JP7730128B1 - Machines, systems, and methods - Google Patents

Abstract

To provide a machine or system that can automatically move an ordered item to a predetermined location, confirm payment, and then hand it over to the orderer.
[Solution] A machine that may be equipped with an image capture device, includes a bipedal robot, a self-propelled vehicle, or an unmanned aerial vehicle, has a communication means, and communicates information with other computers, computing equipment, the cloud, the Internet, and applications, and a method for moving a first object to a predetermined position includes the machine acquiring order information for the first object from a service user, moving the first object to the predetermined position, recognizing that payment for the first object has been completed, and, after recognition, handing the first object over to the orderer.
[Selection diagram] None

Description

The present invention relates to a machine or system.

The statements in this section merely provide background information regarding the present disclosure and do not necessarily constitute prior art.

Patent document 1 discloses a settlement system.

Japanese Patent Application Publication No. 2017-224147

However, the inventors recognized that at least the above embodiment has a drawback in that it does not have a configuration for moving the ordered item to a designated location and handing it over to the orderer after confirming payment.

At least one aspect of the present disclosure provides a method for manufacturing a semiconductor device, comprising:
It is a machine,
Obtain order information for a first target;
Move the first object to a predetermined position,
Recognize the completion of the first payment,
After the recognition, the first object is delivered to the orderer.
Provide machines.

This configuration has the advantage of automatically moving the ordered item to a designated location and handing it over to the orderer after confirming payment.

These and other aspects, features, and advantages of the present disclosure will become apparent from the following detailed written description of preferred embodiments and aspects taken in conjunction with the following drawings, variations and modifications of which may be made without departing from the spirit and scope of the novel concepts of the present disclosure. Aspects of one embodiment of the present disclosure may be combined with or substituted for one or more aspects of another embodiment of the present disclosure, to the extent not inconsistent.

The following disclosure provides many different embodiments and examples for implementing different features of the presented subject matter. To simplify the disclosure, specific examples of components and arrangements are disclosed below. Of course, these are merely examples and are not intended to be limiting. For example, a structure in which a first feature is covered by or in contact with a subsequently disclosed second feature may include an embodiment in which the first feature and the second feature are formed in direct contact, as well as an embodiment in which an additional feature is formed between the first feature and the second feature, preventing direct contact between the first feature and the second feature. Furthermore, the disclosure may repeat reference numbers and/or letters in various examples. Such repetition is for the purposes of brevity and clarity and does not, in itself, require a relationship between the various embodiments and/or configurations described. Furthermore, when a first element is described as being "coupled" or "coupled" to a second element, such description includes embodiments in which the first and second elements are directly coupled or coupled to each other, as well as embodiments in which the first and second elements are indirectly coupled or coupled to each other with one or more other intervening elements therebetween.

As used herein, the phrase "at least one of" encompasses all exemplified variations. For example, the phrase "comprises at least one of A, B, or C" is equivalent to "consisting of A, B, C, and combinations thereof." It encompasses all possible variations of A, B, C, A+B, A+C, B+C, and A+B+C. In this disclosure, disclosure of an embodiment combining two or more components can be implemented as an embodiment in which any one or more components are separated, unless there is a contradiction or unless otherwise specified in this specification. For example, the phrase "implements A, B, and C" is equivalent to "comprising A, B, or C and combinations thereof." It also encompasses all possible variations of A, B, C, A+B, A+C, B+C, and A+B+C.

In this disclosure, disclosures using a machine, electronic operator, or computer may include embodiments of a method, recording medium, apparatus, or program. As used herein, the statement "A is B" may be replaced with "A includes B" unless there is a contradiction or unless otherwise stated in this specification.

The terms in this disclosure, including the terms set forth in the claims, may be interpreted in light of the descriptions and drawings set forth in the specification, and further, unless otherwise indicated inconsistently, by what one or more citizens, past, present, or future, have so called, so designated, so understood, or so performed, or may so do, unless otherwise indicated in the present disclosure.
The operating method used in at least one or more embodiments can take the following embodiments: The following description will be made with reference to JP6456303 (the citation begins below), which clearly explains at least one or more embodiments.

As used herein, the term "computer" generally includes, as known in the art, a processor; memory; at least one information storage/retrieval device, such as a hard drive, disk drive, flash drive, or memory stick, or other non-transitory computer-readable medium or non-transitory storage device; at least one input device, such as a keyboard, mouse, point-and-touch device, touchscreen, or microphone; and a display structure, such as a well-known computer screen. In addition, a computer may include one or more network connections, such as wired or wireless connections. As known in the art, such a computer or computer system may include more or less of the above, including, for example, but not limited to, tablet computers and smart devices, as well as other electronic media and devices.

As used herein, the terms "cloud" or "cloud computing" refer to a centralized and virtualized computing facility in which all computing resources are shared. Application systems and subsystems can no longer be referred to as specific machines, as they are all in the "cloud."

As used herein, the term "Distributed Internet Services System" refers to a distributed Internet services platform that transforms Internet applications to run in a variety of computing environments. The DIS system distributes Internet applications, including content, data, and logic, to any number and type of devices, to whatever extent appropriate and along the network, via Component Distribution Servers/Asset Distribution Servers. Through the DIS, Internet applications can be hosted and centrally managed, with services based on each user's needs, and cached and executed locally on the user's device or nearby locations while maintaining their integrity. Web-enabled computing devices can be upgraded with DIS software to become DIS-enabled, enjoying and running distributed Internet services. The distributed internet services system is more fully described in any one of the following patent families: U.S. Patent Nos. 7,136,857, 7,150,015, 7,181,731, 7,209,921, 7,430,610, 7,685,183, 7,685,577, 7,752,214, 8,326,883, 8,386,525, 8,443,035, 8,458,142, 8,458,222, 8,473,468, 8,527,545, and 8,650,226, and U.S. Patent Publication Nos. 20120005205, and 20130091252, all of which, like the present invention, are commonly owned by OP40 Holdings, Inc., and all of which are incorporated by reference. (End of quote)

The operating method used in at least one embodiment can take the following form for a conventional Internet method that does not use a distributed Internet. Reference is made to JP7113047, which provides a detailed explanation of at least one embodiment (the following is a quote).

Embodiments including those specifically disclosed herein can provide an automated response system that is based on artificial intelligence and is implemented in a manner that resembles a real human conversation, thereby enabling more natural conversations with users while quickly and conveniently handling inquiries, reservations, delivery orders, etc.

The multiple electronic devices 110, 120, 130, and 140 may be fixed or mobile terminals implemented by computer systems. Examples of the multiple electronic devices 110, 120, 130, and 140 include AI speakers, smartphones, mobile phones, navigation systems, personal computers (PCs), notebook PCs, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), tablets, game consoles, wearable devices, Internet of Things (IoT) devices, virtual reality (VR) devices, and augmented reality (AR) devices. As an example, FIG. 1 shows an AI speaker as electronic device 110, but in an embodiment of the present invention, electronic device 110 may represent one of a variety of physical computer systems that can communicate with other electronic devices 120, 130, 140 and/or servers 150, 160 via network 170 using a substantially wireless or wired communication method.

