System

Abstract

An object of a system according to an embodiment is to guarantee the authenticity of a sneaker and to perform a transaction without anxiety.SOLUTION: A system according to an embodiment includes a digital ID allocation unit and a blockchain recording unit. The digital ID assignment unit assigns a unique digital ID to the sneaker. The blockchain recording unit records the digital ID and related information thereof in the blockchain.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] With conventional technology, it has been difficult to prevent the distribution of counterfeit goods in the sneaker market, making it impossible to conduct transactions with confidence.

[0005] The system according to the embodiment aims to guarantee the authenticity of sneakers and allow transactions to be carried out with peace of mind. [Means for solving the problem]

[0006] The system according to the embodiment includes a digital ID assigning unit and a blockchain recording unit. The digital ID assigning unit assigns a unique digital ID to each pair of sneakers. The blockchain recording unit records the digital ID and its associated information on the blockchain. [Effects of the Invention]

[0007] The system according to the embodiment guarantees the authenticity of sneakers, allowing transactions to be carried out with peace of mind. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A trading service according to an embodiment of the present invention is a system that prevents the distribution of counterfeit sneakers in the sneaker market and enables secure transactions in the secondhand market. This system assigns a unique digital ID to each sneaker and records that information on a blockchain. This allows the trading service to immutably verify the authenticity and ownership history of each sneaker and prevent the distribution of counterfeit goods.

[0029] A trading service according to an embodiment includes a digital ID assignment unit and a blockchain recording unit. The digital ID assignment unit assigns a unique digital ID to sneakers. For example, the digital ID assignment unit assigns a digital ID generated when the sneakers are manufactured. The digital ID assignment unit can also associate specific information about the sneakers (e.g., manufacturing date, manufacturing location, serial number, etc.) with the digital ID. The digital ID assignment unit can also generate the digital ID using cryptographic technology to ensure the uniqueness of the sneakers. The blockchain recording unit records the digital ID and its associated information on a blockchain. For example, the blockchain recording unit uses Gemini's blockchain technology to immutably record the digital ID and its associated information. The blockchain recording unit can also record sneaker manufacturing information, ownership history, transaction history, etc. on a blockchain. The blockchain recording unit can also maintain the integrity of the information using blockchain technology to prevent tampering with the recorded information. This allows the trading service according to an embodiment to immutably maintain the authenticity and ownership history of sneakers and prevent the distribution of counterfeit goods. For example, a buyer can use the sneakers' digital ID to verify their authenticity. By scanning the digital ID using a smartphone app and comparing it with information on the blockchain, sneakers can be verified as authentic. The sneaker ownership history is also recorded on the blockchain, making it possible to track the path the sneakers have taken. For example, when sneakers are traded on the secondhand market, it is possible to check their past owners and transaction history.

[0030] The digital ID assignment unit can associate minute physical features generated during the manufacturing process of the sneakers with the digital ID. For example, the digital ID assignment unit photographs the minute physical features generated during the manufacturing process of the sneakers with a high-resolution camera and associates the image data with the digital ID. This further strengthens the uniqueness of the sneakers. The digital ID assignment unit can also associate physical features such as minute scratches and color variations that occur during manufacturing with the digital ID. For example, by associating minute scratches that occur during manufacturing with the digital ID, the uniqueness of the sneakers is ensured. The digital ID assignment unit can also associate the texture of the sneakers' surface and subtle color differences with the digital ID. This further strengthens the uniqueness of the sneakers and more reliably confirms their authenticity.

[0031] The digital ID assignment unit can include detailed information about the materials used in manufacturing the sneakers in the digital ID. For example, the digital ID assignment unit can include information about the supplier of the materials used in manufacturing the sneakers in the digital ID. For example, the digital ID can record the supplier of a particular leather or the manufacturer of a particular fabric. The digital ID assignment unit can also include production lot information about the sneakers in the digital ID. For example, by recording the production lot number in the digital ID, detailed information about the sneaker manufacturing process can be tracked. The digital ID assignment unit can also include information about the type and quality of the sneaker materials in the digital ID. This allows detailed information about the sneaker manufacturing process to be tracked, strengthening authentication verification.

[0032] The digital ID assignment unit can assign unique digital IDs to fashion items other than sneakers and record that information on the blockchain. The digital ID assignment unit can assign unique digital IDs to fashion items other than sneakers and record that information on the blockchain. For example, manufacturing information and ownership history of bags and watches can be associated with the digital ID. The digital ID assignment unit can also build a similar authenticity verification system for fashion items other than sneakers. For example, authenticity can be verified by recording manufacturing information and ownership history of bags and watches on the blockchain. The digital ID assignment unit can also assign unique digital IDs to fashion items other than sneakers and record that information on the blockchain, thereby verifying authenticity. This makes it possible to apply the authenticity verification system to fashion items other than sneakers.

[0033] The digital ID assignment unit generates digital IDs not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process. For example, the digital ID assignment unit generates digital IDs not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process. For example, information about distributors and retailers is included in the digital ID. Furthermore, by recording detailed information about the distribution and sales processes of sneakers in the digital ID, the digital ID assignment unit can track detailed information about the entire process of sneakers. For example, by recording information about distributors and retailers in the digital ID, the digital ID assignment unit can track detailed information about the distribution and sales processes of sneakers. Furthermore, by generating digital IDs not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and recording detailed information about each process, the digital ID assignment unit can track detailed information about the entire process of sneakers. This records detailed information about the entire process of sneakers, strengthening authentication verification.

[0034] The blockchain recording unit can record the usage of the sneakers in the blockchain. For example, the blockchain recording unit detects the frequency of sneaker use using a sensor and records the data in the blockchain. For example, the frequency of use can be measured using a pedometer or an acceleration sensor. The blockchain recording unit can also detect the environment in which the sneakers are used using a sensor and record the data in the blockchain. For example, the environment can be measured using a temperature sensor or a humidity sensor. The blockchain recording unit can also detect the amount of time the sneakers are used using a sensor and record the data in the blockchain. This allows for detailed recording of sneaker usage and strengthens authentication verification.

