System

Abstract

An object of a system according to an embodiment is to evaluate a market value of an article and automatically match an optimal buyer.SOLUTION: A system includes an article photographing part, a value calculation part, a matching part, and a selling procedure part. The article photographing unit photographs an article. The value calculation unit analyzes the image of the article photographed by the article photographing unit and calculates a market supposition value. The matching unit matches an optimal buyer based on the market supposition value calculated by the value calculation unit. The selling procedure unit automates a selling procedure with the buyer selected by the matching unit.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 techniques have had the problem of making it difficult to accurately assess the market value of goods and find the most suitable buyer.

[0005] The system according to the embodiment aims to evaluate the market value of an item and automatically match it with the most suitable buyer. [Means for solving the problem]

[0006] The system according to the embodiment includes an item photographing unit, a value calculation unit, a matching unit, and a sales procedure unit. The item photographing unit photographs an item. The value calculation unit analyzes the image of the item photographed by the item photographing unit and calculates an estimated market value. The matching unit matches the item with an optimal buyer based on the estimated market value calculated by the value calculation unit. The sales procedure unit automates the sales procedure with the buyer selected by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the market value of an item and automatically match it with the most suitable buyer. [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 product sales support system according to an embodiment of the present invention is a system in which users take a photo of an item, the AI ​​generator calculates the estimated market value, and if the user selects to sell, the system even purchases the item. This allows users to sell their items without any hassle, improving the recycling rate.

[0029] An item sales support system according to an embodiment includes an item photographing unit, a value calculation unit, a matching unit, and a sales procedure unit. The item photographing unit photographs an item. For example, a user photographs an item with a smartphone. The item photographing unit can also photograph video. The value calculation unit analyzes the image of the item photographed by the item photographing unit and calculates the expected market value. For example, a generation AI analyzes the image of the item and calculates the value taking into account its condition and market demand. The value calculation unit can also analyze video of the item and calculate the expected market value taking into account its operating condition and usability. The matching unit matches the optimal buyer based on the expected market value calculated by the value calculation unit. For example, the generation AI selects a company that can properly process the item, such as a recycle shop or a recycling company. The sales procedure unit automates the sales procedure with the buyer selected by the matching unit. For example, the generation AI automates negotiations with the buyer, creating a contract, and arranging for the delivery of the item. This allows the item sales support system according to an embodiment to sell items without hassle. For example, a user can take a photo of an item with their smartphone, and the generating AI will calculate its estimated market value, match the item with the most suitable buyer, and automate the selling process, enabling a quick and accurate sale.

[0030] The item photography unit can analyze videos of items and calculate the expected market value by taking into account their operating conditions and usability. For example, the item photography unit allows a user to shoot a video of an item with a smartphone, and the generation AI analyzes the video. For example, it can analyze videos of home appliances in operation or furniture in use, and calculate the expected market value by taking into account their operating conditions and usability. The generation AI also analyzes videos of items and evaluates their operating conditions and usability. For example, it can analyze the sound of a machine in operation and the smoothness of its movement, and calculate the expected market value based on that. The user can also shoot a video of an item, and the generation AI can analyze the video to evaluate its operating conditions and usability. For example, it can analyze the condition and movement of clothing in use, and calculate the expected market value based on that. This makes it possible to calculate the expected market value by taking into account the operating conditions and usability of an item.

[0031] The value calculation unit can automatically collect information about an item's history, year of manufacture, and manufacturer, and calculate its value based on that information. For example, the generation AI analyzes images of an item and automatically collects information about the item's history, year of manufacture, and manufacturer. For example, it identifies the year of manufacture and manufacturer of antique furniture or vintage home appliances, and calculates the estimated market value based on that information. Alternatively, a user can take an image of an item, and the generation AI analyzes the image to collect information about the item's history, year of manufacture, and manufacturer. For example, it identifies the year of manufacture and manufacturer of an old clock or camera, and calculates the estimated market value based on that information. Alternatively, the generation AI can analyze images of an item and automatically collect information about the item's history, year of manufacture, and manufacturer. For example, it identifies the year of manufacture and manufacturer of an old book or painting, and calculates the estimated market value based on that information. This makes it possible to calculate the estimated market value taking into account information about the item's history, year of manufacture, and manufacturer.

[0032] The value calculation unit can calculate the value by taking into account the repair and maintenance history of the item. For example, the value calculation unit has a user take an image of the item, and the generation AI analyze the image to take into account the repair and maintenance history of the item. For example, it calculates the value of a home appliance with a repair history or furniture that has undergone maintenance. The generation AI also analyzes images of the item and evaluates the repair and maintenance history. For example, it calculates the value of a car or motorcycle with a repair history. A user can also take an image of the item, and the generation AI analyzes the image to take into account the repair and maintenance history. For example, it calculates the value of a watch or camera that has undergone maintenance. This makes it possible to calculate the estimated market value by taking into account the repair and maintenance history of the item.

[0033] The value calculation unit can evaluate the eco-friendliness of an item and calculate its value based on that evaluation. For example, the value calculation unit allows a user to take a picture of an item, and the generation AI analyzes the image to evaluate the eco-friendliness of the item. For example, it calculates the value of furniture or home appliances made from recyclable materials. The generation AI also analyzes images of items to evaluate their eco-friendliness. For example, it calculates the value of products that have undergone environmentally friendly manufacturing processes. Also, a user can take a picture of an item, and the generation AI analyzes the image to evaluate their eco-friendliness. For example, it calculates the value of clothing or accessories made from reusable materials. This makes it possible to calculate the expected market value of an item taking into account its eco-friendliness.

