System

Abstract

An object of a system according to an embodiment is to improve customer experience at a self-service register and provide interaction with and support for a customer.SOLUTION: A system includes a pick-up part, a commodity guide part, and an operation support part. The pick-up section picks up the customer with a friendly voice. The product information section conveys the product name and price each time a product is read. The operation support unit copes with an operation mistake or a question.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technology has had the problem of creating a static customer experience at self-checkouts, lacking in customer interaction and support.

[0005] The system according to the embodiment aims to improve the customer experience at self-checkouts and provide interaction and assistance to customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a welcoming unit, a product information unit, and an operation support unit. The welcoming unit greets customers with a friendly voice. The product information unit announces the product name and price each time a product is scanned. The operation support unit responds to operational errors and questions with compassion. [Effects of the Invention]

[0007] The system according to the embodiment can improve the customer experience at the self-checkout and provide customer interaction and assistance. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​cash register system according to an embodiment of the present invention is a system that chats with customers while they are shopping at a self-checkout register, just as if they were shopping at a greengrocer or fishmonger. This system greets customers with a friendly voice when they approach the register, tells them the product name and price each time an item is scanned, provides information on special offers and cooking methods, and responds to any operational errors or questions with compassion. This allows customers to enjoy shopping with peace of mind.

[0029] The AI ​​cash register system according to the embodiment includes a welcoming unit, a product information unit, and an operation support unit. The welcoming unit greets customers with a friendly voice. For example, the generation AI uses voice recognition and voice synthesis technologies to provide a friendly greeting to customers. For example, it greets customers by saying, "Hello! What are you looking for today?" The product information unit announces the product name and price in a friendly voice each time it scans a product. For example, it may provide information such as, "These apples are on sale this week. They're perfect for making apple pie." The operation support unit also provides friendly support when customers make mistakes or have questions. For example, if a customer says, "Sorry, I made a mistake," it responds by saying, "It's okay. How can I help you?" The generation AI also provides appropriate answers to customer questions. For example, in response to a question such as, "I can't read the barcode for this product," the system will respond by saying, "Please try scanning the barcode again. If that doesn't work, I'll call a member of staff." In this way, the AI ​​cash register system according to the embodiment allows customers to enjoy shopping with peace of mind.

[0030] The welcoming unit can provide an individually customized greeting based on the customer's past purchase history. For example, the welcoming unit can provide an individually customized greeting based on the customer's past purchase history. For example, the welcoming unit can greet the customer in the form of "Hello, Tanaka-san! Are you looking for fish today?" This allows for a more friendly greeting to the customer.

[0031] The welcoming unit can entertain customers not only with sound but also with visuals by displaying animations and characters on the display. For example, when welcoming a customer, the welcoming unit can entertain customers not only with sound but also with visuals by displaying animations and characters on the display. For example, an animated character may wave and say "Hello!" This can entertain customers visually as well.

[0032] The welcoming unit can work in conjunction with a smartphone app to display a greeting message on the app before the customer approaches the cash register. The welcoming unit can, for example, work in conjunction with a smartphone app to display a greeting message on the app before the customer approaches the cash register. For example, it can display a message such as "Hello! You're on your way to the cash register." This makes it possible to display a greeting message before the customer approaches the cash register.

[0033] When a product is scanned, the product information section can provide information about the producer and place of origin of the product. For example, when a product is scanned, the generation AI provides information about the producer and place of origin of the product. For example, it can provide information such as "These apples are from Aomori Prefecture." This makes it possible to provide customers with information about the producer and place of origin of the product.

[0034] When a product is scanned, the product information section can provide information about the nutritional value and health benefits of that product. For example, when a product is scanned, the generation AI provides information about the nutritional value and health benefits of that product. For example, it may provide information such as "This apple is rich in vitamin C." This makes it possible to provide customers with information about the nutritional value and health benefits of the product.

[0035] When a product is scanned, the product guidance unit can display a recipe video for that product on the display, providing visual guidance on how to cook it. For example, when a product is scanned, the product guidance unit can have the generation AI display a recipe video for that product on the display, providing visual guidance on how to cook it. For example, the guidance can be in the form of "I'll show you how to make apple pie using these apples." This allows customers to visually guide them on how to cook it.

