System

Abstract

To provide a system for accurately grasping the trend of a sneaker market and utilizing it for investment and collection management.SOLUTION: The system includes a market analyzer, a trend predictor, a price estimator, an investment adviser, a portfolio manager, an advice provider, and a notifier. The market analysis unit analyzes the trend of the sneaker market. The trend prediction unit predicts that a specific model or brand will increase in the future based on the trend of the sneaker market. The price estimator evaluates the current market value of the sneaker. The investment advice unit proposes a buying time and a selling time to the investor. The portfolio manager uses the generated AI to track the collector's sneaker collection and manage the value of the entire portfolio. The advice providing unit stores information on the sneaker collection and provides advice on sale or storage. The notification unit provides information according to the user's interest.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to accurately grasp trends in the sneaker market and use them for investment and collection management.

[0005] The system according to the embodiment aims to accurately grasp trends in the sneaker market and to use this information for investment and collection management. [Means for solving the problem]

[0006] The system according to the embodiment includes a market analysis unit, a trend prediction unit, a price estimation unit, an investment advice unit, a portfolio management unit, an advice provision unit, and a notification unit. The market analysis unit uses a generative AI to analyze trends in the sneaker market. The trend prediction unit predicts future growth of specific models and brands based on sneaker market trends analyzed by the market analysis unit. The price estimation unit uses a generative AI to evaluate the current market value of rare sneakers. The investment advice unit suggests buying and selling times to investors based on the market value evaluated by the price estimation unit. The portfolio management unit uses a generative AI to track a collector's sneaker collection and manage the value of the entire portfolio. The advice provision unit stores information about the sneaker collection managed by the portfolio management unit and provides advice on selling or storing. The notification unit uses a generative AI to provide information tailored to the user's interests. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp trends in the sneaker market and be useful for investment and collection management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A sneaker market support system according to an embodiment of the present invention uses AI technology to support various activities in the sneaker market. This system provides four main functions: market analysis and trend forecasting, price estimation and investment advice, portfolio management, and customized notifications. This allows the sneaker market support system to efficiently and effectively support various activities in the sneaker market. For example, investors can buy and sell sneakers at the right time, and collectors can maximize the value of their collections. Users can also obtain the latest information without missing out.

[0029] A sneaker market support system according to an embodiment includes a market analysis unit, a trend prediction unit, a price estimation unit, an investment advice unit, a portfolio management unit, an advice provision unit, and a notification unit. The market analysis unit uses a generation AI to analyze sneaker market trends. For example, the generation AI analyzes past sales data and social media trends to predict the next popular sneaker. The market analysis unit uses the generation AI to perform market analysis based on data and trend information related to the sneaker market. The trend prediction unit predicts the likelihood of a specific model or brand becoming popular in the future based on the sneaker market trends analyzed by the market analysis unit. For example, the generation AI performs trend prediction using time series analysis and machine learning models. The price estimation unit uses the generation AI to evaluate the current market value of rare sneakers. For example, the generation AI analyzes auction data and transaction history to estimate the market value of sneakers. The investment advice unit suggests buying and selling times to investors based on the market value evaluated by the price estimation unit. For example, the generation AI provides investment advice based on risk assessment and investment strategy. The portfolio management unit uses the generation AI to track a collector's sneaker collection and manage the value of the entire portfolio. For example, the generation AI records the purchase date, condition, and current market value of each sneaker, updating the portfolio value in real time. The advice provision unit stores information about the sneaker collection managed by the portfolio management unit and provides advice on selling and storing them. For example, the generation AI suggests the optimal time to sell and how to store the sneakers based on their condition and market value. The notification unit uses the generation AI to provide information tailored to the user's interests. For example, the generation AI notifies the user of release information and auction information for sneakers that interest the user. This allows the sneaker market support system to efficiently and effectively support various activities in the sneaker market. For example, investors can buy and sell sneakers at the right time, and collectors can maximize the value of their collections. Users can also access the latest information without missing out.

[0030] The market analysis unit analyzes data related to sneaker designs and materials to predict the next popular design and material trends. For example, the market analysis unit collects data related to sneaker designs and materials, and the generation AI analyzes that data to identify trends. For example, it predicts the next popular design and material based on past sales data and information from fashion shows. The market analysis unit also takes user preferences and purchasing history into account when predicting design and material trends. For example, it identifies user groups for whom specific designs and materials are popular and reflects that information in the trend prediction. The market analysis unit also collects opinions from industry experts and influencers to predict design and material trends, and the generation AI analyzes that information. For example, it analyzes fashion blogs and interview articles to identify the next popular design and material. In this way, by analyzing data related to sneaker designs and materials, it is possible to predict the next popular design and material trends.