[0034] The matching unit can analyze the buyer's past transaction history and ratings, and prioritize matching with highly reliable vendors. For example, the generation AI in the matching unit analyzes the buyer's past transaction history and prioritizes matching with highly reliable vendors. For example, it selects vendors that have received high ratings in the past. Also, when a user sells an item, the generation AI analyzes the buyer's rating and prioritizes matching with highly reliable vendors. For example, it selects vendors with good past transaction history. Also, the generation AI analyzes the buyer's past transaction history and ratings, and prioritizes matching with highly reliable vendors. For example, it selects vendors that have received good feedback from users. This prioritizes matching with highly reliable vendors, and can improve user satisfaction.

[0035] The matching unit can select the most convenient dealer for the user by taking into account the geographic location information of the buyer. For example, the generation AI analyzes the geographic location information of the buyer and selects the most convenient dealer for the user. For example, it prioritizes matching with dealers near the user. Also, when the user sells an item, the generation AI considers the geographic location information of the buyer and selects the most convenient dealer. For example, it selects a dealer close to the user's residence. Also, the generation AI analyzes the geographic location information of the buyer and selects the most convenient dealer for the user. For example, it prioritizes matching with dealers in locations with good transportation access. This allows the item selling process to be carried out smoothly by selecting the most convenient dealer for the user.

[0036] The matching unit can analyze the specialties and areas of expertise of potential buyers and select the best suited vendor for the item. For example, the generation AI in the matching unit analyzes the specialties of potential buyers and selects the best suited vendor for the item. For example, it selects a vendor that specializes in antique furniture. Furthermore, when a user sells an item, the generation AI analyzes the specialties of potential buyers and selects the best suited vendor. For example, it selects a vendor that specializes in home appliances. Furthermore, the generation AI analyzes the specialties and areas of expertise of potential buyers and selects the best suited vendor for the item. For example, it selects a vendor that specializes in clothing. In this way, by selecting the best suited vendor for the item, the selling procedure can be carried out efficiently.

[0037] The matching unit can analyze the inventory status and demand of potential buyers and select the vendor that can offer the highest price. For example, the generation AI analyzes the inventory status of potential buyers and selects the vendor that can offer the highest price. For example, it prioritizes matching with vendors with low inventory. Also, when a user sells an item, the generation AI analyzes the demand of potential buyers and selects the vendor that can offer the highest price. For example, it selects a vendor with high demand. Also, the generation AI analyzes the inventory status and demand of potential buyers and selects the vendor that can offer the highest price. For example, it prioritizes matching with vendors with low inventory and high demand. This maximizes the user's profits by selecting the vendor that can offer the highest price.

[0038] The sales procedure unit can take into account the user's past transaction history and propose the optimal procedure. In the sales procedure unit, for example, the generation AI analyzes the user's past transaction history and proposes the optimal sales procedure. For example, it refers to procedures that have been successful in the past. In addition, when a user sells an item, the generation AI takes into account the past transaction history and proposes the optimal procedure. For example, it prioritizes procedures that have not caused any problems in the past. In addition, the generation AI analyzes the user's past transaction history and proposes the optimal sales procedure. For example, it refers to procedures that have received high ratings in the past. This makes it possible to propose the optimal procedure taking into account the user's past transaction history.

[0039] The sales procedure unit can automatically check legal requirements and regulations and take appropriate procedures. For example, the sales procedure unit automatically checks legal requirements and regulations and takes appropriate procedures when the generation AI goes through the sales procedure. For example, it ensures that the contents of the contract are legally correct. Also, when a user sells an item, the generation AI automatically checks legal requirements and regulations and takes appropriate procedures. For example, it automatically generates the necessary documents. Also, the generation AI automatically checks legal requirements and regulations and takes appropriate procedures when the generation AI goes through the sales procedure. For example, it ensures that the transaction is legally problem-free. In this way, legal problems can be avoided by automatically checking legal requirements and regulations and taking appropriate procedures.

[0040] The sales procedure unit can consider the user's preferences and lifestyle and propose the optimal procedure. In the sales procedure unit, for example, the generation AI analyzes the user's preferences and lifestyle and proposes the optimal sales procedure. For example, it prioritizes the procedure method that the user prefers. In addition, when a user sells an item, the generation AI considers the user's preferences and lifestyle and proposes the optimal procedure. For example, it proposes a procedure that suits the user's daily rhythm. In addition, the generation AI analyzes the user's preferences and lifestyle and proposes the optimal sales procedure. For example, it prioritizes a procedure that the user does not feel stressed about. This makes it possible to propose the optimal procedure that takes the user's preferences and lifestyle into consideration.

[0041] The sales procedure unit can consider the user's schedule and time of day to propose the most convenient procedure. In the sales procedure unit, for example, the generation AI analyzes the user's schedule and proposes the most convenient sales procedure. For example, the procedure is carried out during a time when the user is free. Also, when the user sells an item, the generation AI considers the schedule and time of day to propose the most convenient procedure. For example, it proposes a procedure that suits the user's convenience. Also, the generation AI analyzes the user's schedule and proposes the most convenient sales procedure. For example, it carries out the procedure during a time when the user is not busy. This makes it possible to propose the most convenient procedure taking into account the user's schedule and time of day.

[0042] The sales procedure unit can analyze a user's past transaction history and behavioral patterns and display the most appropriate advertisement. In the sales procedure unit, for example, the generation AI analyzes a user's past transaction history and displays the most appropriate advertisement. For example, it displays advertisements related to products purchased in the past. The generation AI also analyzes the user's behavioral patterns and displays the most appropriate advertisement. For example, it displays advertisements for products and services that the user may be interested in. The generation AI also analyzes a user's past transaction history and behavioral patterns and displays the most appropriate advertisement. For example, it displays advertisements for products in categories that the user frequently uses. This makes it possible to display the most appropriate advertisement taking into account the user's past transaction history and behavioral patterns.

[0043] The sales procedure unit can analyze the user's interests and display customized advertisements based on that. In the sales procedure unit, for example, the generation AI analyzes the user's interests and displays customized advertisements based on that. For example, it displays product advertisements in categories that interest the user. The generation AI also analyzes the user's interests and displays customized advertisements. For example, it displays advertisements related to keywords that the user has searched for in the past. The generation AI also analyzes the user's interests and displays customized advertisements based on that. For example, it displays advertisements related to websites that the user frequently visits. This makes it possible to display customized advertisements that take the user's interests and concerns into consideration.