[0035] The blockchain recording unit can record the maintenance history of the sneakers on the blockchain. For example, the blockchain recording unit records the cleaning history of the sneakers on the blockchain. For example, the cleaning company and cleaning date and time can be associated with the digital ID. The blockchain recording unit can also record the repair history of the sneakers on the blockchain. For example, the repair company and repair details can be associated with the digital ID. The blockchain recording unit can also understand the condition of the sneakers by recording the maintenance history of the sneakers in detail. For example, by recording the cleaning and repair history, the condition of the sneakers can be understood in detail. This allows the maintenance history of the sneakers to be recorded in detail, strengthening authentication verification.

[0036] The blockchain recording unit can apply blockchain technology to fashion items other than sneakers to build an authenticity verification system. The blockchain recording unit can apply blockchain technology to fashion items other than sneakers to build an authenticity verification system. For example, manufacturing information and ownership history of bags and watches are recorded on the blockchain. The blockchain recording unit can also build a similar authenticity verification system for fashion items other than sneakers. For example, authenticity can be verified by recording manufacturing information and ownership history of bags and watches on the blockchain. The blockchain recording unit can also apply blockchain technology to fashion items other than sneakers to build an authenticity verification system to verify authenticity. This makes it possible to apply an authenticity verification system to fashion items other than sneakers.

[0037] The blockchain recording unit records information not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and is able to record detailed information about each process. The blockchain recording unit, for example, records information not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process. For example, information about distributors and retailers is recorded on the blockchain. Furthermore, by recording detailed information about the distribution and sales processes of sneakers on the blockchain, detailed information about the entire process of sneakers can be tracked. For example, by recording information about distributors and retailers on the blockchain, detailed information about the distribution and sales processes of sneakers can be tracked. Furthermore, the blockchain recording unit records information not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process, and is able to track detailed information about the entire process of sneakers. This allows detailed information about the entire process of sneakers to be recorded, strengthening authentication verification.

[0038] The authenticity verification unit can add a function to the smartphone app that automatically recognizes the physical characteristics of the sneakers when scanning the sneaker's digital ID. The authenticity verification unit can, for example, add an image recognition function using a high-resolution camera to the smartphone app to automatically recognize the shape and color of the sneakers. This makes it possible to verify the authenticity by matching the digital ID with the physical characteristics. The authenticity verification unit can also verify the authenticity of the sneakers by automatically recognizing the physical characteristics of the sneakers. For example, by adding an image recognition function to the smartphone app to automatically recognize the shape and color of the sneakers, it makes it possible to verify the authenticity by matching the digital ID with the physical characteristics. The authenticity verification unit can also verify the authenticity of the sneakers by automatically recognizing the physical characteristics of the sneakers. This makes it possible to automatically recognize the physical characteristics of the sneakers and strengthen authenticity verification.

[0039] The authenticity verification unit can build a similar authenticity verification system by adding a function to the smartphone app that scans the digital IDs of fashion items other than sneakers. The authenticity verification unit can build an authenticity verification system by adding a function to the smartphone app that scans the digital IDs of fashion items such as bags and watches. For example, the authenticity verification unit associates manufacturing information and ownership history of each item with the digital ID. The authenticity verification unit can also build a similar authenticity verification system for fashion items other than sneakers. For example, authenticity can be verified by associating manufacturing information and ownership history of bags and watches with the digital ID. The authenticity verification unit can also verify authenticity by assigning unique digital IDs to fashion items other than sneakers and recording that information on the blockchain. This allows the authenticity verification system to be applied to fashion items other than sneakers.

[0040] The authenticity verification unit can add a function to the smartphone app that displays the usage status of the sneakers when the sneaker's digital ID is scanned. The authenticity verification unit, for example, adds a function to the smartphone app that displays the usage status of the sneakers, and displays the frequency of use and the usage environment when the digital ID is scanned. For example, data from a pedometer or an acceleration sensor is displayed. The authenticity verification unit can also verify the authenticity of the sneakers by displaying the usage status of the sneakers. For example, the authenticity verification unit can verify the authenticity of the sneakers by adding a function to the smartphone app that displays the usage status, and displays the frequency of use and the usage environment when the digital ID is scanned. The authenticity verification unit can also verify the authenticity of the sneakers by displaying the usage status of the sneakers. This allows the sneaker usage status to be displayed in detail, strengthening the verification of authenticity.

[0041] The ownership history management unit can include sneaker usage status in the ownership history. The ownership history management unit can, for example, include usage frequency data in the sneaker ownership history. For example, data from a pedometer or acceleration sensor can be recorded in the ownership history. The ownership history management unit can also include sneaker usage environment data in the ownership history. For example, data from a temperature sensor or humidity sensor can be recorded in the ownership history. The ownership history management unit can also include sneaker usage time data in the ownership history. This allows sneaker usage status to be included in the ownership history, enabling detailed history management.

[0042] The ownership history management unit can include the maintenance history of the sneakers in the ownership history. The ownership history management unit, for example, records the cleaning history of the sneakers in the ownership history. For example, it associates the dry cleaner and the cleaning date and time with the ownership history. The ownership history management unit can also record the repair history of the sneakers in the ownership history. For example, it can associate the repairer and the repair content with the ownership history. The ownership history management unit can also understand the condition of the sneakers by recording the maintenance history of the sneakers in detail. For example, it can understand the condition of the sneakers in detail by recording the cleaning and repair history. This allows the maintenance history of the sneakers to be included in the ownership history, thereby realizing detailed history management.

