JP2025048960A - system - Google Patents

Abstract

[Problem] The system according to the embodiment aims to provide nutritionally balanced meals to elderly people living in areas where access to food is difficult. [Solution] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives information about the user's ownership of cooking utensils and cooking know-how skills. The collection unit collects ingredient information from at least one of nearby convenience stores, available mail order sites, and supermarkets. The generation unit generates recipes based on the information collected by the reception unit and the collection unit. The provision unit provides the recipes generated by the generation unit. [Selected Figure] Figure 1

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description of the chatbot character and an associated instruction sentence, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

JP 2022-180282 A

Conventional technology has the problem that it is difficult for elderly people living in areas with limited food access to easily obtain nutritionally balanced meals.

The system of the embodiment aims to provide nutritionally balanced meals to elderly people living in areas where food access is difficult.

The system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives the user's ownership of cooking utensils and cooking know-how skills. The collection unit collects ingredient information from at least one of nearby convenience stores, available mail order sites, and supermarkets. The generation unit generates recipes based on the information collected by the reception unit and the collection unit. The provision unit provides the recipes generated by the generation unit.

The system according to the embodiment can provide nutritionally balanced meals to elderly people living in areas where food access is difficult.

1 is a conceptual diagram showing an example of a configuration of a data processing system according to a first embodiment. 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. FIG. 11 is a conceptual diagram showing an example of a configuration of a data processing system according to a second embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped.

Below, an example of an embodiment of a system related to the technology disclosed herein is described with reference to the attached drawings.

First, let us explain the terminology used in the following explanation.

In the following embodiments, the signed processor (hereinafter simply referred to as the "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. The processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

In the following embodiments, a signed random access memory (RAM) is a memory in which information is temporarily stored and is used as a working memory by the processor.

In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

In the following embodiments, a communication I/F (Interface) with a code is an interface including a communication processor and an antenna. The communication I/F controls communication between multiple computers. Examples of communication standards applied to the communication I/F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." In other words, "A and/or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same concept as "A and/or B" is also applied when three or more things are expressed by connecting them with "and/or."

[First embodiment]
FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form of expression that the user can perceive (e.g., voice and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(Example 1)
The nutritional balance suggestion system according to the embodiment of the present invention is a system that suggests nutritionally balanced meals for elderly people living in food desert areas such as mountainous areas. In this nutritional balance suggestion system, the user inputs the possession of cooking utensils and cooking know-how skills, collects food ingredient information from nearby convenience stores, available mail-order stores, supermarkets, etc., and the generation AI suggests recipes suitable for the user's cooking skills and cooking utensils based on the collected food ingredient information. This suggestion allows even elderly people living alone to be provided with healthy meals, and prepares for the declining birthrate and aging population. For example, the user inputs the possession of his/her own cooking utensils and cooking know-how skills. For example, the user inputs the presence or absence of cooking utensils such as frying pans, pots, and microwaves, and cooking skills such as stir-frying and stewing. This information is input to the generation AI. Next, the generation AI collects food ingredient information from nearby convenience stores, available mail-order stores, supermarkets, etc. For example, information such as vegetables and meat sold at convenience stores and seasonings that can be purchased by mail order is collected. This creates a list of ingredients that can be used by the user. Based on the collected food ingredient information, the generation AI suggests recipes suitable for the user's cooking skills and cooking utensils. For example, for a user who only has a frying pan, a recipe for a stir-fry that can be easily made with the frying pan is proposed. Also, for a user who is good at simmering, a recipe for simmering is proposed. In this way, recipes tailored to the user's situation are provided. This makes it possible to provide healthy meals even for elderly people living alone. Since the user can receive recipes tailored to their own cooking skills and cooking utensils, they can cook without difficulty. In addition, by purchasing ingredients from nearby convenience stores, mail order sites, supermarkets, etc., it becomes easy to procure ingredients. This makes it possible to provide nutritionally balanced meals and prepare for the declining birthrate and aging population. This allows the nutritional balance proposal system to provide appropriate recipes based on the user's ownership of cooking utensils and cooking know-how skills.

The nutritional balance proposal system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives the user's cooking utensils and cooking know-how skills. The user's cooking utensils include, for example, a frying pan, a pot, an oven, and the like, but are not limited to such examples. The cooking know-how skills include, for example, levels such as beginner, intermediate, and advanced, but are not limited to such examples. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. The collection unit collects food information from nearby convenience stores, available mail order stores, supermarkets, and the like. The food information includes, for example, the type, price, and inventory status of food ingredients, but are not limited to such examples. The collection unit obtains food information for each store through, for example, an online API. The collection unit can also collect food information from the nearest store based on the user's location information. The generation unit uses a generation AI to generate a recipe based on the information collected by the reception unit and the collection unit. The generation AI generates recipes using, for example, a text generation AI (for example, LLM). The generation unit generates recipes suitable for the cooking skills and cooking utensils owned by the user. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at simmering. The provision unit provides the recipe generated by the generation unit to the user. The provision unit displays the recipe through, for example, a web application or a mobile application. The provision unit can also print the recipe and provide it on paper. Thereby, the nutritional balance proposal system according to the embodiment can provide an appropriate recipe based on the user's cooking utensil possession status and cooking know-how skills. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's cooking skills and information on the cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

The reception unit receives the user's cooking utensils and cooking know-how skills. The user's cooking utensils include, but are not limited to, frying pans, pots, ovens, etc., for example. Specifically, an interface is provided for inputting a list of cooking utensils owned by the user, allowing the user to register the cooking utensils owned by the user in detail. For example, detailed information such as the size and material of a frying pan, the type and capacity of a pot, and the function and maximum temperature of an oven can be input. In addition, the cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced, for example. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. For example, the reception unit asks the user questions such as "Are you confident in using a knife?" and "Can you cook by looking at a complicated recipe?", and automatically determines the skill level based on the answers. In addition, a more accurate skill evaluation can be performed by uploading photos and recipes of dishes that the user has created in the past. This allows the reception unit to grasp the user's cooking utensils and cooking skills in detail, and collect basic information for suggesting optimal recipes to each user. Furthermore, the reception unit can also receive information about the user's preferences and allergies, making it possible to suggest recipes based on the user's health condition and preferences. For example, if a user is allergic to a particular ingredient, recipes that do not contain that ingredient can be suggested.

The collection unit collects food information from nearby convenience stores, available mail order stores, supermarkets, etc. The food information includes, for example, the type of food, price, and stock status, but is not limited to such examples. Specifically, the collection unit obtains food information from each store through an online API. For example, the database of each store can be accessed to obtain the latest food information in real time. The collection unit can also collect food information from the nearest store based on the user's location information. This allows the user to preferentially provide information on stores that are easily accessible to the user. Furthermore, the collection unit can also collect nutritional information and production area information on the food, which allows the user to be suggested healthier and safer food. For example, food information suitable for health-conscious users, such as organically grown vegetables and low-calorie food, can be provided. The collection unit can also collect information on seasonal food ingredients and suggest recipes with a seasonal feel based on this. For example, seasonal suggestions are possible, such as recipes using fresh spring vegetables in spring and cold dishes and barbecue recipes in summer. This allows the collection unit to collect a variety of food information according to the user's needs, improving the accuracy and convenience of the entire system.

