System

Abstract

An object of a system according to an embodiment is to improve the efficiency of searching for items and exchanging information with sellers by using generated AI.SOLUTION: A system according to an embodiment includes a commodity search part, a seller exchange part, a purchase procedure part, a selling procedure part, and a progress management part. The commodity retrieval unit retrieves a commodity by using the generated AI. The seller communication part communicates with the seller on the basis of the information on the commodity retrieved by the commodity retrieval part. The purchase procedure unit performs a purchase procedure based on the information acquired by the exhibitor exchange unit. The listing procedure unit performs a procedure for listing the product purchased by the purchase procedure unit. The progress management part manages the progress of the transaction.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] Conventional technologies have had the problem that searching for products on auction sites and flea market sites and communicating with sellers is complicated and burdensome for users.

[0005] The system of the embodiment aims to use generative AI to make product searches and interactions with sellers more efficient. [Means for solving the problem]

[0006] The system according to the embodiment includes a product search unit, a seller interaction unit, a purchase procedure unit, a listing procedure unit, and a progress management unit. The product search unit searches for products using a generation AI. The seller interaction unit interacts with sellers based on information about the products searched by the product search unit. The purchase procedure unit performs the purchase procedure based on the information acquired by the seller interaction unit. The listing procedure unit performs the procedure to list the product purchased by the purchase procedure unit. The progress management unit manages the progress of the transaction. [Effects of the Invention]

[0007] The system according to the embodiment can use generation AI to streamline product searches and interactions with sellers. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 assistant system according to the embodiment of the present invention uses a generative AI to search for products on auction sites and flea market sites and to communicate with sellers. This allows users to eliminate cumbersome operations and communication, making transactions more convenient.

[0029] The assistant system according to the embodiment includes a product search unit, a seller interaction unit, a purchase procedure unit, a listing procedure unit, and a progress management unit. When a user inputs information about a product they want, the product search unit uses a generation AI to search for the most suitable product based on that information. For example, if a user inputs "I want red sneakers," the generation AI searches auction sites and flea market sites to list relevant products. Furthermore, when a user inputs "I want more details about this product," the generation AI automatically generates appropriate questions for the seller and obtains answers. Furthermore, when a user inputs "I want this product to be a little cheaper," the generation AI negotiates the price with the seller. The seller interaction unit interacts with the seller based on the information about the product searched by the product search unit. For example, the seller interaction unit inquires about detailed product information and negotiates the price. The purchase procedure unit performs the purchase procedure based on the information obtained by the seller interaction unit. For example, when a user inputs "I want to purchase this product," the generation AI automatically performs the purchase procedure on the auction site or flea market site. This includes entering payment information and specifying a shipping address. The listing procedure unit performs procedures for listing the product purchased by the purchase procedure unit. For example, when a user inputs "I want to list this product," the generation AI automatically generates a listing page based on the product description and photos, and lists the product on an auction site or flea market site. The progress management unit manages the progress of the transaction and notifies the user. For example, it automatically checks the product's shipping status and payment status, and notifies the user as appropriate. As a result, the assistant system according to the embodiment allows users to eliminate cumbersome operations and communication, allowing them to make transactions more conveniently.

[0030] Generation AI can analyze a user's past search history and purchase history to provide product search results optimized for each individual user. For example, generation AI can analyze a user's past search history to identify frequently searched keywords and categories. This allows it to quickly display related products when the user searches for the same product again. Generation AI can also suggest related or complementary products based on a user's purchase history. For example, for a user who previously purchased a camera, it can suggest accessories such as lenses and camera bags. Furthermore, generation AI can use a user's past behavioral data to provide product search results optimized for each individual user. For example, for a user who prefers a particular brand or price range, it can prioritize the display of products from that brand or price range. This allows it to provide product search results optimized for the user.

[0031] Generative AI can analyze market price trends in real time and prioritize displaying the best value products. For example, Generative AI collects market price data in real time and analyzes price fluctuations. This allows it to display the currently best value products to users. Generative AI can also integrate data from price comparison sites and auction sites to identify the cheapest products. For example, if the same product is being sold on multiple sites, it will present the user with the cheapest option. Furthermore, Generative AI can predict price trends and notify users of products whose prices are likely to fall. For example, if the price of a particular product tends to fall based on past data, it will provide that information to the user. This allows it to provide users with the best value products.

[0032] Generative AI can use image recognition technology to search for similar products based on images uploaded by users. For example, generative AI analyzes images of products uploaded by users and searches for similar products. For example, if a user uploads an image of sneakers with a specific design, generative AI searches for sneakers with a similar design. Generative AI also uses image recognition technology to extract features from the uploaded image and search for products with the same features. For example, it prioritizes displaying products with a specific color or shape. Furthermore, generative AI suggests accessories and complementary products related to the product based on images of the product taken by the user. For example, if a user uploads an image of a camera, generative AI suggests lenses and camera bags. This allows similar products to be provided based on the images uploaded by the user.