[0043] The ownership history management unit can apply the ownership history management to fashion items other than sneakers and build a similar history management system. The ownership history management unit can apply the ownership history management system to fashion items other than sneakers and build a similar history management system, for example. For example, the ownership history of bags and watches is recorded on a blockchain. The ownership history management unit can also build a similar history management system for fashion items other than sneakers. For example, ownership history management can be performed by recording the ownership history of bags and watches on a blockchain. The ownership history management unit can also apply the ownership history management system to fashion items other than sneakers and build a similar history management system, for example. This makes it possible to apply the history management system to fashion items other than sneakers.

[0044] The ownership history management unit records ownership history management not only during the sneaker manufacturing process, but also during the distribution and sales processes, and is able to record detailed information about each process. The ownership history management unit, for example, records ownership history not only during the sneaker manufacturing process, but also during the distribution and sales processes. For example, information about distributors and retailers is included in the ownership history. The ownership history management unit also records detailed information about the sneaker distribution and sales processes in the ownership history, making it possible to track detailed information about the sneaker's entire process. For example, by recording information about distributors and retailers in the ownership history, it is possible to track detailed information about the sneaker's distribution and sales processes. The ownership history management unit also records ownership history not only during the sneaker manufacturing process, but also during the distribution and sales processes, and is able to track detailed information about each process, making it possible to track detailed information about the sneaker's entire process. This makes it possible to record detailed information about the sneaker's entire process and achieve detailed history management.

[0045] The secondhand market trading unit can add a function to automatically recognize the physical characteristics of sneakers when scanning the sneakers' digital IDs during transactions on the secondhand market. For example, the secondhand market trading unit can add an image recognition function using a high-resolution camera to a smartphone app to automatically recognize the shape and color of sneakers. This allows the digital ID to be matched with the physical characteristics to confirm authenticity. The secondhand market trading unit can also confirm the authenticity of sneakers by automatically recognizing the physical characteristics of sneakers. For example, the secondhand market trading unit can add an image recognition function to a smartphone app to automatically recognize the shape and color of sneakers to automatically recognize the shape and color of sneakers, which allows the digital ID to be matched with the physical characteristics to confirm authenticity. The secondhand market trading unit can also confirm the authenticity of sneakers by automatically recognizing the physical characteristics of sneakers. This allows the physical characteristics of sneakers to be automatically recognized during transactions on the secondhand market, strengthening authentication verification.

[0046] The secondhand market trading unit can add a function to perform real-time authentication from the sneaker manufacturer when the sneaker's digital ID is scanned during a transaction on the secondhand market. The secondhand market trading unit, for example, adds a real-time authentication function to a smartphone app, and communicates with the manufacturer's server for authentication when the sneaker's digital ID is scanned. This allows for instant confirmation of the authenticity of the sneakers. The secondhand market trading unit can also confirm the authenticity of the sneakers by performing real-time authentication from the sneaker manufacturer. For example, the secondhand market trading unit can add a real-time authentication function to a smartphone app, and communicates with the manufacturer's server for authentication when the sneaker's digital ID is scanned. This allows for instant confirmation of the authenticity of the sneakers. The secondhand market trading unit can also confirm the authenticity of the sneakers by performing real-time authentication from the sneaker manufacturer. This allows for real-time authentication from the sneaker manufacturer during a transaction on the secondhand market, strengthening authenticity confirmation.

[0047] The secondhand market trading unit can add a function to display the usage status of sneakers to the trading system for the secondhand market. For example, the secondhand market trading unit can add a function to display the usage status of sneakers to the trading system for the secondhand market, and display the frequency of use and the environment of use when scanning a digital ID. For example, data from a pedometer or an accelerometer can be displayed. Furthermore, the secondhand market trading unit can confirm the authenticity of sneakers by displaying the usage status. For example, the secondhand market trading unit can confirm the authenticity of sneakers by displaying the usage status of sneakers to the trading system for the secondhand market, and display the frequency of use and the environment of use when scanning a digital ID. Furthermore, the secondhand market trading unit can confirm the authenticity of sneakers by displaying the usage status of sneakers. This allows detailed display of the usage status of sneakers when trading on the secondhand market, improving the reliability of transactions.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The trading service can provide personalized sneaker recommendations based on the user's purchase history. For example, it can analyze the user's preferred brands and styles from their purchase history and suggest new sneakers based on that. It can also provide specific sale information and discount coupons based on the user's purchase frequency and budget. Furthermore, it can predict future purchasing trends based on the user's past purchase history and notify the user of recommended products at the appropriate time. This can improve the user's purchasing experience.

[0050] The trading service can assess the value of sneakers based on information associated with the sneakers' digital ID. For example, it can calculate their current market value based on the sneaker's production year and limited edition model information. It can also reflect changes in value in real time, taking into account the sneaker's ownership history and usage. Furthermore, by including the sneaker's maintenance and repair history in the value assessment, a more accurate assessment can be made. This allows users to understand the fair price when buying and selling sneakers.

[0051] The trading service can display the eco-footprint of sneakers based on the information associated with the sneakers' digital ID. For example, it can calculate the carbon dioxide emissions during manufacturing and the environmental impact of the materials used and provide this information to users. It can also provide information about the sneakers' recyclability and the environmental impact of disposal. Furthermore, it can promote environmentally conscious consumer behavior by providing users with information on the procedures and recycling companies for recycling sneakers. This helps users make environmentally friendly choices.

[0052] The trading service can offer customization options for the sneakers based on the information associated with the sneakers' digital ID. For example, the trading service can allow users to select specific colors or designs to create their own unique sneakers. It can also allow users to customize the fit to fit their foot size and shape. It can also offer users the option to engrave their name or initials on the sneakers, allowing them to create unique, personalized sneakers.

[0053] A trading service can provide a sneaker rental service based on the information associated with the sneaker's digital ID. For example, users can rent sneakers for a specific event or for a limited time. The service can also record usage and maintenance history during the rental period in the digital ID and provide it to the next user. Furthermore, the service can evaluate the condition of the sneakers after the rental ends and perform any necessary maintenance to prepare them for the next rental. This allows users to easily try on expensive sneakers.