[0044] The sales procedure unit can analyze the user's geographical location information and display region-specific advertisements. In the sales procedure unit, for example, the generation AI analyzes the user's geographical location information and displays region-specific advertisements. For example, advertisements for stores and services in the area where the user lives are displayed. The generation AI also analyzes the user's location information and displays region-specific advertisements. For example, advertisements for events and sales being held near the user are displayed. The generation AI also analyzes the user's geographical location information and displays region-specific advertisements. For example, advertisements for local products and tourist spots in the area where the user lives are displayed. This makes it possible to display region-specific advertisements that take the user's geographical location information into consideration.

[0045] The sales procedure unit can analyze the user's social media activity and display customized advertisements based on that. In the sales procedure unit, for example, the generation AI analyzes the user's social media activity and displays customized advertisements based on that. For example, it displays advertisements related to accounts the user follows. In addition, the generation AI analyzes the content of the user's social media posts and displays customized advertisements. For example, it displays advertisements for products and services that the user may be interested in. In addition, the generation AI analyzes the user's social media activity and displays customized advertisements based on that. For example, it displays advertisements related to groups and events the user participates in. This makes it possible to display customized advertisements that take the user's social media activity into consideration.

[0046] The sales procedure unit can analyze the history and background of an item when sorting out belongings or selling assets, and calculate its value based on that. For example, the generation AI in the sales procedure unit analyzes the history and background of an item when sorting out belongings or selling assets, and calculates its value based on that. For example, it evaluates the history and background of antique furniture or vintage home appliances and calculates its value based on that. Also, when a user sorts out belongings or sells assets, the generation AI analyzes the history and background of the item and calculates its value based on that. For example, it evaluates the history and background of old books or paintings and calculates their value based on that. Also, the generation AI analyzes the history and background of an item when sorting out belongings or selling assets, and calculates its value based on that. For example, it evaluates the history and background of an item filled with family memories and calculates its value based on that. This makes it possible to calculate a value that takes into account the history and background of an item.

[0047] The sales procedure unit can analyze the opinions of the user's family and friends and propose the optimal selling method based on that. In the sales procedure unit, for example, the generation AI analyzes the opinions of the user's family and friends and proposes the optimal selling method based on that. For example, it prioritizes the selling method recommended by family and friends. In addition, when a user organizes belongings or sells assets, the generation AI analyzes the opinions of family and friends and proposes the optimal selling method based on that. For example, it proposes a selling method that family and friends agree with. In addition, the generation AI analyzes the opinions of the user's family and friends and proposes the optimal selling method based on that. For example, it selects a company that family and friends trust. This makes it possible to propose the optimal selling method taking into account the opinions of the user's family and friends.

[0048] The sales procedure unit can analyze the user's lifestyle and values ​​and propose the optimal selling method based on that. In the sales procedure unit, for example, the generation AI analyzes the user's lifestyle and values ​​and proposes the optimal selling method based on that. For example, it proposes a selling method that matches the values ​​that the user holds dear. Also, when a user is sorting out their belongings or selling assets, the generation AI analyzes their lifestyle and values ​​and proposes the optimal selling method based on that. For example, it proposes a selling method that matches the user's daily rhythm. Also, the generation AI analyzes the user's lifestyle and values ​​and proposes the optimal selling method based on that. For example, it prioritizes a selling method that does not cause the user stress. This makes it possible to propose the optimal selling method that takes into account the user's lifestyle and values.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The product sales support system can also consider the user's past transaction history and propose the optimal sales procedure. For example, it can refer to the procedures of successful transactions in the past to propose the most efficient procedure for the user. In addition, by prioritizing the proposal of procedures that have not caused any problems in the past, it can support the user so that they can proceed with the procedure with peace of mind. Furthermore, by analyzing the user's past transaction history and proposing the optimal sales procedure, it is possible to improve user satisfaction.

[0051] The product sales support system can also consider the user's lifestyle and values ​​to propose optimal sales procedures. For example, by proposing sales procedures that match the values ​​that the user values, it can support the user so that they can proceed with the procedures with a sense of satisfaction. Also, by proposing procedures that match the user's lifestyle, it can help the user proceed with the procedures without feeling stressed. Furthermore, by proposing optimal procedures that take the user's lifestyle and values ​​into consideration, it is possible to improve user satisfaction.

[0052] The product sales support system can also consider the user's schedule and time of day to propose the most convenient procedure. For example, by carrying out the procedure during a time when the user is free, the user can proceed with the procedure without feeling stressed. Also, by proposing a procedure that suits the user's convenience, the user can carry out the procedure while avoiding busy times. Furthermore, by proposing the most convenient procedure that takes the user's schedule and time of day into consideration, user satisfaction can be improved.

[0053] The product selling support system can also analyze the opinions of the user's family and friends and suggest the optimal selling method based on that. For example, by prioritizing the selling method recommended by family and friends, the system can support the user so that they can proceed with the procedure with peace of mind. Also, by suggesting a selling method that family and friends agree with, the system can help the user proceed with the procedure with confidence. Furthermore, by suggesting the optimal selling method that takes into account the opinions of the user's family and friends, the system can improve user satisfaction.