The generation unit uses the generation AI to generate recipes based on the information collected by the reception unit and the collection unit. The generation AI generates recipes using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI receives the user's cooking utensil ownership status, cooking skills, and collected ingredient information as input data, and generates optimal recipes based on this. For example, for a user who only has a frying pan, it generates a stir-fry recipe that can be easily made in a frying pan. In addition, it can generate a stew recipe for a user who is good at simmering. The generation AI learns past recipe data and cooking knowledge, and can create new recipes based on this. For example, it learns combinations and cooking methods of specific ingredients and applies them to propose new recipes. In addition, the generation AI can generate recipes taking into account the user's preferences and allergy information. For example, if a user is allergic to a specific ingredient, it can generate a recipe that does not include that ingredient. In addition, the generation AI can also generate recipes that take nutritional balance into consideration. For example, it can propose recipes that optimize the balance of calories and nutrients according to the user's health condition and goals. This allows the generation unit to generate a variety of recipes that meet the needs of users, improving the convenience and satisfaction of the entire system.

The providing unit provides the recipe generated by the generating unit to the user. Specifically, the recipe is displayed through a web application or a mobile application. For example, when the user accesses the application, the generated recipes are displayed in a list, and the user can check detailed cooking procedures and a list of necessary ingredients. The providing unit can also print the recipe and provide it on paper. For example, if the user wishes, the recipe can be downloaded in PDF format and printed on a home printer. The providing unit can also provide a recipe sharing function. For example, the user can share the generated recipe with friends and family via SNS or email. The providing unit can also collect user feedback and use it to improve the recipe. For example, the user can input ratings and comments after trying the recipe, and the generating AI can improve the recipe based on the feedback. This allows the providing unit to provide the recipe to the user quickly and appropriately, and improve the satisfaction of the entire system. Furthermore, the providing unit can also make individually customized recipe suggestions based on the user's past usage history. For example, new recipes can be suggested based on recipes that were well-received in the past or frequently used ingredients. This allows the providing unit to provide personalized services according to the user's preferences and needs, and further improve the convenience and satisfaction of the entire system.

The collection unit can collect ingredient information from at least one of convenience stores, mail order, and supermarkets. The collection unit collects, for example, information on vegetables and meat sold at convenience stores and seasonings available for mail order. Ingredient information includes, for example, the type of ingredient, price, and stock status, but is not limited to these examples. The collection unit obtains ingredient information for each store through, for example, an online API. The collection unit can also collect ingredient information from the nearest store based on the user's location information. This allows the collection unit to create a list of ingredients available to the user. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the ingredient information obtained through the online API to AI, and have the AI organize and classify the ingredient information.

The generation unit can generate recipes suitable for the cooking skills of the user and cooking utensils owned by the user. The generation unit generates recipes based on the cooking skills of the user and cooking utensils owned by the user, for example. Cooking skills include, but are not limited to, beginner, intermediate, and advanced levels. Cooking utensils owned by the user include, but are not limited to, a frying pan, a pot, an oven, and the like, for example. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at simmering. This allows the generation unit to generate recipes suitable for the cooking skills of the user and cooking utensils owned by the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input information on the cooking skills of the user and cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

The providing unit can provide the generated recipe to the user. The providing unit, for example, provides the recipe generated by the generating unit to the user. The recipe includes, for example, an ingredient list, cooking steps, cooking time, etc., but is not limited to such examples. The providing unit, for example, displays the recipe through a web application or a mobile application. The providing unit can also print the recipe and provide it on paper. This allows the providing unit to allow the user to receive an appropriate recipe. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the recipe generated by the generating unit to AI and cause the AI to execute a method for providing the recipe.

The reception unit can analyze the user's past cooking history and suggest the optimal input method. The reception unit can, for example, analyze the user's past cooking history and suggest the optimal input method. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. The reception unit can, for example, automatically display as candidates cooking utensils and cooking skills that the user has used frequently in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest cooking utensils and cooking skills to be used in a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past cooking history data to AI and cause AI to execute the suggestion of the optimal input method.

The reception unit can filter based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills. The reception unit filters based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills, for example. The health condition includes, but is not limited to, allergy information, nutritional status, and the like. The dietary preferences include, but are not limited to, favorite ingredients, disliked ingredients, and dietary styles, for example. For example, when the user inputs the health condition, the reception unit can suggest appropriate cooking utensils and cooking skills based on the information. In addition, when the user inputs the dietary preferences, the reception unit can also suggest appropriate cooking utensils and cooking skills based on the information. Furthermore, when the user inputs allergy information, the reception unit can also suggest safe cooking utensils and cooking skills based on the information. As a result, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data about the user's health condition and food preferences into the AI and have the AI perform filtering.

The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting cooking utensils and cooking skills. For example, when inputting cooking utensils and cooking skills, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The highly relevant information includes, for example, nearby store information, local specialty products, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the reception unit can suggest cooking utensils and skills that are easy to obtain in that area. In addition, if the user lives in an urban area, the reception unit can also suggest cooking utensils and skills that are common in urban areas. Furthermore, if the user is traveling to a specific area, the reception unit can also suggest cooking utensils and skills that use local specialty products. As a result, the reception unit can preferentially input highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the AI and have the AI prioritize highly relevant information.

The reception unit can analyze the user's social media activity and input related information when inputting cooking utensils and cooking skills. The reception unit can analyze the user's social media activity and input related information when inputting cooking utensils and cooking skills, for example. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, and the like. Related information includes, but is not limited to, the user's interests and trend information, and the like. For example, the reception unit can analyze photos of dishes shared by the user on social media and suggest the cooking utensils and skills used. The reception unit can also suggest related cooking utensils and skills based on information on cooking accounts followed by the user. Furthermore, the reception unit can also suggest related cooking utensils and skills based on information on dishes that the user has "liked" on social media. In this way, the reception unit can input related information by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to AI and have the AI perform analysis of the related information.

When collecting food ingredient information, the collection unit can select the optimal collection method by analyzing the user's past purchase history. For example, when collecting food ingredient information, the collection unit analyzes the user's past purchase history to select the optimal collection method. The past purchase history includes, for example, the type of food ingredient purchased, the purchase frequency, and the purchase location, but is not limited to such examples. For example, the collection unit collects related food ingredient information based on food ingredients that the user frequently purchased in the past. The collection unit can also collect food ingredient information purchased in a specific season from the user's past purchase history. Furthermore, the collection unit can analyze the user's past purchase history and collect nutritionally balanced food ingredient information. In this way, the collection unit can select the optimal collection method by analyzing the user's past purchase history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past purchase history data into AI and cause AI to select the optimal collection method.

When collecting food ingredient information, the collection unit can filter based on the user's health condition and dietary preferences. When collecting food ingredient information, the collection unit, for example, filters based on the user's health condition and dietary preferences. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, and eating style, but is not limited to such examples. For example, when a user inputs a health condition, the collection unit collects appropriate food ingredient information based on the information. In addition, when a user inputs dietary preferences, the collection unit can also collect appropriate food ingredient information based on the information. Furthermore, when a user inputs allergy information, the collection unit can also collect safe food ingredient information based on the information. In this way, the collection unit can collect appropriate food ingredient information by filtering based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and dietary preferences to AI and have the AI perform filtering.

When collecting food ingredient information, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information. For example, when collecting food ingredient information, the collection unit prioritizes collecting highly relevant information in consideration of the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, etc. Examples of highly relevant information include, but are not limited to, nearby store information, local specialty products, etc. For example, if the user lives in a mountainous area, the collection unit collects food ingredient information that is easily available in that area. Also, if the user lives in an urban area, the collection unit can collect food ingredient information that is common in urban areas. Furthermore, if the user is traveling to a specific area, the collection unit can collect food ingredient information of local specialty products in that area. In this way, the collection unit can prioritize collecting highly relevant information by considering the user's geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause AI to execute the priority order of highly relevant information.