[0033] The generation AI accepts voice input and allows users to search for products simply by speaking to it. For example, when a user searches for a product by voice, the generation AI uses voice recognition technology to analyze the user's request and search for the most suitable product. For example, if the user says, "Looking for red sneakers," the generation AI will display the corresponding products. The generation AI can also use voice input to provide detailed information about products to users. For example, if the user says, "Looking for a waterproof camera," the generation AI will search for products that meet those criteria. Furthermore, the generation AI analyzes the voice input, understands the user's intent, and suggests products. For example, if the user says, "Looking for the perfect product for a gift," the generation AI will suggest products suitable for gifts. This allows users to search for products simply by speaking.

[0034] Generative AI can convert a user's vague requests into specific product information using natural language processing technology. For example, generative AI uses natural language processing technology to analyze a user's vague request and convert it into specific product information. For example, if a user enters "I want stylish shoes," the generative AI analyzes the definition of "stylish shoes" and suggests specific products. Generative AI also uses natural language processing technology to analyze the user's request in detail and extract related keywords and categories. For example, if a user enters "I want a camera suitable for traveling," the generative AI will search for products based on the keywords "travel," "camera," and "suitable." Furthermore, to convert a user's vague requests into specific product information, generative AI refers to past data and the user's behavioral history. For example, it suggests specific products based on the behavioral data of users who previously searched for "stylish shoes." This allows the user's vague requests to be converted into specific product information.

[0035] The generation AI can analyze user input and automatically display reviews and ratings of related products. For example, the generation AI analyzes user input and automatically displays reviews and ratings of related products. For example, if a user inputs, "What is the rating of this camera?", the generation AI will display reviews and ratings of that camera. The generation AI also collects reviews and ratings of related products based on the product information entered by the user and provides them to the user. For example, if a user inputs, "I want to see reviews of these sneakers," the generation AI will display reviews of those sneakers. Furthermore, the generation AI analyzes user input and builds a system that automatically displays reviews and ratings of related products. For example, if a user inputs, "What is the rating of this product?", the generation AI will display reviews and ratings of that product. This makes it possible to automatically provide users with reviews and ratings of related products.

[0036] The generation AI can suggest sets of related products based on user input. For example, the generation AI analyzes the user's input and suggests sets of related products. For example, if the user inputs "I want a camera and lens," the generation AI will suggest a set of camera and lens. The generation AI also builds a system that suggests sets of related products based on the user's input. For example, if the user inputs "I want items necessary for travel," the generation AI will suggest a set of items necessary for travel. The generation AI also analyzes the user's input and suggests sets of related products. For example, if the user inputs "I want furniture for my home office," the generation AI will suggest a set of desk, chair, and storage furniture. This makes it possible to suggest sets of related products to the user.

[0037] The generation AI can suggest customizable products based on user input. The generation AI, for example, analyzes the user's input and suggests customizable products. For example, if the user inputs, "I want sneakers that I can customize to my liking," the generation AI will suggest customizable sneakers. The generation AI also builds a system that suggests customizable products based on the user's input. For example, if the user inputs, "I want customizable furniture," the generation AI will suggest customizable furniture. The generation AI also analyzes the user's input and suggests customizable products. For example, if the user inputs, "I want accessories that I can personalize," the generation AI will suggest customizable accessories. This makes it possible to suggest customizable products to the user.

[0038] The generation AI can analyze the history of interactions with sellers and automatically select the optimal communication strategy. For example, the generation AI can analyze the history of past interactions with sellers and select the optimal communication strategy. For example, it can suggest a similar strategy based on negotiation methods that have been successful in the past. The generation AI can also analyze the history of interactions with sellers and send messages at the appropriate time. For example, it can identify the time of day when the seller is most likely to reply and send messages at that time. Furthermore, the generation AI can provide appropriate advice to users based on the history of interactions with sellers. For example, it can suggest the optimal negotiation method to users based on successful examples obtained from past interactions. This makes it possible to optimize interactions with sellers.

[0039] The generation AI can analyze the seller's rating and reliability and provide appropriate advice to the user. For example, the generation AI can analyze the seller's rating and reliability and provide appropriate advice to the user. For example, it can recommend transactions with sellers with high ratings. The generation AI can also analyze the seller's past transaction history and build a system to evaluate reliability. For example, it can advise the user to avoid sellers with a history of problems. Furthermore, the generation AI can evaluate the risk of transactions for the user based on the seller's rating and reliability. For example, it can warn the user to avoid transactions with sellers with low reliability. This makes it possible to provide appropriate advice to the user.

[0040] The generation AI supports multiple languages ​​and can automatically translate interactions with sellers who speak different languages. For example, the generation AI supports multiple languages ​​and automatically translates interactions with sellers who speak different languages. For example, it translates a message entered by a user in Japanese into English and sends it to an English-speaking seller. The generation AI also uses an automatic translation function to smoothly communicate with sellers who speak different languages. For example, it translates a message replied by a seller in French into Japanese and provides it to the user. Furthermore, the generation AI supports multiple languages ​​and builds a system that automatically translates interactions with sellers who speak different languages. For example, it translates a message entered by a user in Chinese into English and sends it to an English-speaking seller. This makes it possible to automatically translate interactions with sellers who speak different languages.