[0054] The trading service can provide sneaker insurance services based on the information associated with the sneaker's digital ID. For example, it can allow users to select an insurance option when purchasing sneakers, providing compensation for loss, theft, and damage. It can also calculate insurance premiums based on the sneaker's usage and maintenance history. Furthermore, when making an insurance claim, the digital ID can be used to check the sneaker's condition and respond quickly. This allows users to use their sneakers with peace of mind.

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

[0056] Step 1: The digital ID assigner assigns a unique digital ID to each pair of sneakers. For example, the digital ID is generated when the sneakers are manufactured. The digital ID can also be associated with specific information about the sneakers (such as the manufacturing date, manufacturing location, and serial number). Furthermore, the digital ID can be generated using cryptography to ensure the uniqueness of each pair of sneakers. Step 2: The blockchain recorder records the digital ID and related information on the blockchain. For example, Gemini's blockchain technology can be used to record the digital ID and related information as immutable data. Additionally, the manufacturing information, ownership history, and transaction history of the sneakers can be recorded on the blockchain. Furthermore, blockchain technology can be used to maintain the integrity of the information, preventing tampering.

[0057] (Example 2) A trading service according to an embodiment of the present invention is a system that prevents the distribution of counterfeit sneakers in the sneaker market and enables secure transactions in the secondhand market. This system assigns a unique digital ID to each sneaker and records that information on a blockchain. This allows the trading service to immutably verify the authenticity and ownership history of each sneaker and prevent the distribution of counterfeit goods.

[0058] A trading service according to an embodiment includes a digital ID assignment unit and a blockchain recording unit. The digital ID assignment unit assigns a unique digital ID to sneakers. For example, the digital ID assignment unit assigns a digital ID generated when the sneakers are manufactured. The digital ID assignment unit can also associate specific information about the sneakers (e.g., manufacturing date, manufacturing location, serial number, etc.) with the digital ID. The digital ID assignment unit can also generate the digital ID using cryptographic technology to ensure the uniqueness of the sneakers. The blockchain recording unit records the digital ID and its associated information on a blockchain. For example, the blockchain recording unit uses Gemini's blockchain technology to immutably record the digital ID and its associated information. The blockchain recording unit can also record sneaker manufacturing information, ownership history, transaction history, etc. on a blockchain. The blockchain recording unit can also maintain the integrity of the information using blockchain technology to prevent tampering with the recorded information. This allows the trading service according to an embodiment to immutably maintain the authenticity and ownership history of sneakers and prevent the distribution of counterfeit goods. For example, a buyer can use the sneakers' digital ID to verify their authenticity. By scanning the digital ID using a smartphone app and comparing it with information on the blockchain, sneakers can be verified as authentic. The sneaker ownership history is also recorded on the blockchain, making it possible to track the path the sneakers have taken. For example, when sneakers are traded on the secondhand market, it is possible to check their past owners and transaction history.

[0059] The digital ID assignment unit can associate minute physical features generated during the manufacturing process of the sneakers with the digital ID. For example, the digital ID assignment unit photographs the minute physical features generated during the manufacturing process of the sneakers with a high-resolution camera and associates the image data with the digital ID. This further strengthens the uniqueness of the sneakers. The digital ID assignment unit can also associate physical features such as minute scratches and color variations that occur during manufacturing with the digital ID. For example, by associating minute scratches that occur during manufacturing with the digital ID, the uniqueness of the sneakers is ensured. The digital ID assignment unit can also associate the texture of the sneakers' surface and subtle color differences with the digital ID. This further strengthens the uniqueness of the sneakers and more reliably confirms their authenticity.

[0060] The digital ID assignment unit can include detailed information about the materials used in manufacturing the sneakers in the digital ID. For example, the digital ID assignment unit can include information about the supplier of the materials used in manufacturing the sneakers in the digital ID. For example, the digital ID can record the supplier of a particular leather or the manufacturer of a particular fabric. The digital ID assignment unit can also include production lot information about the sneakers in the digital ID. For example, by recording the production lot number in the digital ID, detailed information about the sneaker manufacturing process can be tracked. The digital ID assignment unit can also include information about the type and quality of the sneaker materials in the digital ID. This allows detailed information about the sneaker manufacturing process to be tracked, strengthening authentication verification.

[0061] The digital ID assignment unit can use the emotion estimation function to record the emotion of the user when purchasing sneakers and associate the emotion data with the digital ID. The digital ID assignment unit, for example, analyzes the emotion of the user when purchasing sneakers in real time and associates the data with the digital ID. For example, it calculates an emotion score by analyzing facial expressions and voice at the time of purchase. The digital ID assignment unit can also emotionally track the purchase history by associating the emotional data of the user at the time of purchase with the digital ID. For example, by recording the emotional data at the time of purchase, the user's purchase history can be understood in detail. The digital ID assignment unit can also emotionally track the purchase history by using the emotion estimation function to record the emotion of the user at the time of purchase and associate the data with the digital ID. In this way, it is possible to record the emotional data of the user at the time of purchase and emotionally track the purchase history.

[0062] The digital ID assignment unit can assign unique digital IDs to fashion items other than sneakers and record that information on the blockchain. The digital ID assignment unit can assign unique digital IDs to fashion items other than sneakers and record that information on the blockchain. For example, manufacturing information and ownership history of bags and watches can be associated with the digital ID. The digital ID assignment unit can also build a similar authenticity verification system for fashion items other than sneakers. For example, authenticity can be verified by recording manufacturing information and ownership history of bags and watches on the blockchain. The digital ID assignment unit can also assign unique digital IDs to fashion items other than sneakers and record that information on the blockchain, thereby verifying authenticity. This makes it possible to apply the authenticity verification system to fashion items other than sneakers.