[0031] The market analysis department can analyze data for each sneaker sales region and predict trends for each region. For example, the market analysis department collects data for each sneaker sales region, and the generation AI analyzes that data to identify trends for each region. For example, it identifies brands and models that are popular in a particular region and reflects that information in trend predictions. The market analysis department also takes into account the local culture and economic situation when predicting trends for each region. For example, it predicts the next sneaker trend based on the region's economic growth and consumer purchasing power. The market analysis department also analyzes local events and seasonal demand to predict trends for each region. For example, it predicts the impact of sporting events and festivals held in a specific region on sneaker demand. In this way, by analyzing data for each sneaker sales region, it is possible to predict trends for each region.

[0032] The market analysis unit can analyze data from fashion markets other than the sneaker market to predict crossover trends. For example, the market analysis unit collects data from fashion markets other than the sneaker market, and the generation AI analyzes that data to identify crossover trends. For example, it predicts factors that will affect the sneaker market based on trends in apparel and accessories. The market analysis unit also evaluates the relevance between different markets when analyzing fashion market data. For example, if a particular design or material is popular in the apparel market, it predicts how that trend will affect the sneaker market. The market analysis unit also collects opinions from industry experts and influencers to analyze fashion market data, and the generation AI analyzes that information. For example, it analyzes fashion shows and magazine articles to identify crossover trends. This makes it possible to predict crossover trends by analyzing data from fashion markets other than the sneaker market.

[0033] The market analysis unit can combine sneaker trend predictions with trend predictions for other sports goods and accessories. For example, when predicting sneaker trends, the market analysis unit collects data on other sports goods and accessories, and the generation AI analyzes that data to identify overall trends. For example, it predicts factors that will affect the sneaker market based on trends in sportswear and bags. The market analysis unit also evaluates the relevance between different markets to combine sports goods and accessories trend predictions. For example, if a particular sports item is popular, it predicts how that trend will affect the sneaker market. The market analysis unit also collects opinions from industry experts and influencers to combine sports goods and accessories trend predictions, and the generation AI analyzes that information. For example, it analyzes sporting events and magazine articles to identify overall trends. This allows sneaker trend predictions to be combined with trend predictions for other sports goods and accessories to predict overall trends.

[0034] The price estimation unit can analyze the manufacturing and distribution costs of sneakers and reflect them in price estimation. For example, the price estimation unit collects data on the manufacturing and distribution costs of sneakers, and the generation AI analyzes that data and reflects it in price estimation. For example, the market value of sneakers is estimated based on raw material costs and manufacturing process costs. The price estimation unit also takes into account regional cost differences when analyzing manufacturing and distribution costs. For example, it evaluates the impact on price of differences in manufacturing bases and distribution channels and reflects this in price estimation. The price estimation unit also analyzes fluctuations in manufacturing and distribution costs over time and reflects this in price estimation. For example, it predicts fluctuations in raw material prices and logistics costs and uses them in price estimation. In this way, the manufacturing and distribution costs of sneakers can be analyzed and reflected in price estimation.

[0035] The price estimation unit can analyze information about sneakers' rarity and limited edition and reflect it in price estimation. For example, the price estimation unit collects data about sneakers' rarity and limited edition, and the generation AI analyzes that data and reflects it in price estimation. For example, the market value of sneakers is estimated based on the production number of limited editions and the sales region. In addition, the price estimation unit takes into account past sales data and auction data when analyzing information about rarity and limited edition. For example, it reflects data about limited edition sneakers that have been traded at high prices in the past in price estimation. In addition, the price estimation unit analyzes information about rarity and limited edition along a time axis and reflects it in price estimation. For example, it predicts price fluctuations based on the release date and sales period of the limited edition and uses this in price estimation. In this way, information about sneakers' rarity and limited edition can be analyzed and reflected in price estimation.

[0036] The price estimation unit can provide price estimates and investment advice for collector's items other than sneakers. For example, the price estimation unit collects data on collector's items other than sneakers, and the generation AI analyzes the data to provide price estimates and investment advice. For example, the unit estimates the market value of watches and bags and suggests to investors the best times to buy and sell. The price estimation unit also takes into account information about rarity and limited editions when estimating the price of collector's items. For example, the unit estimates the market value of watches and bags based on the number of limited editions produced and the sales region. The price estimation unit also analyzes past sales data and auction data to provide price estimates and investment advice for collector's items. For example, the price estimate is based on data on items that have been traded at high prices in the past. This makes it possible to provide price estimates and investment advice for collector's items other than sneakers.