[0036] When a product is scanned, the product information section provides allergen information for that product, and can warn customers with allergies. For example, when a product is scanned, the product information section uses the generation AI to provide allergen information for that product, and warn customers with allergies. For example, the section provides information in the form of "This apple does not contain any allergens." This allows the allergen information to be provided to customers, and warnings can be made.

[0037] When an operation error occurs, the operation support unit can automatically analyze the cause and propose specific solutions. For example, when an operation error occurs, the generation AI automatically analyzes the cause and proposes specific solutions. For example, it might respond by saying, "The barcode may be dirty. Please wipe it and scan again." This makes it possible to automatically analyze the cause of an operation error and propose specific solutions.

[0038] When a question is raised, the operation support unit can record the content of the question and build a database to be used in future responses. For example, when a question is raised, the operation support unit has the generation AI record the content of the question and build a database to be used in future responses. For example, the operation support unit can record the question, "I can't read the barcode of this product," and build a database to be used in future responses. This makes it possible to record the content of the question and build a database to be used in future responses.

[0039] When a question arises, the operation support unit can send an answer to the question to the smartphone app so that the customer can check it later. For example, when a question arises, the operation support unit can send an answer to the question to the smartphone app so that the customer can check it later. For example, the operation support unit can send an answer to the question, "I can't read the barcode of this product," to the app. This allows the answer to the question to be sent to the smartphone app so that the customer can check it later.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The greeter can also provide a customized greeting based on the customer's hobbies and interests, as well as their purchasing history. For example, if a customer is a sports fan, the greeter can say, "Hello! Are you going to see a game today?" Or, if the customer is a music fan, the greeter can say, "Hello! What's your favorite song these days?" This allows for a more personalized greeting for customers.

[0042] When scanning a product, the product information section can provide information about the product's environmental impact and eco-friendly features. For example, it can provide information such as "These apples were grown organically." It can also provide information such as "This product is made from recycled materials." This makes it possible to provide customers with information about the product's environmental impact.

[0043] The operation support unit can provide visual guidance in real time as customers perform operations. For example, it can display the operation procedure as an animation on the screen, showing the customer which button they should press. It can also use audio guidance, such as "Please press this button next." This allows customers to perform operations smoothly.

[0044] The welcoming section can provide information about other services and events in the store as the customer approaches the cash register. For example, it can provide information such as, "We're holding a wine tasting event in the store today." It can also provide information such as, "We've opened a new cafe, so please stop by." This makes it possible to provide customers with information about other services and events in the store.

[0045] When a product is scanned, the product information section can provide information about the product's storage method and expiration date. For example, it can provide information such as "Please store this apple in the refrigerator." It can also provide information such as "Please consume this product by next week." This makes it possible to provide customers with information about the product's storage method and expiration date.

[0046] The operation support unit can provide tactile feedback in addition to audio when customers perform operations. For example, a slight vibration when pressing a button can confirm that the operation was successful. In addition, if an operation error occurs, a different vibration pattern can be used to notify the customer. This allows customers to perform operations more intuitively.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The welcoming department greets the customer with a friendly voice. For example, the generative AI uses speech recognition and speech synthesis technologies to greet the customer in a friendly manner. Specifically, it greets the customer with something like, "Hello! What are you looking for today?" Step 2: Each time a product is scanned, the product information section will announce the product name and price in a friendly voice. For example, it might say, "This apple costs 100 yen." The generation AI will also provide information such as deals and cooking methods using the product. For example, it might say, "These apples are on sale this week. They're perfect for making apple pie." Step 3: In the operation support section, the generated AI will respond sympathetically even if the customer makes a mistake in operation or has a question. For example, if a customer says, "Sorry, I made a mistake," the AI ​​will respond by saying, "It's okay. How may I help you?" It will also provide appropriate answers to customer questions. For example, if a customer asks, "I can't read the barcode for this product," the AI ​​will respond by saying, "Please try scanning the barcode again. If that doesn't work, I'll call a member of staff."