[0054] The product sales support system can further analyze the user's interests and display advertisements customized based on the results. For example, by displaying product advertisements in categories that interest the user, advertisements that are likely to interest the user can be provided. Also, by analyzing the user's interests and displaying advertisements related to keywords searched in the past, advertisements that are likely to interest the user can be provided. Furthermore, by analyzing the user's interests and displaying advertisements customized based on the results, user satisfaction can be improved.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The item photographing unit photographs an item. For example, a user photographs an image of the item with a smartphone. The item photographing unit can also photograph a video. Step 2: The value calculation unit analyzes the images of the item taken by the item photography unit and calculates the expected market value. For example, the generation AI analyzes the images of the item and calculates the value taking into account its condition and market demand. The value calculation unit can also analyze videos of the item and calculate the expected market value taking into account its operating condition and usability. Step 3: The matching unit matches the optimal buyer based on the estimated market value calculated by the value calculation unit. For example, the generation AI selects a company that can properly process the item, such as a recycle shop or a collection company. Step 4: The sales procedure unit automates the sales procedures with the buyer selected by the matching unit. For example, the generation AI automates negotiations with the buyer, creating contracts, and arranging for the delivery of goods.

[0057] (Example 2) The product sales support system according to an embodiment of the present invention is a system in which users take a photo of an item, the AI ​​generator calculates the estimated market value, and if the user selects to sell, the system even purchases the item. This allows users to sell their items without any hassle, improving the recycling rate.

[0058] An item sales support system according to an embodiment includes an item photographing unit, a value calculation unit, a matching unit, and a sales procedure unit. The item photographing unit photographs an item. For example, a user photographs an item with a smartphone. The item photographing unit can also photograph video. The value calculation unit analyzes the image of the item photographed by the item photographing unit and calculates the expected market value. For example, a generation AI analyzes the image of the item and calculates the value taking into account its condition and market demand. The value calculation unit can also analyze video of the item and calculate the expected market value taking into account its operating condition and usability. The matching unit matches the optimal buyer based on the expected market value calculated by the value calculation unit. For example, the generation AI selects a company that can properly process the item, such as a recycle shop or a recycling company. The sales procedure unit automates the sales procedure with the buyer selected by the matching unit. For example, the generation AI automates negotiations with the buyer, creating a contract, and arranging for the delivery of the item. This allows the item sales support system according to an embodiment to sell items without hassle. For example, a user can take a photo of an item with their smartphone, and the generating AI will calculate its estimated market value, match the item with the most suitable buyer, and automate the selling process, enabling a quick and accurate sale.

[0059] The item photography unit can analyze videos of items and calculate the expected market value by taking into account their operating conditions and usability. For example, the item photography unit allows a user to shoot a video of an item with a smartphone, and the generation AI analyzes the video. For example, it can analyze videos of home appliances in operation or furniture in use, and calculate the expected market value by taking into account their operating conditions and usability. The generation AI also analyzes videos of items and evaluates their operating conditions and usability. For example, it can analyze the sound of a machine in operation and the smoothness of its movement, and calculate the expected market value based on that. The user can also shoot a video of an item, and the generation AI can analyze the video to evaluate its operating conditions and usability. For example, it can analyze the condition and movement of clothing in use, and calculate the expected market value based on that. This makes it possible to calculate the expected market value by taking into account the operating conditions and usability of an item.

[0060] The value calculation unit can automatically collect information about an item's history, year of manufacture, and manufacturer, and calculate its value based on that information. For example, the generation AI analyzes images of an item and automatically collects information about the item's history, year of manufacture, and manufacturer. For example, it identifies the year of manufacture and manufacturer of antique furniture or vintage home appliances, and calculates the estimated market value based on that information. Alternatively, a user can take an image of an item, and the generation AI analyzes the image to collect information about the item's history, year of manufacture, and manufacturer. For example, it identifies the year of manufacture and manufacturer of an old clock or camera, and calculates the estimated market value based on that information. Alternatively, the generation AI can analyze images of an item and automatically collect information about the item's history, year of manufacture, and manufacturer. For example, it identifies the year of manufacture and manufacturer of an old book or painting, and calculates the estimated market value based on that information. This makes it possible to calculate the estimated market value taking into account information about the item's history, year of manufacture, and manufacturer.

[0061] The value calculation unit can analyze the user's emotions and reflect those emotions in the estimated market value. For example, the value calculation unit has the user take a picture of an item, and the generation AI analyzes the image to estimate the user's emotions. For example, the value calculation unit analyzes the emotions the user has toward an item that they cherish and reflects those emotions in the estimated market value. The emotion estimation function can also be used to analyze the emotions the user has toward an item. For example, the emotion the user has toward an item that is filled with memories can be evaluated and the estimated market value can be calculated based on that. The generation AI can also analyze the image of the item and estimate the user's emotions. For example, the emotion the user has toward an item that they are attached to can be analyzed and reflected in the estimated market value. This makes it possible to calculate the estimated market value taking the user's emotions into account.

[0062] The value calculation unit can calculate the value by taking into account the repair and maintenance history of the item. For example, the value calculation unit has a user take an image of the item, and the generation AI analyze the image to take into account the repair and maintenance history of the item. For example, it calculates the value of a home appliance with a repair history or furniture that has undergone maintenance. The generation AI also analyzes images of the item and evaluates the repair and maintenance history. For example, it calculates the value of a car or motorcycle with a repair history. A user can also take an image of the item, and the generation AI analyzes the image to take into account the repair and maintenance history. For example, it calculates the value of a watch or camera that has undergone maintenance. This makes it possible to calculate the estimated market value by taking into account the repair and maintenance history of the item.

[0063] The value calculation unit can evaluate the eco-friendliness of an item and calculate its value based on that evaluation. For example, the value calculation unit allows a user to take a picture of an item, and the generation AI analyzes the image to evaluate the eco-friendliness of the item. For example, it calculates the value of furniture or home appliances made from recyclable materials. The generation AI also analyzes images of items to evaluate their eco-friendliness. For example, it calculates the value of products that have undergone environmentally friendly manufacturing processes. Also, a user can take a picture of an item, and the generation AI analyzes the image to evaluate their eco-friendliness. For example, it calculates the value of clothing or accessories made from reusable materials. This makes it possible to calculate the expected market value of an item taking into account its eco-friendliness.