[0041] The generation AI interacts with sellers via voice, allowing users to communicate simply by speaking to it. The generation AI uses voice recognition technology, for example, to interact with sellers simply by speaking to it. For example, if a user says, "Tell me more about this item," the generation AI will send a question to the seller. The generation AI also uses voice input to build a system that allows users to interact smoothly with sellers. For example, if a user says, "Negotiate the price of this item," the generation AI will negotiate the price with the seller. Furthermore, the generation AI uses voice recognition technology to interact with sellers simply by speaking to it. For example, if a user says, "Check the shipping status of this item," the generation AI will make an inquiry to the seller. This allows users to interact with sellers simply by speaking to it.

[0042] The generation AI can analyze the content of interactions with sellers and provide a summary of important information to the user. For example, the generation AI analyzes the content of interactions with sellers and summarizes important information to provide to the user. For example, it summarizes and displays the seller's response or the results of price negotiations. The generation AI also analyzes the content of interactions with sellers and builds a system that summarizes and provides important information to the user. For example, it summarizes and displays detailed product information and shipping status. Furthermore, the generation AI analyzes the content of interactions with sellers and summarizes and provides important information to the user. For example, it extracts the main points of messages from sellers and provides them to the user. This makes it possible to provide a summary of important information to the user.

[0043] Generative AI can visualize interactions with sellers, allowing users to intuitively understand. For example, generative AI can build a system that visualizes interactions with sellers, allowing users to intuitively understand. For example, it can display message exchanges in charts and graphs. Generative AI can also visualize interactions with sellers, allowing users to grasp important information at a glance. For example, it can display the progress of price negotiations in a timeline. Furthermore, generative AI can visualize interactions with sellers, allowing users to intuitively understand. For example, it can display detailed product information and shipping status using icons and diagrams. This makes it possible to visualize interactions with sellers in a way that users can intuitively understand.

[0044] The generation AI can record interactions with sellers so that users can refer to them later. For example, the generation AI can build a system that records interactions with sellers so that users can refer to them later. For example, it can save past message history so that it can be searched when needed. The generation AI can also record interactions with sellers so that users can check the transaction history. For example, it can save the results of price negotiations and detailed product information so that it can be referred to later. Furthermore, the generation AI can record interactions with sellers so that users can refer to them later. For example, it can record the shipping status of products and payment confirmation so that users can understand the progress of the transaction. This makes it possible to record interactions with sellers so that users can refer to them later.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The assistant system can also analyze a user's past search history and purchase history to provide product search results optimized for each individual user. For example, it can prioritize the display of related products based on keywords and categories that the user has searched for in the past. It can also suggest related or complementary products based on the user's purchase history. Furthermore, for users who prefer specific brands or price ranges, it can prioritize the display of products of those brands or price ranges. This allows the system to provide product search results optimized for each user.

[0047] The assistant system can also analyze market price trends in real time and prioritize displaying the most cost-effective products. For example, by collecting market price data in real time and analyzing price fluctuations, it can display the currently most cost-effective products to users. It can also integrate data from price comparison sites and auction sites to identify the cheapest products. It can also predict price trends and notify users of products that are likely to fall in price. This allows it to provide users with the best value products.

[0048] The assistant system can also use image recognition technology to search for similar products based on images uploaded by users. For example, it can analyze images of products uploaded by users and search for similar products, thereby displaying products with similar designs. It can also use image recognition technology to extract features from uploaded images and search for products with the same features. Furthermore, it can suggest accessories and complementary products related to the product based on images of the product taken by the user. This allows it to provide similar products based on images uploaded by users.

[0049] The assistant system can also accept voice input and perform product searches simply by the user speaking. For example, when a user searches for a product by voice, the system uses voice recognition technology to analyze the user's request, search for the most suitable product, and display the relevant product. The system can also use voice input to provide detailed information about the product. Furthermore, the system can analyze the voice input, understand the user's intention, and suggest products. This allows the user to perform product searches simply by speaking.

[0050] The assistant system can also use natural language processing technology to convert a user's vague request into specific product information. For example, the system can analyze a vague request entered by a user using natural language processing technology and convert it into specific product information, thereby suggesting related products. Natural language processing technology can also be used to analyze a user's request in detail and extract related keywords and categories. Furthermore, past data and the user's behavior history can be referenced to convert a user's vague request into specific product information. This allows the system to convert a user's vague request into specific product information.

[0051] The assistant system can also analyze user input and automatically display reviews and ratings of related products. For example, by analyzing user input and automatically displaying reviews and ratings of related products, when a user inputs "What is the rating of this camera?", reviews and ratings of the camera are displayed. In addition, based on the product information input by the user, reviews and ratings of related products can be collected and provided to the user. Furthermore, it is possible to build a system that analyzes user input and automatically displays reviews and ratings of related products. This makes it possible to automatically provide reviews and ratings of related products to the user.