[0049] (Example 2) The AI ​​cash register system according to an embodiment of the present invention is a system that chats with customers while they are shopping at a self-checkout register, just as if they were shopping at a greengrocer or fishmonger. This system greets customers with a friendly voice when they approach the register, tells them the product name and price each time an item is scanned, provides information on special offers and cooking methods, and responds to any operational errors or questions with compassion. This allows customers to enjoy shopping with peace of mind.

[0050] The AI ​​cash register system according to the embodiment includes a welcoming unit, a product information unit, and an operation support unit. The welcoming unit greets customers with a friendly voice. For example, the generation AI uses voice recognition and voice synthesis technologies to provide a friendly greeting to customers. For example, it greets customers by saying, "Hello! What are you looking for today?" The product information unit announces the product name and price in a friendly voice each time it scans a product. For example, it may provide information such as, "These apples are on sale this week. They're perfect for making apple pie." The operation support unit also provides friendly support when customers make mistakes or have questions. For example, if a customer says, "Sorry, I made a mistake," it responds by saying, "It's okay. How can I help you?" The generation AI also provides appropriate answers to customer questions. For example, in response to a question such as, "I can't read the barcode for this product," the system will respond by saying, "Please try scanning the barcode again. If that doesn't work, I'll call a member of staff." In this way, the AI ​​cash register system according to the embodiment allows customers to enjoy shopping with peace of mind.

[0051] The welcoming unit can provide an individually customized greeting based on the customer's past purchase history. For example, the welcoming unit can provide an individually customized greeting based on the customer's past purchase history. For example, the welcoming unit can greet the customer in the form of "Hello, Tanaka-san! Are you looking for fish today?" This allows for a more friendly greeting to the customer.

[0052] The welcoming unit can use facial expression recognition technology to greet the customer according to their mood. For example, the welcoming unit can use facial expression recognition technology to greet the customer according to their mood. For example, if the customer is smiling, the welcoming unit can greet them with something like, "It's a nice day today!" This makes it possible to greet the customer according to their mood.

[0053] The welcoming unit can use the emotion estimation function to estimate the emotion of the customer and deliver a greeting that elicits positive emotions. For example, the welcoming unit can use the emotion estimation function to estimate the emotion of the customer and deliver a greeting that elicits positive emotions. For example, the welcoming unit can greet the customer by asking, "Is there anything special happening today?" This makes it possible to deliver a greeting that elicits positive emotions from the customer.

[0054] The welcoming unit can entertain customers not only with sound but also with visuals by displaying animations and characters on the display. For example, when welcoming a customer, the welcoming unit can entertain customers not only with sound but also with visuals by displaying animations and characters on the display. For example, an animated character may wave and say "Hello!" This can entertain customers visually as well.

[0055] The welcoming unit can work in conjunction with a smartphone app to display a greeting message on the app before the customer approaches the cash register. The welcoming unit can, for example, work in conjunction with a smartphone app to display a greeting message on the app before the customer approaches the cash register. For example, it can display a message such as "Hello! You're on your way to the cash register." This makes it possible to display a greeting message before the customer approaches the cash register.

[0056] The welcoming unit uses the emotion estimation function to analyze the walking speed and posture of a customer as they approach the cash register, and can greet them at an appropriate time. For example, the welcoming unit uses the emotion estimation function to analyze the walking speed and posture of a customer as they approach the cash register, and can greet them at an appropriate time. For example, if the customer is walking slowly, the welcoming unit can greet them by saying, "Please take your time and enjoy your shopping." This makes it possible to analyze the walking speed and posture of a customer as they approach the cash register, and can greet them at an appropriate time.

[0057] When a product is scanned, the product information section can provide information about the producer and place of origin of the product. For example, when a product is scanned, the generation AI provides information about the producer and place of origin of the product. For example, it can provide information such as "These apples are from Aomori Prefecture." This makes it possible to provide customers with information about the producer and place of origin of the product.

[0058] When a product is scanned, the product information section can provide information about the nutritional value and health benefits of that product. For example, when a product is scanned, the generation AI provides information about the nutritional value and health benefits of that product. For example, it may provide information such as "This apple is rich in vitamin C." This makes it possible to provide customers with information about the nutritional value and health benefits of the product.

