WO2020090117A1 - Information processing device, information processing system, learning device, learned estimation model, and method for collecting data for learning - Google Patents

Abstract

This information processing device is provided with a region-specifying unit for specifying, in an image obtained by imaging a guest using a camera, a facial region indicating the guest's face, and a body region indicating the guest's body; an image feature-extracting unit for extracting a first feature amount from the facial region of the image, and a second feature amount from the body region of the image; a sound feature-extracting unit for extracting a third feature amount from a portion of sounds collected by a microphone corresponding to the guest's speech; a learned estimation model for receiving the first feature amount, the second feature amount, and the third feature amount as inputs, and outputting, as a result of estimation, the possibility of whether each of a plurality of apparel items is one that should be recommended; and a display unit for displaying an apparel item suitable for the guest on the basis of the result of estimation.

Description

Information processing device, information processing system, learning device, learned estimation model, and learning data collection method

The present invention relates to a technique for proposing a clothing item suitable for a plurality of clothing items.

Many clothing items are displayed at stores that sell clothing items, and it is not easy for prospective purchasers to find the desired clothing item.

For example, Japanese Patent Laying-Open No. 2017-215667 (Patent Document 1) can easily recommend a recommended product to a customer who visits a store based on what the customer owns or the sales product that the customer sees. In response to such a problem that did not exist, the customer who came to the store was photographed with a photograph of the item worn by the customer or the sale product displayed at the position where the customer is in the store. Disclosed is a configuration that proposes product information related to recommended products of a type corresponding to owner information of an item or the like shown in a photograph.

In WO 2003/069526 (Patent Document 2), when a physical feature is input, a first database device that outputs data of fashion content that matches the feature is output, and the first database device outputs the data. Disclosed is a fashion advising system including a second database device that outputs data of a store that provides fashion contents based on the data of the fashion contents.

Japanese Patent Laid-Open No. 2001-502090 (Patent Document 3) discloses a method of fashion shopping by a customer, and specifically, a method of helping to select an appropriate fashion for purchase based on data about the customer. To do.

JP, 2017-215667, A International Publication No. 2003/069526 Japanese Patent Publication No. 2001-502090

The configuration disclosed in Patent Document 1 is a product different from the type of the product shown in the photograph, for example, a product in which design elements such as color, shape, and pattern match the product, or owner information is secondly owned. In the case of personal information, recommended products are, for example, products that are the same as the type of the product shown in the photograph, and recommend products that have design elements such as color, shape, and pattern that match the product. It's not like recommending products according to your taste.

With the configuration disclosed in Patent Document 2, when the physical characteristics of the customer are input, the main purpose is to determine the fashion content that matches those characteristics, and to provide the fashion according to the taste of the customer. It's not like that.

The configuration disclosed in Patent Document 3 is for supporting the selection of clothing items for purchase by acquiring personal information including measurement values of bust, hip, waist, arm length, height, and front center. , Personal information is required to propose clothing items, which is not suitable for general-purpose operation.

An object of the present invention is to provide a technique for proposing a clothing item suitable for a customer from a plurality of clothing items with higher accuracy based on a feature amount representing the characteristics of the customer.

According to an aspect of the present invention, there is provided an information processing device that proposes a clothing item suitable for a customer from a plurality of clothing items based on a feature amount that represents the characteristics of the customer. An information processing apparatus includes a camera for capturing an image of a customer, a microphone for collecting voice, a face area representing the face of the customer, and a body of the customer in an image obtained by capturing the image of the customer with the camera. A region specifying unit for specifying the body region to be expressed, an image feature extracting unit for extracting the first feature amount from the face region of the image, and a second feature amount from the body region of the image, and a microphone. A voice feature extraction unit for extracting a third feature amount from the voice of a portion corresponding to the utterance of the customer in the collected voice, and receiving the input of the first feature amount, the second feature amount, and the third feature amount , To display the clothing item according to the customer based on the learned estimation model that outputs each possibility that each of the plurality of clothing items is a clothing item to be proposed as an estimation result, and the estimation result. And the display section of . The estimation model is generated by a learning process using the learning data set, and the learning data set is used for the image obtained by imaging the other customer and the voice uttered by the other customer. It includes a plurality of learning data labeled with the fashion items purchased by the customer.

The display unit displays a list of categories indicating the classification of clothing items before collecting voice by the microphone, and also displays a message prompting the customer to select one of the categories displayed in the list by voice. You may do so.

The area specifying unit may specify, as a body area, a portion representing the clothes worn by the customer.

Each of the plurality of clothing items may belong to any one of a plurality of predetermined categories. The information processing apparatus may further include a voice analysis unit for identifying a category selected by the customer from the plurality of categories based on the voice uttered by the customer. Among the clothing items displayed based on the estimation result, the display unit displays clothing items belonging to the category specified by the voice analysis unit and clothing items not belonging to the specified category in different display modes. You can

An information processing system according to another aspect of the present invention is an information processing apparatus that inputs a feature amount representing a feature of a customer to a learned estimation model and proposes a clothing item corresponding to the customer from a plurality of clothing items. , And a learning device for generating the estimation model. An information processing apparatus includes a camera for capturing an image of a customer, a microphone for collecting voice, a face area representing a face of the customer, and a body of the customer in an input image obtained by capturing the image of the customer with the camera. And an image feature extraction unit for extracting the first feature amount from the face region of the input image and the second feature amount from the body region of the input image. , And a voice feature extraction unit for extracting a third feature amount from the voice of the portion corresponding to the utterance of the customer in the voice collected by the microphone. The estimation model receives inputs of the first feature amount, the second feature amount, and the third feature amount, receives inputs of the first feature amount, the second feature amount, and the third feature amount, and each of the plurality of clothing items Is learned to output each possibility that is a clothing item to be proposed as an estimation result. The information processing device further includes a display unit for displaying a clothing item according to a customer based on the estimation result. The learning device includes an acquisition unit for acquiring the learning data set. The learning data set is a learning data obtained by labeling a clothing item purchased by another customer with respect to a learning image obtained by imaging another customer and a learning voice spoken by the other customer. Including multiple. The learning device further includes an area specifying unit for specifying a face area representing the face of another customer and a body area representing the body of another customer in the learning image, and a face area of the learning image. An image feature extraction unit for extracting the first learning feature amount and a second learning feature amount from the body region of the learning image, and a portion of the learning voice corresponding to the utterance of another customer. A speech feature extraction unit for extracting the third learning feature amount from the voice, and an estimation model for the first learning feature amount, the second learning feature amount, and the third learning feature amount extracted from the learning data. Includes a learning unit for optimizing the estimation model so that the estimation result input to and output to approaches the purchase record of the clothing item labeled in the learning data.

According to still another aspect of the present invention, learning for receiving an input of a feature amount representing a feature of a customer and generating an estimation model used for suggesting a clothing item according to the customer from a plurality of clothing items. A device is provided. The learning device includes an acquisition unit for acquiring the learning data set. The learning data set includes a plurality of learning data in which the clothing item purchased by the customer is labeled with respect to the image obtained by imaging the customer and the voice uttered by the customer. The learning device extracts, in the image, a face area representing a customer's face and a body area representing a customer's body, and a first feature amount from the face area of the image. Image feature extraction unit for extracting the second feature amount from the body region, a voice feature extraction unit for extracting the third feature amount from the voice of the portion of the voice corresponding to the utterance of the customer, and learning data The estimation result output by inputting the first feature amount, the second feature amount, and the third feature amount extracted from the estimation model approaches the purchase record of the clothing item labeled in the learning data. Thus, a learning unit for optimizing the estimation model is included.

According to still another aspect of the present invention, there is provided a learned estimation model used for suggesting a clothing item corresponding to the customer from a plurality of clothing items in response to the input of the feature amount representing the characteristics of the customer. It The estimation model is generated by a learning process using the learning data set. The learning data set includes a plurality of learning data in which the clothing item purchased by the customer is labeled with respect to the image obtained by imaging the customer and the voice uttered by the customer. In the learning process, for each of the learning data, a step of identifying a face area representing the face of the customer and a body area representing the body of the customer in the image, and extracting the first feature amount from the face area of the image. At the same time, a step of extracting the second feature amount from the body region of the image, a step of extracting the third feature amount from the voice of the portion corresponding to the utterance of the customer in the voice, the first feature amount, the second feature amount, and And optimizing the estimation model so that the estimation result output by inputting the third feature amount into the estimation model approaches the purchase record of the clothing item labeled in the learning data.

According to still another aspect of the present invention, learning used for learning an estimation model used for suggesting a clothing item corresponding to a customer from a plurality of clothing items by receiving a feature amount representing a customer feature. A method of collecting data for use is provided. The learning data is collected by inputting an image obtained by capturing an image of a customer and a voice including a utterance of the customer, and inputting a plurality of feature amounts extracted from the image and the voice into a learned estimation model. Generate a suggestion of a furnishing item according to the customer, generate identification information, and issue a medium for promoting purchase of a furnishing item, including the suggestion of the produced furnishing item and the generated identification information. A step of associating the generated identification information with the image and sound; a step of acquiring the identification information included in the medium and a clothing item purchased by the customer; a step of associating the identification information obtained from the medium with the clothing purchased by the customer The step of associating with the item and the image and sound with the identification information as a key are associated with the clothing item purchased by the customer to learn the estimation model. And a step of storing as learning data to be needed.

According to the present invention, it is possible to propose a clothing item suitable for a customer from a plurality of clothing items with higher accuracy based on the feature amount representing the characteristics of the customer.