The communication method is not limited, and may include not only communication methods using communication networks that network 170 can include (for example, a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.), but also short-range wireless communication between devices. For example, network 170 may include one or more of the following networks: a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a broadband network (BBN), the Internet, etc. Furthermore, network 170 may include any one or more of the following network topologies, including, but not limited to, a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree or hierarchical network, etc.

Servers 150 and 160 may each be realized by one or more computer devices that communicate with multiple electronic devices 110, 120, 130, and 140 via network 170 and provide instructions, code, files, content, services, etc. For example, server 150 may be a system that provides a first service to multiple electronic devices 110, 120, 130, and 140 connected via network 170, and server 160 may be a system that provides a second service to multiple electronic devices 110, 120, 130, and 140 connected via network 170. As a more specific example, server 150 may provide a service (such as an automated response service, for example) targeted by an application, which is a computer program installed and executed on multiple electronic devices 110, 120, 130, and 140, as a first service to the multiple electronic devices 110, 120, 130, and 140. As another example, the server 160 may provide, as a second service, a service for distributing files for installing and executing the above-mentioned application to multiple electronic devices 110, 120, 130, and 140.

Figure 2 is a block diagram illustrating the internal configuration of an electronic device and a server in one embodiment of the present invention. In Figure 2, the internal configuration of electronic device 110 and server 150 are described as examples of electronic devices. In addition, other electronic devices 120, 130, 140 and server 160 may also have the same or similar internal configuration as electronic device 110 or server 150 described above.

The electronic device 110 and the server 150 may include memory 211, 221, processors 212, 222, communication modules 213, 223, and input/output interfaces 214, 224. The memory 211, 221 may be a non-transitory computer-readable recording medium and may include a non-transitory mass storage device such as a random access memory (RAM), a read-only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, etc. Here, the non-transitory mass storage device such as a ROM, an SSD, a flash memory, or a disk drive may be included in the electronic device 110 or the server 150 as a separate non-transitory storage device distinct from the memory 211, 221. The memories 211 and 221 may also store an operating system and at least one program code (e.g., code for a browser installed and executed on the electronic device 110, or code for an application installed on the electronic device 110 to provide a specific service). Such software components may be loaded from a computer-readable recording medium separate from the memories 211 and 221. Such a separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD/CD-ROM drive, or a memory card. In other embodiments, the software components may be loaded into the memories 211 and 221 through communication modules 213 and 223, which are not computer-readable recording media. For example, at least one program may be loaded into the memories 211 and 221 based on a computer program (e.g., the above-mentioned application) that is installed by a file provided over the network 170 by a developer or a file distribution system that distributes application installation files (e.g., the above-mentioned server 160).

Processors 212, 222 may be configured to process computer program instructions by performing basic arithmetic, logic, and input/output operations. The instructions may be provided to processors 212, 222 by memories 211, 221 or communication modules 213, 223. For example, processors 212, 222 may be configured to execute instructions received according to program code stored in a storage device such as memories 211, 221.

The communication modules 213, 223 may provide functionality for the electronic device 110 and the server 150 to communicate with each other via the network 170, or may provide functionality for the electronic device 110 and/or the server 150 to communicate with other electronic devices (for example, the electronic device 120) or other servers (for example, the server 160). For example, a request generated by the processor 212 of the electronic device 110 in accordance with program code recorded in a recording device such as the memory 211 may be transmitted to the server 150 via the network 170 under the control of the communication module 213. Conversely, control signals, instructions, content, files, etc. provided under the control of the processor 222 of the server 150 may be received by the electronic device 110 via the communication module 213 of the electronic device 110 via the communication module 223 and the network 170. For example, control signals, commands, content, files, etc. from the server 150 received through the communication module 213 may be transmitted to the processor 212 or memory 211, and the content, files, etc. may be recorded on a recording medium (the non-transitory recording device described above) that the electronic device 110 may further include.

The input/output interface 214 may be a means for interfacing with the input/output device 215. For example, the input device may include a keyboard, mouse, microphone, camera, etc., while the output device may include a display, speaker, haptic feedback device, etc. As another example, the input/output interface 214 may be a means for interfacing with a device that integrates input and output functions into one, such as a touchscreen. The input/output device 215 may be configured as a single device together with the electronic device 110. Furthermore, the input/output interface 224 of the server 150 may be a means for interfacing with an input or output device (not shown) that may be connected to or included in the server 150. As a more specific example, when the processor 212 of the electronic device 110 processes instructions from a computer program loaded into the memory 211, service screens or content configured using data provided by the server 150 or the electronic device 120 may be displayed on a display via the input/output interface 214.

In other embodiments, the electronic device 110 and the server 150 may include more components than those shown in FIG. 2. However, it is not necessary to explicitly depict most of the conventional components. For example, the electronic device 110 may be implemented to include at least some of the input/output devices 215 described above, and may further include other components such as a transceiver, a camera, various sensors, a database, etc. As a more specific example, if the electronic device 110 is an AI speaker, the electronic device 110 may further include various components typically found in AI speakers, such as various sensors, a camera module, various physical buttons, touch-sensitive buttons, input/output ports, and a vibrator for vibration. (End of quote)

A machine is disclosed. According to at least one embodiment, a user terminal comprises a control unit, RAM, storage unit, graphics processing unit, communication interface, and interface unit, each connected by an internal bus. In at least one embodiment, the user terminal includes a terminal owned by the user. On the other hand, it includes not only terminals owned by the user, but also terminals owned by someone other than the user (including sellers or traders of goods or services, governments, and local governments). As an example, it includes terminals (including those transferred or loaned) provided for use by recipients of the provision, advertising, or promotion of goods or services (hereinafter referred to in this paragraph as "the provision, etc."), terminals used for the provision, etc., and terminals related to the provision, etc. of goods or services by recipients of the provision, etc. In other words, it includes terminals owned by others that the user is only temporarily permitted to use, and terminals loaned to the user.

According to at least one embodiment, the control unit is composed of a CPU and ROM. The control unit executes programs stored in the storage unit and controls the user terminal. The RAM is the work area of the control unit. The storage unit is a memory area for saving programs and data. The control unit reads and processes programs and data from the RAM. The control unit processes the programs and data loaded into the RAM and outputs drawing commands to the graphics processing unit.

According to at least one embodiment, the graphics processing unit is connected to the display unit. The display unit has a display screen. When the control unit outputs a drawing command to the graphics processing unit, the graphics processing unit outputs a video signal for displaying an image on the display screen. Here, the display unit may be a touch panel equipped with a touch sensor. The touch panel of this display unit functions as the input unit.

According to at least one embodiment, the communication interface can be connected to a communication network wirelessly or via a wired connection, and can send and receive data to and from a server device via the communication network. Data received via the communication interface is loaded into RAM, where it is processed by the control unit. An external memory (e.g., an SD card) is connected to the interface unit.