[0059] The product information unit can use the emotion estimation function to provide stories and anecdotes about products that customers may be interested in. For example, the product information unit can use the emotion estimation function to provide stories and anecdotes about products that customers may be interested in. For example, the product information unit can provide information in the form of, "This apple is a variety that has been loved locally for a long time." This makes it possible to provide product information that will interest customers.

[0060] When a product is scanned, the product guidance unit can display a recipe video for that product on the display, providing visual guidance on how to cook it. For example, when a product is scanned, the product guidance unit can have the generation AI display a recipe video for that product on the display, providing visual guidance on how to cook it. For example, the guidance can be in the form of "I'll show you how to make apple pie using these apples." This allows customers to visually guide them on how to cook it.

[0061] When a product is scanned, the product information section provides allergen information for that product, and can warn customers with allergies. For example, when a product is scanned, the product information section uses the generation AI to provide allergen information for that product, and warn customers with allergies. For example, the section provides information in the form of "This apple does not contain any allergens." This allows the allergen information to be provided to customers, and warnings can be made.

[0062] The product information unit can use the emotion estimation function to recommend other products that the customer may be interested in and suggest related products. For example, the product information unit can use the emotion estimation function to recommend other products that the customer may be interested in and suggest related products. For example, the product information unit can provide guidance in the form of "Would you like some cheese with this apple?" This makes it possible to suggest related products to the customer.

[0063] When an operation error occurs, the operation support unit can automatically analyze the cause and propose specific solutions. For example, when an operation error occurs, the generation AI automatically analyzes the cause and proposes specific solutions. For example, it might respond by saying, "The barcode may be dirty. Please wipe it and scan again." This makes it possible to automatically analyze the cause of an operation error and propose specific solutions.

[0064] When a question is raised, the operation support unit can record the content of the question and build a database to be used in future responses. For example, when a question is raised, the operation support unit has the generation AI record the content of the question and build a database to be used in future responses. For example, the operation support unit can record the question, "I can't read the barcode of this product," and build a database to be used in future responses. This makes it possible to record the content of the question and build a database to be used in future responses.

[0065] The operation support unit can use the emotion estimation function to provide relaxing music or messages to customers when they are feeling stressed. For example, the operation support unit can use the emotion estimation function to have the generation AI provide relaxing music or messages to customers when they are feeling stressed. For example, the operation support unit can respond by saying, "It's okay, let's take it easy." This makes it possible to provide relaxing music or messages to customers when they are feeling stressed.

[0066] When a question arises, the operation support unit can send an answer to the question to the smartphone app so that the customer can check it later. For example, when a question arises, the operation support unit can send an answer to the question to the smartphone app so that the customer can check it later. For example, the operation support unit can send an answer to the question, "I can't read the barcode of this product," to the app. This allows the answer to the question to be sent to the smartphone app so that the customer can check it later.

[0067] The operation support unit can use the emotion estimation function to analyze the emotion a customer feels when they make an operation error and provide feedback to improve how they will be handled next time and onwards. The operation support unit can, for example, use the emotion estimation function to analyze the emotion a customer feels when they make an operation error and provide feedback to improve how they will be handled next time and onwards. For example, the operation support unit can respond by saying, "If the customer feels stressed, we will provide a more detailed explanation next time." This makes it possible to analyze the emotion a customer feels when they make an operation error and provide feedback to improve how they will be handled next time and onwards.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The greeter can also provide a customized greeting based on the customer's hobbies and interests, as well as their purchasing history. For example, if a customer is a sports fan, the greeter can say, "Hello! Are you going to see a game today?" Or, if the customer is a music fan, the greeter can say, "Hello! What's your favorite song these days?" This allows for a more personalized greeting for customers.

[0070] When scanning a product, the product information section can provide information about the product's environmental impact and eco-friendly features. For example, it can provide information such as "These apples were grown organically." It can also provide information such as "This product is made from recycled materials." This makes it possible to provide customers with information about the product's environmental impact.

[0071] The operation support unit can provide visual guidance in real time as customers perform operations. For example, it can display the operation procedure as an animation on the screen, showing the customer which button they should press. It can also use audio guidance, such as "Please press this button next." This allows customers to perform operations smoothly.