The collection unit can analyze the user's social media activities and collect related information when collecting the ingredient information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting the ingredient information. The social media activities include, for example, but are not limited to, the content of posts, the number of likes, the number of followers, etc. The related information includes, for example, but are not limited to, the user's interests and trend information. For example, the collection unit can analyze photos of dishes shared by the user on social media and collect related ingredient information. The collection unit can also collect related ingredient information based on information on cooking accounts followed by the user. Furthermore, the collection unit can collect related ingredient information based on information on dishes that the user has "liked" on social media. In this way, the collection unit can collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI perform analysis of the related information.

When generating a recipe, the generation unit can analyze the user's past cooking history to generate an optimal recipe. For example, when generating a recipe, the generation unit analyzes the user's past cooking history to generate an optimal recipe. The past cooking history includes, for example, the type of food cooked, the frequency, and the success rate, but is not limited to such examples. For example, the generation unit generates a related recipe based on the food cooked by the user in the past. The generation unit can also generate a nutritionally balanced recipe from the user's past cooking history. Furthermore, the generation unit can analyze the user's past cooking history and generate the most efficient recipe. In this way, the generation unit can generate an optimal recipe by analyzing the user's past cooking history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past cooking history data to the generation AI and cause the generation AI to generate an optimal recipe.

The generation unit can customize the recipe based on the user's health condition and dietary preferences when generating the recipe. For example, the generation unit customizes the recipe based on the user's health condition and dietary preferences when generating the recipe. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, and dietary style, but is not limited to such examples. For example, when the user inputs the health condition, the generation unit generates an appropriate recipe based on the information. In addition, when the user inputs the dietary preferences, the generation unit can also generate an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the generation unit can generate a safe recipe based on the information. In this way, the generation unit can generate an appropriate recipe by customizing the recipe based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the user's health condition and dietary preferences to the generation AI and cause the generation AI to customize the recipe.

The generation unit can generate an optimal recipe by considering the geographical location information of the user when generating a recipe. For example, the generation unit generates an optimal recipe by considering the geographical location information of the user when generating a recipe. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The optimal recipe includes, for example, the user's preferences, health condition, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the generation unit generates a recipe using ingredients that are easily available in the area. In addition, if the user lives in an urban area, the generation unit can also generate a recipe using ingredients that are common in the urban area. Furthermore, if the user is traveling to a specific area, the generation unit can generate a recipe using a specialty product of the area. In this way, the generation unit can generate an optimal recipe by considering the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to generate an optimal recipe.

The generation unit can analyze the social media activity of the user at the time of recipe generation and generate a related recipe. The generation unit can analyze the social media activity of the user at the time of recipe generation, for example, and generate a related recipe. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related recipe includes, for example, the user's interests and trend information, but is not limited to such examples. The generation unit can analyze, for example, photos of food shared by the user on social media and generate a related recipe. The generation unit can also generate a related recipe based on information of a food account that the user follows. Furthermore, the generation unit can generate a related recipe based on information of a food that the user "liked" on social media. In this way, the generation unit can generate a related recipe by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's social media activity data to the generation AI and cause the generation AI to generate a related recipe.

When providing a recipe, the providing unit can select the optimal providing method by referring to the user's past cooking history. For example, when providing a recipe, the providing unit selects the optimal providing method by referring to the user's past cooking history. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. For example, the providing unit provides a related recipe based on the food cooked by the user in the past. The providing unit can also provide a nutritionally balanced recipe from the user's past cooking history. Furthermore, the providing unit can analyze the user's past cooking history and provide the most efficient recipe. As a result, the providing unit can select the optimal providing method by referring to the user's past cooking history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past cooking history data to AI and cause the AI to select the optimal providing method.

The providing unit can customize the providing method based on the user's health condition and dietary preferences when providing a recipe. For example, the providing unit customizes the providing method based on the user's health condition and dietary preferences when providing a recipe. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, and dietary style, but is not limited to such examples. For example, when the user inputs a health condition, the providing unit provides an appropriate recipe based on the information. In addition, when the user inputs dietary preferences, the providing unit can also provide an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the providing unit can also provide a safe recipe based on the information. In this way, the providing unit can provide an appropriate recipe by customizing the providing method based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's health condition and dietary preferences into AI and cause the AI to customize the providing method.

When providing a recipe, the providing unit can select the optimal providing method in consideration of the geographical location information of the user. For example, when providing a recipe, the providing unit selects the optimal providing method in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user lives in a mountainous area, the providing unit provides a recipe using ingredients that are easily available in that area. In addition, when the user lives in an urban area, the providing unit can also provide a recipe using ingredients that are common in the urban area. Furthermore, when the user is traveling to a specific area, the providing unit can also provide a recipe using a specialty product of that area. In this way, the providing unit can select the optimal providing method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI to select the optimal providing method.

The providing unit can analyze the social media activity of the user when providing a recipe and provide a related recipe. The providing unit can analyze the social media activity of the user when providing a recipe and provide a related recipe. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. Related recipes include, for example, the user's interests and trend information, but is not limited to such examples. The providing unit can analyze, for example, photos of dishes shared by the user on social media and provide a related recipe. The providing unit can also provide a related recipe based on information on cooking accounts followed by the user. Furthermore, the providing unit can provide a related recipe based on information on dishes that the user has "liked" on social media. In this way, the providing unit can provide a related recipe by analyzing the user's social media activity. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and cause the AI to provide a related recipe.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The reception unit can receive the user's dietary preferences and allergy information in addition to the user's possession of cooking utensils and cooking know-how skills. For example, if the user has an allergy to a particular ingredient, the generation unit can generate a recipe that avoids the allergy by inputting that information. Also, if the user prefers a particular ingredient, the generation unit can preferentially generate recipes that use that ingredient by inputting that information. Furthermore, if the user prefers a particular dietary style (e.g., vegetarian, gluten-free, etc.), the generation unit can generate a recipe suitable for that style by inputting that information. In this way, the reception unit can provide more personalized recipes by accepting input that takes into account the user's dietary preferences and allergy information.

The collection unit can analyze the user's purchasing history and collect ingredient information based on information about ingredients purchased in the past. For example, by preferentially collecting ingredients that the user frequently purchased in the past, it is possible to provide ingredient information that matches the user's preferences. In addition, it is possible to analyze ingredients that the user purchases in a particular season and collect ingredient information suitable for that season. Furthermore, it is possible to analyze the nutritional value of ingredients purchased by the user in the past and collect ingredient information that is nutritionally balanced. In this way, the collection unit can provide more personalized ingredient information by collecting ingredient information based on the user's purchasing history.

The generation unit can generate recipes based on the user's health condition and nutritional goals, in addition to the user's cooking skills and cooking utensils. For example, if the user has a specific health condition (e.g., high blood pressure, diabetes, etc.), a health-conscious recipe can be generated based on that information. Also, if the user has a specific nutritional goal (e.g., increasing protein intake, restricting calories, etc.), a nutritionally balanced recipe can be generated based on that information. Furthermore, if the user prefers a specific eating style (e.g., low carbohydrate, high protein, etc.), a recipe suitable for that style can be generated based on that information. In this way, the generation unit can provide recipes that take into account the user's health condition and nutritional goals.

When providing the generated recipe to the user, the providing unit can provide it in the optimal format depending on the type of device and usage situation of the user. For example, if the user is using a smartphone, the recipe can be displayed in a mobile-friendly format. Also, if the user is using a tablet, the recipe can be displayed in a format suitable for a large screen. Furthermore, if the user has a printer, the recipe can be printed and provided on paper. This allows the providing unit to provide the recipe in the optimal format depending on the type of device and usage situation of the user.