[0063] The digital ID assignment unit generates digital IDs not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process. For example, the digital ID assignment unit generates digital IDs not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process. For example, information about distributors and retailers is included in the digital ID. Furthermore, by recording detailed information about the distribution and sales processes of sneakers in the digital ID, the digital ID assignment unit can track detailed information about the entire process of sneakers. For example, by recording information about distributors and retailers in the digital ID, the digital ID assignment unit can track detailed information about the distribution and sales processes of sneakers. Furthermore, by generating digital IDs not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and recording detailed information about each process, the digital ID assignment unit can track detailed information about the entire process of sneakers. This records detailed information about the entire process of sneakers, strengthening authentication verification.

[0064] The digital ID assignment unit can use the emotion estimation function to record the emotion of the user when trying on sneakers in real time and associate the data with the digital ID. For example, the digital ID assignment unit can analyze the emotion of the user when trying on sneakers in real time and associate the data with the digital ID. For example, the digital ID assignment unit can calculate an emotion score by analyzing facial expressions and voice when trying on sneakers. The digital ID assignment unit can also associate the emotion data of the user when trying on sneakers with the digital ID, thereby emotionally tracking the try-on history. For example, by recording the emotion data when trying on sneakers, the user's try-on history can be grasped in detail. The digital ID assignment unit can also use the emotion estimation function to record the emotion of the user when trying on sneakers and associate the data with the digital ID, thereby emotionally tracking the try-on history. In this way, the user's emotion data when trying on sneakers can be recorded and the try-on history can be emotionally tracked.

[0065] The blockchain recording unit can record the usage of the sneakers in the blockchain. For example, the blockchain recording unit detects the frequency of sneaker use using a sensor and records the data in the blockchain. For example, the frequency of use can be measured using a pedometer or an acceleration sensor. The blockchain recording unit can also detect the environment in which the sneakers are used using a sensor and record the data in the blockchain. For example, the environment can be measured using a temperature sensor or a humidity sensor. The blockchain recording unit can also detect the amount of time the sneakers are used using a sensor and record the data in the blockchain. This allows for detailed recording of sneaker usage and strengthens authentication verification.

[0066] The blockchain recording unit can record the maintenance history of the sneakers on the blockchain. For example, the blockchain recording unit records the cleaning history of the sneakers on the blockchain. For example, the cleaning company and cleaning date and time can be associated with the digital ID. The blockchain recording unit can also record the repair history of the sneakers on the blockchain. For example, the repair company and repair details can be associated with the digital ID. The blockchain recording unit can also understand the condition of the sneakers by recording the maintenance history of the sneakers in detail. For example, by recording the cleaning and repair history, the condition of the sneakers can be understood in detail. This allows the maintenance history of the sneakers to be recorded in detail, strengthening authentication verification.

[0067] The blockchain recording unit can use the emotion estimation function to record the emotions of a user when using the sneakers and record the emotion data in the blockchain. The blockchain recording unit, for example, analyzes the emotions of a user when using the sneakers in real time and records the data in the blockchain. For example, it calculates an emotion score by analyzing facial expressions and voices during use. The blockchain recording unit can also emotionally track the usage history by recording the emotion data of the user during use in the blockchain. For example, by recording the emotion data during use, the user's usage history can be understood in detail. The blockchain recording unit can also emotionally track the usage history by using the emotion estimation function to record the emotions of a user during use and record the data in the blockchain. This allows the emotion data of a user during use to be recorded and the usage history to be emotionally tracked.

[0068] The blockchain recording unit can apply blockchain technology to fashion items other than sneakers to build an authenticity verification system. The blockchain recording unit can apply blockchain technology to fashion items other than sneakers to build an authenticity verification system. For example, manufacturing information and ownership history of bags and watches are recorded on the blockchain. The blockchain recording unit can also build a similar authenticity verification system for fashion items other than sneakers. For example, authenticity can be verified by recording manufacturing information and ownership history of bags and watches on the blockchain. The blockchain recording unit can also apply blockchain technology to fashion items other than sneakers to build an authenticity verification system to verify authenticity. This makes it possible to apply an authenticity verification system to fashion items other than sneakers.

[0069] The blockchain recording unit records information not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and is able to record detailed information about each process. The blockchain recording unit, for example, records information not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process. For example, information about distributors and retailers is recorded on the blockchain. Furthermore, by recording detailed information about the distribution and sales processes of sneakers on the blockchain, detailed information about the entire process of sneakers can be tracked. For example, by recording information about distributors and retailers on the blockchain, detailed information about the distribution and sales processes of sneakers can be tracked. Furthermore, the blockchain recording unit records information not only during the manufacturing process of sneakers, but also during the distribution and sales processes, and records detailed information about each process, and is able to track detailed information about the entire process of sneakers. This allows detailed information about the entire process of sneakers to be recorded, strengthening authentication verification.

[0070] The blockchain recording unit can use the emotion estimation function to record the emotions of a user when trying on sneakers in real time and record the data in the blockchain. The blockchain recording unit, for example, analyzes the emotions of a user when trying on sneakers in real time and records the data in the blockchain. For example, it calculates an emotion score by analyzing facial expressions and voices when trying on sneakers. The blockchain recording unit can also emotionally track the try-on history by recording the emotion data of the user when trying on sneakers in the blockchain. For example, by recording the emotion data when trying on sneakers, the user's try-on history can be grasped in detail. The blockchain recording unit can also emotionally track the try-on history by using the emotion estimation function to record the emotion of a user when trying on sneakers and record the data in the blockchain. In this way, it is possible to record the emotion data of a user when trying on sneakers and emotionally track the try-on history.