[0037] The price estimation unit can combine the price estimate of sneakers with price estimates of other fashion items and accessories. For example, when estimating the price of sneakers, the price estimation unit collects data on other fashion items and accessories, and the generation AI analyzes that data to generate a comprehensive price estimate. For example, the price of sneakers is estimated based on the market value of apparel and bags. The price estimation unit also evaluates the relevance between different markets to combine price estimates of fashion items and accessories. For example, if a particular design or material is popular in the apparel market, it predicts how that trend will affect the sneaker market. The price estimation unit also collects opinions from industry experts and influencers to combine price estimates of fashion items and accessories, and the generation AI analyzes that information. For example, it analyzes fashion shows and magazine articles to generate a comprehensive price estimate. This allows the price estimate of sneakers to be combined with price estimates of other fashion items and accessories to generate a comprehensive price estimate.

[0038] The portfolio management department can analyze the storage environment and storage method of sneakers and propose the optimal storage method. For example, the portfolio management department collects data on the sneaker storage environment, and the generation AI analyzes that data to propose the optimal storage method. For example, it proposes a storage method that prevents sneakers from deteriorating based on temperature and humidity management methods. The portfolio management department also takes into account the material and design of the sneakers when analyzing the storage environment and storage method. For example, it identifies environments in which certain materials are prone to deterioration and proposes the optimal storage method based on that information. The portfolio management department also analyzes data on the storage environment and storage method over time to identify areas for improvement in the storage method. For example, it predicts seasonal fluctuations in the storage environment and proposes the optimal storage method. In this way, it is possible to propose the optimal storage method by analyzing the sneaker storage environment and storage method.

[0039] The portfolio management department can analyze sneaker maintenance information and suggest the optimal maintenance method. For example, the portfolio management department collects data on sneaker maintenance, and the generation AI analyzes that data to suggest the optimal maintenance method. For example, it suggests a maintenance method that keeps sneakers in optimal condition based on cleaning and moisturizing methods. The portfolio management department also takes into account the material and design of the sneakers when analyzing maintenance information. For example, it identifies maintenance methods that are prone to deterioration of certain materials and suggests the optimal maintenance method based on that information. The portfolio management department also analyzes maintenance information data along a time axis to identify areas for improvement in maintenance methods. For example, it predicts seasonal fluctuations in maintenance methods and suggests the optimal maintenance method. In this way, it is possible to suggest the optimal maintenance method by analyzing sneaker maintenance information.

[0040] The portfolio management unit can manage portfolios of collector's items other than sneakers. For example, the portfolio management unit collects data on collector's items other than sneakers, and the generation AI analyzes that data to manage the portfolio. For example, it manages the market value of watches and bags and provides advice to collectors on selling and storing them. The portfolio management unit also takes into account information about rarity and limited editions when managing the portfolio of collector's items. For example, it manages the market value of watches and bags based on the number of limited editions produced and the sales region. The portfolio management unit also analyzes past sales data and auction data to manage the portfolio of collector's items. For example, it reflects data on items that have been traded at high prices in the past in portfolio management. This makes it possible to manage portfolios of collector's items other than sneakers.

[0041] The portfolio management unit can combine sneaker portfolio management with portfolio management of other fashion items and accessories. For example, when managing a sneaker portfolio, the portfolio management unit collects data on other fashion items and accessories, and the generation AI analyzes that data to perform comprehensive portfolio management. For example, the sneaker portfolio is managed based on the market value of apparel and bags. The portfolio management unit also evaluates the relevance between different markets to combine portfolio management of fashion items and accessories. For example, if a particular design or material is popular in the apparel market, it predicts how that trend will affect the sneaker market. The portfolio management unit also collects opinions from industry experts and influencers to combine portfolio management of fashion items and accessories, and the generation AI analyzes that information. For example, it analyzes fashion shows and magazine articles to perform comprehensive portfolio management. This allows sneaker portfolio management to be combined with portfolio management of other fashion items and accessories to perform comprehensive portfolio management.

[0042] The notification unit can analyze a user's past purchase history and browsing history to provide optimal information. For example, the notification unit collects a user's past purchase history and browsing history, and the generation AI analyzes that data to provide optimal information. For example, related new product information is provided based on the brand and model of sneakers previously purchased. The notification unit also takes into account the user's preferences and interests when analyzing the purchase history and browsing history. For example, information on specific designs or materials is preferentially provided to users who prefer those designs or materials. The notification unit also analyzes purchase history and browsing history data along a time axis to identify areas for improvement in information provision. For example, it predicts seasonal fluctuations in purchase history and browsing history and provides optimal information. In this way, optimal information can be provided by analyzing a user's past purchase history and browsing history.