It is a schematic diagram which shows an example of the external appearance of the store in which the clothing suggestion system according to this Embodiment is arrange | positioned. It is a figure for demonstrating the process in the display terminal which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the process in the display terminal which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the visitor who uses the coupon output from the display terminal which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the production | generation process of the learning data set in the clothing suggestion system according to this Embodiment. FIG. 1 is a schematic diagram showing an example of a system configuration of a clothing suggestion system according to the present embodiment. It is a schematic diagram which shows an example of the hardware constitutions of the display terminal which comprises the clothing suggestion system according to this Embodiment. It is a schematic diagram which shows an example of the hardware constitutions of the POS terminal which comprises the clothing suggestion system according to this Embodiment. It is a schematic diagram which shows an example of the hardware constitutions of the management apparatus which comprises the clothing suggestion system according to this Embodiment. It is a schematic diagram which shows an example of a functional structure of the display terminal which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the processing content in the proposal item estimation function of the display terminal which comprises the clothing suggestion system according to this Embodiment. FIG. 12 is a diagram for explaining area specifying processing by the area specifying module shown in FIG. 11. It is a figure for demonstrating the process of the area identification by the area identification module shown in FIG. It is a schematic diagram which shows the network structural example of the estimation model shown in FIG. It is a figure for demonstrating the processing content in the display control function 150 and the coupon issue control function of the display terminal which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the processing content in the image sound preservation | save function 170 of the display terminal which comprises the clothing suggestion system according to this Embodiment. 7 is a flowchart showing a processing procedure of item estimation processing in the display terminal which constitutes the clothing suggestion system according to the present embodiment. It is a schematic diagram which shows an example of a functional structure of the POS terminal which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the processing content in the sales information storage function 250 of the POS terminal which comprises the clothing suggestion system according to this Embodiment. 7 is a flowchart showing a processing procedure of sales management processing in a POS terminal which constitutes the clothing suggestion system according to the present embodiment. It is a figure for explaining the outline of the learning phase in the clothing suggestion system according to the present embodiment. It is a schematic diagram which shows an example of a functional structure of the management apparatus which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the processing content in the learning data set production | generation function 350 of the management apparatus which comprises the clothing suggestion system according to this Embodiment. It is a figure for demonstrating the processing content in the learning function 360 of the management apparatus which comprises the clothing suggestion system according to this Embodiment. 7 is a flowchart showing a processing procedure of a learning process in the management device configuring the clothing suggestion system according to the present embodiment. It is a schematic diagram which shows an example of a system configuration of the clothing suggestion system according to the modification 1 of this Embodiment. It is a figure for demonstrating the item proposal screen displayed on the display terminal of the clothing suggestion system according to the modification 2 of this Embodiment. It is a figure for demonstrating the processing content in the display control function and coupon issue control function of the display terminal which comprises the clothing suggestion system according to the modification 2 of this Embodiment. It is a figure for demonstrating the processing content in the proposal item estimation function of the display terminal which comprises the clothing suggestion system according to the modification 3 of this Embodiment. It is a figure for demonstrating the processing content in the proposal item estimation function of the display terminal which comprises the clothing suggestion system according to the modification 4 of this Embodiment. It is a schematic diagram which shows the usage example of the clothing suggestion system according to the modification 5 of this Embodiment. It is a schematic diagram which shows the example of mounting of the clothing suggestion system according to the modification 5 of this Embodiment.

Embodiments of the present invention will be described in detail with reference to the drawings. It should be noted that the same or corresponding parts in the drawings are designated by the same reference numerals and the description thereof will not be repeated.

<A. Outline of clothing proposal system>
First, an outline of the clothing suggestion system 1 according to the present embodiment will be described as a typical example of the information processing system according to the present invention.

In the present specification, “clothing” means all clothes (clothing) and accessories (decoration) worn by a person. “Fashion item” is a term indicating any product included in the clothing. For the sake of simplicity of description, the “fashion item” may be simply referred to as “item”.

In the present specification, “customer” means a general user who has some intention to purchase a fashion item. In the following description, customers who visit the store are also referred to as “visitors”. A customer who uses the system according to the present embodiment via a mobile terminal is also referred to as a “net user”.

FIG. 1 is a schematic diagram showing an example of the exterior of a store in which clothing suggestion system 1 according to the present embodiment is arranged. 2 and 3 are diagrams for explaining the process in display terminal 100 configuring clothing suggestion system 1 according to the present embodiment.

As shown in FIG. 1, it is assumed that a customer (hereinafter, also referred to as “visitor 40”) enters the store 30. A display terminal 100, which is an example of an information processing device, is arranged near the entrance of the store 30. The display terminal 100 includes a relatively large-sized display 102, and a human sensor 128, a camera 130, and a microphone 132 arranged near the display 102. A printer 120 is arranged below the display 102.

When the visitor 40 approaches the display terminal 100 (FIG. 2A), the human sensor 128 detects the approach, and the category selection acceptance screen 50 is displayed on the display 102 (FIG. 2B). .. In this state, the visitor 40 is imaged by the camera 130 of the display terminal 100. That is, the display terminal 100 acquires an image showing the visitor 40 (hereinafter, also referred to as “captured image 136”).

The category selection reception screen 50 displays a list of one or more categories. At the same time, a message “Please select a category by voice” is displayed to prompt the visitor 40 to speak.

After that, the collection of voice is started by the microphone 132 of the display terminal 100, and when the visitor 40 emits a voice (“jacket” in the example shown in FIG. 2) indicating the desired category (FIG. 2C), the display is displayed. The item proposal screen 52 is output to 102 (FIG. 3A). At this time, the display terminal 100 acquires the voice uttered by the visitor 40 (hereinafter, also referred to as “collected voice 138”).

As described above, the display 102 displays a list of categories indicating the classification of clothing items and collects voice by selecting any of the categories displayed in the list before collecting the voice by the microphone 132. A message prompting the person 40 is displayed.

The item suggestion screen 52 includes a list display 54 of clothing items estimated to be “recommended” according to the taste of the visitor 40. The items displayed in a list on the item proposal screen 52 are determined based on the estimation result obtained by executing the item estimation process using a learned model as described later. As described above, the display terminal 100, which is an example of the information processing apparatus, selects a customer from a plurality of clothing items based on the feature amount (typically, the captured image 136 and the collected sound 138) representing the feature of the customer. We propose clothing items according to your needs.

The item proposal screen 52 further has a coupon issue button 56. In response to pressing the coupon issue button 56, the coupon 10 is output from the printer 120.

In addition to the discount amount display 12, the coupon 10 output from the printer 120 includes the list display 14 corresponding to the list display 54 included in the item proposal screen 52, and the items included in the list display 14 in any of the stores. And a map 16 indicating whether the position is present (FIG. 3 (b)).

Further, the coupon 10 includes an identification image 18 such as a QR code (registered trademark) indicating a coupon ID described later. By using the coupon ID indicated by the identification image 18, a learning data set used for learning the estimation model is generated.

FIG. 4 is a diagram for explaining a visitor 40 who uses the coupon 10 output from the display terminal 100 configuring the clothing suggestion system 1 according to the present embodiment. The visitor 40 can enjoy shopping while referring to the content printed on the coupon 10 (FIG. 4A). Since the discount is applied by presenting the coupon 10, the visitor 40 usually presents the coupon 10 output from the display terminal 100 at the time of checkout (FIG. 4B).

FIG. 5 is a diagram for explaining a learning data set generation process in the clothing suggestion system 1 according to the present embodiment. With reference to FIG. 5, the captured image 136 and the collected voice 138 acquired in the display terminal 100, and the information of the purchased item (hereinafter, also referred to as “sales information 218”) are the coupon 10 (to be exact). , Coupon ID 166) as an intermediary. In this way, the captured image 136, the collected voice 138, and the sales information 218 that are associated with each other are used as a learning data set for learning the estimation model.

As described above, in the clothing suggestion system 1 according to the present embodiment, an item is proposed based on the taste of the visitor 40 at the time of entering the store, and information of the item actually purchased by the visitor 40 is used to identify the item. It is possible to learn an estimation model for making a proposal.

<B. Hardware configuration example of clothing suggestion system>
Next, a system configuration example of the clothing suggestion system 1 according to the present embodiment will be described. First, the overall configuration example of the clothing suggestion system 1 will be described, and then the hardware configuration example of the main device included in the clothing suggestion system 1 will be described.

(B1: System configuration example)
FIG. 6 is a schematic diagram showing an example of a system configuration of clothing suggestion system 1 according to the present embodiment. With reference to FIG. 6, the clothing suggestion system 1 includes one or more display terminals 100, one or more POS terminals 200, and a management apparatus 300 which are connected via a local network 2.

The display terminal 100 is typically arranged near the entrance of the store 30, and proposes clothing according to the taste of the visitor. More specifically, the display terminal 100 acquires an image by capturing an image of a visitor, and collects a voice uttered by the visitor. The display terminal 100 inputs the image of the visitor (hereinafter, also referred to as “captured image”) and the voice (hereinafter, also referred to as “collected voice”) to the learned model, so that the preference of the visitor can be obtained. The fitness (hereinafter, also referred to as “score”) is calculated for each item for sale. The display terminal 100 proposes an item having a higher score to the store visitor. The display terminal 100 can also issue a coupon on which the item proposed to the visitor is printed.

The display terminal 100 can also transmit the captured image and the collected sound to the management device 300 in response to a request.

POS terminal 200 executes accounting processing for items that the visitor desires to purchase. The POS terminal 200 can also generate information (sales information) on the purchased item and transmit it to the management device 300 in response to a request.

The management device 300 manages and updates the learned model used by the display terminal 100. More specifically, the management device 300 acquires the captured image and the collected voice from the display terminal 100, and acquires the sales information from the POS terminal 200. Then, the management device 300 generates a learning data set from the acquired captured image and collected voice and the acquired sales information. The management device 300 uses the generated learning data set to perform learning of the learned model (which may include both new learning and additional learning).

The learned model generated or updated by the management device 300 is transmitted to the display terminal 100.

(B2: display terminal 100)
FIG. 7 is a schematic diagram showing an example of the hardware configuration of display terminal 100 that constitutes clothing suggestion system 1 according to the present embodiment. The display terminal 100 may be realized by using a general-purpose computer.

Referring to FIG. 7, the display terminal 100 includes a display 102, a processor 104, a memory 106, a network controller 108, a storage 110, a printer 120, an optical drive 122, and a touch as main hardware elements. The detection unit 126, a human sensor 128, a camera 130, and a microphone 132 are included.

The display 102 outputs a category selection acceptance screen 50, an item proposal screen 52, and the like. The display 102 is composed of, for example, an LCD (Liquid Crystal Display) or an organic EL (Electroluminescence) display.

The processor 104 is an arithmetic entity that executes processing required to realize the display terminal 100 by executing various programs as will be described later. The processor 104 may be, for example, one or more CPUs (Central Processing Unit). And GPU (Graphics Processing Unit). A CPU or GPU having multiple cores may be used.

The memory 106 provides a storage area for temporarily storing a program code, a work memory, etc. when the processor 104 executes a program. As the memory 106, for example, a volatile memory device such as a DRAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory) may be used.

The network controller 108 transmits / receives data to / from any information processing device including the management device 300 via the local network 2. The network controller 108 may support any communication method such as Ethernet (registered trademark), wireless LAN (Local Area Network), and Bluetooth (registered trademark).

The storage 110 is an item image for generating an OS (Operating System) 112 executed by the processor 104, an application program 114 for realizing a functional configuration described later, a learned model 116, and an item proposal screen 52. For example, 118 is stored. As the storage 110, for example, a non-volatile memory device such as a hard disk or SSD (Solid State Drive) may be used. Further, the storage 110 may store a picked-up image obtained by picking up an image of a visitor and a collected voice that is a voice emitted by the visitor.

The library or function module provided as a standard by the OS 112 may be used as a part of the library or function module required when the application program 114 is executed by the processor 104. In this case, the application program 114 alone does not include all of the program modules necessary to realize the corresponding function, but by installing it under the execution environment of the OS 112, the function as described later is provided. The configuration can be realized. Therefore, even a program that does not include such a part of the library or the functional module can be included in the technical scope of the present invention.

The printer 120 issues a coupon printed with the item proposed to the visitor. As the printer 120, any printing method such as an electrophotographic method, an inkjet method, and a thermal paper method can be adopted.