[0064] The value calculation unit can analyze the user's emotions in real time and make suggestions that will elicit positive emotions. For example, when a user takes a picture of an item, the generation AI in the value calculation unit analyzes the user's emotions in real time and makes suggestions that will elicit positive emotions. For example, an encouraging message is displayed when the user takes a picture of the item. The emotion estimation function is also used to analyze the user's emotions in real time when taking a picture of the item. For example, suggestions are made to elicit positive emotions when the user takes a picture of the item. The generation AI also analyzes the user's emotions in real time when taking a picture of the item and makes suggestions that will elicit positive emotions. For example, the generation AI guides the user to have positive emotions when taking a picture of the item. This makes it possible to analyze the user's emotions in real time and make suggestions that will elicit positive emotions.

[0065] The matching unit can analyze the buyer's past transaction history and ratings, and prioritize matching with highly reliable vendors. For example, the generation AI in the matching unit analyzes the buyer's past transaction history and prioritizes matching with highly reliable vendors. For example, it selects vendors that have received high ratings in the past. Also, when a user sells an item, the generation AI analyzes the buyer's rating and prioritizes matching with highly reliable vendors. For example, it selects vendors with good past transaction history. Also, the generation AI analyzes the buyer's past transaction history and ratings, and prioritizes matching with highly reliable vendors. For example, it selects vendors that have received good feedback from users. This prioritizes matching with highly reliable vendors, and can improve user satisfaction.

[0066] The matching unit can select the most convenient dealer for the user by taking into account the geographic location information of the buyer. For example, the generation AI analyzes the geographic location information of the buyer and selects the most convenient dealer for the user. For example, it prioritizes matching with dealers near the user. Also, when the user sells an item, the generation AI considers the geographic location information of the buyer and selects the most convenient dealer. For example, it selects a dealer close to the user's residence. Also, the generation AI analyzes the geographic location information of the buyer and selects the most convenient dealer for the user. For example, it prioritizes matching with dealers in locations with good transportation access. This allows the item selling process to be carried out smoothly by selecting the most convenient dealer for the user.

[0067] The matching unit can analyze the user's emotions and prioritize matching with traders who have positive emotions. For example, the generation AI in the matching unit analyzes the user's emotions and prioritizes matching with traders who have positive emotions. For example, it selects traders with whom the user has had a good experience in the past. In addition, it uses an emotion estimation function to analyze the user's emotions toward buyers and prioritizes matching with traders with positive emotions. For example, it selects traders that the user trusts. In addition, the generation AI analyzes the user's emotions and prioritizes matching with traders with positive emotions. For example, it selects traders with whom the user has been satisfied in the past. This makes it possible to match traders that take the user's emotions into consideration.

[0068] The matching unit can analyze the specialties and areas of expertise of potential buyers and select the best suited vendor for the item. For example, the generation AI in the matching unit analyzes the specialties of potential buyers and selects the best suited vendor for the item. For example, it selects a vendor that specializes in antique furniture. Furthermore, when a user sells an item, the generation AI analyzes the specialties of potential buyers and selects the best suited vendor. For example, it selects a vendor that specializes in home appliances. Furthermore, the generation AI analyzes the specialties and areas of expertise of potential buyers and selects the best suited vendor for the item. For example, it selects a vendor that specializes in clothing. In this way, by selecting the best suited vendor for the item, the selling procedure can be carried out efficiently.

[0069] The matching unit can analyze the inventory status and demand of potential buyers and select the vendor that can offer the highest price. For example, the generation AI analyzes the inventory status of potential buyers and selects the vendor that can offer the highest price. For example, it prioritizes matching with vendors with low inventory. Also, when a user sells an item, the generation AI analyzes the demand of potential buyers and selects the vendor that can offer the highest price. For example, it selects a vendor with high demand. Also, the generation AI analyzes the inventory status and demand of potential buyers and selects the vendor that can offer the highest price. For example, it prioritizes matching with vendors with low inventory and high demand. This maximizes the user's profits by selecting the vendor that can offer the highest price.

[0070] The matching unit can analyze the emotions of the buyer's dealer and prioritize matching users with positive emotions. For example, the generation AI in the matching unit analyzes the emotions of the buyer's dealer and prioritizes matching users with positive emotions. For example, it selects users with whom the dealer has had good transactions in the past. It also uses an emotion estimation function to analyze the emotions the buyer's dealer has toward the user and prioritizes matching users with positive emotions. For example, it selects users the dealer trusts. It also analyzes the emotions of the buyer's dealer and prioritizes matching users with positive emotions. For example, it selects users the dealer has been satisfied with in the past. This makes it possible to match users taking into account the dealer's emotions.

[0071] The sales procedure unit can take into account the user's past transaction history and propose the optimal procedure. In the sales procedure unit, for example, the generation AI analyzes the user's past transaction history and proposes the optimal sales procedure. For example, it refers to procedures that have been successful in the past. In addition, when a user sells an item, the generation AI takes into account the past transaction history and proposes the optimal procedure. For example, it prioritizes procedures that have not caused any problems in the past. In addition, the generation AI analyzes the user's past transaction history and proposes the optimal sales procedure. For example, it refers to procedures that have received high ratings in the past. This makes it possible to propose the optimal procedure taking into account the user's past transaction history.

[0072] The sales procedure unit can automatically check legal requirements and regulations and take appropriate procedures. For example, the sales procedure unit automatically checks legal requirements and regulations and takes appropriate procedures when the generation AI goes through the sales procedure. For example, it ensures that the contents of the contract are legally correct. Also, when a user sells an item, the generation AI automatically checks legal requirements and regulations and takes appropriate procedures. For example, it automatically generates the necessary documents. Also, the generation AI automatically checks legal requirements and regulations and takes appropriate procedures when the generation AI goes through the sales procedure. For example, it ensures that the transaction is legally problem-free. In this way, legal problems can be avoided by automatically checking legal requirements and regulations and taking appropriate procedures.