The reception unit can analyze the user's past cooking history and suggest the optimal input method. For example, it can automatically display as candidates cooking utensils and cooking skills that the user has used frequently in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest cooking utensils and cooking skills to be used during a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history.

When inputting cooking utensil ownership status and cooking skills, the reception unit can perform filtering based on the user's health condition and dietary preferences. For example, if the user inputs allergy information, safe cooking utensils and cooking skills can be suggested based on that information. Also, if the user has a preference for specific ingredients, appropriate cooking utensils and cooking skills can be suggested based on that information. Furthermore, if the user has a preference for a specific dietary style (e.g., vegetarian, gluten-free, etc.), appropriate cooking utensils and cooking skills can be suggested based on that information. In this way, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences.

When inputting cooking utensils and cooking skills, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a mountainous area, it can suggest cooking utensils and skills that are easily available in that area. Also, if the user lives in an urban area, it can suggest cooking utensils and skills that are common in urban areas. Furthermore, if the user is traveling to a specific area, it can suggest cooking utensils and skills that use local specialties of that area. In this way, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information.

When inputting cooking utensils and cooking skills, the reception unit can analyze the user's social media activity and input related information. For example, it can analyze photos of dishes shared by the user on social media and suggest the cooking utensils and skills used. It can also suggest related cooking utensils and skills based on information about cooking accounts the user follows. It can also suggest related cooking utensils and skills based on information about dishes that the user has "liked" on social media. This allows the reception unit to input related information by analyzing the user's social media activity.

The processing flow of Example 1 is briefly explained below.

Step 1: The reception unit receives the user's cooking utensils and cooking know-how skills. The user's cooking utensils include, but are not limited to, frying pans, pots, ovens, etc. The cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers.
Step 2: The collection unit collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The ingredient information includes, but is not limited to, the type of ingredient, price, and stock status. The collection unit obtains ingredient information from each store, for example, through an online API. The collection unit can also collect ingredient information from the nearest store based on the user's location information.
Step 3: The generation unit uses the generation AI to generate a recipe based on the information collected by the reception unit and the collection unit. The generation AI generates a recipe using, for example, a text generation AI (e.g., LLM). The generation unit also generates a recipe suitable for the user's cooking skills and cooking utensils. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at stews.
Step 4: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the recipe through, for example, a web application or a mobile application. The providing unit can also print the recipe and provide it on paper.

(Example 2)
The nutritional balance suggestion system according to the embodiment of the present invention is a system that suggests nutritionally balanced meals for elderly people living in food desert areas such as mountainous areas. In this nutritional balance suggestion system, the user inputs the possession of cooking utensils and cooking know-how skills, collects food ingredient information from nearby convenience stores, available mail-order stores, supermarkets, etc., and the generation AI suggests recipes suitable for the user's cooking skills and cooking utensils based on the collected food ingredient information. This suggestion allows even elderly people living alone to be provided with healthy meals, and prepares for the declining birthrate and aging population. For example, the user inputs the possession of his/her own cooking utensils and cooking know-how skills. For example, the user inputs the presence or absence of cooking utensils such as frying pans, pots, and microwaves, and cooking skills such as stir-frying and stewing. This information is input to the generation AI. Next, the generation AI collects food ingredient information from nearby convenience stores, available mail-order stores, supermarkets, etc. For example, information such as vegetables and meat sold at convenience stores and seasonings that can be purchased by mail order is collected. This creates a list of ingredients that can be used by the user. Based on the collected food ingredient information, the generation AI suggests recipes suitable for the user's cooking skills and cooking utensils. For example, for a user who only has a frying pan, a recipe for a stir-fry that can be easily made with the frying pan is proposed. Also, for a user who is good at simmering, a recipe for simmering is proposed. In this way, recipes tailored to the user's situation are provided. This makes it possible to provide healthy meals even for elderly people living alone. Since the user can receive recipes tailored to their own cooking skills and cooking utensils, they can cook without difficulty. In addition, by purchasing ingredients from nearby convenience stores, mail order sites, supermarkets, etc., it becomes easy to procure ingredients. This makes it possible to provide nutritionally balanced meals and prepare for the declining birthrate and aging population. This allows the nutritional balance proposal system to provide appropriate recipes based on the user's ownership of cooking utensils and cooking know-how skills.

The nutritional balance proposal system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives the user's cooking utensils and cooking know-how skills. The user's cooking utensils include, for example, a frying pan, a pot, an oven, and the like, but are not limited to such examples. The cooking know-how skills include, for example, levels such as beginner, intermediate, and advanced, but are not limited to such examples. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. The collection unit collects food information from nearby convenience stores, available mail order stores, supermarkets, and the like. The food information includes, for example, the type, price, and stock status of food ingredients, but are not limited to such examples. The collection unit obtains food information for each store through, for example, an online API. The collection unit can also collect food information from the nearest store based on the user's location information. The generation unit uses a generation AI to generate a recipe based on the information collected by the reception unit and the collection unit. The generation AI generates recipes using, for example, a text generation AI (for example, LLM). The generation unit generates recipes suitable for the user's cooking skills and cooking utensils. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at simmering. The provision unit provides the recipe generated by the generation unit to the user. The provision unit displays the recipe through, for example, a web application or a mobile application. The provision unit can also print the recipe and provide it on paper. Thereby, the nutritional balance proposal system according to the embodiment can provide an appropriate recipe based on the user's cooking utensil possession status and cooking know-how skills. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's cooking skills and information on the cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

The reception unit receives the user's cooking utensils and cooking know-how skills. The user's cooking utensils include, but are not limited to, frying pans, pots, ovens, etc., for example. Specifically, an interface is provided for inputting a list of cooking utensils owned by the user, allowing the user to register the cooking utensils owned by the user in detail. For example, detailed information such as the size and material of a frying pan, the type and capacity of a pot, and the function and maximum temperature of an oven can be input. In addition, the cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced, for example. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. For example, the reception unit asks the user questions such as "Are you confident in using a knife?" and "Can you cook by looking at a complicated recipe?", and automatically determines the skill level based on the answers. In addition, a more accurate skill evaluation can be performed by uploading photos and recipes of dishes that the user has created in the past. This allows the reception unit to grasp the user's cooking utensils and cooking skills in detail, and collect basic information for suggesting optimal recipes to each user. Furthermore, the reception unit can also receive information about the user's preferences and allergies, making it possible to suggest recipes based on the user's health condition and preferences. For example, if a user is allergic to a particular ingredient, recipes that do not contain that ingredient can be suggested.

The collection unit collects food information from nearby convenience stores, available mail order stores, supermarkets, etc. The food information includes, for example, the type of food, price, and stock status, but is not limited to such examples. Specifically, the collection unit obtains food information from each store through an online API. For example, the database of each store can be accessed to obtain the latest food information in real time. The collection unit can also collect food information from the nearest store based on the user's location information. This allows the user to preferentially provide information on stores that are easily accessible to the user. Furthermore, the collection unit can also collect nutritional information and production area information on the food, which allows the user to be suggested healthier and safer food. For example, food information suitable for health-conscious users, such as organically grown vegetables and low-calorie food, can be provided. The collection unit can also collect information on seasonal food ingredients and suggest recipes with a seasonal feel based on this. For example, seasonal suggestions are possible, such as recipes using fresh spring vegetables in spring and cold dishes and barbecue recipes in summer. This allows the collection unit to collect a variety of food information according to the user's needs, improving the accuracy and convenience of the entire system.