The optical drive 122 reads information such as programs stored in the optical disc 124 such as a CD-ROM (Compact Disc Read Only Memory) and a DVD (Digital Versatile Disc). The optical disc 124 is an example of a non-transitory recording medium, and is distributed in a state in which an arbitrary program is stored in a nonvolatile manner. The optical drive 122 reads the program from the optical disk 124 and installs the program in the storage 110, whereby the display terminal 100 according to the present embodiment can be configured. Therefore, the subject matter of the present invention may be the program itself installed in the storage 110 or the like, or a recording medium such as the optical disk 124 storing the program for implementing the functions and processes according to the present embodiment.

FIG. 7 shows an optical recording medium such as the optical disk 124 as an example of a non-transitory recording medium, but the present invention is not limited to this, and a semiconductor recording medium such as a flash memory or a magnetic recording medium such as a hard disk or a storage tape. , MO (Magneto-Optical disk) or the like may be used.

Alternatively, the program for implementing the display terminal 100 may be distributed not only by being stored in an arbitrary recording medium as described above and distributed, but also by being downloaded from a server device or the like via the Internet or an intranet. ..

The touch detection unit 126 is arranged in association with the display 102 and detects an input operation on the display 102. As the touch detection unit 126, an arbitrary detection method such as a capacitance method, a resistance film method, or an ultrasonic surface acoustic wave method can be adopted.

The human sensor 128 detects the approach of a visitor to the display terminal 100 by using infrared rays or the like.

The camera 130 is a device that images a visitor, is arranged near the display area of the display 102, etc., and is configured to include the visitor facing the display 102 in the field of view. The camera 130 may continuously capture an image of the visual field range in a predetermined cycle, or may capture an image in accordance with a command issued from the processor 104 or the like.

The microphone 132 is a device for collecting voice, and is arranged in the vicinity of the display area of the display 102 or the like in which the voice emitted by the visitor can be collected. The microphone 132 preferably collects only the voice of the visitor who directly faces the display 102, and therefore preferably has a sharp directivity.

FIG. 7 shows a configuration example in which the general-purpose computer (processor 104) realizes the display terminal 100 by executing the application program 114. However, all or a part of the functions necessary for realizing the display terminal 100 are It may be realized by using a hard-wired circuit such as an integrated circuit. For example, it may be realized by using an ASIC (Application Specific Integrated Circuit) or FPGA (Field-Programmable Gate Array).

(B3: POS terminal 200)
FIG. 8 is a schematic diagram showing an example of the hardware configuration of the POS terminal 200 configuring the clothing suggestion system 1 according to the present embodiment. Referring to FIG. 8, the POS terminal 200 includes a display 202, a processor 204, a memory 206, a network controller 208, a storage 210, a printer 220, an optical drive 222, and a touch as main hardware elements. It includes a detection unit 226, an optical reader 228, an input unit 230, and a payment processing unit 232.

The display 202 displays information necessary for item accounting processing. The display 202 is composed of, for example, an LCD or an organic EL display.

The processor 204 is an arithmetic operation body that executes processing required to realize the POS terminal 200 by executing various programs to be described later. The processor 204 includes, for example, one or a plurality of CPUs. .. A CPU having a plurality of cores may be used.

The memory 206 provides a storage area for temporarily storing a program code, a work memory, etc. when the processor 204 executes a program. As the memory 206, for example, a volatile memory device such as DRAM or SRAM may be used.

The network controller 208 transmits / receives data to / from any information processing device including the management device 300 via the local network 2. The network controller 208 may be adapted to any communication method such as Ethernet, wireless LAN, and Bluetooth.

The storage 210 includes an OS 212 executed by the processor 204, an application program 214 for realizing a functional configuration described later, item information 216 including price and attribute information of each item required for accounting processing, and purchased. The sales information 218, which is the information of the item, is stored. As the storage 210, for example, a non-volatile memory device such as a hard disk or SSD may be used.

The library or function module provided as standard by the OS 212 may be used as a part of the library or function module required when the application program 214 is executed by the processor 204. In this case, the application program 214 alone does not include all the program modules necessary to realize the corresponding function, but the function as described below is obtained by being installed under the execution environment of the OS 212. The configuration can be realized. Therefore, even a program that does not include such a part of the library or the functional module can be included in the technical scope of the present invention.

The printer 220 issues a receipt in which the results of accounting processing are printed. As the printer 220, any printing method such as an electrophotographic method, an inkjet method, and a thermal paper method can be adopted.

The optical drive 222 reads information such as programs stored in the optical disc 224 such as a CD-ROM or a DVD. The optical disk 224 is an example of a non-transitory recording medium, and is distributed in a state where an arbitrary program is stored in a nonvolatile manner. The optical drive 222 reads the program from the optical disk 224 and installs it in the storage 210, so that the POS terminal 200 according to the present embodiment can be configured. Therefore, the subject matter of the present invention may be a program itself installed in the storage 210 or the like, or a recording medium such as an optical disk 224 storing the program for implementing the functions and processes according to the present embodiment.

FIG. 8 shows an optical recording medium such as the optical disk 224 as an example of a non-transitory recording medium, but the present invention is not limited to this, and a semiconductor recording medium such as a flash memory or a magnetic recording medium such as a hard disk or a storage tape. , MO, etc. may be used.

Alternatively, the program for realizing the POS terminal 200 may be distributed not only by being stored in an arbitrary recording medium as described above and distributed, but also by being downloaded from a server device or the like via the Internet or an intranet. ..

The touch detection unit 226 is arranged in association with the display 202 and detects an input operation on the display 202. As the touch detection unit 226, an arbitrary detection method such as a capacitance method, a resistance film method, or an ultrasonic surface acoustic wave method can be adopted.

The optical reader 228 optically reads information such as the item tag information attached to the item and the QR code included in the coupon. As the optical reader 228, any detection method such as a laser scan method or an image sensing method can be adopted.

The input unit 230 receives an input operation such as the type of amount. As the input unit 230, for example, a cashier key, a keyboard, a mouse, a touch panel, a pen or the like may be used.

The payment processing unit 232 includes a mechanism necessary for cash payment and a mechanism necessary for electronic payment such as a credit card. More specifically, the payment processing unit 232 includes a cash storage unit for storing banknotes and coins, a sales management unit for managing the sales amount, and the like for cash payment. The payment processing unit 232 includes a mechanism for reading the information stored in the credit card and exchanging the payment information with a payment center for electronic payment.

FIG. 8 shows a configuration example in which the general-purpose computer (processor 204) executes the application program 214 to realize the POS terminal 200. However, all or a part of the functions necessary for realizing the POS terminal 200 are It may be realized using a hard-wired circuit such as an integrated circuit. For example, you may implement | achieve using ASIC, FPGA, etc.

(B4: management device 300)
FIG. 9 is a schematic diagram showing an example of a hardware configuration of management device 300 that constitutes clothing suggestion system 1 according to the present embodiment. With reference to FIG. 9, the management device 300 includes a display 302, a processor 304, a memory 306, a network controller 308, a storage 310, and an input unit 330 as main hardware elements.

The display 302 displays information necessary for processing by the management device 300. The display 302 is composed of, for example, an LCD or an organic EL display.

The processor 304 is a computing entity that executes the processes required to realize the management device 300 by executing various programs to be described later. The processor 304 includes, for example, one or more CPUs or GPUs. To be done. A CPU or GPU having multiple cores may be used. In the management device 300, it is preferable to adopt a GPU suitable for the learning process for generating the learned model.

The memory 306 provides a storage area for temporarily storing a program code, a work memory, etc. when the processor 304 executes a program. As the memory 306, for example, a volatile memory device such as DRAM or SRAM may be used.

The network controller 308 transmits / receives data to / from any information processing device including the display terminal 100 and the POS terminal 200 via the local network 2. The network controller 308 may support any communication method such as Ethernet, wireless LAN, and Bluetooth.

The storage 310 is a pre-process for generating a learning data set 324 from the OS 312 executed by the processor 304, an application program 314 for realizing a functional configuration described below, image / sound information 320, and sales information 322. A program 316 and a learning program 318 for generating a learned model 326 using the learning data set 324 are stored.

The image / sound information 320 includes a captured image 136 acquired from the display terminal 100 and a collected sound 138. The sales information 322 includes sales information 218 acquired from the POS terminal 200. The acquisition processing of the image / sound information 320 and the sales information 322 will be described in detail later.

The learning data set 324 is a training data set in which the sales information 322 is added as a label (or tag) to the image / sound information 320. The learned model 326 is an estimation model obtained by executing a learning process using the learning data set 324.

A non-volatile memory device such as a hard disk or SSD may be used as the storage 310.

The library or function module provided as standard by the OS 312 may be used as a part of the library or function module required when the application program 314, the preprocessing program 316, and the learning program 318 are executed by the processor 304. .. In this case, each of the application program 314, the preprocessing program 316, and the learning program 318 alone does not include all of the program modules necessary to realize the corresponding function, but under the execution environment of the OS 312. By being installed in, the functional configuration described below can be realized. Therefore, even a program that does not include such a part of the library or the functional module can be included in the technical scope of the present invention.

The application program 314, the preprocessing program 316, and the learning program 318 include an optical recording medium such as an optical disk, a semiconductor recording medium such as a flash memory, a magnetic recording medium such as a hard disk or a storage tape, and a magneto-optical recording medium such as an MO. It may be stored in a non-transitory recording medium, distributed, and installed in the storage 310. Therefore, the subject of the present invention may be the program itself installed in the storage 310 or the like, or a recording medium storing the program for implementing the functions and processes according to the present embodiment.

Alternatively, the program for realizing the management device 300 may be distributed not only by being stored in an arbitrary recording medium as described above and distributed, but also by being downloaded from a server device or the like via the Internet or an intranet. ..

The input unit 330 receives various input operations. As the input unit 330, for example, a keyboard, a mouse, a touch panel, a pen or the like may be used.

FIG. 9 shows a configuration example in which the general-purpose computer (processor 304) executes the application program 314, the preprocessing program 316, and the learning program 318 to realize the management apparatus 300. All or part of the necessary functions may be realized by using a hardwired circuit such as an integrated circuit. For example, you may implement | achieve using ASIC, FPGA, etc.

(B5: Integrated configuration / Cloud configuration)
6 to 9 exemplarily show a configuration in which the display terminal 100, the POS terminal 200, and the management device 300 each have a processor for realizing the function in charge of the respective items, but the present invention is not limited to this. It is also possible to adopt an integrated configuration in which the functions required for realizing the clothing suggestion system 1 are realized by a smaller number of calculation subjects.

As an example of such an integrated configuration, the management device 300 realizes the functions of the display terminal 100 and the POS terminal 200, and the display terminal 100 and the POS terminal 200 have only a user interface like a thin client. May be provided.

Furthermore, the management device 300 may also be realized by explicitly or implicitly cooperating with a plurality of computers connected via a computer network. When a plurality of computers work together, some of the computers may be unspecified computers on a network, which are so-called cloud computers.