[0043] The notification unit can analyze a user's social media activity and provide information tailored to their interests. For example, the notification unit collects the user's social media posts and the accounts they follow, and the generation AI analyzes that data to provide the most appropriate information. For example, it can provide relevant new product information based on information about brands and influencers the user follows. The notification unit also takes into account the user's preferences and interests when analyzing social media activity. For example, it can provide relevant information based on posts that frequently use specific hashtags or keywords. The notification unit also analyzes social media activity data over time to identify areas for improvement in information provision. For example, it can predict how specific events or campaigns will affect the user's interests and provide the most appropriate information. In this way, it is possible to provide information tailored to the user's interests by analyzing the user's social media activity.

[0044] The notification unit can provide information on fashion items and accessories other than sneakers. For example, the notification unit collects data on fashion items and accessories other than sneakers, and the generation AI analyzes the data to provide optimal information. For example, it provides information on new apparel and bag products. The notification unit also takes into account the user's preferences and interests when providing information on fashion items and accessories. For example, it prioritizes providing information on specific designs and materials to users who prefer those designs and materials. The notification unit also analyzes information on fashion items and accessories along a time axis to identify areas for improvement in the information provided. For example, it predicts seasonal trends and the release dates of new products and provides optimal information. This makes it possible to provide information on fashion items and accessories other than sneakers.

[0045] The notification unit can combine information tailored to the user's interests with information on other hobbies and interests. For example, when providing information tailored to the user's interests, the notification unit collects data on other hobbies and interests, and the generation AI analyzes that data to provide comprehensive information. For example, information on new music and movie releases is provided. The notification unit also evaluates the relevance between different fields to combine information on hobbies and interests. For example, a user who likes a particular music genre or movie can be provided with information on fashion items related to that hobby. The notification unit also collects opinions from industry experts and influencers to combine information on hobbies and interests, and the generation AI analyzes that information. For example, music and movie reviews and interview articles are analyzed to provide comprehensive information. This allows comprehensive information to be provided by combining information tailored to the user's interests with information on other hobbies and interests.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The sneaker market support system can also analyze a user's health data to help them choose sneakers. For example, it can suggest the most suitable type of sneakers based on the user's number of steps and amount of exercise. It can also analyze the shape of the user's feet and walking patterns to recommend sneakers that put less strain on the feet. It can also suggest appropriate sneaker maintenance methods and replacement times based on the user's health condition and activity level. This allows users to choose sneakers that are best suited to their health condition and ensure a comfortable fit.

[0048] The sneaker market support system can also analyze a user's purchasing history and predict the sneakers they are likely to purchase next. For example, it can identify a user's preferred brands and designs based on past purchase data, and then use that information to suggest sneakers they are likely to purchase next. When analyzing a user's purchasing history, it can also take into account purchasing patterns according to seasons and events, predicting the sneakers they are likely to purchase at a particular time. This allows users to efficiently find sneakers that suit their preferences and lifestyle.

[0049] The sneaker market support system can also analyze users' social media activity and provide information tailored to their interests. For example, it can collect users' social media posts and the accounts they follow, and the generation AI can analyze that data to provide the most appropriate information. It can provide relevant new product information based on information about the brands and influencers the user follows. It can also provide related information based on posts that frequently use specific hashtags or keywords. This makes it possible to provide information tailored to a user's interests by analyzing their social media activity.

[0050] The sneaker market support system can also analyze a user's health data to help them choose sneakers. For example, it can suggest the most suitable type of sneakers based on the user's number of steps and amount of exercise. It can also analyze the shape of the user's feet and walking patterns to recommend sneakers that put less strain on the feet. It can also suggest appropriate sneaker maintenance methods and replacement times based on the user's health condition and activity level. This allows users to choose sneakers that are best suited to their health condition and ensure a comfortable fit.

[0051] The sneaker market support system can also analyze a user's purchasing history and predict the sneakers they are likely to purchase next. For example, it can identify a user's preferred brands and designs based on past purchase data, and then use that information to suggest sneakers they are likely to purchase next. When analyzing a user's purchasing history, it can also take into account purchasing patterns according to seasons and events, predicting the sneakers they are likely to purchase at a particular time. This allows users to efficiently find sneakers that suit their preferences and lifestyle.