[0071] The authenticity verification unit can add a function to the smartphone app that automatically recognizes the physical characteristics of the sneakers when scanning the sneaker's digital ID. The authenticity verification unit can, for example, add an image recognition function using a high-resolution camera to the smartphone app to automatically recognize the shape and color of the sneakers. This makes it possible to verify the authenticity by matching the digital ID with the physical characteristics. The authenticity verification unit can also verify the authenticity of the sneakers by automatically recognizing the physical characteristics of the sneakers. For example, by adding an image recognition function to the smartphone app to automatically recognize the shape and color of the sneakers, it makes it possible to verify the authenticity by matching the digital ID with the physical characteristics. The authenticity verification unit can also verify the authenticity of the sneakers by automatically recognizing the physical characteristics of the sneakers. This makes it possible to automatically recognize the physical characteristics of the sneakers and strengthen authenticity verification.

[0072] The authenticity verification unit uses the emotion estimation function to record the emotion of the user when checking the authenticity of the sneakers, and can improve the accuracy of authenticity verification based on the emotion data. The authenticity verification unit, for example, analyzes the emotion of the user when checking the authenticity of the sneakers in real time, and improves the accuracy of authenticity verification based on the data. For example, it calculates an emotion score by analyzing facial expressions and voice at the time of verification. The authenticity verification unit can also improve the accuracy of authenticity verification based on the emotion data of the user at the time of verification. For example, by recording the emotion data at the time of verification, it is possible to grasp the emotion of the user at the time of verification in detail. The authenticity verification unit can also use the emotion estimation function to record the emotion of the user at the time of verification, and can improve the accuracy of authenticity verification based on the data. In this way, it is possible to record the emotion data of the user at the time of verification and improve the accuracy of authenticity verification.

[0073] The authenticity verification unit can build a similar authenticity verification system by adding a function to the smartphone app that scans the digital IDs of fashion items other than sneakers. The authenticity verification unit can build an authenticity verification system by adding a function to the smartphone app that scans the digital IDs of fashion items such as bags and watches. For example, the authenticity verification unit associates manufacturing information and ownership history of each item with the digital ID. The authenticity verification unit can also build a similar authenticity verification system for fashion items other than sneakers. For example, authenticity can be verified by associating manufacturing information and ownership history of bags and watches with the digital ID. The authenticity verification unit can also verify authenticity by assigning unique digital IDs to fashion items other than sneakers and recording that information on the blockchain. This allows the authenticity verification system to be applied to fashion items other than sneakers.

[0074] The authenticity verification unit can add a function to the smartphone app that displays the usage status of the sneakers when the sneaker's digital ID is scanned. The authenticity verification unit, for example, adds a function to the smartphone app that displays the usage status of the sneakers, and displays the frequency of use and the usage environment when the digital ID is scanned. For example, data from a pedometer or an acceleration sensor is displayed. The authenticity verification unit can also verify the authenticity of the sneakers by displaying the usage status of the sneakers. For example, the authenticity verification unit can verify the authenticity of the sneakers by adding a function to the smartphone app that displays the usage status, and displays the frequency of use and the usage environment when the digital ID is scanned. The authenticity verification unit can also verify the authenticity of the sneakers by displaying the usage status of the sneakers. This allows the sneaker usage status to be displayed in detail, strengthening the verification of authenticity.

[0075] The authenticity verification unit uses the emotion estimation function to record the emotions of the user when trying on sneakers in real time, and can improve the accuracy of authenticity verification based on the data. The authenticity verification unit, for example, analyzes the emotions of the user when trying on sneakers in real time, and can improve the accuracy of authenticity verification based on the data. For example, the authenticity verification unit calculates an emotion score by analyzing facial expressions and voices when trying on sneakers. The authenticity verification unit can also improve the accuracy of authenticity verification based on emotion data of the user when trying on sneakers. For example, by recording the emotion data when trying on sneakers, the user's emotions when trying on sneakers can be grasped in detail. The authenticity verification unit can also use the emotion estimation function to record the emotions of the user when trying on sneakers, and can improve the accuracy of authenticity verification based on the data. In this way, the emotion data of the user when trying on sneakers is recorded, and the accuracy of authenticity verification can be improved.

[0076] The ownership history management unit can include sneaker usage status in the ownership history. The ownership history management unit can, for example, include usage frequency data in the sneaker ownership history. For example, data from a pedometer or acceleration sensor can be recorded in the ownership history. The ownership history management unit can also include sneaker usage environment data in the ownership history. For example, data from a temperature sensor or humidity sensor can be recorded in the ownership history. The ownership history management unit can also include sneaker usage time data in the ownership history. This allows sneaker usage status to be included in the ownership history, enabling detailed history management.

[0077] The ownership history management unit can include the maintenance history of the sneakers in the ownership history. The ownership history management unit, for example, records the cleaning history of the sneakers in the ownership history. For example, it associates the dry cleaner and the cleaning date and time with the ownership history. The ownership history management unit can also record the repair history of the sneakers in the ownership history. For example, it can associate the repairer and the repair content with the ownership history. The ownership history management unit can also understand the condition of the sneakers by recording the maintenance history of the sneakers in detail. For example, it can understand the condition of the sneakers in detail by recording the cleaning and repair history. This allows the maintenance history of the sneakers to be included in the ownership history, thereby realizing detailed history management.

[0078] The ownership history management unit can apply the ownership history management to fashion items other than sneakers and build a similar history management system. The ownership history management unit can apply the ownership history management system to fashion items other than sneakers and build a similar history management system, for example. For example, the ownership history of bags and watches is recorded on a blockchain. The ownership history management unit can also build a similar history management system for fashion items other than sneakers. For example, ownership history management can be performed by recording the ownership history of bags and watches on a blockchain. The ownership history management unit can also apply the ownership history management system to fashion items other than sneakers and build a similar history management system, for example. This makes it possible to apply the history management system to fashion items other than sneakers.