[0073] The sales procedure unit can analyze the user's emotions and make suggestions to reduce the stress and anxiety felt during the sales procedure. In the sales procedure unit, for example, the generation AI analyzes the user's emotions and makes suggestions to reduce the stress and anxiety felt during the sales procedure. For example, it displays the progress of the procedure in an easy-to-understand manner. It also uses an emotion estimation function to analyze the stress and anxiety felt by the user during the sales procedure and makes suggestions to reduce it. For example, it suggests simplifying the procedure. The generation AI also analyzes the user's emotions and makes suggestions to reduce the stress and anxiety felt during the sales procedure. For example, it displays a support message. This makes it possible to take the user's emotions into consideration and make suggestions to reduce stress and anxiety during the sales procedure.

[0074] The sales procedure unit can consider the user's preferences and lifestyle and propose the optimal procedure. In the sales procedure unit, for example, the generation AI analyzes the user's preferences and lifestyle and proposes the optimal sales procedure. For example, it prioritizes the procedure method that the user prefers. In addition, when a user sells an item, the generation AI considers the user's preferences and lifestyle and proposes the optimal procedure. For example, it proposes a procedure that suits the user's daily rhythm. In addition, the generation AI analyzes the user's preferences and lifestyle and proposes the optimal sales procedure. For example, it prioritizes a procedure that the user does not feel stressed about. This makes it possible to propose the optimal procedure that takes the user's preferences and lifestyle into consideration.

[0075] The sales procedure unit can consider the user's schedule and time of day to propose the most convenient procedure. In the sales procedure unit, for example, the generation AI analyzes the user's schedule and proposes the most convenient sales procedure. For example, the procedure is carried out during a time when the user is free. Also, when the user sells an item, the generation AI considers the schedule and time of day to propose the most convenient procedure. For example, it proposes a procedure that suits the user's convenience. Also, the generation AI analyzes the user's schedule and proposes the most convenient sales procedure. For example, it carries out the procedure during a time when the user is not busy. This makes it possible to propose the most convenient procedure taking into account the user's schedule and time of day.

[0076] The sales procedure unit can analyze the user's emotions in real time and make suggestions that elicit positive emotions. For example, the sales procedure unit uses a generation AI to analyze the user's emotions during the sales procedure in real time and make suggestions that elicit positive emotions. For example, it communicates the progress of the procedure in a positive manner. In addition, it uses an emotion estimation function to analyze the user's emotions during the sales procedure in real time and make suggestions that elicit positive emotions. For example, it displays an encouraging message. In addition, the generation AI analyzes the user's emotions during the sales procedure in real time and makes suggestions that elicit positive emotions. For example, it displays a message praising the progress of the procedure. This makes it possible to analyze the user's emotions in real time and make suggestions that elicit positive emotions.

[0077] The sales procedure unit can analyze a user's past transaction history and behavioral patterns and display the most appropriate advertisement. In the sales procedure unit, for example, the generation AI analyzes a user's past transaction history and displays the most appropriate advertisement. For example, it displays advertisements related to products purchased in the past. The generation AI also analyzes the user's behavioral patterns and displays the most appropriate advertisement. For example, it displays advertisements for products and services that the user may be interested in. The generation AI also analyzes a user's past transaction history and behavioral patterns and displays the most appropriate advertisement. For example, it displays advertisements for products in categories that the user frequently uses. This makes it possible to display the most appropriate advertisement taking into account the user's past transaction history and behavioral patterns.

[0078] The sales procedure unit can analyze the user's interests and display customized advertisements based on that. In the sales procedure unit, for example, the generation AI analyzes the user's interests and displays customized advertisements based on that. For example, it displays product advertisements in categories that interest the user. The generation AI also analyzes the user's interests and displays customized advertisements. For example, it displays advertisements related to keywords that the user has searched for in the past. The generation AI also analyzes the user's interests and displays customized advertisements based on that. For example, it displays advertisements related to websites that the user frequently visits. This makes it possible to display customized advertisements that take the user's interests and concerns into consideration.

[0079] The sales procedure unit can analyze the user's emotions and display advertisements that elicit positive emotions. In the sales procedure unit, for example, the generation AI analyzes the user's emotions and displays advertisements that elicit positive emotions. For example, advertisements for products and services that the user is likely to be interested in are displayed. The emotion estimation function is also used to analyze the emotions the user has toward advertisements and display advertisements that elicit positive emotions. For example, advertisements in a style that the user prefers are displayed. The generation AI also analyzes the user's emotions and displays advertisements that elicit positive emotions. For example, advertisements for events and campaigns that the user is likely to be interested in are displayed. This makes it possible to display advertisements that elicit positive emotions by taking the user's emotions into consideration.

[0080] The sales procedure unit can analyze the user's geographical location information and display region-specific advertisements. In the sales procedure unit, for example, the generation AI analyzes the user's geographical location information and displays region-specific advertisements. For example, advertisements for stores and services in the area where the user lives are displayed. The generation AI also analyzes the user's location information and displays region-specific advertisements. For example, advertisements for events and sales being held near the user are displayed. The generation AI also analyzes the user's geographical location information and displays region-specific advertisements. For example, advertisements for local products and tourist spots in the area where the user lives are displayed. This makes it possible to display region-specific advertisements that take the user's geographical location information into consideration.

[0081] The sales procedure unit can analyze the user's social media activity and display customized advertisements based on that. In the sales procedure unit, for example, the generation AI analyzes the user's social media activity and displays customized advertisements based on that. For example, it displays advertisements related to accounts the user follows. In addition, the generation AI analyzes the content of the user's social media posts and displays customized advertisements. For example, it displays advertisements for products and services that the user may be interested in. In addition, the generation AI analyzes the user's social media activity and displays customized advertisements based on that. For example, it displays advertisements related to groups and events the user participates in. This makes it possible to display customized advertisements that take the user's social media activity into consideration.