[0052] The assistant system can also suggest sets of related products based on the user's input. For example, by analyzing the user's input and suggesting sets of related products, if the user inputs "I want a camera and a lens," a set of a camera and a lens will be suggested. It is also possible to build a system that suggests sets of related products based on the user's input. Furthermore, it is also possible to analyze the user's input and suggest sets of related products. This makes it possible to suggest sets of related products to the user.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: When a user inputs information about a product they want, the product search unit uses the generation AI to search for the most suitable product based on that information. For example, if a user inputs "I want red sneakers," the generation AI will search auction sites and flea market sites across the board and list relevant products. Also, if a user inputs "I would like to know more about this product," the generation AI will automatically generate appropriate questions to ask the seller and obtain the answer. Furthermore, if a user inputs "I would like this product to be sold a little cheaper," the generation AI will negotiate the price with the seller. Step 2: The seller interaction unit interacts with the seller based on the information about the product searched by the product search unit. For example, the seller inquires about detailed product information or negotiates the price. Step 3: The purchase procedure unit carries out the purchase procedure based on the information obtained by the seller interaction unit. For example, if a user inputs "I would like to purchase this product," the generation AI automatically carries out the purchase procedure on an auction site or flea market site. This includes entering payment information and specifying the shipping address. Step 4: The listing procedure unit carries out the procedures for listing the product purchased by the purchase procedure unit. For example, if a user inputs "I would like to list this product," the generation AI automatically generates a listing page based on the product description and photos, and lists the product on an auction site or flea market site. Step 5: The progress management unit manages the progress of the transaction and notifies the user. For example, it automatically checks the shipping status of the product and payment, and notifies the user as appropriate.

[0055] (Example 2) The assistant system according to the embodiment of the present invention uses a generative AI to search for products on auction sites and flea market sites and to communicate with sellers. This allows users to eliminate cumbersome operations and communication, making transactions more convenient.

[0056] The assistant system according to the embodiment includes a product search unit, a seller interaction unit, a purchase procedure unit, a listing procedure unit, and a progress management unit. When a user inputs information about a product they want, the product search unit uses a generation AI to search for the most suitable product based on that information. For example, if a user inputs "I want red sneakers," the generation AI searches auction sites and flea market sites to list relevant products. Furthermore, when a user inputs "I want more details about this product," the generation AI automatically generates appropriate questions for the seller and obtains answers. Furthermore, when a user inputs "I want this product to be a little cheaper," the generation AI negotiates the price with the seller. The seller interaction unit interacts with the seller based on the information about the product searched by the product search unit. For example, the seller interaction unit inquires about detailed product information and negotiates the price. The purchase procedure unit performs the purchase procedure based on the information obtained by the seller interaction unit. For example, when a user inputs "I want to purchase this product," the generation AI automatically performs the purchase procedure on the auction site or flea market site. This includes entering payment information and specifying a shipping address. The listing procedure unit performs procedures for listing the product purchased by the purchase procedure unit. For example, when a user inputs "I want to list this product," the generation AI automatically generates a listing page based on the product description and photos, and lists the product on an auction site or flea market site. The progress management unit manages the progress of the transaction and notifies the user. For example, it automatically checks the product's shipping status and payment status, and notifies the user as appropriate. As a result, the assistant system according to the embodiment allows users to eliminate cumbersome operations and communication, allowing them to make transactions more conveniently.

[0057] Generation AI can analyze a user's past search history and purchase history to provide product search results optimized for each individual user. For example, generation AI can analyze a user's past search history to identify frequently searched keywords and categories. This allows it to quickly display related products when the user searches for the same product again. Generation AI can also suggest related or complementary products based on a user's purchase history. For example, for a user who previously purchased a camera, it can suggest accessories such as lenses and camera bags. Furthermore, generation AI can use a user's past behavioral data to provide product search results optimized for each individual user. For example, for a user who prefers a particular brand or price range, it can prioritize the display of products from that brand or price range. This allows it to provide product search results optimized for the user.

[0058] Generative AI can analyze market price trends in real time and prioritize displaying the best value products. For example, Generative AI collects market price data in real time and analyzes price fluctuations. This allows it to display the currently best value products to users. Generative AI can also integrate data from price comparison sites and auction sites to identify the cheapest products. For example, if the same product is being sold on multiple sites, it will present the user with the cheapest option. Furthermore, Generative AI can predict price trends and notify users of products whose prices are likely to fall. For example, if the price of a particular product tends to fall based on past data, it will provide that information to the user. This allows it to provide users with the best value products.

[0059] Using its emotion estimation function, the generation AI can analyze the emotions a user feels when searching and prioritize the display of products that elicit positive emotions. For example, the generation AI analyzes the user's facial expressions and voice when searching and calculates an emotion score. For example, if a user searches with a smile, it will prioritize the display of products that elicit positive emotions. The generation AI also uses its emotion estimation function to monitor the emotions a user feels when searching in real time and suggest products that elicit positive emotions. For example, if a user is feeling stressed, it will display products that have a relaxing effect. Furthermore, based on the user's emotion data, the generation AI can identify products that have elicited positive emotions in the past and prioritize the display of similar products. For example, if a product purchased in the past brought joy to the user, it will suggest products in the same category. This makes it possible to provide products that elicit positive emotions in the user.