Those skilled in the art will be able to realize the clothing suggestion system 1 according to the present embodiment by appropriately using the technology according to the era in which the present invention is implemented.

<C. Functions and Processing of Display Terminal 100>
Next, the function and processing of display terminal 100 that constitutes clothing suggestion system 1 according to the present embodiment will be described. In the clothing suggestion system 1, the display terminal 100 takes charge of the operation phase of suggesting clothing using a learned model (estimated model) and also takes charge of part of the learning phase for constructing the learned model. Become.

(C1: Functional configuration of display terminal 100)
FIG. 10 is a schematic diagram showing an example of the functional configuration of the display terminal 100 configuring the clothing suggestion system 1 according to the present embodiment. Each function shown in FIG. 10 may be typically implemented by the processor 104 of the display terminal 100 executing the OS 112 and the application program 114 (see FIG. 7 for both).

Referring to FIG. 10, the display terminal 100 has a suggested item estimation function 140, a display control function 150, a coupon issuance control function 160, and an image / sound storage function 170 as functional configurations.

The proposed item estimation function 140 receives, as an input, a captured image 136 obtained by capturing an image of a visitor with the camera 130, and a collected voice 138 obtained by collecting the voice uttered by the visitor with the microphone 132, The estimation result is output by inputting it into the learned model 116.

The display control function 150 receives the estimation result from the suggestion item estimation function 140 and generates a screen for suggesting clothing according to the taste of the visitor.

The coupon issue control function 160 receives the information of the item proposed by the display control function 150 to the visitor, generates a coupon ID 166, and issues the coupon 10 on which the suggested item and the coupon ID 166 are printed.

The image / sound saving function 170 adds the coupon ID 166 generated by the coupon issuance control function 160 to the captured image 136 and the collected sound 138 received by the proposed item estimation function 140 as input and saves them. The captured image 136 and the collected voice 138 (to which the coupon ID 166 is added) stored by the image and voice storage function 170 are transmitted to the management device 300, as will be described later, and a learning process for generating a learned model. Used for.

(C2: Proposed item estimation function 140)
Next, details of the proposed item estimation function 140 of the display terminal 100 shown in FIG. 10 will be described.

FIG. 11 is a diagram for explaining the processing contents in the suggested item estimation function 140 of the display terminal 100 that constitutes the clothing suggestion system 1 according to the present embodiment. With reference to FIG. 11, the display terminal 100 includes an area identification module 141, size adjustment modules 142 and 143, a section identification module 144, and a resampling module 145 as the proposed item estimation function 140.

The area specifying module 141 analyzes a subject (visitor) included in the captured image 136 and specifies a face area and a body area. That is, the area specifying module 141 specifies a face area representing the customer's face and a body area representing the customer's body in the image obtained by capturing the customer with the camera 130. The area specifying module 141 extracts the face area partial image 147 and the body area partial image 148 corresponding to the specified face area and body area from the captured image 136 and outputs them.

Typically, the region identification module 141 extracts face features such as eyes and nose and skeletal features such as limbs to identify a face region and a body region. At this time, the area specifying module 141 may specify the portion representing the clothing worn by the customer as the body area.

FIG. 12 is a diagram for explaining the area specifying processing by the area specifying module 141 shown in FIG. With reference to FIG. 12, the region specifying module 141 extracts a region including the visitor's face as a face region partial image 147, and extracts a region below the visitor's face as a body region partial image 148.

The face area partial image 147 is considered to include attribute information such as the gender and age of the visitor, and the body area partial image 148 stores information about the current clothes of the visitor (that is, information indicating the taste preference of the clothing). It is considered to include.

Referring again to FIG. 11, the face area partial image 147 extracted from the captured image 136 by the area specifying module 141 is output to the size adjusting module 142. Similarly, the body region partial image 148 extracted from the captured image 136 by the region specifying module 141 is output to the size adjusting module 143.

In the size adjustment modules 142 and 143, the face area partial image 147 and the body area partial image 148 are converted into a feature amount (feature amount vector) having a predetermined dimension and given to the estimation model 1400. Here, since the image sizes of the face area partial image 147 and the body area partial image 148 extracted by the area specifying module 141 may vary, the size adjusting modules 142 and 143 standardize the image sizes.

More specifically, the size adjustment module 142 adjusts the face area partial image 147 from the area identification module 141 to an image having a predetermined number of pixels, and then adjusts the pixel value of each pixel forming the adjusted image. Is input to the estimation model 1400 as a face area feature amount 1410.

Similarly, the size adjustment module 143 adjusts the body region partial image 148 from the region specifying module 141 to an image having a predetermined number of pixels, and then sets the pixel value of each pixel forming the adjusted image to the body region. The feature amount 1420 is input to the estimation model 1400.

As described above, the size adjustment modules 142 and 143 extract the face area feature amount 1410 (first feature amount) from the face area partial image 147 (image face area), and the body area partial image 148 (image area of the image). ), The body region feature amount 1420 (second feature amount) is extracted.

The section specifying module 144 specifies the section of the voice uttered by the visitor included in the collected voice 138, extracts the specific section voice 149, and outputs it. Typically, the section identifying module 144 analyzes the temporal change of the voice indicated by the collected voice 138, and identifies the section in which the amplitude or frequency of the noise component around the display terminal 100 has changed. As a result, the specific section voice 149 is extracted.

FIG. 13 is a diagram for explaining the section specifying processing by the section specifying module 144 shown in FIG. 11. With reference to FIG. 13, the section identifying module 144 identifies, as a speech section by the visitor, a section showing a significant change with respect to a temporal change before and after the temporal change of the voice shown by the collected voice 138. Then, it is extracted as the specific section voice 149.

Since the specific section voice 149 is a voice uttered by the visitor in the desired category, the specific section voice 149 includes information for specifying the desired category. Further, the specific section voice 149 is considered to include information indicating the current feeling (mood) of the visitor.

In this way, the section identifying module 144 and the resampling module 145 extract the voice feature amount 1430 (third feature amount) from the voice of the part corresponding to the utterance of the customer among the voices collected by the microphone 132.

Referring again to FIG. 11, the specific section voice 149 extracted from the collected voice 138 by the resampling module 145 is output to the resampling module 145. In the resampling module 145, the specific section voice 149 is converted into a feature amount (feature amount vector) having a predetermined dimension and given to the estimation model 1400. Here, since the time length of the voice of the specific section voice 149 specified by the section specifying module 144 may vary, the resampling module 145 standardizes the number of audio samples.

More specifically, the resampling module 145 samples the time waveform of the voice indicated by the specific section voice 149 from the section specifying module 144 by a predetermined number of samples, and thus the amplitude value at each sampling point is voiced. The feature amount 1430 is input to the estimation model 1400.

The estimation model 1400 is constructed based on the learned model 116 that defines the network structure and corresponding parameters. The face area feature amount 1410, the body area feature amount 1420, and the voice feature amount 1430 are input to the estimation model 1400, whereby the calculation process defined by the estimation model 1400 is executed, and the score for each item is calculated as the estimation result 1450. To be done. Here, the score for each item is a value indicating the possibility that each clothing item is a clothing item to be proposed.

The estimation model 1400 is generated by a learning process using a learning data set as described later. As will be described later, the learning data set is a learning data set in which the clothing item purchased by the other customer is labeled with respect to the image obtained by imaging the other customer and the voice uttered by the other customer. Contains multiple data.

As described above, the estimation model 1400, which is the learned estimation model, includes the face region feature amount 1410 (first feature amount), the body region feature amount 1420 (second feature amount), and the voice feature amount 1430 (third feature amount). In response to the input, the possibility (score) of each of the plurality of clothing items that is a clothing item to be proposed is output as the estimation result 1450.

(C3: Estimated model 1400)
FIG. 14 is a schematic diagram showing a network configuration example of the estimation model 1400 shown in FIG. With reference to FIG. 14, the estimation model 1400 is a network classified as a DNN (Deep Neural Network). The estimation model 1400 includes preprocessing networks 1460, 1470, 1480 classified into CNN (Convolutional Neural Network), an intermediate layer 1490, an activation function 1492 corresponding to an output layer, and a Softmax function 1494.

The preprocessing networks 1460, 1470, and 1480 extract the feature amount effective for calculating the estimation result 1450 from the face region feature amount 1410, the body region feature amount 1420, and the voice feature amount 1430 having a relatively large order. It is planned to function as a kind of filter. Each of the preprocessing networks 1460, 1470, 1480 has a configuration in which convolutional layers (CONV) and pooling layers (Pooling) are alternately arranged. Note that the number of convolutional layers and the number of pooling layers do not have to be the same, and an activation function such as ReLU (rectified linear unit) is placed on the output side of the convolutional layer.

More specifically, the preprocessing network 1460 receives the face area feature amount 1410 (x ₁₁ , x ₁₂ , ..., X _1r ) and receives attribute information such as the gender and age of the visitor. It is constructed to output the feature quantity. Preprocessing the network 1470, body region feature _{1420 (x 21, x 22,} ···, x 2s) receives an input of information about the current clothing online user (i.e., information indicating the preference tendency of the clothing) Is constructed so as to output an internal feature amount indicating. The pre-processing network 1480 receives the input of the voice feature amount 1430 (x ₃₁ , x ₃₂ , ..., x _3t ), information for specifying the category, and the current feeling (feeling) of the visitor. Is constructed so as to output an internal feature amount indicating information indicating.

The middle layer 1490 is composed of a fully connected network having a predetermined number of layers, and the outputs from each of the preprocessing networks 1460, 1470, 1480 are sequentially combined for each node using the weights and biases determined for each node. To do.

An activation function 1492 such as ReLU is arranged on the output side of the intermediate layer 1490, and finally normalized to a probability distribution by the Softmax function 1494, and then the estimation result 1450 (y ₁ , y ₂ , ... .., y _N ) is output.

In the learning phase, which will be described later, the parameters of each element that constructs the network of the estimation model 1400 are optimized.

(C4: display control function 150 and coupon issue control function 160)
Next, details of the display control function 150 and the coupon issue control function 160 of the display terminal 100 shown in FIG. 10 will be described.

FIG. 15 is a diagram for explaining the processing contents in display control function 150 and coupon issue control function 160 of display terminal 100 that constitutes clothing suggestion system 1 according to the present embodiment. Referring to FIG. 15, display terminal 100 includes a display control module 152 as display control function 150.

The display control module 152 receives the estimation result 1450 calculated by the suggestion item estimation function 140 and generates the item suggestion screen 52 using the item image 118 corresponding to the item having the higher score in the estimation result 1450. The display control module 152 outputs the generated item proposal screen 52 to the display 102. That is, the display 102 displays the clothing item according to the customer based on the estimation result 1450.

The item image 118 includes an image of each item associated with the item identification information. The display control module 152 extracts a necessary image from the images included in the item image 118 based on the estimation result 1450.

The display terminal 100 also includes a coupon issue control module 162 and a coupon ID generation module 164 as the coupon issue control function 160. The coupon issue control module 162 receives the proposal item from the display control module 152 and the coupon ID 166 from the coupon ID generation module 164, and issues the coupon 10 on which the information is printed from the printer.