[0082] The sales procedure unit can analyze user emotions in real time and display advertisements that elicit positive emotions. In the sales procedure unit, for example, the generation AI analyzes the user's emotions in real time while an advertisement is being displayed and displays advertisements that elicit positive emotions. For example, it displays advertisements for products and services that the user is likely to be interested in. In addition, it uses an emotion estimation function to analyze the user's emotions in real time while an advertisement is being displayed and displays advertisements that elicit positive emotions. For example, it displays advertisements in a style that the user prefers. In addition, the generation AI analyzes the user's emotions in real time while an advertisement is being displayed and displays advertisements that elicit positive emotions. For example, it displays advertisements for events or campaigns that the user is likely to be interested in. This makes it possible to analyze the user's emotions in real time and display advertisements that elicit positive emotions.

[0083] The sales procedure unit can analyze the emotional value of items when sorting out belongings or selling assets, and based on that, propose the optimal selling method. For example, the generation AI in the sales procedure unit analyzes the emotional value of items when sorting out belongings or selling assets, and based on that, propose the optimal selling method. For example, it evaluates the emotional value of furniture or photographs filled with family memories and proposes a selling method based on that. Also, when a user sorts out belongings or sells assets, the generation AI analyzes the emotional value of items and based on that, proposes the optimal selling method. For example, it proposes putting items with high emotional value up for auction. Also, the generation AI analyzes the emotional value of items when sorting out belongings or selling assets, and based on that, proposes the optimal selling method. For example, it proposes entrusting items with high emotional value to a specialized company. This makes it possible to propose the optimal selling method taking the emotional value of items into consideration.

[0084] The sales procedure unit can analyze the history and background of an item when sorting out belongings or selling assets, and calculate its value based on that. For example, the generation AI in the sales procedure unit analyzes the history and background of an item when sorting out belongings or selling assets, and calculates its value based on that. For example, it evaluates the history and background of antique furniture or vintage home appliances and calculates its value based on that. Also, when a user sorts out belongings or sells assets, the generation AI analyzes the history and background of the item and calculates its value based on that. For example, it evaluates the history and background of old books or paintings and calculates their value based on that. Also, the generation AI analyzes the history and background of an item when sorting out belongings or selling assets, and calculates its value based on that. For example, it evaluates the history and background of an item filled with family memories and calculates its value based on that. This makes it possible to calculate a value that takes into account the history and background of an item.

[0085] The sales procedure unit can analyze the user's emotions, evaluate their emotions regarding sorting out belongings and selling assets, and make suggestions that elicit positive emotions. In the sales procedure unit, for example, the generation AI analyzes the user's emotions, evaluates their emotions regarding sorting out belongings and selling assets, and makes suggestions that elicit positive emotions. For example, it provides support so that the user does not feel emotionally burdened. It also uses an emotion estimation function to analyze the user's emotions regarding sorting out belongings and selling assets, and makes suggestions that elicit positive emotions. For example, it proposes a sales method that will satisfy the user. It also analyzes the user's emotions, evaluates their emotions regarding sorting out belongings and selling assets, and makes suggestions that elicit positive emotions. For example, it provides support that makes the user feel emotionally secure. This makes it possible to take the user's emotions into consideration and make suggestions that elicit positive emotions regarding sorting out belongings and selling assets.

[0086] The sales procedure unit can analyze the opinions of the user's family and friends and propose the optimal selling method based on that. In the sales procedure unit, for example, the generation AI analyzes the opinions of the user's family and friends and proposes the optimal selling method based on that. For example, it prioritizes the selling method recommended by family and friends. In addition, when a user organizes belongings or sells assets, the generation AI analyzes the opinions of family and friends and proposes the optimal selling method based on that. For example, it proposes a selling method that family and friends agree with. In addition, the generation AI analyzes the opinions of the user's family and friends and proposes the optimal selling method based on that. For example, it selects a company that family and friends trust. This makes it possible to propose the optimal selling method taking into account the opinions of the user's family and friends.

[0087] The sales procedure unit can analyze the user's lifestyle and values ​​and propose the optimal selling method based on that. In the sales procedure unit, for example, the generation AI analyzes the user's lifestyle and values ​​and proposes the optimal selling method based on that. For example, it proposes a selling method that matches the values ​​that the user holds dear. Also, when a user is sorting out their belongings or selling assets, the generation AI analyzes their lifestyle and values ​​and proposes the optimal selling method based on that. For example, it proposes a selling method that matches the user's daily rhythm. Also, the generation AI analyzes the user's lifestyle and values ​​and proposes the optimal selling method based on that. For example, it prioritizes a selling method that does not cause the user stress. This makes it possible to propose the optimal selling method that takes into account the user's lifestyle and values.

[0088] The sales procedure unit can analyze the user's emotions in real time and make suggestions that draw out positive emotions during the estate sorting or asset sale process. In the sales procedure unit, for example, the generation AI analyzes the user's emotions in real time during the estate sorting or asset sale process and makes suggestions that draw out positive emotions. For example, it communicates the progress of the process in a positive manner. In addition, it uses an emotion estimation function to analyze the user's emotions in real time during the estate sorting or asset sale process and makes suggestions that draw out positive emotions. For example, it displays encouraging messages. In addition, the generation AI analyzes the user's emotions in real time during the estate sorting or asset sale process and makes suggestions that draw out positive emotions. For example, it displays messages praising the progress of the process. This makes it possible to analyze the user's emotions in real time and make suggestions that draw out positive emotions during the estate sorting or asset sale process.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The item selling support system can further estimate the user's emotions and optimize the selling process based on the estimated emotions. For example, the system can display encouraging messages to the user to reduce the anxiety and stress the user feels when selling an item. In addition, to elicit positive emotions felt by the user during the selling process, the system can clearly display the progress of the process and support the user so that the user can proceed with the process with peace of mind. Furthermore, by analyzing the user's emotions during the selling process in real time and displaying support messages at appropriate times, user satisfaction can be improved.