[0079] The ownership history management unit records ownership history management not only during the sneaker manufacturing process, but also during the distribution and sales processes, and is able to record detailed information about each process. The ownership history management unit, for example, records ownership history not only during the sneaker manufacturing process, but also during the distribution and sales processes. For example, information about distributors and retailers is included in the ownership history. The ownership history management unit also records detailed information about the sneaker distribution and sales processes in the ownership history, making it possible to track detailed information about the sneaker's entire process. For example, by recording information about distributors and retailers in the ownership history, it is possible to track detailed information about the sneaker's distribution and sales processes. The ownership history management unit also records ownership history not only during the sneaker manufacturing process, but also during the distribution and sales processes, and is able to track detailed information about each process, making it possible to track detailed information about the sneaker's entire process. This makes it possible to record detailed information about the sneaker's entire process and achieve detailed history management.

[0080] The ownership history management unit can use the emotion estimation function to record the emotion of the user when trying on sneakers in real time and include the data in the ownership history. The ownership history management unit, for example, analyzes the emotion of the user when trying on sneakers in real time and includes the data in the ownership history. For example, it calculates an emotion score by analyzing facial expressions and voices when trying on sneakers. The ownership history management unit can also emotionally track the try-on history by including the emotion data of the user when trying on sneakers in the ownership history. For example, by recording the emotion data when trying on sneakers, the user's try-on history can be grasped in detail. The ownership history management unit can also emotionally track the try-on history by using the emotion estimation function to record the emotion of the user when trying on sneakers and include the data in the ownership history. In this way, it is possible to record the emotion data of the user when trying on sneakers and emotionally track the ownership history.

[0081] The secondhand market trading unit can add a function to automatically recognize the physical characteristics of sneakers when scanning the sneakers' digital IDs during transactions on the secondhand market. For example, the secondhand market trading unit can add an image recognition function using a high-resolution camera to a smartphone app to automatically recognize the shape and color of sneakers. This allows the digital ID to be matched with the physical characteristics to confirm authenticity. The secondhand market trading unit can also confirm the authenticity of sneakers by automatically recognizing the physical characteristics of sneakers. For example, the secondhand market trading unit can add an image recognition function to a smartphone app to automatically recognize the shape and color of sneakers to automatically recognize the shape and color of sneakers, which allows the digital ID to be matched with the physical characteristics to confirm authenticity. The secondhand market trading unit can also confirm the authenticity of sneakers by automatically recognizing the physical characteristics of sneakers. This allows the physical characteristics of sneakers to be automatically recognized during transactions on the secondhand market, strengthening authentication verification.

[0082] The secondhand market trading unit can add a function to perform real-time authentication from the sneaker manufacturer when the sneaker's digital ID is scanned during a transaction on the secondhand market. The secondhand market trading unit, for example, adds a real-time authentication function to a smartphone app, and communicates with the manufacturer's server for authentication when the sneaker's digital ID is scanned. This allows for instant confirmation of the authenticity of the sneakers. The secondhand market trading unit can also confirm the authenticity of the sneakers by performing real-time authentication from the sneaker manufacturer. For example, the secondhand market trading unit can add a real-time authentication function to a smartphone app, and communicates with the manufacturer's server for authentication when the sneaker's digital ID is scanned. This allows for instant confirmation of the authenticity of the sneakers. The secondhand market trading unit can also confirm the authenticity of the sneakers by performing real-time authentication from the sneaker manufacturer. This allows for real-time authentication from the sneaker manufacturer during a transaction on the secondhand market, strengthening authenticity confirmation.

[0083] The secondhand market trading unit uses the emotion estimation function to record the emotions of users when purchasing used sneakers, and can improve the sense of security of transactions based on the emotion data. The secondhand market trading unit, for example, analyzes the emotions of users when purchasing used sneakers in real time, and improves the sense of security of transactions based on the data. For example, it calculates an emotion score by analyzing facial expressions and voice at the time of purchase. The secondhand market trading unit can also improve the sense of security of transactions based on the emotion data of users at the time of purchase. For example, by recording the emotion data at the time of purchase, it is possible to grasp in detail the emotions of users at the time of purchase. The secondhand market trading unit can also use the emotion estimation function to record the emotions of users at the time of purchase, and improve the sense of security of transactions based on the data. In this way, it is possible to record the emotion data of users at the time of purchase and improve the sense of security of transactions.

[0084] The secondhand market trading unit can add a function to display the usage status of sneakers to the trading system for the secondhand market. For example, the secondhand market trading unit can add a function to display the usage status of sneakers to the trading system for the secondhand market, and display the frequency of use and the environment of use when scanning a digital ID. For example, data from a pedometer or an accelerometer can be displayed. Furthermore, the secondhand market trading unit can confirm the authenticity of sneakers by displaying the usage status. For example, the secondhand market trading unit can confirm the authenticity of sneakers by displaying the usage status of sneakers to the trading system for the secondhand market, and display the frequency of use and the environment of use when scanning a digital ID. Furthermore, the secondhand market trading unit can confirm the authenticity of sneakers by displaying the usage status of sneakers. This allows detailed display of the usage status of sneakers when trading on the secondhand market, improving the reliability of transactions.

[0085] The secondhand market trading unit uses the emotion estimation function to record the emotions of a user when trying on used sneakers in real time, and can improve the sense of security of the transaction based on the data. The secondhand market trading unit, for example, analyzes the emotions of a user when trying on used sneakers in real time, and can improve the sense of security of the transaction based on the data. For example, the secondhand market trading unit calculates an emotion score by analyzing facial expressions and voices when trying on sneakers. The secondhand market trading unit can also improve the sense of security of the transaction based on emotion data of the user when trying on sneakers. For example, by recording emotion data when trying on sneakers, it is possible to grasp in detail the emotions of the user when trying on sneakers. The secondhand market trading unit can also use the emotion estimation function to record the emotions of a user when trying on sneakers, and can improve the sense of security of the transaction based on the data. In this way, it is possible to record emotion data of a user when trying on sneakers and improve the sense of security of the transaction.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The trading service can provide personalized sneaker recommendations based on the user's purchase history. For example, it can analyze the user's preferred brands and styles from their purchase history and suggest new sneakers based on that. It can also provide specific sale information and discount coupons based on the user's purchase frequency and budget. Furthermore, it can predict future purchasing trends based on the user's past purchase history and notify the user of recommended products at the appropriate time. This can improve the user's purchasing experience.