[0060] Generative AI can use image recognition technology to search for similar products based on images uploaded by users. For example, generative AI analyzes images of products uploaded by users and searches for similar products. For example, if a user uploads an image of sneakers with a specific design, generative AI searches for sneakers with a similar design. Generative AI also uses image recognition technology to extract features from the uploaded image and search for products with the same features. For example, it prioritizes displaying products with a specific color or shape. Furthermore, generative AI suggests accessories and complementary products related to the product based on images of the product taken by the user. For example, if a user uploads an image of a camera, generative AI suggests lenses and camera bags. This allows similar products to be provided based on the images uploaded by the user.

[0061] The generation AI accepts voice input and allows users to search for products simply by speaking to it. For example, when a user searches for a product by voice, the generation AI uses voice recognition technology to analyze the user's request and search for the most suitable product. For example, if the user says, "Looking for red sneakers," the generation AI will display the corresponding products. The generation AI can also use voice input to provide detailed information about products to users. For example, if the user says, "Looking for a waterproof camera," the generation AI will search for products that meet those criteria. Furthermore, the generation AI analyzes the voice input, understands the user's intent, and suggests products. For example, if the user says, "Looking for the perfect product for a gift," the generation AI will suggest products suitable for gifts. This allows users to search for products simply by speaking.

[0062] Using its emotion estimation function, the generation AI can monitor the emotions felt by the user in real time when searching and dynamically adjust search results. For example, the generation AI can monitor the user's emotions in real time when searching and prioritize the display of products that evoke positive emotions. For example, if the user is feeling stressed, it can display products that have a relaxing effect. The generation AI also uses its emotion estimation function to dynamically adjust search results according to the user's emotions. For example, if the user is excited, it can suggest products that are suitable for an active lifestyle. Furthermore, based on the user's emotion data, the generation AI can identify products that have previously evoked positive emotions and prioritize the display of similar products. For example, if a product purchased in the past brought joy to the user, it can suggest products in the same category. This allows the search results to be dynamically adjusted according to the user's emotions.

[0063] Generative AI can convert a user's vague requests into specific product information using natural language processing technology. For example, generative AI uses natural language processing technology to analyze a user's vague request and convert it into specific product information. For example, if a user enters "I want stylish shoes," the generative AI analyzes the definition of "stylish shoes" and suggests specific products. Generative AI also uses natural language processing technology to analyze the user's request in detail and extract related keywords and categories. For example, if a user enters "I want a camera suitable for traveling," the generative AI will search for products based on the keywords "travel," "camera," and "suitable." Furthermore, to convert a user's vague requests into specific product information, generative AI refers to past data and the user's behavioral history. For example, it suggests specific products based on the behavioral data of users who previously searched for "stylish shoes." This allows the user's vague requests to be converted into specific product information.

[0064] The generation AI can analyze user input and automatically display reviews and ratings of related products. For example, the generation AI analyzes user input and automatically displays reviews and ratings of related products. For example, if a user inputs, "What is the rating of this camera?", the generation AI will display reviews and ratings of that camera. The generation AI also collects reviews and ratings of related products based on the product information entered by the user and provides them to the user. For example, if a user inputs, "I want to see reviews of these sneakers," the generation AI will display reviews of those sneakers. Furthermore, the generation AI analyzes user input and builds a system that automatically displays reviews and ratings of related products. For example, if a user inputs, "What is the rating of this product?", the generation AI will display reviews and ratings of that product. This makes it possible to automatically provide users with reviews and ratings of related products.

[0065] The generation AI can use the emotion estimation function to infer emotions from user input and make product suggestions based on those emotions. For example, the generation AI analyzes the user's input and uses the emotion estimation function to infer the user's emotions. For example, if the user inputs "I don't like this product," the generation AI infers negative emotions and instead suggests products that evoke positive emotions. The generation AI also uses the emotion estimation function to infer emotions from the user's input and makes product suggestions based on those emotions. For example, if the user inputs "I like this product," the generation AI infers positive emotions and suggests similar products. Furthermore, the generation AI builds a system that infers emotions based on the user's input and makes product suggestions based on those emotions. For example, if the user inputs "This product is too expensive," the generation AI infers negative emotions and suggests lower-priced products. This makes it possible to make product suggestions based on the user's emotions.

[0066] The generation AI can suggest sets of related products based on user input. For example, the generation AI analyzes the user's input and suggests sets of related products. For example, if the user inputs "I want a camera and lens," the generation AI will suggest a set of camera and lens. The generation AI also builds a system that suggests sets of related products based on the user's input. For example, if the user inputs "I want items necessary for travel," the generation AI will suggest a set of items necessary for travel. The generation AI also analyzes the user's input and suggests sets of related products. For example, if the user inputs "I want furniture for my home office," the generation AI will suggest a set of desk, chair, and storage furniture. This makes it possible to suggest sets of related products to the user.