[0072] The welcoming unit can use the emotion estimation function to greet a customer who appears tired in a way that will help them relax. For example, it can greet a customer by saying, "You seem tired today. Please take your time and enjoy your shopping." If the customer is feeling stressed, it can respond by saying, "Please let us know if there's anything we can help you with." This makes it possible to greet a customer according to their emotions.

[0073] The product information section can use the emotion estimation function to provide information about the history and cultural background of products that customers may be interested in. For example, it can provide information such as, "This apple has been cultivated since ancient Roman times." It can also provide information such as, "This product is traditionally made in a specific region." This makes it possible to provide customers with information about the history and cultural background of products.

[0074] The welcoming section can provide information about other services and events in the store as the customer approaches the cash register. For example, it can provide information such as, "We're holding a wine tasting event in the store today." It can also provide information such as, "We've opened a new cafe, so please stop by." This makes it possible to provide customers with information about other services and events in the store.

[0075] The operation support unit can use the emotion estimation function to provide more detailed guidance when a customer is having trouble operating the device. For example, it can provide guidance such as, "If you are having trouble operating the device, please press this button." If the customer is feeling anxious, it can respond by saying, "It's okay, please proceed slowly." This makes it possible to provide appropriate support when a customer is having trouble operating the device.

[0076] When a product is scanned, the product information section can provide information about the product's storage method and expiration date. For example, it can provide information such as "Please store this apple in the refrigerator." It can also provide information such as "Please consume this product by next week." This makes it possible to provide customers with information about the product's storage method and expiration date.

[0077] The welcoming unit can use the emotion estimation function to provide detailed information about a particular product when the customer shows interest in that product. For example, it can provide information such as, "Are you interested in this apple? This apple is a special variety that is very sweet." It can also provide information such as, "This product is only available in limited quantities, so please order it early." This makes it possible to provide detailed information about products that customers are interested in.

[0078] The operation support unit can provide tactile feedback in addition to audio when customers perform operations. For example, a slight vibration when pressing a button can confirm that the operation was successful. In addition, if an operation error occurs, a different vibration pattern can be used to notify the customer. This allows customers to perform operations more intuitively.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The welcoming department greets the customer with a friendly voice. For example, the generative AI uses speech recognition and speech synthesis technologies to greet the customer in a friendly manner. Specifically, it greets the customer with something like, "Hello! What are you looking for today?" Step 2: Each time a product is scanned, the product information section will announce the product name and price in a friendly voice. For example, it might say, "This apple costs 100 yen." The generation AI will also provide information such as deals and cooking methods using the product. For example, it might say, "These apples are on sale this week. They're perfect for making apple pie." Step 3: In the operation support section, the generated AI will respond sympathetically even if the customer makes a mistake in operation or has a question. For example, if a customer says, "Sorry, I made a mistake," the AI ​​will respond by saying, "It's okay. How may I help you?" It will also provide appropriate answers to customer questions. For example, if a customer asks, "I can't read the barcode for this product," the AI ​​will respond by saying, "Please try scanning the barcode again. If that doesn't work, I'll call a member of staff."

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A welcoming section that greets customers with a friendly voice, A product information section that displays the product name and price each time a product is scanned, An operation support unit that responds sympathetically to operational errors and questions. A system characterized by:

2. The receiving section is Using facial expression recognition technology to greet customers according to their mood 2. The system of claim 1.

3. The receiving section is In addition to audio, the display also displays animations and characters for visual enjoyment.

2. The system of claim 1.

4. The product information section When scanning a product, information about the product's producer and origin is provided.

2. The system of claim 1.

5. The operation support unit When the above-mentioned operational error occurs, the cause is automatically analyzed and a specific solution is proposed.

2. The system of claim 1.

6. The receiving section is Estimate the customer's emotions and greet them in a way that elicits positive emotions 2. The system of claim 1.

7. The product information section Provide stories and anecdotes about products that may be of interest to the customer 2. The system of claim 1.

8. The operation support unit If the customer is feeling stressed, provide them with relaxing music or messages.

2. The system of claim 1.

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