The coupon ID generation module 164 generates a coupon ID 166, which is unique identification information, by an arbitrary method. The coupon ID 166 may be printed on the coupon 10 in the form of a QR code or the like. In this case, the coupon ID generation module 164 may randomly generate a predetermined number of character strings and also generate a QR code corresponding to the generated character strings. As described later, the coupon ID 166 is used as a key for generating the learning data set 324.

(C5: Image / sound saving function 170)
Next, details of the image / sound saving function 170 of the display terminal 100 shown in FIG. 10 will be described.

FIG. 16 is a diagram for explaining the processing contents in the image / sound saving function 170 of the display terminal 100 which constitutes the clothing suggestion system 1 according to the present embodiment. Referring to FIG. 16, display terminal 100 includes an association module 172 and an image / sound storage unit 174 as image / sound storage function 170.

In response to the coupon issue control module 162 (see FIG. 15) issuing the coupon 10, the association module 172 accepts the coupon ID 166 assigned to the issued coupon 10 and uses it for issuing the coupon 10. The received captured image 136 and collected voice 138 are associated with the received coupon ID 166. The associating module 172 stores the associated coupon ID 166, the captured image 136 and the collected sound 138 in the image and sound storage unit 174 as one body.

The image / sound storage unit 174 is realized by using at least a part of a storage area provided by the memory 106 or the storage 110 (see FIG. 7). The image / sound storage unit 174 stores data in units of a data set including the coupon ID 166, the captured image 136, and the collected sound 138.

(C6: Processing procedure)
Next, the item estimation process executed in the display terminal 100 that constitutes the clothing suggestion system 1 will be described.

FIG. 17 is a flowchart showing a processing procedure of item estimation processing in display terminal 100 which constitutes clothing suggestion system 1 according to the present embodiment. Each step shown in FIG. 17 may be typically implemented by the processor 104 of the display terminal 100 executing the OS 112 and the application program 114 (see FIG. 7 for both).

Referring to FIG. 17, first, display terminal 100 determines whether or not a visitor is detected (step S100). In step S100, typically, it is determined whether or not a visitor exists based on the detection result from human sensor 128 (see FIG. 7). If no store visitor is detected (NO in step S100), the process of step S100 is repeated.

When the visitor is detected (YES in step S100), the display terminal 100 displays the category selection acceptance screen (see FIG. 2) on the display 102 (step S102).

Subsequently, the display terminal 100 acquires a captured image 136 by capturing an image of a visitor who faces the display terminal 100 with the camera 130 (step S104). At the same time, the display terminal 100 starts collecting voice (step S106). Then, the display terminal 100 determines whether or not the speech of the visitor has been detected based on the collected voice (step S108). In step S108, as shown in FIG. 13, it is determined whether or not a section (speech section) showing a significant change with respect to the temporal change of the voice has appeared. If the speech of the visitor is not detected (NO in step S108), the processes of steps S106 and S108 are repeated. As described above, the display terminal 100 deals with the process of acquiring the captured image 136 obtained by imaging the customer and the collected voice 138 including the utterance of the customer.

When the speech of the visitor is detected (YES in step S108), the display terminal 100 extracts the face area partial image 147 and the body area partial image 148 from the captured image 136 (step S110). Then, the display terminal 100 adjusts the sizes of the extracted face area partial image 147 and body area partial image 148 to extract the face area feature amount 1410 and the body region feature amount 1420 (step S112).

In addition, the display terminal 100 extracts the utterance section included in the collected voice 138 as the specific section voice 149 (step S114). Then, the display terminal 100 resamples the extracted specific section voice 149 to extract the voice feature amount 1430 (step S116).

The display terminal 100 inputs the face area characteristic amount 1410 and the body area characteristic amount 1420 generated in step S112, and the audio characteristic amount 1430 generated in step S116 into the estimation model 1400 to generate an estimation result 1450 (step S118). ).

In this way, the display terminal 100 inputs a plurality of feature amounts extracted from the captured image 136 and the collected sound 138 into the learned estimation model 1400, and executes a process of generating a suggestion of a clothing item according to the customer. To do.

The display terminal 100 generates and outputs the item proposal screen 52 based on the item with the higher score in the estimation result 1450 generated in step S118 (step S120).

The display terminal 100 determines whether the coupon issue button has been pressed (step S122). If the coupon issue button is not pressed (NO in step S122), the process of step S122 is repeated.

When the coupon issue button is pressed (YES in step S122), display terminal 100 generates coupon ID 166 and issues coupon 10 on which the proposed item and coupon ID 166 are printed (step S124). That is, the display terminal 100 executes the process of generating the coupon ID 166, which is the identification information, and the process of issuing the coupon 10, which is the medium. The coupon 10 includes the proposal of the generated clothing item and the generated coupon ID 166 (identification information), and the content of the discount for prompting the purchase of the clothing item is displayed.

Finally, the display terminal 100 stores the captured image 136 and the collected voice 138 in association with the coupon ID 166 (step S126). That is, the display terminal 100 executes a process of associating the generated coupon ID 166 (identification information) with the captured image 136 and the collected voice 138.

With the above, the item estimation process for one visitor is completed.
(C7: Summary)
Display terminal 100 according to the present embodiment has a face area feature amount 1410 generated from face area partial image 147 of a visitor, a body area feature amount 1420 generated from body area partial image 148 of a visitor, and a visitor. By providing the estimation model 1400 with the voice feature amount 1430 generated from the specific section voice 149 of 1, the clothing according to the taste of the visitor can be proposed.

Further, display terminal 100 according to the present embodiment can provide a platform for generating a learning data set used in a learning phase, which will be described later, by issuing coupon 10 including coupon ID 166.

<D. Functional configuration of POS terminal 200>
Next, the function and processing of POS terminal 200 configuring fashion suggestion system 1 according to the present embodiment will be described. In the clothing suggestion system 1, the POS terminal 200 is mainly responsible for part of the learning phase for constructing the learned model.

(D1: Functional configuration of POS terminal 200)
FIG. 18 is a schematic diagram showing an example of the functional configuration of the POS terminal 200 configuring the clothing suggestion system 1 according to the present embodiment. Each function shown in FIG. 18 may be typically realized by the processor 204 of the POS terminal 200 executing the OS 212 and the application program 214 (see FIG. 8 for both).

Referring to FIG. 18, the POS terminal 200 has an accounting function 240 and a sales information storage function 250 as functional configurations.

The accounting function 240 is in charge of payment processing when a visitor purchases an item. More specifically, the accounting function 240 calculates the price, discount amount, payment amount, etc. of the purchase target item based on the information of the item tag 20 attached to the purchase target item and the coupon ID 166 read from the coupon. At the same time, the settlement process is executed. The accounting function 240 outputs sales information 218 indicating the item that has been settled.

The sales information saving function 250 adds the coupon ID 166 read from the coupon 10 to the sales information 218 output from the accounting function 240 and saves it. The sales information 218 (provided with the coupon ID 166) saved by the sales information saving function 250 is transmitted to the management device 300 and used for learning processing for generating a learned model, as described later.

(D2: Sales information storage function 250)
Next, details of the sales information storage function 250 of the POS terminal 200 shown in FIG. 18 will be described.

FIG. 19 is a diagram for explaining processing contents in the sales information storage function 250 of the POS terminal 200 that constitutes the clothing suggestion system 1 according to the present embodiment. Referring to FIG. 19, POS terminal 200 includes a correlation module 252 and a sales information storage unit 254 as sales information storage function 250.

In response to the sales information 218 and the coupon ID 166 being input from the accounting function 240 (see FIG. 18), the association module 252 has been attached to the coupon 10 used when the item related to the sales information 218 was purchased. The coupon ID 166 is accepted and associated with the sales information 218. The associating module 252 integrally stores the associated coupon ID 166 and the sales information 218 in the sales information storage unit 254.

The sales information storage unit 254 is realized by using at least a part of a storage area provided by the memory 106 or the storage 110 (see FIG. 7). The sales information storage unit 254 stores data in units of a data set including a coupon ID 166 and sales information 218.

The sales information 218 typically stores the number of units sold for each item type (item 1, item 2, ..., Item N).

(D3: processing procedure)
Next, a sales management process executed in the POS terminal 200 that constitutes the clothing suggestion system 1 will be described.

FIG. 20 is a flowchart showing a processing procedure of sales management processing in the POS terminal 200 configuring the clothing suggestion system 1 according to the present embodiment. The steps shown in FIG. 20 may be typically implemented by the processor 204 of the POS terminal 200 executing the OS 212 and the application program 214 (see FIG. 8 for both).

Referring to FIG. 20, first, the POS terminal 200 determines whether or not the coupon 10 has been read by the optical reader 228 (see FIG. 8) (step S200). When coupon 10 is read (YES in step S200), POS terminal 200 acquires coupon ID 166 of read coupon 10 (step S202). On the other hand, if coupon 10 is not read (NO in step S200), the process of step S202 is skipped.

Subsequently, the POS terminal 200 determines whether or not the item tag 20 attached to the item to be purchased is read by the optical reader 228 (see FIG. 8) (step S204). When the item tag 20 is read (YES in step S204), the POS terminal 200 adds the item information of the read item tag 20 to the sales information 218 (step S206).

Then, the POS terminal 200 determines whether or not an instruction to finish reading the item tag has been given (step S208). If the instruction to finish reading the item tag is not given (NO in step S208), the processes in step S204 and subsequent steps are repeated.

When the instruction to finish reading the item tag is given (YES in step S208), the POS terminal 200 calculates the payment amount based on the presence / absence of the coupon 10 and the current sales information 218 (step S210). Then, the POS terminal 200 executes the settlement process for the payment amount calculated in step S210 (step S212).

Subsequently, the POS terminal 200 determines whether or not the coupon ID 166 has been acquired (step S214). That is, in step S200, it is determined whether the coupon 10 has been read.

If coupon ID 166 has been acquired (YES in step S214), POS terminal 200 stores sales information 218 in association with coupon ID 166 (step S216). On the other hand, if coupon ID 166 has not been acquired (NO in step S214), the process of step S216 is skipped. With the above, the sales management process for one visitor is completed.

(D4: Summary)
The POS terminal 200 according to the present embodiment executes the settlement processing of the item purchased by the visitor, reads the coupon ID 166 given to the coupon 10 presented at that time, and associates it with the purchased item. save. The stored information of purchased items (sales information 218) is used to generate a learning data set used in a learning phase as described later.

<E. Outline of learning phase>
Next, the outline of the learning phase in clothing suggestion system 1 according to the present embodiment will be described.

The clothing suggestion system 1 according to the present embodiment associates the captured image 136 and the collected voice 138 saved in the display terminal 100 with the sales information 218 saved in the POS terminal 200 for the same visitor, The learning data set 324 is generated, and the estimation model is learned using the generated learning data set 324.