[0052] The sneaker market support system can also analyze users' social media activity and provide information tailored to their interests. For example, it can collect users' social media posts and the accounts they follow, and the generation AI can analyze that data to provide the most appropriate information. It can provide relevant new product information based on information about the brands and influencers the user follows. It can also provide related information based on posts that frequently use specific hashtags or keywords. This makes it possible to provide information tailored to a user's interests by analyzing their social media activity.

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

[0054] Step 1: The market analysis department uses the generative AI to analyze trends in the sneaker market. For example, the generative AI analyzes past sales data and social media trends to predict the next trending sneakers. The market analysis department also uses the generative AI to conduct market analysis based on data and trend information on the sneaker market. Step 2: The trend forecasting unit predicts the future potential of specific models and brands based on the sneaker market trends analyzed by the market analysis unit. For example, the generative AI uses time series analysis and machine learning models to make trend predictions. Step 3: The price estimation unit uses the generation AI to estimate the current market value of the rare sneakers. For example, the generation AI analyzes auction data and transaction history to estimate the sneakers' market value. Step 4: The investment advice unit suggests buying and selling times to investors based on the market value assessed by the price estimation unit. For example, the generation AI provides investment advice based on risk assessment and investment strategy. Step 5: The Portfolio Management department uses Generative AI to track the collector's sneaker collection and manage the value of the entire portfolio. For example, Generative AI records the purchase date, condition, and current market value of each sneaker, and updates the portfolio value in real time. Step 6: The advice provider stores the information on the sneaker collection managed by the portfolio manager and provides advice on selling and storing the sneakers. For example, the generator AI suggests the best time to sell and how to store the sneakers based on their condition and market value. Step 7: The notification unit uses the generation AI to provide information tailored to the user's interests. For example, the generation AI notifies the user of sneaker release information or auction information that interests the user.

[0055] (Example 2) A sneaker market support system according to an embodiment of the present invention uses AI technology to support various activities in the sneaker market. This system provides four main functions: market analysis and trend forecasting, price estimation and investment advice, portfolio management, and customized notifications. This allows the sneaker market support system to efficiently and effectively support various activities in the sneaker market. For example, investors can buy and sell sneakers at the right time, and collectors can maximize the value of their collections. Users can also obtain the latest information without missing out.

[0056] A sneaker market support system according to an embodiment includes a market analysis unit, a trend prediction unit, a price estimation unit, an investment advice unit, a portfolio management unit, an advice provision unit, and a notification unit. The market analysis unit uses a generation AI to analyze sneaker market trends. For example, the generation AI analyzes past sales data and social media trends to predict the next popular sneaker. The market analysis unit uses the generation AI to perform market analysis based on data and trend information related to the sneaker market. The trend prediction unit predicts the likelihood of a specific model or brand becoming popular in the future based on the sneaker market trends analyzed by the market analysis unit. For example, the generation AI performs trend prediction using time series analysis and machine learning models. The price estimation unit uses the generation AI to evaluate the current market value of rare sneakers. For example, the generation AI analyzes auction data and transaction history to estimate the market value of sneakers. The investment advice unit suggests buying and selling times to investors based on the market value evaluated by the price estimation unit. For example, the generation AI provides investment advice based on risk assessment and investment strategy. The portfolio management unit uses the generation AI to track a collector's sneaker collection and manage the value of the entire portfolio. For example, the generation AI records the purchase date, condition, and current market value of each sneaker, updating the portfolio value in real time. The advice provision unit stores information about the sneaker collection managed by the portfolio management unit and provides advice on selling and storing them. For example, the generation AI suggests the optimal time to sell and how to store the sneakers based on their condition and market value. The notification unit uses the generation AI to provide information tailored to the user's interests. For example, the generation AI notifies the user of release information and auction information for sneakers that interest the user. This allows the sneaker market support system to efficiently and effectively support various activities in the sneaker market. For example, investors can buy and sell sneakers at the right time, and collectors can maximize the value of their collections. Users can also access the latest information without missing out.

[0057] The market analysis unit analyzes user emotional data and can predict the impact of emotional fluctuations on the market. For example, the market analysis unit collects emotional data from users' social media posts and reviews, and the generative AI analyzes the data to identify emotional fluctuations. For example, it identifies brands and models with increasing positive emotions and reflects their impact in market forecasts. In addition, when analyzing emotional data, the market analysis unit evaluates the impact of specific events and campaigns on user emotions. For example, it analyzes how new product launches and sales events affect user emotions and uses this information in market forecasts. In addition, the market analysis unit analyzes user emotional data over time to identify patterns of emotional fluctuations. For example, it predicts seasonal emotional fluctuations and emotional peaks at specific times and reflects this in market trends. In this way, by analyzing user emotional data, it can predict the impact of emotional fluctuations on the market.