According to at least one embodiment, the user terminal is a computing device having a display screen and an input section, but is not limited thereto. Examples of user terminals include conventional mobile phones, tablet devices, smartphones, and desktop and laptop personal computers. VR goggles may also be configured with a screen (or two display panels, one for each eye) attached to a frame (or headset) strapped or attached to the head. The user terminal also has an audio output section.

According to at least one embodiment, the user terminal is capable of communicating with the server device via a communications network. The user terminal can transmit or receive information via the communications network.

According to at least one embodiment, the server device includes at least a control unit, RAM, storage unit, and communication interface, each connected via an internal bus.

According to at least one embodiment, the control unit is composed of a CPU and ROM, executes programs stored in the storage unit, and controls the server device. The control unit also has an internal timer for measuring time. The RAM is the work area of the control unit. The storage unit is a memory area for saving programs and data. The control unit reads the programs and data from the RAM and performs program execution processing based on information received from the user terminal, etc.

This document discloses AI. According to at least one embodiment, artificial intelligence includes machine learning, deep learning, generative AI, large-scale language models, LLMs, foundational models, and generative AI. Generative AI uses transformers and employs a number of mechanisms called attention. It uses self-supervised learning and extract prediction. In this case, the AI can guess the next word. Given a sentence, it guesses the next word based on the sentence up to that point. A large number of supervised learning problems are created. These create an AI that can guess the next word. Generative AI can predict grammatical structure, topic connections, and the type of sentence a person with a certain writing style is likely to write. Furthermore, generative AI can learn the structure, causal relationships, and knowledge behind the next sentence simply by guessing it. Generative AI is scalable, and the larger the number of parameters, the higher its accuracy. In conventional statistics and machine learning, overfitting occurs when model parameters are set too large compared to the sample size of the data. In LLM, the higher the number of parameters, the higher the accuracy. One generative AI has 175 billion parameters, and is overlaid with supervised learning to ensure smooth dialogue. It is trained not to say strange things. It writes reviews and acts as a call center operator.

According to at least one embodiment, large language models (LLMs) are machine learning natural language processing models built non-inclusively using large datasets and deep learning techniques. They are typically adapted to various natural language processing (NLP) tasks, such as text classification/generation, sentiment analysis, text summarization, and question answering, using a task-specific training technique known as "fine-tuning." According to at least one embodiment, self-supervised learning is similar to intrinsic human intelligence. When humans behave, they constantly predict the next event and the next input. In the process, they learn the structure of the external world. Predicting the next word is considered intrinsic intelligence and is similar to what the cerebral cortex does. According to at least one embodiment, large language models memorize all input information but generalize only to the extent necessary to predict the next word. They do not attempt to generalize all information from the beginning. Large language models require capacity to memorize information, which in turn requires parameters. According to at least one embodiment, the large-scale language model includes eight models with 175 billion parameters or 220 billion parameters.

According to at least one embodiment, videos and images are represented as a collection of visual patches, which are small units of data similar to text tokens in LLMs. Patches effectively represent models of visual data and serve as a highly scalable and effective representation for training generative models on a wide variety of videos and images. Videos are converted to patches by first compressing them into a low-dimensional latent space and then decomposing the representation into spatiotemporal patches.

According to at least one embodiment, a video compression network is a network that reduces the dimensionality of visual data, taking raw video as input and outputting a temporally and spatially compressed latent representation. An AI is trained on this compressed latent space and then generates video within this compressed latent space.

According to at least one embodiment, Spacetime Latent Patches, given a compressed input video, extracts a set of spacetime patches that act as Transformer tokens. The patch-based representation allows Sora to be trained on videos and images of various resolutions, lengths, and aspect ratios, and controls the size of the generated video by placing randomly initialized patches on an appropriately sized grid during inference.

According to at least one embodiment, the AI is a diffusion model, trained to predict the original "clean" patch when fed a noisy patch (and conditioning information such as a text prompt). The AI is a diffusion transformer, which exhibits remarkable scaling properties in a variety of domains, including language modeling, computer vision, and image generation. Diffusion transformers are also effective as video generation models. The AI significantly improves the quality of the samples as the training computational effort increases.

According to at least one embodiment, the AI applies caption regeneration techniques to train a highly descriptive caption model, which is then used to generate text captions for all videos in the training set. Training highly descriptive captions improves the fidelity of the text as well as the overall quality of the generated videos. GPT is leveraged to convert short user prompts into long, detailed captions that are sent to the model. This allows the AI to generate high-quality videos that accurately follow the user prompts.

According to at least one embodiment, AI can perform vectorization in natural language processing according to the following process. First, a preprocessing step is performed on the given text. This preprocessing step involves removing unnecessary words, such as JavaScript code and HTML tags, from the text. These codes are used for displaying information online and are therefore not generally used in natural language processing. Next, the text is divided into words using morphological analysis. Morphological analysis is the process of classifying natural language sentences written in text into the smallest meaningful linguistic units. Morphological analysis tools available include "MeCab," "JUMAN," and "JANOME." Normalization involves unifying words with the same meaning, such as variations in spelling, into a single word. Stop words are words that are not considered for processing because they cannot be used in natural language processing. Examples of stop words include particles and auxiliary verbs, which have no meaning on their own. When calculating vectors, these words may be removed, leaving only meaningful words. Vectorization may also be performed without removing these stop words. Vectorization is the process of converting words, which are strings of characters, into vectors. Vectorization converts word data into numerical data. Converting words into vectors is done using methods called bag of words or distributed representations. Bag of words is a method of vectorizing a sentence using the number of words that appear in a given sentence. It focuses on how often a word appears in a sentence, and does not take into account the order of the words or sentences. Distributed representation is a vectorization method that focuses on the meaning of words. By vectorizing word meanings, it is possible to assign vectors that are similar to words with similar meanings or usages, and the relationships between words can also be expressed as vectors. Representation as vectors makes it possible to add and subtract word meanings. In applied processing, natural language converted into numerical data can be used as input for machine learning. Specifically, vectorized natural language is input into a classifier to classify the sentences. Tools used here include "TensorFlow," "scikit-learn," and "PyTorch."

A machine is disclosed. In at least one embodiment, the machine may combine one or more of the embodiments or features of this disclosure.