FIG. 21 is a diagram for explaining an outline of a learning phase in clothing suggestion system 1 according to the present embodiment. With reference to FIG. 21, display terminal 100 transmits captured image 136 and collected voice 138 associated with coupon ID 166 acquired at the time of executing the item estimation process to management device 300 (sequence SQ1). Similarly, the POS terminal 200 transmits the sales information 218 associated with the coupon ID 166 acquired at the time of executing the sales management process to the management device 300 (sequence SQ2).

The management device 300 associates the captured image 136 and the collected voice 138 transmitted from the display terminal 100 with the sales information 218 transmitted from the POS terminal 200 by using the coupon ID 166 as a key, thereby creating the learning data set 324. Generate (sequence SQ3). That is, the sequence SQ3 corresponds to preprocessing for generating the learning data set 324.

The management device 300 uses the generated learning data set 324 to learn the estimated model or perform additional learning to generate the learned model 326 (sequence SQ4). Then, management device 300 transmits the generated learned model 326 to each of display terminals 100 (sequence SQ5). The display terminal 100 stores the learned model 326 transmitted from the management device 300 as the learned model 116. That is, the learned model 116 of the display terminal 100 is set or updated.

As shown in FIG. 21, in clothing suggestion system 1 according to the present embodiment, coupon ID 166 assigned to coupon 10 can be used to combine pieces of information acquired by each of display terminal 100 and POS terminal 200. It is possible to easily generate the learning data set 324 for increasing the estimation accuracy of the estimation model without imposing a burden on the visitor.

<F. Functional configuration of management device 300>
Next, the function and processing of management device 300 that constitutes clothing suggestion system 1 according to the present embodiment will be described. In the clothing suggestion system 1, the management device 300 mainly takes charge of a part of the learning phase for constructing the learned model.

(F1: Functional configuration of management device 300)
FIG. 22 is a schematic diagram showing an example of the functional configuration of the management device 300 configuring the clothing suggestion system 1 according to the present embodiment. The functions shown in FIG. 22 are typically realized by the processor 304 of the management apparatus 300 executing the OS 312, the application program 314, the preprocessing program 316, and the learning program 318 (see FIG. 9 for all). May be.

With reference to FIG. 22, the management device 300 has a captured image / collected voice / sales information acquisition function 340, a learning data set generation function 350, and a learning function 360 as functional configurations.

The captured image / collected voice / sales information acquisition function 340 stores the captured image 136 and the collected voice 138 associated with the coupon ID 166 stored in the display terminal 100, and the coupon ID 166 stored in the POS terminal 200. The sales information 218 associated with is acquired. These data will be used as a learning data set. That is, the captured image / collected voice / sales information acquisition function 340 of the management device 300 corresponds to a configuration for acquiring the learning data set.

As a method of acquiring data from the display terminal 100 and the POS terminal 200, for example, some instruction may be given to the display terminal 100 and the POS terminal 200 so that the display terminal 100 and the POS terminal 200 respectively transmit data. Alternatively, the management device 300 may access the display terminal 100 and the POS terminal 200 to acquire data respectively. Alternatively, the display terminal 100 and the POS terminal 200 may transmit data to the management device 300 at each predetermined cycle.

The learning data set generation function 350 includes the captured image 136 and the collected voice 138 associated with the coupon ID 166 acquired from the display terminal 100, and the sales information 218 associated with the coupon ID 166 acquired from the POS terminal 200. Then, the learning data set 324 is generated.

The learning function 360 generates the learned model 326 by learning the estimation model using the learning data set 324 generated by the learning data set generation function 350. The generated learned model 326 is transmitted to the display terminal 100.

(F2: learning data set generation function 350)
Next, details of the learning data set generation function 350 of the management device 300 shown in FIG. 22 will be described.

FIG. 23 is a diagram for explaining the processing contents in the learning data set generation function 350 of the management device 300 that constitutes the clothing suggestion system 1 according to the present embodiment. With reference to FIG. 23, regarding the learning data set generation function 350, the management device 300 acquires the captured image 136 and the collected voice 138 associated with the coupon ID 166 acquired from the display terminal 100, and the POS terminal 200. The sales information 218 with which the coupon ID 166 is associated is compared, and the data having the same coupon ID 166 are associated with each other.

In FIG. 23, as an example, a data set of the captured image 136 and the collected voice 138 to which “01”, “02”, and “03” are assigned as the coupon ID 166, and the coupon ID 166 of “02” and “02”, respectively. Sales information 218 to which "03" and "08" are respectively given is shown. Of these, for the data to which the coupon ID 166 is assigned “02” and “03”, the captured image 136, the collected voice 138, and the sales information 218 are all collected. These three types of data can be determined as learning data (relationship between input information and the correct answer value of the estimation result). The learning data set 324 can be generated by generating learning data for each of the plurality of coupon IDs 166.

At this time, the sales information 218 is used as a label (tag) in order to adapt it to a learning process described later. That is, the learning data set 324 includes a captured image 136 (learning image) obtained by capturing an arbitrary customer and a collected voice 138 (learning voice) uttered by the arbitrary customer. The clothing item purchased by the customer (sales information 218) is labeled.

(F3: learning function 360)
Next, details of the learning function 360 of the management apparatus 300 shown in FIG. 22 will be described.

FIG. 24 is a diagram for explaining processing contents in learning function 360 of management device 300 which constitutes clothing suggestion system 1 according to the present embodiment. With reference to FIG. 24, the management device 300 includes an area identification module 141, size adjustment modules 142 and 143, a section identification module 144, and a resampling module 145 as the learning function 360. These modules are substantially the same as the modules that the display terminal 100 has as the proposed item estimation function 140. Therefore, detailed description of these modules will not be repeated.

Further, the management device 300 includes a parameter optimization module 362 as a learning function 360. The parameter optimization module 362 generates the learned model 326 by optimizing the model parameter 364 for defining the estimation model 1400.

The parameter optimization module 362 optimizes the model parameter 364 using each set (learning data) of the captured image 136, the collected voice 138, and the sales information 218 included in the learning data set 324.

More specifically, the parameter optimization module 362 calculates the face area feature amount 1410, the body region feature amount 1420, and the voice feature amount 1430 from the captured image 136 and the collected voice 138 of each set included in the learning data set 324. The estimation result 1450 is calculated by generating it and inputting it to the estimation model 1400. Then, the parameter optimization module 362 calculates an error by comparing the estimation result 1450 output from the estimation model 1400 with the corresponding sales information 218 (correct answer label), and calculates the error of the model parameter 364 according to the calculated error. Optimize (adjust) the value.

That is, the parameter optimization module 362 corresponds to a learning unit and is extracted from the learning data (the sales information 218 is labeled on the captured image 136 and the collected sound 138) and the face area feature amount 1410 (first Feature amount), body region feature amount 1420 (second feature amount), and voice feature amount 1430 (third feature amount) are input to the estimation model 1400, and an estimation result 1450 is output to the learning data. The estimation model 1400 is optimized so as to approach the purchase record (sales information 218) of the clothing item that is being displayed. In other words, the parameter optimization module 362 extracts the feature quantity from the captured image 136 and the collected voice 138 included in the learning data and inputs the feature quantity to the estimation model 1400, and the sales result 218 corresponding to the estimation result 1450 calculated. Adjust the model parameter 364 to match

In the same procedure, the learning is performed by repeatedly optimizing the model parameter 364 of the estimation model 1400 based on each learning data (captured image 136, collected voice 138, and sales information 218) included in the learning data set 324. The completed model 326 is generated.

When the parameter optimization module 362 optimizes the value of the model parameter 364, any optimization algorithm can be used. More specifically, the optimization algorithms include, for example, SGD (Stochastic Gradient Descent), Momentum SGD (SGD with inertial term addition), AdaGrad, RMSprop, AdaDelta, and Adam (Adaptive moment estimation). Gradient methods can be used.

In addition, when outputting each element of the estimation result 1450 output from the estimation model 1400 as a normalized probability, the item type (item 1, item 2, ..., Item N) included in the sales information 218 is output. It is preferable to normalize the number of units sold (see FIG. 19).

The estimation model 1400 having the model parameters 364 optimized by the parameter optimization module 362 corresponds to the learned model 326 and is transmitted to the display terminal 100.

(F4: processing procedure)
Next, a learning process executed in the management device 300 that constitutes the clothing suggestion system 1 will be described.

FIG. 25 is a flowchart showing a processing procedure of a learning process in management device 300 which constitutes clothing suggestion system 1 according to the present embodiment. The steps shown in FIG. 25 are typically realized by the processor 304 of the management device 300 executing the OS 312, the application program 314, the preprocessing program 316, and the learning program 318 (see FIG. 9 for all). May be.

Referring to FIG. 25, management device 300 acquires captured image 136 to which coupon ID 166 is added and collected voice 138 from display terminal 100 (step S300). At the same time, the management device 300 acquires the sales information 218 to which the coupon ID 166 is added from the POS terminal 200 (step S302). That is, the management device 300 executes a process of acquiring the coupon ID 166 (identification information) included in the coupon 10 that is a medium and the clothing item (sales information 218) purchased by the customer.

Then, the management device 300 creates the learning data set 324 by associating the captured image 136 and the collected voice 138 with the sales information 218 using the coupon ID 166 as a key (step S304). That is, the management device 300 executes a process of associating the coupon ID 166 (identification information) acquired from the coupon 10 that is a medium with the clothing item (sales information 218) purchased by the customer, and further uses the coupon ID 166 as a key for imaging. A process of associating the image 136, the collected voice 138, and the sales information 218 with each other and saving them as learning data used for learning the estimation model 1400 is executed.

The management device 300 selects one data set (learning data) from the generated learning data set 324 (step S306).

The management apparatus 300 extracts the face area partial image 147 and the body area partial image 148 from the captured image 136 of the selected data (step S308). Then, the management device 300 adjusts the sizes of the extracted face area partial image 147 and body area partial image 148 to extract the face area feature amount 1410 and the body region feature amount 1420 (step S310).

In this way, the management device 300 executes the process of specifying the face area representing the customer's face and the body area representing the customer's body in the captured image 136 of each learning data. Then, the management device 300 extracts the face area feature amount 1410 (first feature amount) from the face area of the captured image 136, and extracts the body region feature amount 1420 (second feature amount) from the body region of the captured image 136. Execute the process.

At the same time, the management device 300 extracts the utterance section included in the collected voice 138 of the selected data as the specific section voice 149 (step S312). Then, the management device 300 resamples the extracted specific section voice 149 to extract the voice feature amount 1430 (step S314). In this way, the management apparatus 300 executes the process of extracting the voice feature amount 1430 (third feature amount) from the voice of the portion of the collected voice 138 corresponding to the utterance of the customer.

The management apparatus 300 inputs the face area feature amount 1410 and the body area feature amount 1420 generated in step S310, and the voice feature amount 1430 generated in step S314 into the estimation model 1400 to generate an estimation result 1450 (step S316). ).

The management device 300 optimizes the model parameter 364 of the estimation model based on the error between the sales information 218 of the selected data and the estimation result 1450 generated in step S316 (step S318).