[0058] The market analysis unit analyzes data related to sneaker designs and materials to predict the next popular design and material trends. For example, the market analysis unit collects data related to sneaker designs and materials, and the generation AI analyzes that data to identify trends. For example, it predicts the next popular design and material based on past sales data and information from fashion shows. The market analysis unit also takes user preferences and purchasing history into account when predicting design and material trends. For example, it identifies user groups for whom specific designs and materials are popular and reflects that information in the trend prediction. The market analysis unit also collects opinions from industry experts and influencers to predict design and material trends, and the generation AI analyzes that information. For example, it analyzes fashion blogs and interview articles to identify the next popular design and material. In this way, by analyzing data related to sneaker designs and materials, it is possible to predict the next popular design and material trends.

[0059] The market analysis department can analyze data for each sneaker sales region and predict trends for each region. For example, the market analysis department collects data for each sneaker sales region, and the generation AI analyzes that data to identify trends for each region. For example, it identifies brands and models that are popular in a particular region and reflects that information in trend predictions. The market analysis department also takes into account the local culture and economic situation when predicting trends for each region. For example, it predicts the next sneaker trend based on the region's economic growth and consumer purchasing power. The market analysis department also analyzes local events and seasonal demand to predict trends for each region. For example, it predicts the impact of sporting events and festivals held in a specific region on sneaker demand. In this way, by analyzing data for each sneaker sales region, it is possible to predict trends for each region.

[0060] The market analysis unit can analyze data from fashion markets other than the sneaker market to predict crossover trends. For example, the market analysis unit collects data from fashion markets other than the sneaker market, and the generation AI analyzes that data to identify crossover trends. For example, it predicts factors that will affect the sneaker market based on trends in apparel and accessories. The market analysis unit also evaluates the relevance between different markets when analyzing fashion market data. For example, if a particular design or material is popular in the apparel market, it predicts how that trend will affect the sneaker market. The market analysis unit also collects opinions from industry experts and influencers to analyze fashion market data, and the generation AI analyzes that information. For example, it analyzes fashion shows and magazine articles to identify crossover trends. This makes it possible to predict crossover trends by analyzing data from fashion markets other than the sneaker market.

[0061] The market analysis unit can combine sneaker trend predictions with trend predictions for other sports goods and accessories. For example, when predicting sneaker trends, the market analysis unit collects data on other sports goods and accessories, and the generation AI analyzes that data to identify overall trends. For example, it predicts factors that will affect the sneaker market based on trends in sportswear and bags. The market analysis unit also evaluates the relevance between different markets to combine sports goods and accessories trend predictions. For example, if a particular sports item is popular, it predicts how that trend will affect the sneaker market. The market analysis unit also collects opinions from industry experts and influencers to combine sports goods and accessories trend predictions, and the generation AI analyzes that information. For example, it analyzes sporting events and magazine articles to identify overall trends. This allows sneaker trend predictions to be combined with trend predictions for other sports goods and accessories to predict overall trends.

[0062] The market analysis unit uses the emotion estimation function to predict the characteristics of sneakers that users will want to purchase next, and can predict trends based on that information. For example, the market analysis unit uses the emotion estimation function to identify the characteristics of sneakers that users will want to purchase next. For example, it collects emotion data from users' social media posts and reviews, and the generation AI analyzes that data to predict purchase intent. In addition, when analyzing user emotion data, the market analysis unit evaluates the impact of specific designs and materials on users' purchase intent. For example, it identifies designs and materials that evoke strong positive emotions and reflects that information in trend predictions. In addition, the market analysis unit uses the emotion estimation function to analyze users' purchase intent over time and identify patterns of emotional fluctuation. For example, it predicts seasonal purchase intent and peaks in purchase intent at specific times, and reflects this in market trends. As a result, the emotion estimation function can be used to predict the characteristics of sneakers that users will want to purchase next, and trends can be predicted based on that information.

[0063] The price estimation unit can analyze user emotional data and predict the impact of emotional fluctuations on prices. For example, the price estimation unit collects emotional data from users' social media posts and reviews, and the generation AI analyzes the data to identify emotional fluctuations. For example, it predicts the likelihood of price increases for brands or models with increasing positive emotions. When analyzing the emotional data, the price estimation unit also evaluates the impact of specific events or campaigns on users' emotions. For example, it analyzes how new product launches and sales events affect users' emotions and uses this information to estimate prices. The price estimation unit also analyzes user emotional data over time to identify patterns of emotional fluctuations. For example, it predicts seasonal emotional fluctuations and emotional peaks at specific times and reflects this in price estimates. In this way, by analyzing user emotional data, it is possible to predict the impact of emotional fluctuations on prices.