The generation unit uses the generation AI to generate recipes based on the information collected by the reception unit and the collection unit. The generation AI generates recipes using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI receives the user's cooking utensil possession status, cooking skills, and collected ingredient information as input data, and generates optimal recipes based on this. For example, for a user who only has a frying pan, it generates a stir-fry recipe that can be easily made in a frying pan. In addition, it can generate a stew recipe for a user who is good at simmering. The generation AI learns past recipe data and cooking knowledge, and can create new recipes based on this. For example, it learns combinations and cooking methods of specific ingredients and proposes new recipes by applying them. In addition, the generation AI can generate recipes taking into account the user's preferences and allergy information. For example, if a user is allergic to a specific ingredient, it can generate a recipe that does not include that ingredient. In addition, the generation AI can also generate recipes that take nutritional balance into consideration. For example, it can propose recipes that optimize the balance of calories and nutrients according to the user's health condition and goals. This allows the generation unit to generate a variety of recipes that meet the needs of users, improving the convenience and satisfaction of the entire system.

The providing unit provides the recipe generated by the generating unit to the user. Specifically, the recipe is displayed through a web application or a mobile application. For example, when the user accesses the application, the generated recipes are displayed in a list, and the user can check detailed cooking procedures and a list of necessary ingredients. The providing unit can also print the recipe and provide it on paper. For example, if the user wishes, the recipe can be downloaded in PDF format and printed on a home printer. The providing unit can also provide a recipe sharing function. For example, the user can share the generated recipe with friends and family via SNS or email. The providing unit can also collect user feedback and use it to improve the recipe. For example, the user can input ratings and comments after trying the recipe, and the generating AI can improve the recipe based on the feedback. This allows the providing unit to provide the recipe to the user quickly and appropriately, and improve the satisfaction of the entire system. Furthermore, the providing unit can also make individually customized recipe suggestions based on the user's past usage history. For example, new recipes can be suggested based on recipes that were well-received in the past or frequently used ingredients. This allows the providing unit to provide personalized services according to the user's preferences and needs, and further improve the convenience and satisfaction of the entire system.

The collection unit can collect ingredient information from at least one of convenience stores, mail order, and supermarkets. The collection unit collects, for example, information on vegetables and meat sold at convenience stores and seasonings available for mail order. Ingredient information includes, for example, the type of ingredient, price, and stock status, but is not limited to these examples. The collection unit obtains ingredient information for each store through, for example, an online API. The collection unit can also collect ingredient information from the nearest store based on the user's location information. This allows the collection unit to create a list of ingredients available to the user. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the ingredient information obtained through the online API to AI, and have the AI organize and classify the ingredient information.

The generation unit can generate recipes suitable for the cooking skills of the user and cooking utensils owned by the user. The generation unit generates recipes based on the cooking skills of the user and cooking utensils owned by the user, for example. Cooking skills include, but are not limited to, beginner, intermediate, and advanced levels. Cooking utensils owned by the user include, but are not limited to, a frying pan, a pot, an oven, and the like, for example. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at simmering. This allows the generation unit to generate recipes suitable for the cooking skills of the user and cooking utensils owned by the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input information on the cooking skills of the user and cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

The providing unit can provide the generated recipe to the user. The providing unit, for example, provides the recipe generated by the generating unit to the user. The recipe includes, for example, an ingredient list, cooking steps, cooking time, etc., but is not limited to such examples. The providing unit, for example, displays the recipe through a web application or a mobile application. The providing unit can also print the recipe and provide it on paper. This allows the providing unit to allow the user to receive an appropriate recipe. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the recipe generated by the generating unit to AI and cause the AI to execute a method for providing the recipe.

The reception unit can estimate the user's emotions and adjust the input method of cooking utensils and cooking skills based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and adjusts the input method of cooking utensils and cooking skills based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input cooking utensils and cooking skills. As a result, the reception unit can adjust the input method according to the user's emotions, allowing the user to input without feeling stress. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

The reception unit can analyze the user's past cooking history and suggest the optimal input method. The reception unit can, for example, analyze the user's past cooking history and suggest the optimal input method. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. The reception unit can, for example, automatically display as candidates cooking utensils and cooking skills that the user has used frequently in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest cooking utensils and cooking skills to be used in a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past cooking history data to AI and cause AI to execute the suggestion of the optimal input method.

The reception unit can filter based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills. The reception unit filters based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills, for example. The health condition includes, but is not limited to, allergy information, nutritional status, and the like. The dietary preferences include, but are not limited to, favorite ingredients, disliked ingredients, and dietary styles, for example. For example, when the user inputs the health condition, the reception unit can suggest appropriate cooking utensils and cooking skills based on the information. In addition, when the user inputs the dietary preferences, the reception unit can also suggest appropriate cooking utensils and cooking skills based on the information. Furthermore, when the user inputs allergy information, the reception unit can also suggest safe cooking utensils and cooking skills based on the information. As a result, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data about the user's health condition and food preferences into the AI and have the AI perform filtering.

The reception unit can estimate the user's emotions and determine the priority order of cooking utensils and cooking skills to be input based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and determines the priority order of cooking utensils and cooking skills to be input based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is stressed, the reception unit preferentially suggests easy-to-use cooking utensils and basic cooking skills. In addition, when the user is relaxed, the reception unit can also suggest complex cooking utensils and advanced cooking skills. Furthermore, when the user is in a hurry, the reception unit can also preferentially suggest cooking utensils and skills that can cook quickly. As a result, the reception unit determines the priority order of cooking utensils and cooking skills according to the user's emotions, so that the user can input without feeling stress. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting cooking utensils and cooking skills. For example, when inputting cooking utensils and cooking skills, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The highly relevant information includes, for example, nearby store information, local specialty products, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the reception unit can suggest cooking utensils and skills that are easy to obtain in that area. In addition, if the user lives in an urban area, the reception unit can also suggest cooking utensils and skills that are common in urban areas. Furthermore, if the user is traveling to a specific area, the reception unit can also suggest cooking utensils and skills that use local specialty products. As a result, the reception unit can preferentially input highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographic location information to the AI and have the AI prioritize highly relevant information.

The reception unit can analyze the user's social media activity and input related information when inputting cooking utensils and cooking skills. The reception unit can analyze the user's social media activity and input related information when inputting cooking utensils and cooking skills, for example. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, and the like. Related information includes, but is not limited to, the user's interests and trend information, and the like. For example, the reception unit can analyze photos of dishes shared by the user on social media and suggest the cooking utensils and skills used. The reception unit can also suggest related cooking utensils and skills based on information on cooking accounts followed by the user. Furthermore, the reception unit can also suggest related cooking utensils and skills based on information on dishes that the user has "liked" on social media. In this way, the reception unit can input related information by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to AI and have the AI perform analysis of the related information.

The collection unit can estimate the user's emotions and adjust the method of collecting ingredient information based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the method of collecting ingredient information based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is stressed, the collection unit preferentially collects ingredient information that can be easily cooked. In addition, when the user is relaxed, the collection unit can also collect ingredient information that takes time to cook but is nutritious. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect ingredient information that can be cooked immediately. In this way, the collection unit can adjust the method of collecting ingredient information according to the user's emotions, so that the user can collect ingredient information without feeling stressed. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

When collecting food ingredient information, the collection unit can select the optimal collection method by analyzing the user's past purchase history. For example, when collecting food ingredient information, the collection unit analyzes the user's past purchase history to select the optimal collection method. The past purchase history includes, for example, the type of food ingredient purchased, the purchase frequency, and the purchase location, but is not limited to such examples. For example, the collection unit collects related food ingredient information based on food ingredients that the user frequently purchased in the past. The collection unit can also collect food ingredient information purchased in a specific season from the user's past purchase history. Furthermore, the collection unit can analyze the user's past purchase history and collect nutritionally balanced food ingredient information. In this way, the collection unit can select the optimal collection method by analyzing the user's past purchase history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past purchase history data into AI and cause AI to select the optimal collection method.