Machines include machines that can move on their own or machines that are equipped with power and can move on their own. Machines may be equipped with image capture devices. Machines include robots. Machines have communication means and can communicate information with other computers, computing facilities, the cloud, the Internet, and applications. As an example, machines include machines that move on at least one wheel or self-propelled vehicles. A self-propelled vehicle is an unmanned vehicle or machine that moves on its own. Furthermore, machines include machines that have at least one moving part and can walk or run. As an example, machines are capable of bipedal walking. As an example, machines include humanoid robots. A humanoid robot has at least two moving parts, which correspond to feet. Machines include robots or humanoid robots that combine artificial intelligence (AI) and robotics. These machines can act autonomously while recognizing their surroundings using self-driving technology and machine learning algorithms. Machines are equipped with AI technology and sensors. This allows them to recognize their surrounding environment in real time and select appropriate actions. For example, they can move while avoiding obstacles and communicate with nearby people. Machines include machines that fly using power. Examples include unmanned aerial vehicles. Aircraft include airplanes, rotorcraft, gliders, airships, and other equipment designated by government ordinance that can be used for aviation. Aircraft may have one or more propellers. Unmanned aerial vehicles are equipped with image capture devices. Unmanned aerial vehicles are capable of capturing images. Images include thermography.

An image capture device or means is disclosed. In at least one embodiment, the image capture device can be any of the machines or devices described in this disclosure. As a non-exhaustive example, an image capture device includes a device that captures image data and converts it into digital data. Examples include digital cameras (which capture still and video images and store them on a recording medium), video cameras (which primarily capture video), webcams (which are connected to a PC and used for video conferencing and live streaming), surveillance cameras (which are installed for security purposes and continuously record video), and smartphone cameras. In this disclosure, "image" is a term with a very broad meaning. It generally refers to an image created by light, i.e., visual information captured by the eye. In other words, "image" includes thermography (a technology that visualizes invisible heat). Thermography uses an infrared camera to detect infrared light emitted from an object and captures temperature distribution as an image by displaying the intensity of the infrared light in different colors. Naturally, "image capture device" also includes devices that capture thermography.

A method for obtaining order information for a first object is disclosed. In at least one embodiment, a user of the service places an order for a first object through a machine or terminal. An order includes an instruction or request for some kind of service, or an instruction or request for the production or delivery of an object or product. Furthermore, an order includes an instruction or request for the production or delivery of a specified quality, quantity, shape, size, or one or more combinations thereof. A first object includes any goods or service that can be ordered. A user of the service arbitrarily specifies a first object. For example, a user of the service orders ramen noodles as the first object to be produced. For example, a user orders ramen noodles as the first object to be delivered to the user's location. The user electromagnetically communicates this request to another machine, server, etc. through a machine or terminal. For example, the user sends order information to a machine, server, etc. of a party operating the service. The machine acquires the order information sent from the user.

A method for moving a first object to a predetermined location is disclosed. In at least one embodiment, a machine moves the first object to a predetermined location. The predetermined location is determined by a user or a service provider. For example, a user can determine the user's location as the predetermined location. A service provider can determine the cash register in a store as the predetermined location. Alternatively, a service provider can determine the location on the store premises where the user's car is parked as the predetermined location. In this way, predetermined locations include locations that can be freely registered by users of machines or terminals. The machine is capable of moving on its own. The machine may have a location detection means such as GPS. In this case, the machine can drive itself to the predetermined location. The machine holds the first object and moves it to the predetermined location by moving on its own. For example, the machine produces ordered ramen, holds the ramen, and moves it to the user's car on the store premises. Alternatively, the machine holds ramen cooked by another machine or a human and moves it to the user's car on the store premises. Alternatively, the machine may move the first object to the user's home or front door. The machine may be a humanoid robot or a drone. Moving a first object to a predetermined location includes a machine moving the first object to a predetermined location by itself. On the other hand, it also includes a machine moving the first object to a predetermined location in cooperation with other machines or humans. For example, in an embodiment, the first object is delivered from a logistics facility or other facility where it is stored to a distribution center by a self-propelled cart or a vehicle driven by a driver, and then transported from the distribution center to the customer's home (corresponding to the predetermined location) by a machine (such as a humanoid robot). In another embodiment, in a large, membership-based warehouse-style store, the first object is removed from a shelf and transported to the checkout counter by an automated warehouse, conveyor, self-propelled cart, drone, or the like. A humanoid robot located near the checkout counter then receives the first object from the machine, etc., and moves the first object a short distance from that point to a location close to the person at the checkout counter. Thus, the act of moving a first object to a predetermined location includes not only a single machine, but also an embodiment in which other machines, etc., intervene.

As the machine moves, the target object moves in the same way. An example of this action includes holding. In this disclosure, "holding" can be replaced with an embodiment of "the action of moving the target object in the same way as the machine moves." For example, this includes a humanoid robot carrying an object. When carrying an object, this includes embodiments in which the robot holds the object in one hand, holds it in both hands, places it on an object holder on its back, or a combination of these. During such an action, the robot is said to be holding a first object. Furthermore, this includes a drone carrying an object, regardless of the method. For example, this includes an embodiment in which a drone is equipped with a carrier for carrying objects and flies with an object loaded on the carrier. It includes a self-propelled vehicle moving with an object loaded on its cart. Thus, in this disclosure, holding does not necessarily require that the object be detachably fixed to the machine in some way; it is sufficient to move the object in a manner that allows it to be carried, regardless of the method.

A method for recognizing the completion of payment for a first object is disclosed. In at least one embodiment, a user of this service makes a payment to purchase a first object. The payment method can be before the machine moves the first object to a predetermined location, while the machine is moving the first object to the predetermined location, or after the first object has been moved to the predetermined location. This includes a method of making a payment online through the user's terminal or the machine, or a method of making a payment to the machine that has delivered the item. When making a payment to the machine, this includes a method in which the machine itself is equipped with a QR code or barcode reader that can perform code payments. Other methods include a method in which the machine is equipped with an image capture device and determines from the image that cash has been placed in the machine. Online payment methods include a method using electronic payment, or a method in which payment information made by the user is communicated to or stored in a machine or server of a service provider. The machine can recognize that an electronic payment has been made by receiving communication from an external machine or server. These methods recognize the completion of payment for the first object.

This disclosure describes a method for handing the first object to the orderer after the recognition. After the machine recognizes that payment for the first object has been completed, it hands the first object to the orderer. Handing over to the orderer includes releasing the machine from its possession of the first object, transferring possession of the first object to the orderer, and transferring the first object to the orderer. Regardless of the method, any action that can be objectively observed and interpreted as such is considered to be an act of "handing the first object to the orderer." For example, this includes an act of a self-propelled vehicle moving with an item loaded in an item carrying means (such as a basket or platform) and presenting the item in front of the orderer after the recognition. The first object is transferred when the orderer picks up the first object. This also includes an act of a humanoid robot moving while holding an item in its hand and releasing the item after the recognition. By releasing the item in this way and the orderer receiving it, the first object is handed over to the orderer. This includes an act in which a drone loads an item into an item loading means (such as a basket), which has a lid or the like to prevent the item from being taken, and after the recognition, the lid or the like is unlocked, allowing a person to take the item. In this way, when transferring the first object, the drone does not necessarily need to be equipped with a mechanism to lock the first object so that it cannot be transferred, but such a mechanism may be provided. Such an embodiment will be described in more detail later.