As described above, the management apparatus 300 inputs the face area feature amount 1410 (first feature amount), the body region feature amount 1420 (second feature amount) and the voice feature amount 1430 (third feature amount) to the estimation model 1400. A process of optimizing the estimation model 1400 is executed so that the estimated result 1450 output as a result approaches the purchase record (sales information 218) of the clothing item labeled in the learning data.

Then, the management device 300 determines whether or not all of the learning data set 324 generated in step S304 has been processed (step S320). If all of the learning data set 324 has not been processed (NO in step S320), the processes of step S306 and subsequent steps are repeated.

If all the learning data sets 324 have been processed (YES in step S320), the management apparatus 300 transmits the learned model 326 defined by the current model parameter 364 to each display terminal 100 (step S322). .. With the above, the learning process is completed.

(F5: Summary)
Management device 300 according to the present embodiment learns by associating captured image 136 and collected voice 138 acquired from display terminal 100 with sales information 218 acquired from POS terminal 200 by using coupon ID 166 as a key. The data set 324 for business can be easily generated. By using such a learning data set 324, it is possible to construct an estimation model or perform additional learning on the learned model 326. As a result, it is possible to improve the accuracy of suggestion for clothing.

<G. Modification>
In the above-described embodiment, as a typical example, the clothing suggestion system 1 in which the display terminal 100, the POS terminal 200, and the management device 300 are arranged in the single store 30 has been illustrated, but the invention is not limited to this and various modifications are possible. Is possible. Hereinafter, some modified examples will be described.

(G1: Multiple store cooperation: Modification 1)
As a modification, the management device 300 may manage a plurality of stores.

FIG. 26 is a schematic diagram showing an example of the system configuration of the clothing suggestion system 1A according to the first modification of the present embodiment. Referring to FIG. 26, one or a plurality of display terminals 100 and one or a plurality of POS terminals 200 are arranged in each of store 30A and store 30B. Each store 30 is connected to the same management device 300 via the wide area network 4.

The management device 300 acquires necessary information (the captured image 136, the collected voice 138, and the sales information 218) from the display terminal 100 and the POS terminal 200 of the store 30A, and at the same time, acquires the necessary information from the display terminal 100 and the POS terminal 200 of the store 30B. Get information. Then, the management device 300 generates a learned model common to both stores or a learned model for each store based on the collected information.

By adopting the configuration as shown in FIG. 26, it is possible to reduce the number of arrangements of the management device 300 and obtain a larger number of learning data sets, so that the estimation accuracy of the learned model can be improved.

(G2: Item proposal by category: Modification 2)
To the estimation model 1400 (see FIG. 11) according to the above-described embodiment, the voice feature amount 1430 corresponding to any of the categories displayed on the category selection reception screen 50 is input, and thus basically is output. In the estimated result 1450, the items belonging to the uttered category will have a relatively high score. Each of the plurality of clothing items belongs to one of a plurality of predetermined categories (commodity categories).

However, when there are many items belonging to other categories that were purchased at the same time as the items belonging to the selected category, the estimation result 1450 includes a mixture of items belonging to other categories having a relatively high score. obtain. In such a case, items belonging to a category other than the selected category are also suggested on the item suggestion screen 52.

FIG. 27 is a diagram for explaining an item suggestion screen displayed on display terminal 100 of clothing suggestion system 1 according to the second modification of the present embodiment. As shown in FIG. 27A, when the estimation result 1450 has an item belonging to another category having a relatively high score, the list display 54 of the item proposal screen 52 shows items belonging to the other category. (Reference numeral 54M) is included.

The item proposal screen 52 that may include items belonging to such other categories may be displayed. However, as shown in FIG. 27B, items belonging to the selected category and other categories are displayed. The item to which the item belongs may be suggested in a different display mode.

That is, the item suggestion screen 52 shown in FIG. 27B includes a list display 54 including items belonging to the category selected by the visitor 40 by voice, and items belonging to a category other than the category selected by the visitor 40 by voice. And a list display 55. Although the category is different from the selected category, the list display 55 also displays a message such as "How about you" indicating that the item is suitable for the proposal based on the past sales record.

FIG. 28 is a diagram for explaining processing contents in display control function 150A and coupon issue control function 160 of display terminal 100 that constitutes clothing suggestion system 1 according to the second modification of the present embodiment. With reference to FIG. 28, the display terminal 100 has a display control module 152A, a voice analysis module 154, and category / item correspondence information 156 as a display control function 150A.

The voice analysis module 154 performs voice analysis on the collected voice 138 uttered by the visitor 40 to identify the category selected by the visitor 40 by voice. That is, the voice analysis module 154 identifies the category selected by the customer from the plurality of categories based on the voice uttered by the customer. The speech analysis method performed by the speech analysis module 154 can use any known algorithm. The category specified by the voice analysis module 154 is provided to the display control module 152A.

The display control module 152A receives the estimation result 1450 calculated by the proposed item estimation function 140, and identifies the item having a higher score in the estimation result 1450. The display control module 152A refers to the category / item correspondence information 156 to determine whether or not each of the items having the higher scores in the estimation result 1450 belongs to the category specified by the voice analysis module 154. Then, the display control module 152A refers to the item image 118, adds the image of the item belonging to the category specified by the voice analysis module 154 to the list display 54, and the category specified by the voice analysis module 154. For items belonging to categories other than those, the item proposal screen 52A is generated by adding the image to the list display 55. The generated item proposal screen 52A is displayed on the display 102.

By executing the processing in the display control module 152A as described above, the item proposal screen 52A as shown in FIG. 27B is provided. That is, the display 102 displays different clothing items belonging to the category identified by the voice analysis module 154 and clothing items not belonging to the identified category from the clothing items displayed based on the estimation result 1450. Display with. By adopting such a display mode, it is possible to encourage purchase of an item other than the category selected by the visitor 40.

Other processes and functions are substantially the same as the contents described with reference to FIG. 15, so detailed description will not be repeated.

(G3: Network: Modification 3)
In the above-described embodiment, the estimation model 1400 to which the face area feature amount 1410, the body region feature amount 1420, and the voice feature amount 1430 are input has been exemplified, but an estimation model capable of inputting additional information is adopted. Good.

FIG. 29 is a diagram for explaining processing contents in the suggested item estimation function 140 of the display terminal 100 which constitutes the clothing suggestion system 1 according to the third modification of the present embodiment. FIG. 29 shows an estimation model 1400A that receives weather information such as weather and temperature as an input feature amount 1440. Thus, by adding the input information, the estimation accuracy can be improved.

When a feature amount to be input to the estimation model 1400A is added, the information included in the learning data set used for learning of the estimation model 1400A will also be increased in accordance with the input feature amount.

In addition, although the weather information is shown in FIG. 29 as a typical example, the information to be additionally input is not limited to this, and any information that is estimated to have some relevance to the determination of the item to be proposed is arbitrary. Information can be adopted. For example, other weather information such as wind speed and sunshine time, time information such as date and time and day of the week, and information such as the congestion level of the store may be used.

(G4: Network: Modification 4)
In the above-described embodiment, the estimation model 1400 to which the face area feature amount 1410, the body region feature amount 1420, and the voice feature amount 1430 are input is exemplified, but an estimation model that substitutes some information is adopted. May be.

FIG. 30 is a diagram for explaining the processing content in the suggested item estimation function 140 of the display terminal 100 which constitutes the clothing suggestion system 1 according to the fourth modification of the present embodiment. FIG. 30 shows a configuration example in which an input feature amount 1442 indicating a category is input instead of the voice feature amount 1430. The input feature value 1442 may be generated by performing voice analysis on the collected voice 138 spoken by the visitor 40 to specify the category selected by the visitor 40 by voice.

Alternatively, when the visitor 40 selects by touching a portion corresponding to the category on the category selection reception screen 50 displayed on the display terminal 100, the category selected by the touch operation is input feature amount 1442. You may input as.

Although FIG. 30 shows an example of inputting the input feature amount 1442 indicating a category, the input feature amount 1442 may be deleted by adopting the configuration shown in FIG. Good.

(G5: Item proposal using mobile terminal: Modification 5)
As a modified example, instead of the actual store, the item proposal as described above may be made on a personally owned mobile terminal.

FIG. 31 is a schematic diagram showing a usage example of the clothing suggestion system 1B according to the fifth modification of the present embodiment. Referring to FIG. 31, by installing the application of mobile terminal 500, the same function as display terminal 100 can be realized on mobile terminal 500. The internet user executes the application on the mobile terminal 500 and, while capturing an image of himself / herself using the camera mounted on the mobile terminal 500, utters a desired category to receive the suggestion of the above-described clothing. be able to.

As an implementation for realizing the item estimation process according to the present embodiment on the mobile terminal 500, any form can be adopted.

FIG. 32 is a schematic diagram showing an implementation example of the clothing suggestion system according to the fifth modification of the present embodiment.

FIG. 32 (a) shows an implementation example in which the item estimation process is realized by the mobile terminal 500 alone. As shown in FIG. 32A, the application 510 is installed in the portable terminal 500 from the server device 400. The application 510 has a suggestion item estimation function 512, a display control function 514, and a coupon issue control function 516. The suggested item estimation function 512, the display control function 514, and the coupon issue control function 516 are substantially the same as the suggested item estimation function 140, the display control function 150, and the coupon issue control function 160 (see FIG. 10) of the display terminal 100. The process of is executed.

In the implementation example shown in FIG. 32A, since the learned model 518 (substantially the same as the learned model 116 arranged in the display terminal 100) is arranged in the mobile terminal 500, communication with the server apparatus 400 Even if the above cannot be performed, the item estimation process can be executed.

FIG. 32B shows an implementation example in which the server device 400 and the mobile terminal 500 cooperate to realize the item estimation process. As shown in FIG. 32B, the application 520 is installed in the mobile terminal 500 from the server device 400. The application 520 has a feature amount generation function 522 and a display function 524. The feature amount generation function 522 extracts the face region feature amount 1410 and the body region feature amount 1420 from the captured image obtained by capturing the net user, and also generates the voice feature amount 1430 from the collected voice 138 issued by the net user. Then, it is transmitted to the server device 400.

The display function 524 outputs the display content from the server device 400 to the display of the mobile terminal 500.

On the other hand, the server device 400 has a suggestion item estimation function 412, a display control function 414, and a coupon issue control function 416. The proposed item estimation function 412 corresponds to the part of the proposed item estimation function 140 (see FIG. 11) of the display terminal 100 excluding the function of extracting a feature amount. The display control function 414 and the coupon issue control function 416 execute substantially the same processing as the display control function 150 and the coupon issue control function 160 (see FIG. 10) of the display terminal 100.

In the implementation example shown in FIG. 32B, since the learned model 518 (substantially the same as the learned model 116 arranged in the display terminal 100) is arranged in the server device 400, the learned in the server device 400 The model 518 can be appropriately updated. Further, in the mobile terminal 500, since only the characteristic amount needs to be extracted, resource consumption on the mobile terminal 500 side can be reduced.