[0064] The price estimation unit can analyze the manufacturing and distribution costs of sneakers and reflect them in price estimation. For example, the price estimation unit collects data on the manufacturing and distribution costs of sneakers, and the generation AI analyzes that data and reflects it in price estimation. For example, the market value of sneakers is estimated based on raw material costs and manufacturing process costs. The price estimation unit also takes into account regional cost differences when analyzing manufacturing and distribution costs. For example, it evaluates the impact on price of differences in manufacturing bases and distribution channels and reflects this in price estimation. The price estimation unit also analyzes fluctuations in manufacturing and distribution costs over time and reflects this in price estimation. For example, it predicts fluctuations in raw material prices and logistics costs and uses them in price estimation. In this way, the manufacturing and distribution costs of sneakers can be analyzed and reflected in price estimation.

[0065] The price estimation unit can analyze information about sneakers' rarity and limited edition and reflect it in price estimation. For example, the price estimation unit collects data about sneakers' rarity and limited edition, and the generation AI analyzes that data and reflects it in price estimation. For example, the market value of sneakers is estimated based on the production number of limited editions and the sales region. In addition, the price estimation unit takes into account past sales data and auction data when analyzing information about rarity and limited edition. For example, it reflects data about limited edition sneakers that have been traded at high prices in the past in price estimation. In addition, the price estimation unit analyzes information about rarity and limited edition along a time axis and reflects it in price estimation. For example, it predicts price fluctuations based on the release date and sales period of the limited edition and uses this in price estimation. In this way, information about sneakers' rarity and limited edition can be analyzed and reflected in price estimation.

[0066] The price estimation unit can provide price estimates and investment advice for collector's items other than sneakers. For example, the price estimation unit collects data on collector's items other than sneakers, and the generation AI analyzes the data to provide price estimates and investment advice. For example, the unit estimates the market value of watches and bags and suggests to investors the best times to buy and sell. The price estimation unit also takes into account information about rarity and limited editions when estimating the price of collector's items. For example, the unit estimates the market value of watches and bags based on the number of limited editions produced and the sales region. The price estimation unit also analyzes past sales data and auction data to provide price estimates and investment advice for collector's items. For example, the price estimate is based on data on items that have been traded at high prices in the past. This makes it possible to provide price estimates and investment advice for collector's items other than sneakers.

[0067] The price estimation unit can combine the price estimate of sneakers with price estimates of other fashion items and accessories. For example, when estimating the price of sneakers, the price estimation unit collects data on other fashion items and accessories, and the generation AI analyzes that data to generate a comprehensive price estimate. For example, the price of sneakers is estimated based on the market value of apparel and bags. The price estimation unit also evaluates the relevance between different markets to combine price estimates of fashion items and accessories. For example, if a particular design or material is popular in the apparel market, it predicts how that trend will affect the sneaker market. The price estimation unit also collects opinions from industry experts and influencers to combine price estimates of fashion items and accessories, and the generation AI analyzes that information. For example, it analyzes fashion shows and magazine articles to generate a comprehensive price estimate. This allows the price estimate of sneakers to be combined with price estimates of other fashion items and accessories to generate a comprehensive price estimate.

[0068] The price estimation unit uses the emotion estimation function to estimate the price of sneakers a user is considering purchasing and can provide emotion-based investment advice. The price estimation unit, for example, uses the emotion estimation function to estimate the price of sneakers a user is considering purchasing. For example, emotion data is collected from users' social media posts and reviews, and the generation AI analyzes the data to estimate the price. In addition, when analyzing the user's emotion data, the price estimation unit evaluates the impact of specific designs and materials on the user's willingness to purchase. For example, it identifies designs and materials that evoke strong positive emotions and reflects that information in the price estimation. In addition, the price estimation unit uses the emotion estimation function to analyze the user's willingness to purchase over time and identify patterns of emotional fluctuation. For example, it predicts seasonal purchasing intent or peaks in purchasing intent at specific times and reflects this in the price estimation. In this way, the emotion estimation function can estimate the price of sneakers a user is considering purchasing and provide emotion-based investment advice.