This embodiment offers convenience and industrial applicability by automatically moving at least the ordered item to a designated location and handing it over to the orderer after confirming payment. After careful consideration by the inventors, it has been determined that by moving the ordered item to a designated location and handing the first item over to the orderer after confirming that payment for the first item has been completed, there is no risk of the first item being fraudulently taken. Prior art has only been able to transport designated items to designated locations, and has no solution to the problem of the first item being fraudulently taken. This effect was not known in prior art, and is novel and has a significant effect.

A method for releasing the hold of the first object after the recognition is disclosed. In at least one embodiment, the machine holds the first object. In this embodiment, holding includes physically preventing the item from leaving the machine. When observing the embodiment, any device that has the effect of physically preventing the item from leaving the machine, regardless of the means, is considered to fall under this configuration. One example includes a humanoid robot holding an item as if gripping it in its hand, and the item will not leave the robot unless the robot releases its grip. This includes a drone or self-driving vehicle placing an item in an item carrying means (such as a box), but the item carrying means is locked, and a person cannot open the item carrying means and remove it unless the lock is released. In the present disclosure, taking physical measures that can prevent the delivery of the item, regardless of the means, can be considered holding. The machine releases its hold of the first object after recognizing that payment for the first object has been completed. For example, in a large, membership-based warehouse store, the machine may be a humanoid robot that moves the ordered household goods to the cash register (corresponding to a predetermined location), holds the household goods in its hand, recognizes that the customer has completed payment for the household goods at the cash register, and then releases the household goods from its hand. This action allows the customer to receive the household goods from the machine. Meanwhile, the humanoid robot does not release its hold on the first object unless the purchaser completes payment. This embodiment applies not only to humanoid robots, but also to self-propelled carts and drones. These machines do not release their hold on the first object unless payment is completed. In another embodiment, if an item is taken without recognizing that payment for the first object has been completed, the machine may emit an alarm sound or light. If the orderer has already completed payment when the machine moves the first object to the predetermined location, the machine immediately releases its hold on the first object. In this way, the machine can recognize the completion of payment at any time between acquiring order information and moving the first object to the predetermined location. Payment can be made at any time between acquiring order information and moving the first object to the designated location. The system can also be configured so that the machine moves the first object to the designated location even if payment is not complete. In this case, if the machine moves the first object to the designated location and does not recognize that payment for the first object has been completed (equivalent to when the purchaser has placed an order but not paid), the system can also be configured so that the machine does not deliver the first object to the orderer. In this case, the machine can also be configured as a system that returns (takes back) the first object from the designated location to its original location. After careful consideration by the inventors, it has been determined that, compared to a human, returning the first object from the designated location to its original location when a purchaser has placed an order but not paid for it does not require much effort. Therefore, by allowing for such machine effort, delivery of the first object can be started immediately upon receipt of an order, and payment can be postponed, resulting in faster delivery of the first object. This effect was not known in conventional systems where delivery is performed by humans, and is therefore novel and has a significant effect.

This embodiment at least prevents unpaid items from being taken by users, and offers convenience and industrial applicability by automating the entire process from transporting items to payment. After careful consideration by the inventors, the machine transporting the items recognizes that payment has been completed and releases its hold on the items, thereby preventing theft and shoplifting of items. This effect was not known in prior art, and is therefore novel and has a significant effect.

A method is disclosed for requesting an AI to determine whether a first object can be delivered to an orderer, and delivering the first object to the orderer when the determination is affirmative. In at least one embodiment, a machine requests an AI to determine whether the first object can be delivered. The AI is capable of diverse information processing. The machine or a user transmits information to the AI for the AI to make a decision. As an example, the machine captures an image of the user's face using an image capture device and transmits the image to the AI. The machine acquires the user's voiceprint information using an audio capture device. A voiceprint is an analysis of characteristics such as voice frequency, pitch, dialect, and vocal cord shape, and includes information used to identify an individual. When ordering a first object, the user can also transmit the user's personal identification information (such as their My Number) to the AI. The user can also transmit an image of their face, a video of their My Number card or driver's license, or their own voice to the AI. Biometric information (including vein authentication, palm print authentication, finger vein authentication, facial authentication, voiceprint authentication, and iris authentication) may also be used. The machine moves the first object to a predetermined position and, before handing it over to the customer, acquires information about the person in front of it and transmits that information to the AI. The information may include facial video, video footage of a My Number card or driver's license, audio, biometric information, or a combination of one or more of these. Based on this information, the machine requests the AI to determine whether it is okay to hand over the first object to the person in front of the machine. Based on the information transmitted from the machine and the information transmitted from the user, the AI determines whether the person in front of the machine is the same as or similar to the user. For example, the AI may use image analysis to determine whether the video transmitted from the machine and the video transmitted from the customer are the same face. In the case of voiceprints, the machine determines whether the user and the person in front of it are the same by assessing the similarity of characteristics such as voice frequency, pitch, dialect, and vocal cord shape. Similarity of information can also be used as a basis for judgment regarding other biometric information. The AI determines that it is OK to hand over the first object to the orderer if it determines that the user and the person in front of it are the same person. When the machine receives information that the AI has determined that it is OK to hand over the first object to the orderer, it hands over the first object to the orderer. The handover method can employ any of the embodiments described herein. According to this embodiment, the first object can be handed over only to the person who issued the order information based on at least highly reliable information, providing convenience and industrial applicability by preventing fraudulent use. After careful consideration by the inventors, this embodiment has been found to be particularly effective in locations with large numbers of people. For example, in a large, members-only warehouse-style store, if many people are waiting in line for an item, a method of releasing the machine's hold on the first object only if the machine successfully authenticates the user as described above prevents the machine from malfunctioning and handing over the first object to the wrong person. This effect was not known in prior art and is novel and has significant benefits.

This disclosure discloses a method of requesting AI to determine whether it is OK to hand over the first object to the orderer, and releasing the hold of the first object when it is determined that it is OK. The method of releasing the hold can employ any of the embodiments described in this specification. According to this embodiment, the first object can be handed over only to at least the person who issued the order information, which is convenient in that it prevents fraudulent use and has industrial applicability.

This paper discloses a method for requesting an AI to determine whether a first object can be handed over to an orderer, and forbidding the first object from being handed over to the orderer if the AI determines that the first object is handed over. The machine receives determination information from the AI indicating whether the first object can be handed over. The machine has a mechanism for holding the first object. If the machine does not receive determination information from the AI indicating that the first object can be handed over, it continues to hold the first object. For example, the machine is a humanoid robot that carries ordered household goods to a cash register, recognizes that the customer has completed payment for the household goods at the cash register, holds the household goods in its hands, and does not release the household goods from its hands until it receives a determination from the AI. In this case, the customer cannot release the robot's grip on the robot by itself and cannot forcibly take the household goods from the robot. This includes, for example, cases where the person in front of the customer is not recognized as the person who issued the order information or where payment has not yet been made. After receiving determination information from the AI indicating that the first object can be handed over, the robot releases the household goods from its hands. This action allows the customer to receive the household goods from the machine. According to this embodiment, it is possible to prevent the first object from being handed over to an unauthorized person, which provides convenience and industrial applicability by preventing fraudulent use.