[0091] The product sales support system can also consider the user's past transaction history and propose the optimal sales procedure. For example, it can refer to the procedures of successful transactions in the past to propose the most efficient procedure for the user. In addition, by prioritizing the proposal of procedures that have not caused any problems in the past, it can support the user so that they can proceed with the procedure with peace of mind. Furthermore, by analyzing the user's past transaction history and proposing the optimal sales procedure, it is possible to improve user satisfaction.

[0092] The product sales support system can also consider the user's lifestyle and values ​​to propose optimal sales procedures. For example, by proposing sales procedures that match the values ​​that the user values, it can support the user so that they can proceed with the procedures with a sense of satisfaction. Also, by proposing procedures that match the user's lifestyle, it can help the user proceed with the procedures without feeling stressed. Furthermore, by proposing optimal procedures that take the user's lifestyle and values ​​into consideration, it is possible to improve user satisfaction.

[0093] The item selling support system can also analyze the user's emotions in real time and make suggestions to elicit positive emotions. For example, the system can display encouraging messages to the user to reduce the anxiety and stress the user feels when selling an item. In addition, to elicit positive emotions felt by the user during the selling process, the system can clearly display the progress of the process and support the user so that they can proceed with the process with peace of mind. Furthermore, by analyzing the user's emotions during the selling process in real time and displaying support messages at appropriate times, user satisfaction can be improved.

[0094] The product sales support system can also consider the user's schedule and time of day to propose the most convenient procedure. For example, by carrying out the procedure during a time when the user is free, the user can proceed with the procedure without feeling stressed. Also, by proposing a procedure that suits the user's convenience, the user can carry out the procedure while avoiding busy times. Furthermore, by proposing the most convenient procedure that takes the user's schedule and time of day into consideration, user satisfaction can be improved.

[0095] The item selling support system can further analyze the user's emotions, evaluate the user's feelings toward sorting out belongings or selling assets, and make suggestions that will elicit positive emotions. For example, the system can display encouraging messages to the user to support the user so that they do not feel emotionally burdened. In addition, by suggesting a selling method that will satisfy the user emotionally, the system can allow the user to proceed with the process with peace of mind. Furthermore, by analyzing the user's emotions and making suggestions that will elicit positive emotions toward sorting out belongings or selling assets, user satisfaction can be improved.

[0096] The product selling support system can also analyze the opinions of the user's family and friends and suggest the optimal selling method based on that. For example, by prioritizing the selling method recommended by family and friends, the system can support the user so that they can proceed with the procedure with peace of mind. Also, by suggesting a selling method that family and friends agree with, the system can help the user proceed with the procedure with confidence. Furthermore, by suggesting the optimal selling method that takes into account the opinions of the user's family and friends, the system can improve user satisfaction.

[0097] The item selling support system can further analyze the user's emotions in real time and make suggestions to elicit positive emotions during the sorting and asset sale process. For example, the system can display encouraging messages to the user to reduce the anxiety and stress the user feels during the sorting and asset sale process. In addition, to elicit positive emotions during the sorting and asset sale process, the system can clearly display the progress of the process and support the user so that they can proceed with the process with peace of mind. Furthermore, by analyzing the emotions the user feels during the sorting and asset sale process in real time and displaying support messages at appropriate times, user satisfaction can be improved.

[0098] The product sales support system can further analyze the user's interests and display advertisements customized based on the results. For example, by displaying product advertisements in categories that interest the user, advertisements that are likely to interest the user can be provided. Also, by analyzing the user's interests and displaying advertisements related to keywords searched in the past, advertisements that are likely to interest the user can be provided. Furthermore, by analyzing the user's interests and displaying advertisements customized based on the results, user satisfaction can be improved.

[0099] The product sales support system can further analyze the user's emotions and display advertisements that elicit positive emotions. For example, by displaying advertisements for products and services that the user is likely to be interested in, it is possible to provide advertisements that are likely to elicit positive emotions in the user. In addition, by using the emotion estimation function to analyze the emotions the user has toward advertisements and display advertisements that elicit positive emotions, it is possible to provide advertisements that are likely to interest the user. Furthermore, by analyzing the user's emotions and displaying advertisements that elicit positive emotions, it is possible to improve user satisfaction.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The item photographing unit photographs an item. For example, a user photographs an image of the item with a smartphone. The item photographing unit can also photograph a video. Step 2: The value calculation unit analyzes the images of the item taken by the item photography unit and calculates the expected market value. For example, the generation AI analyzes the images of the item and calculates the value taking into account its condition and market demand. The value calculation unit can also analyze videos of the item and calculate the expected market value taking into account its operating condition and usability. Step 3: The matching unit matches the optimal buyer based on the estimated market value calculated by the value calculation unit. For example, the generation AI selects a company that can properly process the item, such as a recycle shop or a collection company. Step 4: The sales procedure unit automates the sales procedures with the buyer selected by the matching unit. For example, the generation AI automates negotiations with the buyer, creating contracts, and arranging for the delivery of goods.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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. an article photographing unit that photographs an article; a value calculation unit that analyzes the image of the product photographed by the product photographing unit and calculates an estimated market value; a matching unit that matches an optimal buyer based on the estimated market value calculated by the value calculation unit; a sales procedure unit that automates the sales procedure with the buyer selected by the matching unit. A system characterized by:

2. The article photographing unit is Analyze videos of items and calculate their estimated market value by taking into account their operating condition and usability.

2. The system of claim 1.

3. The value calculation unit Automatically collect information on the item's history, manufacturing year, and manufacturer, and calculate its value based on that information 2. The system of claim 1.

4. The value calculation unit Analyzing user sentiment and reflecting it in the estimated market value 2. The system of claim 1.

5. The value calculation unit Calculate the value by taking into account the repair and maintenance history of the item 2. The system of claim 1.

Images