[0067] The generation AI can suggest customizable products based on user input. The generation AI, for example, analyzes the user's input and suggests customizable products. For example, if the user inputs, "I want sneakers that I can customize to my liking," the generation AI will suggest customizable sneakers. The generation AI also builds a system that suggests customizable products based on the user's input. For example, if the user inputs, "I want customizable furniture," the generation AI will suggest customizable furniture. The generation AI also analyzes the user's input and suggests customizable products. For example, if the user inputs, "I want accessories that I can personalize," the generation AI will suggest customizable accessories. This makes it possible to suggest customizable products to the user.

[0068] The generation AI can use the emotion estimation function to infer emotions from the user's input and suggest product categories that correspond to the emotions. For example, the generation AI analyzes the user's input and uses the emotion estimation function to infer the user's emotions. For example, if the user inputs "I want products that help me relax," the generation AI will suggest product categories with a relaxing effect. The generation AI also uses the emotion estimation function to infer emotions from the user's input and suggest product categories that correspond to the emotions. For example, if the user inputs "I want exciting products," the generation AI will suggest product categories that are suitable for an active lifestyle. Furthermore, the generation AI builds a system that infers emotions based on the user's input and suggests product categories that correspond to the emotions. For example, if the user inputs "I want products that have a soothing effect," the generation AI will suggest product categories with a soothing effect. This makes it possible to suggest product categories that correspond to the user's emotions.

[0069] The generation AI can analyze the history of interactions with sellers and automatically select the optimal communication strategy. For example, the generation AI can analyze the history of past interactions with sellers and select the optimal communication strategy. For example, it can suggest a similar strategy based on negotiation methods that have been successful in the past. The generation AI can also analyze the history of interactions with sellers and send messages at the appropriate time. For example, it can identify the time of day when the seller is most likely to reply and send messages at that time. Furthermore, the generation AI can provide appropriate advice to users based on the history of interactions with sellers. For example, it can suggest the optimal negotiation method to users based on successful examples obtained from past interactions. This makes it possible to optimize interactions with sellers.

[0070] The generation AI can analyze the seller's rating and reliability and provide appropriate advice to the user. For example, the generation AI can analyze the seller's rating and reliability and provide appropriate advice to the user. For example, it can recommend transactions with sellers with high ratings. The generation AI can also analyze the seller's past transaction history and build a system to evaluate reliability. For example, it can advise the user to avoid sellers with a history of problems. Furthermore, the generation AI can evaluate the risk of transactions for the user based on the seller's rating and reliability. For example, it can warn the user to avoid transactions with sellers with low reliability. This makes it possible to provide appropriate advice to the user.

[0071] The generation AI can use the emotion estimation function to infer emotions from the seller's message and generate an appropriate reply based on those emotions. For example, the generation AI analyzes the seller's message and uses the emotion estimation function to infer the seller's emotions. For example, if the seller is angry, it generates a calm reply. The generation AI also uses the emotion estimation function to infer emotions from the seller's message and builds a system that generates an appropriate reply based on those emotions. For example, if the seller is happy, it generates a positive reply. Furthermore, the generation AI infers emotions based on the seller's message and generates an appropriate reply based on those emotions. For example, if the seller is feeling anxious, it generates a reassuring reply. This makes it possible to generate an appropriate reply based on the seller's emotions.

[0072] The generation AI supports multiple languages ​​and can automatically translate interactions with sellers who speak different languages. For example, the generation AI supports multiple languages ​​and automatically translates interactions with sellers who speak different languages. For example, it translates a message entered by a user in Japanese into English and sends it to an English-speaking seller. The generation AI also uses an automatic translation function to smoothly communicate with sellers who speak different languages. For example, it translates a message replied by a seller in French into Japanese and provides it to the user. Furthermore, the generation AI supports multiple languages ​​and builds a system that automatically translates interactions with sellers who speak different languages. For example, it translates a message entered by a user in Chinese into English and sends it to an English-speaking seller. This makes it possible to automatically translate interactions with sellers who speak different languages.

[0073] The generation AI interacts with sellers via voice, allowing users to communicate simply by speaking to it. The generation AI uses voice recognition technology, for example, to interact with sellers simply by speaking to it. For example, if a user says, "Tell me more about this item," the generation AI will send a question to the seller. The generation AI also uses voice input to build a system that allows users to interact smoothly with sellers. For example, if a user says, "Negotiate the price of this item," the generation AI will negotiate the price with the seller. Furthermore, the generation AI uses voice recognition technology to interact with sellers simply by speaking to it. For example, if a user says, "Check the shipping status of this item," the generation AI will make an inquiry to the seller. This allows users to interact with sellers simply by speaking to it.

[0074] The generation AI can use the emotion estimation function to infer emotions from the seller's messages and propose a negotiation strategy that corresponds to those emotions. For example, the generation AI analyzes the seller's messages and uses the emotion estimation function to infer the seller's emotions. For example, if the seller is angry, it proposes a calm negotiation strategy. The generation AI also uses the emotion estimation function to infer emotions from the seller's messages and builds a system that proposes a negotiation strategy that corresponds to those emotions. For example, if the seller is happy, it proposes a positive negotiation strategy. Furthermore, the generation AI infers emotions based on the seller's messages and proposes a negotiation strategy that corresponds to those emotions. For example, if the seller is feeling anxious, it proposes a negotiation strategy that reassures them. This makes it possible to propose a negotiation strategy that corresponds to the seller's emotions.