Disclosed is an embodiment in which an AI is requested to determine whether it is OK to hand over the first object to the orderer, and if the determination is not OK, the machine continues to hold the first object. In at least one embodiment, the machine begins holding the first object from the time the first object is moved to a predetermined position. The machine may begin holding the first object before moving the first object to the predetermined position, for example, during transportation. In another embodiment, the machine does not hold the first object during transportation (for example, by carrying the first object in a carrier such as a basket), but begins holding the first object (for example, by holding it by hand) just before or after moving the first object to the predetermined position. The machine continues to hold the first object if the AI does not determine that it is OK to hand over the first object to the orderer. In this case, the machine continues to hold the object and waits until the orderer completes payment. In other words, the machine can wait until the orderer completes payment, eliminating the need to rush the orderer to make payment. This improves the orderer's comfort when using the service. Furthermore, the machine not only does not hand over the first object to the purchaser because payment has not been made, but it also prevents the machine from quickly taking the object back to a logistics facility, etc. This embodiment at least prevents the first object from being handed over to unauthorized persons, providing convenience and industrial applicability in preventing fraudulent use.

This disclosure discloses a method for moving a first object to a predetermined location, moving the first object from the predetermined location to the vicinity of an orderer, holding the first object so that the orderer cannot acquire it, and releasing the hold when delivering the first object to the orderer. In at least one embodiment, the machine releases the hold when delivering the first object to the orderer. These methods can employ any of the embodiments described herein. The timing for holding the first object so that the orderer cannot acquire it may be after the first object has been moved from the predetermined location to the vicinity of the orderer. In other words, when moving the first object from the predetermined location to the vicinity of the orderer, a machine or conveyor with high moving speed is used, and when payment is confirmed, the object is held by a machine or conveyor with excellent object holding capabilities. After careful consideration, the inventors have found that this division of labor improves delivery speed. This effect is unknown in conventional technology and has novelty and significant benefits. According to this embodiment, the first object can be delivered only to the person who issued the order information, which provides convenience and industrial applicability by preventing fraudulent use.

The machine is self-propelled, and a method for self-propelling the first object to a predetermined location is disclosed. After careful consideration, the inventors discovered that providing a machine with a self-propelled function and a function for holding the first object so that the orderer cannot retrieve it dramatically improves the convenience of delivering goods to the orderer. Conventional machines could deliver goods to the orderer, but did not have a function for holding the first object so that it could not be retrieved. As a result, goods could be delivered to the wrong person, significantly reducing the convenience of the service. According to this embodiment, the machine has a self-propelled function and further holds the first object so that the orderer cannot retrieve it. By performing two roles, one person can provide a highly mobile service. Furthermore, the first object is no longer handed over to the wrong person. This effect was not known in conventional technology and is therefore novel and has a significant effect.

The machine is a humanoid robot. The inventor has diligently considered the matter and found that convenience would be greatly improved if the machine were a humanoid robot. Because stores, buildings, and other structures are designed for bipedal humans, drones and self-propelled vehicles may not be able to navigate satisfactorily. For example, high-rise apartment buildings are difficult to reach by self-propelled vehicle, and drones have difficulty flying in residential areas with tangled power lines. If a customer is at a subway station and requests that an item be delivered to the subway ticket gate, neither a drone nor a self-propelled cart may be able to accommodate. The inventor has found that a bipedal robot is ideally configured as a machine for delivering goods to subways and other residential locations. That is, because a humanoid robot has a range of motion and size similar to or similar to that of a human, it can enter a high-rise apartment building, reach a specific room, and navigate easily through residential areas with tangled power lines. As a result, humanoid robots can almost reliably reach locations where people are present, and are the most versatile when it comes to moving a first object to a predetermined position. This makes humanoid robots one of the most desirable forms for providing services. This effect is something that was not known in conventional technology, and is a novel and significant effect.

The disclosed method involves a machine, a humanoid robot, that, when moving a first object to a predetermined position, moves the first object from the predetermined location to the vicinity of the orderer, holds the first object in its hands so that the orderer cannot pick it up, and releases the hold when handing the first object to the orderer. In at least one embodiment, the humanoid robot moves the first object from the predetermined location to the vicinity of the orderer. For example, in a store, the robot picks up the first object from a warehouse or storage room and carries it to the cash register. When the orderer is near the cash register, the robot moves the item to the front of the cash register. The robot holds the first object in its hands so that the orderer cannot pick it up. When handing the first object to the orderer, the robot releases the hold. The robot releases the first object from its hands. This embodiment offers convenience and industrial applicability by allowing predetermined items to be automatically handed over to orderers even in places where drones or self-driving vehicles have difficulty navigating.

This disclosure relates to a transfer method in which a device acquires order information for a first object, moves the first object to a predetermined location, recognizes that payment for the first object has been completed, and, after the recognition, hands over the first object to the orderer. The machines and devices described in this specification can also be realized as methods. The same applies to programs that operate any of the machines described in this specification.

This disclosure discloses a method in which, when delivering the first object to the orderer, the machine further verifies the identity of the orderer, and delivers the first object, which is the ordered item, to the orderer only if the identity verification is successful. In at least one embodiment, the identity verification utilizes any of the methods described in this specification. This embodiment has the convenience and industrial applicability of at least preventing the mistake of delivering an item to someone other than the orderer.

A method of transferring a first object is disclosed in which a machine moves a first object to a predetermined location by self-propelled movement. When the machine arrives at the predetermined location, it verifies the identity of the recipient using an image capture device and facial recognition means. If identity verification or payment is successful, the first object is handed over to the recipient. These methods incorporate the embodiments described in this specification. In at least one embodiment, the facial recognition means identifies the person based on the position and size of facial features such as the eyes, nose, and mouth. The machine captures facial images of the person in front of the machine using an image capture device. The user captures facial images (including photographs or videos) of their own face using their own terminal or the machine's image capture device, or transmits already-held facial images to the service provider's terminal, machine, or server. The machine receives, searches, or stores these images. The machine compares the facial images of the target person captured by the machine with the received facial images to confirm whether the person in front is a registered person. To make the determination, the machine makes a judgment based on the ratio of distances related to the positions of the eyes and nose, the positions of facial feature points such as the eyes, nose, and mouth, and the position and size of the facial area. Registration involves a person who wishes to use a goods delivery service registering their personal information, facial images, etc. This embodiment offers convenience and industrial applicability by at least eliminating the mistake of handing goods over to the wrong person.