FIG. 32C shows an implementation example in which the server device 400 and the mobile terminal 500 cooperate with each other to implement the item estimation process. As shown in FIG. 32C, the application 530 is installed from the server device 400 to the mobile terminal 500. The application 530 has an image / sound transmission function 532 and a display function 524. The image / sound transmission function 532 transmits the captured image obtained by capturing the image of the net user and the collected voice 138 generated by the net user to the server device 400.

The display function 534 outputs the display content from the server device 400 to the display of the mobile terminal 500.

On the other hand, the server device 400 has a suggestion item estimation function 412, a display control function 414, and a coupon issue control function 416. The proposed item estimation function 412, the display control function 414, and the coupon issue control function 416 perform substantially the same processing as the display control function 150 and the coupon issue control function 160 (see FIG. 10) of the display terminal 100.

In the implementation example shown in FIG. 32C, since the learned model 518 (substantially the same as the learned model 116 arranged in the display terminal 100) is arranged in the server device 400, the learned in the server device 400. The model 518 can be appropriately updated. Further, in the mobile terminal 500, since the captured image 136 and the collected voice 138 may be transmitted to the server device 400 as they are, the resource consumption on the mobile terminal 500 side can be reduced.

(G6: Other)
It is obvious that various modifications can be made in accordance with the gist of the present invention without being limited to the modifications described above. Moreover, about each modified example mentioned above, 1 or more can also be combined arbitrarily.

<H. Summary>
According to the clothing suggestion system 1 according to the present embodiment, a face area feature amount 1410 generated from the visitor face area partial image 147 and a body region feature amount 1420 generated from the visitor body area partial image 148. , By using the voice feature amount 1430 generated from the visitor's specific section voice 149 as the input information, it is possible to more accurately propose clothing according to the visitor's taste.

Further, according to the clothing suggestion system 1 according to the present embodiment, the coupon 10 is provided with the input information (the captured image 136 and the collected voice 138) acquired from the visitor and the item actually purchased by the visitor. The learning data set 324 can be generated by associating the coupon IDs 166 with each other. By learning the estimation model using such a learning data set 324, the estimation accuracy can be continuously improved, and the estimation model is adapted even when a new item is added. be able to.

Further, since the clothing suggestion system 1 according to the present embodiment issues the coupon 10 with a discounted payment amount, there is an incentive for the visitor to actively use the coupon 10. As a result, the possibility of collecting information for generating the learning data set 324 can be increased.

The embodiments disclosed this time are to be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description of the embodiments but by the claims, and is intended to include meaning equivalent to the claims and all modifications within the scope.

1, 1A, 1B clothing proposal system, 2 local network, 4 wide area network, 10 coupons, 12 discount amount display, 14, 54, 55 list display, 16 map, 18 identification image, 20 item tag, 30, 30A, 30B store , 40 store visitors, 50 category selection reception screen, 52, 52A item proposal screen, 56 coupon issue button, 100 display terminal, 102, 202, 302 display, 104, 204, 304 processor, 106, 206, 306 memory, 108, 208, 308 network controller, 110, 210, 310 storage, 112, 212, 312 OS, 114, 214, 314 application program, 116, 326, 518 learned model, 118 aite Image, 120, 220 printer, 122, 222 optical drive, 124, 224 optical disk, 126, 226 touch detection unit, 128 human sensor, 130 camera, 132 microphone, 136 captured image, 138 collected audio, 140, 412, 512 Proposed item estimation function, 141 area identification module, 142,143 size adjustment module, 144 section identification module, 145 resampling module, 147 face area partial image, 148 body area partial image, 149 specific section audio, 150, 150A, 414, 414 514 display control function, 152, 152A display control module, 154 voice analysis module, 156 category / item correspondence information, 160, 416, 516 coupon issue control function, 162 Coupon issue control module, 164 coupon ID generation module, 166 coupon ID, 170 image and voice storage function, 172,252 correspondence module, 174 image and voice storage unit, 200 POS terminal, 216 item information, 218,322 sales information, 228 optics Reader, 230, 330 input unit, 232 payment processing unit, 240 accounting function, 250 sales information storage function, 254 sales information storage unit, 300 management device, 316 preprocessing program, 318 learning program, 320 voice information, 324 learning Data set, 340 sales information acquisition function, 350 learning data set generation function, 360 learning function, 362 parameter optimization module, 364 model parameters, 400 server device, 500 mobile terminal 510, 520, 530 application, 522 feature amount generation function, 524, 534 display function, 532 image voice transmission function, 1400, 1400A estimation model, 1410 face area feature amount, 1420 body region feature amount, 1430 voice feature amount, 1440 , 1442 input feature amount, 1450 estimation result, 1460, 1470, 1480 preprocessing network, 1490 middle layer, 1492 activation function, 1494 Softmax function.

Claims (8)

An information processing device for proposing a clothing item according to a customer from a plurality of clothing items based on a feature amount representing the characteristics of the customer,
A camera for imaging the customer,
A microphone for collecting audio,
In an image obtained by imaging the customer with the camera, an area specifying unit for specifying a face area representing the face of the customer and a body area representing the body of the customer,
An image feature extraction unit for extracting a first feature amount from the face region of the image and a second feature amount from the body region of the image;
A voice feature extraction unit for extracting a third feature amount from a voice of a portion corresponding to the utterance of the customer in the voice collected by the microphone;
Upon receiving the input of the first characteristic amount, the second characteristic amount, and the third characteristic amount, each possibility that each of the plurality of clothing items is a clothing item to be proposed is output as an estimation result. A trained estimation model,
A display unit for displaying a clothing item according to the customer based on the estimation result,
The estimation model is generated by a learning process using a learning data set, and the learning data set is related to an image obtained by imaging another customer and a voice uttered by the other customer. An information processing device including a plurality of learning data labeled with clothing items purchased by other customers. The display unit displays a list of categories indicating the classification of clothing items before collecting the voice by the microphone, and prompts the customer to select any of the categories displayed in the list by voice. The information processing apparatus according to claim 1, which displays a message. The information processing device according to claim 1 or 2, wherein the area specifying unit specifies a portion representing the clothing worn by the customer as the body area. Each of the plurality of clothing items belongs to one of a plurality of predetermined categories,
The information processing apparatus further includes a voice analysis unit for specifying a category selected by the customer from the plurality of categories based on a voice uttered by the customer,
Among the clothing items displayed based on the estimation result, the display unit displays clothing items that belong to the category specified by the voice analysis unit and clothing items that do not belong to the specified category in different display modes. The information processing apparatus according to any one of claims 1 to 3, which displays the information. An information processing system,
An information processing device that inputs a feature amount representing a feature of a customer to a learned estimation model and proposes a clothing item corresponding to the customer from a plurality of clothing items,
A learning device for generating the estimation model,
The information processing device,
A camera for imaging the customer,
A microphone for collecting audio,
In an input image obtained by imaging the customer with the camera, a region specifying unit for specifying a face region representing the face of the customer and a body region representing the body of the customer,
An image feature extraction unit for extracting a first feature amount from the face region of the input image and a second feature amount from the body region of the input image;
A voice feature extraction unit for extracting a third feature amount from a voice of a portion corresponding to the utterance of the customer in the voice collected by the microphone,
The estimation model receives inputs of the first feature quantity, the second feature quantity and the third feature quantity,
Upon receiving the input of the first characteristic amount, the second characteristic amount, and the third characteristic amount, each possibility that each of the plurality of clothing items is a clothing item to be proposed is output as an estimation result. Is learned as
The information processing device further includes a display unit for displaying a clothing item according to the customer based on the estimation result,
The learning device is
The learning data set includes an acquisition unit for acquiring a learning data set, and the learning data set is related to a learning image obtained by imaging another customer and a learning voice uttered by the other customer. Including a plurality of learning data labeled clothing items purchased by other customers, further, in the learning image, a face area representing the face of the other customer, and a body area representing the body of the other customer An area specifying unit for specifying
An image feature extraction unit for extracting a first learning feature amount from the face region of the learning image and a second learning feature amount from the body region of the learning image;
A voice feature extraction unit for extracting a third learning feature amount from a voice of a portion of the learning voice corresponding to the utterance of the other customer;
An estimation result obtained by inputting the first learning feature amount, the second learning feature amount, and the third learning feature amount extracted from the learning data to the estimation model is An information processing system, comprising: a learning unit for optimizing the estimation model so as to approach the purchase record of the clothing item labeled in the data. A learning device for receiving an input of a feature amount representing a feature of a customer and generating an estimation model used for suggesting a clothing item corresponding to the customer from a plurality of clothing items,
The learning data set includes an acquisition unit for acquiring a learning data set, and the learning data set labels the clothing item purchased by the customer with respect to the image obtained by imaging the customer and the voice uttered by the customer. A plurality of attached learning data, further in the image, a face region representing the face of the customer, a region specifying unit for specifying a body region representing the body of the customer,
An image feature extraction unit for extracting a first feature amount from the face region of the image and a second feature amount from the body region of the image;
A voice feature extraction unit for extracting a third feature amount from the voice of a portion of the voice corresponding to the utterance of the customer;
The estimation result output by inputting the first feature amount, the second feature amount, and the third feature amount extracted from the learning data into the estimation model is labeled in the learning data. And a learning unit for optimizing the estimation model so as to approach the purchase record of the clothing item. A learned estimation model used for suggesting a clothing item according to the customer from a plurality of clothing items upon receiving the input of the feature amount representing the customer's features,
The estimation model is generated by a learning process using a learning data set,
The learning data set includes a plurality of learning data in which the clothing item purchased by the customer is labeled, with respect to the image obtained by imaging the customer and the voice uttered by the customer.
The learning process, for each of the learning data,
In the image, identifying a face area representing the face of the customer and a body area representing the body of the customer;
Extracting a first feature amount from the face region of the image and a second feature amount from the body region of the image;
Extracting a third feature amount from the voice of the portion of the voice corresponding to the utterance of the customer;
The estimation result obtained by inputting the first feature amount, the second feature amount, and the third feature amount into the estimation model approaches the purchase record of the clothing item labeled in the learning data. And a step of optimizing the estimation model. A method of collecting learning data used for learning an estimation model used for suggesting a clothing item according to a customer from a plurality of clothing items that have received an input of a feature amount that represents the characteristics of the customer,
Acquiring an image obtained by imaging the customer and a voice including an utterance of the customer;
Inputting a plurality of feature amounts extracted from the image and the sound to a learned estimation model to generate a clothing item proposal according to the customer;
Generating identification information,
Issuing a medium that encourages the purchase of a fashion item, including the generated fashion item proposal and the generated identification information,
Associating the generated identification information with the image and the sound,
Acquiring the identification information and the clothing item purchased by the customer included in the medium;
Associating the identification information acquired from the medium with the clothing item purchased by the customer;
A method of associating the image and the sound with the clothing item purchased by the customer using the identification information as a key, and storing the data as learning data used for learning the estimation model.

Images