[0075] The generation AI can analyze the content of interactions with sellers and provide a summary of important information to the user. For example, the generation AI analyzes the content of interactions with sellers and summarizes important information to provide to the user. For example, it summarizes and displays the seller's response or the results of price negotiations. The generation AI also analyzes the content of interactions with sellers and builds a system that summarizes and provides important information to the user. For example, it summarizes and displays detailed product information and shipping status. Furthermore, the generation AI analyzes the content of interactions with sellers and summarizes and provides important information to the user. For example, it extracts the main points of messages from sellers and provides them to the user. This makes it possible to provide a summary of important information to the user.

[0076] The generation AI uses the emotion estimation function to express the user's intentions emotionally, facilitating communication with sellers. For example, the generation AI analyzes the user's intentions and generates an emotionally expressed message using the emotion estimation function. For example, if the user inputs "Tell me more about this product," the generation AI generates a polite and friendly message. The generation AI also uses the emotion estimation function to express the user's intentions emotionally, building a system that facilitates communication with sellers. For example, if the user inputs "Negotiate the price of this product," the generation AI generates a friendly negotiation message. The generation AI also analyzes the user's intentions and generates an emotionally expressed message using the emotion estimation function. For example, if the user inputs "Check the shipping status of this product," the generation AI generates a message that gives a sense of security. This allows the user's intentions to be expressed emotionally, facilitating communication with sellers.

[0077] Generative AI can visualize interactions with sellers, allowing users to intuitively understand. For example, generative AI can build a system that visualizes interactions with sellers, allowing users to intuitively understand. For example, it can display message exchanges in charts and graphs. Generative AI can also visualize interactions with sellers, allowing users to grasp important information at a glance. For example, it can display the progress of price negotiations in a timeline. Furthermore, generative AI can visualize interactions with sellers, allowing users to intuitively understand. For example, it can display detailed product information and shipping status using icons and diagrams. This makes it possible to visualize interactions with sellers in a way that users can intuitively understand.

[0078] The generation AI can record interactions with sellers so that users can refer to them later. For example, the generation AI can build a system that records interactions with sellers so that users can refer to them later. For example, it can save past message history so that it can be searched when needed. The generation AI can also record interactions with sellers so that users can check the transaction history. For example, it can save the results of price negotiations and detailed product information so that it can be referred to later. Furthermore, the generation AI can record interactions with sellers so that users can refer to them later. For example, it can record the shipping status of products and payment confirmation so that users can understand the progress of the transaction. This makes it possible to record interactions with sellers so that users can refer to them later.

[0079] The generation AI uses the emotion estimation function to generate messages that reflect the user's emotions, thereby building a relationship of trust with the seller. For example, the generation AI analyzes the user's emotions and uses the emotion estimation function to generate messages that reflect those emotions. For example, if a user wants to express gratitude, it generates a message that expresses that gratitude. The generation AI also uses the emotion estimation function to generate messages that reflect the user's emotions, building a system that builds a relationship of trust with the seller. For example, if a user is feeling anxious, it generates a message that reassures the user. Furthermore, the generation AI analyzes the user's emotions and uses the emotion estimation function to generate messages that reflect those emotions. For example, if a user is feeling happy, it generates a message that shares that joy. In this way, messages that reflect the user's emotions can be generated, and a relationship of trust can be built with the seller.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The assistant system can also analyze a user's past search history and purchase history to provide product search results optimized for each individual user. For example, it can prioritize the display of related products based on keywords and categories that the user has searched for in the past. It can also suggest related or complementary products based on the user's purchase history. Furthermore, for users who prefer specific brands or price ranges, it can prioritize the display of products of those brands or price ranges. This allows the system to provide product search results optimized for each user.

[0082] The assistant system can also analyze market price trends in real time and prioritize displaying the most cost-effective products. For example, by collecting market price data in real time and analyzing price fluctuations, it can display the currently most cost-effective products to users. It can also integrate data from price comparison sites and auction sites to identify the cheapest products. It can also predict price trends and notify users of products that are likely to fall in price. This allows it to provide users with the best value products.

[0083] The assistant system can also use its emotion estimation function to analyze the emotions felt by the user when searching and prioritize the display of products that elicit positive emotions. For example, it can analyze the user's facial expressions and voice when searching and calculate an emotion score to prioritize the display of products that elicit positive emotions. It can also use the emotion estimation function to monitor the emotions felt by the user when searching in real time and suggest products that elicit positive emotions. Furthermore, it can identify products that have elicited positive emotions in the past based on the user's emotion data and prioritize the display of similar products. This makes it possible to provide products that elicit positive emotions in the user.