A method is disclosed in which a speech recognition means capable of supporting two or more foreign languages accepts voice input from an orderer and outputs voice guidance regarding the order in the language selected by the orderer. In at least one embodiment, the machine or the user's terminal acquires the user's voice via a voice capture device. The machine generates text information related to the voice by analyzing the acquired voice using a natural language processing model. "Capable of supporting two or more foreign languages" means that the natural language processing model supports at least two or more foreign languages. The speech recognition means may be any type of voice capture device, including a microphone. The user orders a first item through the user's terminal or the machine. For example, the machine may be a fixed terminal. Fixed terminals include terminals installed on land or buildings, but also include mobile store terminals. Examples include order terminals in drive-throughs and fixed terminals installed in stores. Because the machine is capable of supporting two or more foreign languages, it can also analyze orders made by foreigners in those languages. The machine accepts voice input from the orderer. Accepting includes acquiring voice data from a voice recognition means and receiving voice data from a machine or a server. The machine determines the foreign language of the voice data through a natural language processing model. The machine identifies the foreign language used by the user by determining the foreign language of the user's voice data. The machine uses natural language processing and AI to output voice instructions regarding the order in the language selected by the orderer when delivering the first object to the orderer or when verifying the orderer's identity. The order instructions include some information related to the first object. For example, information such as "I have brought you ramen" or "I have brought you some noodles." The purchaser's name may also be spoken. This information is converted into voice information using natural language processing and output by the machine's voice output unit. AI capable of outputting in at least two languages is used. After careful consideration by the inventor, if the machine delivers the first object, the orderer may be hesitant about whether or not to accept it. In such cases, the machine's speech allows the orderer to receive the item with peace of mind. This effect is completely unknown in conventional technology and has a novel and significant effect. This embodiment at least serves as an alternative to human delivery personnel, and offers convenience and industrial applicability by providing a fully automated delivery service with a human touch.

A method is disclosed in which, upon recognizing the completion of payment for a first target, a device generates transaction information related to the order or payment for the first target, connects to a blockchain network, and records the transaction information on the blockchain network. In at least one embodiment, the blockchain directly connects terminals on the network and includes a database that uses cryptographic technology to process and record transaction records in a distributed manner. Each block constituting the blockchain contains data called a "hash value" that represents the contents of the previous block. If an attempt is made to tamper with data in a previously generated block, the hash value calculated from the changed block will be different from the previous one, and the hash values of all subsequent blocks must be changed. Thus, tampering with data managed by the blockchain is difficult. The transaction information related to the order or payment for the first target may be any information related to the transaction. For example, it may include the orderer's name, personal data, My Number, the product name, quantity, price, shipping method, specified location, desired delivery time to the specified location, or a combination of one or more of these. The machine generates the transaction information and connects to the blockchain network. This network includes machines or networks of providers of this service and machines or networks of service providers that operate payment systems. The machines transmit or store transaction information on the blockchain network. This embodiment offers convenience and industrial applicability by at least improving the confidentiality of transaction information with users.

A machine is disclosed that includes a means for detecting the inventory quantity of a first object and a means for communicating with an inventory management system, and that generates information requesting replenishment of the first object when the inventory quantity of the first object falls below a predetermined value. In at least one embodiment, the first object is stored in a warehouse attached to a store or a more centralized, large warehouse or logistics facility (referred to herein as a "logistics facility, etc."). The logistics facility, etc. includes a means for detecting the inventory quantity of the first object. For example, the logistics facility, etc. obtains information regarding the names and quantities of items being brought into the facility. Furthermore, it obtains information regarding the names and quantities of items being removed from the facility and records the number of items remaining in inventory within the facility. In another embodiment, the logistics facility, etc., includes a video capture device on the rack and counts the number of items remaining on the rack. Alternatively, a self-propelled cart equipped with a video capture device is operated in an automated warehouse, and the number of items present on each rack is determined via video, with communications concealed, from the self-propelled cart. Alternatively, the names and quantities of items in inventory can be determined by reading the barcodes of the items. This information is recorded in an inventory management system of a logistics facility, etc. The machine communicates with this inventory management system. The machine itself may be a self-propelled cart or a self-propelled vehicle. Alternatively, the machine may be a drone or a humanoid robot. The machine moves an item from a logistics facility, etc., by moving a first object to a predetermined location. The machine transmits the name or number of items removed from the logistics facility, etc. to the inventory management system. The inventory management system updates the name or number of items that have been reduced as a result. The machine determines that the inventory quantity of the first object has fallen below a predetermined value by transmitting the name or number of items removed from the logistics facility, etc. to the inventory management system or by communicating with the inventory management system. The predetermined value can be registered by the provider of this service or the operator of the logistics facility, etc. The machine communicates with the inventory management system to obtain information on the predetermined value of the inventory quantity of the first object. Furthermore, the machine communicates with the inventory management system to determine the inventory quantity. If the inventory quantity of the first object falls below a predetermined value due to the machine's own transportation, the machine generates information requesting replenishment of the first object. The information requesting replenishment includes information requesting the purchase of items transported by the machine itself, simply the number of items transported, or a combination of one or more of these pieces of information. This embodiment provides convenience and industrial applicability by automatically linking at least the machines that transport items, etc. with logistics facilities, etc., to prevent inventory shortages.

The following provides an overview of the embodiments described above.

It is a machine,
Obtain order information for a first target;
Move the first object to a predetermined position,
Recognize the completion of the first payment,
After the recognition, the first object is delivered to the orderer.
machine

It is a machine,
Move the first object to a predetermined position,
Recognize the completion of the first payment,
After the recognition, releasing the holding of the first object.
machine.

It is a machine,
Obtain order information for a first target;
Move the first object to a predetermined position,
The AI is asked to determine whether it is okay to hand over the first item to the customer.
If it is judged to be good, the first object is delivered to the orderer.
machine

It is a machine,
Move the first object to a predetermined position,
The AI is asked to determine whether it is okay to hand over the first item to the customer.
If it is determined to be good, release the hold on the first target.
machine.

It is a machine,
Obtain order information for a first target;
Move the first object to a predetermined position,
The AI is asked to determine whether it is okay to hand over the first item to the customer.
If it is not judged to be good, the first object will not be delivered to the customer.
machine

It is a machine,
Move the first object to a predetermined position,
The AI is asked to determine whether it is okay to hand over the first item to the customer.
If it is not determined to be good, continue holding the first object.
machine.

A machine according to any of the above,
When moving the first object to a predetermined position, the first object is moved from the predetermined location to a periphery of the orderer and held so that the orderer cannot acquire the first object;
When the first object is delivered to the orderer, the holding is released.
machine

A machine according to any of the above,
the machine is self-propelled;
The first object is moved to a predetermined position by self-propelling.
machine

A machine according to any of the above,
The machine is a humanoid robot.
machine

A machine according to any of the above,
the machine is a humanoid robot;
When moving the first object to a predetermined position, the first object is moved from the predetermined position to a periphery of the orderer, and the orderer holds the first object with his/her hand so that the first object cannot be acquired by the orderer;
When the first object is delivered to the orderer, the holding is released.
machine

1. A method of transferring, comprising:
Obtain order information for a first target;
Move the first object to a predetermined position,
Recognize the completion of the first payment,
After the recognition, the first object is delivered to the orderer.
method

The machine described above,
When moving the first object to a predetermined position, the first object is moved from the predetermined location to a periphery of the orderer and held so that the orderer cannot acquire the first object;
When the first object is delivered to the orderer, the holding is released.
method

A program that operates any of the machines described above.