When collecting food ingredient information, the collection unit can filter based on the user's health condition and dietary preferences. When collecting food ingredient information, the collection unit, for example, filters based on the user's health condition and dietary preferences. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, and eating style, but is not limited to such examples. For example, when a user inputs a health condition, the collection unit collects appropriate food ingredient information based on the information. In addition, when a user inputs dietary preferences, the collection unit can also collect appropriate food ingredient information based on the information. Furthermore, when a user inputs allergy information, the collection unit can also collect safe food ingredient information based on the information. In this way, the collection unit can collect appropriate food ingredient information by filtering based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and dietary preferences to AI and have the AI perform filtering.

The collection unit can estimate the user's emotions and determine the priority of the food ingredient information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the food ingredient information to be collected based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is stressed, the collection unit preferentially collects food ingredient information that can be easily cooked. In addition, when the user is relaxed, the collection unit can also collect food ingredient information that takes time to cook but is nutritious. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect food ingredient information that can be cooked immediately. In this way, the collection unit can collect food ingredient information without the user feeling stressed by determining the priority of the food ingredient information according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

When collecting food ingredient information, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information. For example, when collecting food ingredient information, the collection unit prioritizes collecting highly relevant information in consideration of the user's geographical location information. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The highly relevant information includes, for example, nearby store information, local specialty products, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the collection unit collects food ingredient information that is easily available in that area. In addition, if the user lives in an urban area, the collection unit can also collect food ingredient information that is common in urban areas. Furthermore, if the user is traveling to a specific area, the collection unit can also collect food ingredient information of local specialty products in that area. In this way, the collection unit can prioritize collecting highly relevant information by considering the user's geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause AI to execute the priority order of highly relevant information.

The collection unit can analyze the user's social media activities and collect related information when collecting the ingredient information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting the ingredient information. The social media activities include, for example, but are not limited to, the content of posts, the number of likes, the number of followers, etc. The related information includes, for example, but are not limited to, the user's interests and trend information. For example, the collection unit can analyze photos of dishes shared by the user on social media and collect related ingredient information. The collection unit can also collect related ingredient information based on information on cooking accounts followed by the user. Furthermore, the collection unit can collect related ingredient information based on information on dishes that the user has "liked" on social media. In this way, the collection unit can collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI perform analysis of the related information.

The generation unit can estimate the user's emotions and adjust the recipe generation method based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the recipe generation method based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is relaxed, the generation AI generates a recipe that proceeds at a leisurely pace. In addition, when the user is in a hurry, the generation AI can also generate a recipe that can be cooked in the shortest time. Furthermore, when the user is excited, the generation AI can also generate a recipe with a visually stimulating effect added. As a result, the generation unit can generate a recipe without the user feeling stressed by adjusting the recipe generation method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

When generating a recipe, the generation unit can analyze the user's past cooking history to generate an optimal recipe. For example, when generating a recipe, the generation unit analyzes the user's past cooking history to generate an optimal recipe. The past cooking history includes, for example, the type of food cooked, the frequency, and the success rate, but is not limited to such examples. For example, the generation unit generates a related recipe based on the food cooked by the user in the past. The generation unit can also generate a nutritionally balanced recipe from the user's past cooking history. Furthermore, the generation unit can analyze the user's past cooking history and generate the most efficient recipe. In this way, the generation unit can generate an optimal recipe by analyzing the user's past cooking history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past cooking history data to the generation AI and cause the generation AI to generate an optimal recipe.

The generation unit can customize the recipe based on the user's health condition and dietary preferences when generating the recipe. For example, the generation unit customizes the recipe based on the user's health condition and dietary preferences when generating the recipe. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, and dietary style, but is not limited to such examples. For example, when the user inputs the health condition, the generation unit generates an appropriate recipe based on the information. In addition, when the user inputs the dietary preferences, the generation unit can also generate an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the generation unit can generate a safe recipe based on the information. In this way, the generation unit can generate an appropriate recipe by customizing the recipe based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the user's health condition and dietary preferences to the generation AI and cause the generation AI to customize the recipe.

The generation unit can estimate the user's emotions and determine the priority of recipes to be generated based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and determines the priority of recipes to be generated based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is stressed, the generation unit preferentially generates recipes that are easy to make. In addition, when the user is relaxed, the generation unit can also generate recipes that take time to cook but are nutritious. Furthermore, when the user is in a hurry, the generation unit can also preferentially generate recipes that can be cooked immediately. In this way, the generation unit can generate recipes without the user feeling stressed by determining the priority of recipes according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

The generation unit can generate an optimal recipe by considering the geographical location information of the user when generating a recipe. For example, the generation unit generates an optimal recipe by considering the geographical location information of the user when generating a recipe. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The optimal recipe includes, for example, the user's preferences, health condition, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the generation unit generates a recipe using ingredients that are easily available in the area. In addition, if the user lives in an urban area, the generation unit can also generate a recipe using ingredients that are common in the urban area. Furthermore, if the user is traveling to a specific area, the generation unit can generate a recipe using a specialty product of the area. In this way, the generation unit can generate an optimal recipe by considering the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to generate an optimal recipe.

The generation unit can analyze the social media activity of the user at the time of recipe generation and generate a related recipe. The generation unit can analyze the social media activity of the user at the time of recipe generation, for example, and generate a related recipe. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related recipe includes, for example, the user's interests and trend information, but is not limited to such examples. The generation unit can analyze, for example, photos of food shared by the user on social media and generate a related recipe. The generation unit can also generate a related recipe based on information of a food account that the user follows. Furthermore, the generation unit can generate a related recipe based on information of a food that the user "liked" on social media. In this way, the generation unit can generate a related recipe by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's social media activity data to the generation AI and cause the generation AI to generate a related recipe.

The providing unit can estimate the user's emotions and adjust the recipe provision method based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the recipe provision method based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is feeling stressed, the providing unit provides a simple and highly visible recipe. In addition, when the user is relaxed, the providing unit can also provide a recipe including detailed explanations. Furthermore, when the user is in a hurry, the providing unit can also provide a concise recipe that covers the main points. Thereby, the providing unit can adjust the recipe provision method according to the user's emotions, so that the user can receive the recipe without feeling stressed. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

When providing a recipe, the providing unit can select the optimal providing method by referring to the user's past cooking history. For example, when providing a recipe, the providing unit selects the optimal providing method by referring to the user's past cooking history. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. For example, the providing unit provides a related recipe based on the food cooked by the user in the past. The providing unit can also provide a nutritionally balanced recipe from the user's past cooking history. Furthermore, the providing unit can analyze the user's past cooking history and provide the most efficient recipe. As a result, the providing unit can select the optimal providing method by referring to the user's past cooking history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past cooking history data to AI and cause the AI to select the optimal providing method.

The providing unit can customize the providing method based on the user's health condition and dietary preferences when providing a recipe. For example, the providing unit customizes the providing method based on the user's health condition and dietary preferences when providing a recipe. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, and dietary style, but is not limited to such examples. For example, when the user inputs a health condition, the providing unit provides an appropriate recipe based on the information. In addition, when the user inputs dietary preferences, the providing unit can also provide an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the providing unit can also provide a safe recipe based on the information. In this way, the providing unit can provide an appropriate recipe by customizing the providing method based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's health condition and dietary preferences into AI and cause the AI to customize the providing method.