[0084] The assistant system can also use image recognition technology to search for similar products based on images uploaded by users. For example, it can analyze images of products uploaded by users and search for similar products, thereby displaying products with similar designs. It can also use image recognition technology to extract features from uploaded images and search for products with the same features. Furthermore, it can suggest accessories and complementary products related to the product based on images of the product taken by the user. This allows it to provide similar products based on images uploaded by users.

[0085] The assistant system can also accept voice input and perform product searches simply by the user speaking. For example, when a user searches for a product by voice, the system uses voice recognition technology to analyze the user's request, search for the most suitable product, and display the relevant product. The system can also use voice input to provide detailed information about the product. Furthermore, the system can analyze the voice input, understand the user's intention, and suggest products. This allows the user to perform product searches simply by speaking.

[0086] The assistant system can also use the emotion estimation function to monitor the emotions felt by the user during a search in real time and dynamically adjust search results. For example, by monitoring the user's emotions in real time during a search and preferentially displaying products that evoke positive emotions, if the user is feeling stressed, products with a relaxing effect will be displayed. The emotion estimation function can also be used to dynamically adjust search results according to the user's emotions. Furthermore, it is possible to identify products that have evoked positive emotions in the past based on the user's emotion data and preferentially display similar products. This allows the search results to be dynamically adjusted according to the user's emotions.

[0087] The assistant system can also use natural language processing technology to convert a user's vague request into specific product information. For example, the system can analyze a vague request entered by a user using natural language processing technology and convert it into specific product information, thereby suggesting related products. Natural language processing technology can also be used to analyze a user's request in detail and extract related keywords and categories. Furthermore, past data and the user's behavior history can be referenced to convert a user's vague request into specific product information. This allows the system to convert a user's vague request into specific product information.

[0088] The assistant system can also analyze user input and automatically display reviews and ratings of related products. For example, by analyzing user input and automatically displaying reviews and ratings of related products, when a user inputs "What is the rating of this camera?", reviews and ratings of the camera are displayed. In addition, based on the product information input by the user, reviews and ratings of related products can be collected and provided to the user. Furthermore, it is possible to build a system that analyzes user input and automatically displays reviews and ratings of related products. This makes it possible to automatically provide reviews and ratings of related products to the user.

[0089] The assistant system can also use the emotion estimation function to infer emotions from the user's input content and make product suggestions based on the emotions. For example, by analyzing the user's input content and using the emotion estimation function to infer the user's emotions, if a negative emotion is estimated, the system can instead suggest products that elicit a positive emotion. The emotion estimation function can also be used to infer emotions from the user's input content and make product suggestions based on the emotions. Furthermore, it is possible to build a system that infers emotions based on the user's input content and makes product suggestions based on the emotions. This makes it possible to make product suggestions based on the user's emotions.

[0090] The assistant system can also suggest sets of related products based on the user's input. For example, by analyzing the user's input and suggesting sets of related products, if the user inputs "I want a camera and a lens," a set of a camera and a lens will be suggested. It is also possible to build a system that suggests sets of related products based on the user's input. Furthermore, it is also possible to analyze the user's input and suggest sets of related products. This makes it possible to suggest sets of related products to the user.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: When a user inputs information about a product they want, the product search unit uses the generation AI to search for the most suitable product based on that information. For example, if a user inputs "I want red sneakers," the generation AI will search auction sites and flea market sites across the board and list relevant products. Also, if a user inputs "I would like to know more about this product," the generation AI will automatically generate appropriate questions to ask the seller and obtain the answer. Furthermore, if a user inputs "I would like this product to be sold a little cheaper," the generation AI will negotiate the price with the seller. Step 2: The seller interaction unit interacts with the seller based on the information about the product searched by the product search unit. For example, the seller inquires about detailed product information or negotiates the price. Step 3: The purchase procedure unit carries out the purchase procedure based on the information obtained by the seller interaction unit. For example, if a user inputs "I would like to purchase this product," the generation AI automatically carries out the purchase procedure on an auction site or flea market site. This includes entering payment information and specifying the shipping address. Step 4: The listing procedure unit carries out the procedures for listing the product purchased by the purchase procedure unit. For example, if a user inputs "I would like to list this product," the generation AI automatically generates a listing page based on the product description and photos, and lists the product on an auction site or flea market site. Step 5: The progress management unit manages the progress of the transaction and notifies the user. For example, it automatically checks the shipping status of the product and payment, and notifies the user as appropriate.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0119] 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.

[0120] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 product search section using generative AI, a seller interaction unit that interacts with sellers based on information about the products searched by the product search unit; a purchase procedure unit that performs a purchase procedure based on the information acquired by the seller interaction unit; a listing procedure unit for listing the product purchased by the purchase procedure unit; A progress management unit that manages the progress of transactions. A system characterized by:

2. The generated AI is Analyze users' past search and purchase history to provide product search results optimized for each individual user 2. The system of claim 1.

3. The generated AI is Analyzes market price trends in real time and prioritizes the best deals 2. The system of claim 1.

4. The generated AI is Analyzes the emotions users feel when searching and prioritizes products that evoke positive emotions 2. The system of claim 1.

5. The generated AI is Uses image recognition technology to search for similar products based on user-uploaded images 2. The system of claim 1.

Images