The method according to the above,
When delivering the first object to the orderer, the machine:
Furthermore, we verify the identity of the orderer,
Only if the identity verification is successful, deliver the first object, which is the ordered item, to the orderer.
method.

A method of transferring, comprising:
The machine moves by itself to move the first object to a predetermined position,
When the machine arrives at the designated location, it will verify the recipient's identity using a video capture device and facial recognition means.
If the identity verification or payment is successful, the first object is delivered to the recipient;
method.

The method according to the above,
Accepting voice input from the orderer using a voice recognition means capable of handling two or more foreign languages;
Audio output of order instructions in the customer's language of choice;
method.

The method according to the above,
Upon recognizing the completion of the settlement of the first object, the device generates transaction information relating to the order or settlement of the first object;
Connect to the blockchain network
Recording transaction information on the blockchain network,
method.

The method according to the above, wherein the apparatus comprises:
The system includes a means for detecting the inventory quantity of the first object and a means for communicating with the inventory management system,
When the inventory quantity of the first object falls below a predetermined value, information requesting replenishment of the first object is generated.
machine.

In at least one embodiment, the orderer can confirm that the product will be handed over, or that entry will be granted if a ticket has been purchased, by viewing the payment screen or showing the payment number or code on the purchase screen. The machine acquires video of the user's terminal via an image capture device. The service provider may offer an online sales homepage, providing a method for purchasing goods, products, etc. online and making the necessary payments. Regardless of the method, once payment is completed, a screen indicating the payment is complete is displayed. This screen may include information indicating that the payment has been completed, a unique payment number, a specific screen that is displayed only upon completion of payment, a code indicating the payment number, or one or more combinations thereof (hereinafter referred to as "screen, etc."). The machine can also read codes displayed on the user's terminal using a code reader. Codes include QR codes, but may also be barcodes, or any other code. When the machine recognizes that payment for the first object has been completed, it checks the screen of the machine or terminal presented by the orderer, and upon confirming the specific video, hands the first object over to the orderer. The specific video includes the screen, etc. described above. Tickets include admission tickets, train tickets, and multi-ride tickets. If the user has purchased an electronic ticket that can be provided online, the machine will allow the purchaser to enter a predetermined area when it confirms a predetermined video or other image. In this case, the machine is equipped with a mechanism that will not allow people to enter a predetermined area if it cannot confirm the predetermined video, either by itself or in cooperation with another machine. One example is an entrance gate. For example, at a concert venue, the machine will unlock the entrance gate when it confirms a predetermined video or other image, and allow anyone who presents that video or other image to enter. If it cannot confirm the predetermined video or other image, the entrance gate will remain locked. This embodiment at least improves the convenience of identity verification and has industrial applicability.

In at least one embodiment, the machine captures a video of the orderer at any timing. This timing includes before payment, when handing over the first object to the orderer after recognizing that payment for the first object has been completed, after handing over, or a combination of one or more of these. The video of the orderer includes an image, a video, or a combination thereof. The video may be one second long, or may capture the entire time the orderer is in front of the machine. The machine stores the video of the orderer or transmits it to another machine or server. The machine or server transmits the video of the orderer to another machine or server. The machine can further store or transmit information about the orderer in association with the video. For example, for orderer A, this may include information that payment was completed quickly and information about how many times the orderer has purchased. The machine further changes its behavior based on the information stored or received from another machine or server. For example, if the orderer has made multiple purchases, the machine may say, "Thank you very much for your continued purchases." On the other hand, if the orderer delays payment or has a history of stealing items without paying, the system will change its behavior by not handing over the item to the orderer or sounding a specified warning sound. This embodiment has the convenience and industrial applicability of at least preventing the possibility of product theft and automatically measuring the trustworthiness of the orderer.

In at least one embodiment, the system can be configured as a medication receiving machine installed in a hospital or pharmacy, allowing patients to receive prescription medications. The machine acquires order information for a first medication and moves the first medication from a medication storage room or storage cabinet to a predetermined location. This predetermined location includes the location where the patient or other person will receive the medication. At the predetermined location, the machine recognizes that payment for the first medication has been completed, and after this recognition, hands the first medication to the patient or other person. In this case, the machine can function as an automated medication receiving machine. After careful consideration by the inventors, this system has been found to be particularly effective in hospitals and pharmacies. Emergency outpatient visits occur in hospitals and pharmacies. However, conventional hospitals and pharmacies require human accounting, making it impossible to deliver medications outside of business hours. Patients who arrive at night and whose condition does not require emergency transport must endure the wait for their medication until the clinic or pharmacy opens the next day. This embodiment automates the delivery of medications, allowing for 24-hour service. Medication can be delivered even at night. This effect was not known in the prior art and is therefore novel and has significant benefits.

In at least one embodiment, the embodiments described herein can also be applied to face-to-face sales, taking restaurant orders, authenticating orders using barcode payments, ride-hailing services, food delivery services, and freight transport services.

The invention according to the present disclosure may have at least one of the above-described effects.

Claims (6)

It is a machine,
Obtain order information for a first target;
Before recognizing the completion of the settlement of the first object, the first object is started to be moved to a predetermined position,
Recognize the completion of the first payment,
After the recognition, the first object is delivered to the orderer;
When moving the first object to a predetermined position, the first object is moved from the predetermined location to a periphery of the orderer of the first object , and is held so that the orderer cannot acquire the first object;
When the first object is delivered to the orderer, the holding is released.
machine.
A method, comprising:
Obtain order information for a first target;
Before recognizing the completion of the settlement of the first object, the first object is started to be moved to a predetermined position,
Recognize the completion of the first payment,
After the recognition, the first object is delivered to the orderer;
When moving the first object to a predetermined position, the first object is moved from the predetermined location to a periphery of the orderer of the first object , and is held so that the orderer cannot acquire the first object;
When the first object is delivered to the orderer, the holding is released.
method.
A system, comprising:
Obtain order information for a first target;
Before recognizing the completion of the settlement of the first object, the first object is started to be moved to a predetermined position,
Recognize the completion of the first payment,
After the recognition, the first object is delivered to the orderer;
When moving the first object to a predetermined position, the first object is moved from the predetermined location to a periphery of the orderer of the first object , and is held so that the orderer cannot acquire the first object;
When the first object is delivered to the orderer, the holding is released.
system.
2. The machine of claim 1,
The machine is a bipedal humanoid robot.
machine.
2. The machine of claim 1,
The machine is a humanoid robot,
When locking and holding, lock by holding with your hand.
machine.
2. The machine of claim 1,
If the first object is taken without recognizing that the first object has been settled, a sound or light is emitted.
machine.