The providing unit can estimate the user's emotions and determine the order of providing recipes based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and determines the order of providing recipes based on the estimated user's emotions. The user's emotions include, for example, stress, relaxation, and hurry, but are not limited to such examples. For example, when the user is feeling stressed, the providing unit preferentially provides recipes that are easy to make. In addition, when the user is relaxed, the providing unit can also provide recipes that take time to cook but are nutritious. Furthermore, when the user is in a hurry, the providing unit can also preferentially provide recipes that can be cooked immediately. In this way, the providing unit can determine the order of providing recipes according to the user's emotions, so that the user can receive the recipes without feeling stressed. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

When providing a recipe, the providing unit can select the optimal providing method in consideration of the geographical location information of the user. For example, when providing a recipe, the providing unit selects the optimal providing method in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user lives in a mountainous area, the providing unit provides a recipe using ingredients that are easily available in that area. In addition, when the user lives in an urban area, the providing unit can also provide a recipe using ingredients that are common in the urban area. Furthermore, when the user is traveling to a specific area, the providing unit can also provide a recipe using a specialty product of that area. In this way, the providing unit can select the optimal providing method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI to select the optimal providing method.

The providing unit can analyze the social media activity of the user when providing a recipe and provide a related recipe. The providing unit can analyze the social media activity of the user when providing a recipe and provide a related recipe. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. Related recipes include, for example, the user's interests and trend information, but is not limited to such examples. The providing unit can analyze, for example, photos of dishes shared by the user on social media and provide a related recipe. The providing unit can also provide a related recipe based on information on cooking accounts followed by the user. Furthermore, the providing unit can provide a related recipe based on information on dishes that the user has "liked" on social media. In this way, the providing unit can provide a related recipe by analyzing the user's social media activity. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and cause the AI to provide a related recipe.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The reception unit can receive the user's dietary preferences and allergy information in addition to the user's possession of cooking utensils and cooking know-how skills. For example, if the user has an allergy to a particular ingredient, the generation unit can generate a recipe that avoids the allergy by inputting that information. Also, if the user prefers a particular ingredient, the generation unit can preferentially generate recipes that use that ingredient by inputting that information. Furthermore, if the user prefers a particular dietary style (e.g., vegetarian, gluten-free, etc.), the generation unit can generate a recipe suitable for that style by inputting that information. In this way, the reception unit can provide more personalized recipes by accepting input that takes into account the user's dietary preferences and allergy information.

The collection unit can analyze the user's purchasing history and collect ingredient information based on information about ingredients purchased in the past. For example, by preferentially collecting ingredients that the user frequently purchased in the past, it is possible to provide ingredient information that matches the user's preferences. In addition, it is possible to analyze ingredients that the user purchases in a particular season and collect ingredient information suitable for that season. Furthermore, it is possible to analyze the nutritional value of ingredients purchased by the user in the past and collect ingredient information that is nutritionally balanced. In this way, the collection unit can provide more personalized ingredient information by collecting ingredient information based on the user's purchasing history.

The generation unit can generate recipes based on the user's health condition and nutritional goals, in addition to the user's cooking skills and cooking utensils. For example, if the user has a specific health condition (e.g., high blood pressure, diabetes, etc.), a health-conscious recipe can be generated based on that information. Also, if the user has a specific nutritional goal (e.g., increasing protein intake, restricting calories, etc.), a nutritionally balanced recipe can be generated based on that information. Furthermore, if the user prefers a specific eating style (e.g., low carbohydrate, high protein, etc.), a recipe suitable for that style can be generated based on that information. In this way, the generation unit can provide recipes that take into account the user's health condition and nutritional goals.

When providing the generated recipe to the user, the providing unit can provide it in the optimal format depending on the type of device and usage situation of the user. For example, if the user is using a smartphone, the recipe can be displayed in a mobile-friendly format. Also, if the user is using a tablet, the recipe can be displayed in a format suitable for a large screen. Furthermore, if the user has a printer, the recipe can be printed and provided on paper. This allows the providing unit to provide the recipe in the optimal format depending on the type of device and usage situation of the user.

The reception unit can estimate the user's emotions and adjust the input method for cooking utensils and cooking skills based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input cooking utensils and cooking skills. In this way, the reception unit can adjust the input method according to the user's emotions, allowing the user to input without feeling stressed.

The reception unit can analyze the user's past cooking history and suggest the optimal input method. For example, it can automatically display as candidates cooking utensils and cooking skills that the user has used frequently in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest cooking utensils and cooking skills to be used during a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history.

When inputting cooking utensil ownership status and cooking skills, the reception unit can perform filtering based on the user's health condition and dietary preferences. For example, if the user inputs allergy information, safe cooking utensils and cooking skills can be suggested based on that information. Also, if the user has a preference for specific ingredients, appropriate cooking utensils and cooking skills can be suggested based on that information. Furthermore, if the user has a preference for a specific dietary style (e.g., vegetarian, gluten-free, etc.), appropriate cooking utensils and cooking skills can be suggested based on that information. In this way, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences.

The reception unit can estimate the user's emotions and determine the priority order of cooking utensils and cooking skills to be input based on the estimated user's emotions. For example, if the user is feeling stressed, easy-to-use cooking utensils and basic cooking skills can be suggested with priority. Also, if the user is relaxed, complex cooking utensils and advanced cooking skills can be suggested with priority. Furthermore, if the user is in a hurry, cooking utensils and skills that allow for quick cooking can be suggested with priority. In this way, the reception unit can determine the priority order of cooking utensils and cooking skills according to the user's emotions, allowing the user to make input without feeling stressed.

When inputting cooking utensils and cooking skills, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a mountainous area, it can suggest cooking utensils and skills that are easily available in that area. Also, if the user lives in an urban area, it can suggest cooking utensils and skills that are common in urban areas. Furthermore, if the user is traveling to a specific area, it can suggest cooking utensils and skills that use local specialties of that area. In this way, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information.

When inputting cooking utensils and cooking skills, the reception unit can analyze the user's social media activity and input related information. For example, it can analyze photos of dishes shared by the user on social media and suggest the cooking utensils and skills used. It can also suggest related cooking utensils and skills based on information about cooking accounts the user follows. It can also suggest related cooking utensils and skills based on information about dishes that the user has "liked" on social media. This allows the reception unit to input related information by analyzing the user's social media activity.

The process flow for Example 2 is briefly explained below.

Step 1: The reception unit receives the user's cooking utensils and cooking know-how skills. The user's cooking utensils include, but are not limited to, frying pans, pots, ovens, etc. The cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers.
Step 2: The collection unit collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The ingredient information includes, but is not limited to, the type of ingredient, price, and stock status. The collection unit obtains ingredient information from each store, for example, through an online API. The collection unit can also collect ingredient information from the nearest store based on the user's location information.
Step 3: The generation unit uses the generation AI to generate a recipe based on the information collected by the reception unit and the collection unit. The generation AI generates a recipe using, for example, a text generation AI (e.g., LLM). The generation unit also generates a recipe suitable for the user's cooking skills and cooking utensils. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at stews.
Step 4: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the recipe through, for example, a web application or a mobile application. The providing unit can also print the recipe and provide it on paper.

The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input for the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. The specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[Second embodiment]
FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is performed in a secure state.

Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input for the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[Third embodiment]
FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is performed in a secure state.

Figure 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Figure 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input for the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[Fourth embodiment]
FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is performed in a secure state.

The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and legs. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating the user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by an AI, the processing is performed in part or in whole by the AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

The emotion identification model 59, which serves as an emotion engine, may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. Emotions that represent states and actions arising from a state of mind are arranged on the outer sides of the concentric circles. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

These emotions are distributed in the three o'clock direction of emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on inside the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotions become.

Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery power, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions that belong to an area called "reaction" where sensation is dominant. The right half of the emotion map is lined with emotions that belong to an area called "situation" where situation recognition is dominant.

The emotion map defines two emotions that encourage learning. The first is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input to a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple learning data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, "peace of mind," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

In the above embodiment, an example has been described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

The specific processing program 56 may also be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The various processors listed below can be used as hardware resources for executing specific processes. Examples of processors include a CPU, which is a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically to execute specific processes. All of these processors have built-in or connected memory, and all of these processors execute specific processes by using the memory.

The hardware resource that executes the specific process may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific process may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured by combining one or more CPUs with software, and this processor functions as a hardware resource that executes a specific process. Secondly, there is a configuration in which a processor is used that realizes the functions of the entire system, including multiple hardware resources that execute a specific process, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the various processors mentioned above as hardware resources.

More specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processing described above is merely an example. It goes without saying that unnecessary steps can be deleted, new steps can be added, and the processing order can be changed without departing from the spirit of the invention.

In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are only examples, and they may be combined with each other, or may be other devices. Also, in the above example, the first and second embodiments have been described separately, but these may be combined.

The above description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, function, action, and effect is an explanation of an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. Also, in order to avoid confusion and to make it easier to understand the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference into this specification to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

(Appendix 1)
A reception unit that receives the user's cooking utensil possession status and cooking know-how skills;
A collection unit that collects food information from at least one of nearby convenience stores, available mail-order stores, and supermarkets;
a generation unit that generates a recipe based on the information collected by the reception unit and the collection unit;
A system comprising: a providing unit that provides the recipe generated by the generating unit.
(Appendix 2)
The collecting unit includes:
The system described in claim 1, characterized in that it collects ingredient information from at least one of convenience stores, mail order stores, and supermarkets.
(Appendix 3)
The generation unit is
The system described in claim 1 is characterized by generating recipes that are suitable for the user's cooking skills and cooking utensils.
(Appendix 4)
The providing unit is
The system of claim 1, further comprising: providing the generated recipe to a user.
(Appendix 5)
The reception unit is
The system according to claim 1, further comprising: estimating a user's emotion; and adjusting a method of inputting cooking utensils and cooking skills based on the estimated user's emotion.
(Appendix 6)
The reception unit is
The system described in claim 1 analyzes a user's past cooking history and suggests an optimal input method.
(Appendix 7)
The reception unit is
The system described in claim 1, wherein filtering is performed based on the user's health condition and dietary preferences when inputting cooking utensil ownership status and cooking skills.
(Appendix 8)
The reception unit is
The system according to claim 1, further comprising: estimating a user's emotion; and determining a priority order of cooking utensils and cooking skills to be input based on the estimated user's emotion.
(Appendix 9)
The reception unit is
The system according to claim 1, characterized in that when inputting cooking utensils and cooking skills, highly relevant information is input preferentially in consideration of the user's geographical location information.
(Appendix 10)
The reception unit is
The system described in claim 1, characterized in that when cooking utensils and cooking skills are input, the system analyzes the user's social media activity and inputs related information.
(Appendix 11)
The collecting unit includes:
The system according to claim 1, further comprising: estimating a user's emotion; and adjusting a method of collecting ingredient information based on the estimated user's emotion.
(Appendix 12)
The collecting unit includes:
The system according to claim 1, characterized in that when collecting food ingredient information, the system analyzes the user's past purchasing history and selects the optimal collection method.
(Appendix 13)
The collecting unit includes:
The system according to claim 1, wherein when collecting food ingredient information, filtering is performed based on the user's health condition and dietary preferences.
(Appendix 14)
The collecting unit includes:
The system according to claim 1, further comprising: estimating a user's emotion; and determining a priority order for the food ingredient information to be collected based on the estimated user's emotion.
(Appendix 15)
The collecting unit includes:
The system according to claim 1, characterized in that when collecting ingredient information, highly relevant information is collected preferentially in consideration of the user's geographical location information.
(Appendix 16)
The collecting unit includes:
The system described in claim 1, characterized in that when collecting ingredient information, the system analyzes the user's social media activity and collects related information.
(Appendix 17)
The generation unit is
The system of claim 1, further comprising: estimating a user's emotions; and adjusting a recipe generation method based on the estimated user's emotions.
(Appendix 18)
The generation unit is
The system described in claim 1 is characterized in that, when generating a recipe, the system analyzes the user's past cooking history to generate an optimal recipe.
(Appendix 19)
The generation unit is
The system described in claim 1, characterized in that when creating a recipe, the recipe is customized based on the user's health condition and dietary preferences.
(Appendix 20)
The generation unit is
The system according to claim 1, further comprising: estimating a user's emotions; and determining a priority order for recipes to be generated based on the estimated user's emotions.
(Appendix 21)
The generation unit is
The system according to claim 1, characterized in that when generating a recipe, the system generates an optimal recipe by taking into account a user's geographical location information.
(Appendix 22)
The generation unit is
The system described in claim 1, characterized in that when generating a recipe, the system analyzes a user's social media activity and generates related recipes.
(Appendix 23)
The providing unit is
The system of claim 1, further comprising: estimating a user's emotions; and adjusting a recipe presentation method based on the estimated user's emotions.
(Appendix 24)
The providing unit is
The system described in claim 1, characterized in that when providing a recipe, the system selects the optimal method of providing the recipe by referring to the user's past cooking history.
(Appendix 25)
The providing unit is
The system described in claim 1, characterized in that when providing a recipe, the method of providing the recipe is customized based on the user's health condition and dietary preferences.
(Appendix 26)
The providing unit is
The system described in claim 1 is characterized in that it estimates a user's emotions and determines the order in which recipes are served based on the estimated user's emotions.
(Appendix 27)
The providing unit is
The system according to claim 1, characterized in that when providing a recipe, the system selects the optimal providing method taking into account the user's geographical location information.
(Appendix 28)
The providing unit is
The system described in claim 1 is characterized in that, when providing a recipe, the system analyzes a user's social media activity and provides related recipes.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (6)

A reception unit that receives the user's cooking utensil possession status and cooking know-how skills;
A collection unit that collects food information from at least one of nearby convenience stores, available mail-order stores, and supermarkets;
a generation unit that generates a recipe based on the information collected by the reception unit and the collection unit;
A system comprising: a providing unit that provides the recipe generated by the generating unit. The generation unit is
The system according to claim 1, characterized in that it generates recipes suited to the user's cooking skills and cooking utensils owned by the user. The reception unit is
The system according to claim 1 , further comprising: estimating a user's emotion; and adjusting a cooking utensil and a cooking skill input method based on the estimated user's emotion. The reception unit is
The system according to claim 1, further comprising: analyzing a user's past cooking history and proposing an optimal input method. The reception unit is
The system according to claim 1, wherein when inputting cooking utensil ownership status and cooking skills, filtering is performed based on the user's health condition and dietary preferences. The reception unit is
The system according to claim 1 , further comprising: estimating a user's emotion; and determining a priority order of cooking utensils and cooking skills to be input based on the estimated user's emotion.

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