[0088] The trading service can assess the value of sneakers based on information associated with the sneakers' digital ID. For example, it can calculate their current market value based on the sneaker's production year and limited edition model information. It can also reflect changes in value in real time, taking into account the sneaker's ownership history and usage. Furthermore, by including the sneaker's maintenance and repair history in the value assessment, a more accurate assessment can be made. This allows users to understand the fair price when buying and selling sneakers.

[0089] The trading service can display the eco-footprint of sneakers based on the information associated with the sneakers' digital ID. For example, it can calculate the carbon dioxide emissions during manufacturing and the environmental impact of the materials used and provide this information to users. It can also provide information about the sneakers' recyclability and the environmental impact of disposal. Furthermore, it can promote environmentally conscious consumer behavior by providing users with information on the procedures and recycling companies for recycling sneakers. This helps users make environmentally friendly choices.

[0090] The trading service can record the emotions a user feels when purchasing sneakers and provide post-purchase support based on that data. For example, if a user expresses high satisfaction at the time of purchase, it can provide follow-up emails and special offers. Also, if a user expresses anxiety or doubts at the time of purchase, customer support can quickly contact the user to resolve the issue. Furthermore, it can analyze the user's emotional data and identify areas for improvement to increase post-purchase satisfaction. This can improve the user's purchasing experience.

[0091] The trading service can offer customization options for the sneakers based on the information associated with the sneakers' digital ID. For example, the trading service can allow users to select specific colors or designs to create their own unique sneakers. It can also allow users to customize the fit to fit their foot size and shape. It can also offer users the option to engrave their name or initials on the sneakers, allowing them to create unique, personalized sneakers.

[0092] The trading service can record users' emotions when purchasing sneakers and use that data to encourage them to leave reviews after the purchase. For example, users who expressed high satisfaction with their purchase can be asked to leave a review. In addition, customers who expressed concerns or doubts after the purchase can receive follow-up support before leaving a review. Furthermore, by analyzing users' emotional data and understanding trends in review content, it is possible to identify areas for improvement in products and services. This allows for effective use of user feedback.

[0093] A trading service can provide a sneaker rental service based on the information associated with the sneaker's digital ID. For example, users can rent sneakers for a specific event or for a limited time. The service can also record usage and maintenance history during the rental period in the digital ID and provide it to the next user. Furthermore, the service can evaluate the condition of the sneakers after the rental ends and perform any necessary maintenance to prepare them for the next rental. This allows users to easily try on expensive sneakers.

[0094] The trading service can record users' emotions when trying on sneakers and use that data to improve the try-on experience. For example, users who show high satisfaction when trying on sneakers can be offered rewards to encourage them to make a purchase. Furthermore, for users who show anxiety or doubts while trying on sneakers, the trading service can follow up with them after trying them on to resolve any questions or concerns they may have about their purchase. Furthermore, by analyzing users' emotional data and identifying areas for improvement in the try-on experience, the trading service can provide a better try-on environment. This can improve the user's try-on experience.

[0095] The trading service can provide sneaker insurance services based on the information associated with the sneaker's digital ID. For example, it can allow users to select an insurance option when purchasing sneakers, providing compensation for loss, theft, and damage. It can also calculate insurance premiums based on the sneaker's usage and maintenance history. Furthermore, when making an insurance claim, the digital ID can be used to check the sneaker's condition and respond quickly. This allows users to use their sneakers with peace of mind.

[0096] The trading service can record users' emotions when using sneakers and provide community functions based on that data. For example, it can connect users who expressed high satisfaction with sneakers and provide a forum for exchanging information and sharing reviews about them. It can also provide support and advice within the community for users who expressed concerns or doubts about sneakers. Furthermore, it can analyze users' emotional data and identify trends and popular sneakers within the community. This can promote interaction between users and allow them to share information about sneakers.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The digital ID assigner assigns a unique digital ID to each pair of sneakers. For example, the digital ID is generated when the sneakers are manufactured. The digital ID can also be associated with specific information about the sneakers (such as the manufacturing date, manufacturing location, and serial number). Furthermore, the digital ID can be generated using cryptography to ensure the uniqueness of each pair of sneakers. Step 2: The blockchain recorder records the digital ID and related information on the blockchain. For example, Gemini's blockchain technology can be used to record the digital ID and related information as immutable data. Additionally, the manufacturing information, ownership history, and transaction history of the sneakers can be recorded on the blockchain. Furthermore, blockchain technology can be used to maintain the integrity of the information, preventing tampering.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a digital ID assignment unit that assigns a unique digital ID to each sneaker; a blockchain recording unit that records the digital ID and related information in a blockchain. A system characterized by:

2. The digital ID assignment unit Linking the digital ID to minute physical characteristics generated during the manufacturing process of the sneakers 2. The system of claim 1.

3. The digital ID assignment unit Unique digital IDs will be assigned to fashion items other than sneakers, and that information will be recorded on the blockchain.

2. The system of claim 1.

4. The blockchain recording unit Record the usage of the sneakers on the blockchain 2. The system of claim 1.

5. The authenticity verification unit: Adding functionality to the smartphone app that automatically recognizes the sneaker's physical characteristics when scanning the sneaker's digital ID.

2. The system of claim 1.

6. The Ownership History Management Department: Include usage of said sneakers in the ownership history 2. The system of claim 1.

7. The Secondhand Market Trading Department Adding a feature that automatically recognizes the sneakers' physical characteristics when scanning their digital IDs during a secondhand market transaction.

2. The system of claim 1.

8. The digital ID assignment unit Using emotion estimation, the emotion a user feels when purchasing sneakers is recorded and the emotion data is associated with the digital ID.

2. The system of claim 1.

Images