[0069] The portfolio management unit can analyze a user's emotional data and predict the impact of emotional fluctuations on the value of a collection. For example, the portfolio management unit collects emotional data from users' social media posts and reviews, and the generative AI analyzes the data to identify emotional fluctuations. For example, it predicts the likelihood that the value of sneakers with increasing positive emotions will increase. When analyzing the emotional data, the portfolio management unit also evaluates the impact of specific events and campaigns on the user's emotions. For example, it analyzes how new product launches and sales events affect the user's emotions and uses this information to manage the value of the collection. The portfolio management unit also analyzes the user's emotional data over time to identify patterns of emotional fluctuation. For example, it predicts seasonal emotional fluctuations and emotional peaks at specific times and reflects this in the value management of the collection. In this way, by analyzing the user's emotional data, it is possible to predict the impact of emotional fluctuations on the value of the collection.

[0070] The portfolio management department can analyze the storage environment and storage method of sneakers and propose the optimal storage method. For example, the portfolio management department collects data on the sneaker storage environment, and the generation AI analyzes that data to propose the optimal storage method. For example, it proposes a storage method that prevents sneakers from deteriorating based on temperature and humidity management methods. The portfolio management department also takes into account the material and design of the sneakers when analyzing the storage environment and storage method. For example, it identifies environments in which certain materials are prone to deterioration and proposes the optimal storage method based on that information. The portfolio management department also analyzes data on the storage environment and storage method over time to identify areas for improvement in the storage method. For example, it predicts seasonal fluctuations in the storage environment and proposes the optimal storage method. In this way, it is possible to propose the optimal storage method by analyzing the sneaker storage environment and storage method.

[0071] The portfolio management department can analyze sneaker maintenance information and suggest the optimal maintenance method. For example, the portfolio management department collects data on sneaker maintenance, and the generation AI analyzes that data to suggest the optimal maintenance method. For example, it suggests a maintenance method that keeps sneakers in optimal condition based on cleaning and moisturizing methods. The portfolio management department also takes into account the material and design of the sneakers when analyzing maintenance information. For example, it identifies maintenance methods that are prone to deterioration of certain materials and suggests the optimal maintenance method based on that information. The portfolio management department also analyzes maintenance information data along a time axis to identify areas for improvement in maintenance methods. For example, it predicts seasonal fluctuations in maintenance methods and suggests the optimal maintenance method. In this way, it is possible to suggest the optimal maintenance method by analyzing sneaker maintenance information.

[0072] The portfolio management unit can manage portfolios of collector's items other than sneakers. For example, the portfolio management unit collects data on collector's items other than sneakers, and the generation AI analyzes that data to manage the portfolio. For example, it manages the market value of watches and bags and provides advice to collectors on selling and storing them. The portfolio management unit also takes into account information about rarity and limited editions when managing the portfolio of collector's items. For example, it manages the market value of watches and bags based on the number of limited editions produced and the sales region. The portfolio management unit also analyzes past sales data and auction data to manage the portfolio of collector's items. For example, it reflects data on items that have been traded at high prices in the past in portfolio management. This makes it possible to manage portfolios of collector's items other than sneakers.

[0073] The portfolio management unit can combine sneaker portfolio management with portfolio management of other fashion items and accessories. For example, when managing a sneaker portfolio, the portfolio management unit collects data on other fashion items and accessories, and the generation AI analyzes that data to perform comprehensive portfolio management. For example, the sneaker portfolio is managed based on the market value of apparel and bags. The portfolio management unit also evaluates the relevance between different markets to combine portfolio management of fashion items and accessories. For example, if a particular design or material is popular in the apparel market, it predicts how that trend will affect the sneaker market. The portfolio management unit also collects opinions from industry experts and influencers to combine portfolio management of fashion items and accessories, and the generation AI analyzes that information. For example, it analyzes fashion shows and magazine articles to perform comprehensive portfolio management. This allows sneaker portfolio management to be combined with portfolio management of other fashion items and accessories to perform comprehensive portfolio management.

[0074] The portfolio management unit can use the emotion estimation function to propose a portfolio management method that will most satisfy the user. For example, the portfolio management unit uses the emotion estimation function to identify a portfolio management method that will most satisfy the user. For example, emotion data is collected from users' social media posts and reviews, and the generation AI analyzes the data to predict satisfaction. In addition, when analyzing the user's emotion data, the portfolio management unit evaluates the impact of a specific management method on the user's satisfaction. For example, it identifies management methods that generate strong positive emotions and reflects that information in portfolio management. In addition, the portfolio management unit uses the emotion estimation function to analyze user satisfaction over time and identify patterns of emotional fluctuation. For example, it predicts seasonal satisfaction levels and peaks in satisfaction at specific times, and reflects this information in portfolio management. In this way, the emotion estimation function can be used to propose a portfolio management method that will most satisfy the user.

[0075] The notification unit can analyze a user's emotional data and predict the impact of emotional fluctuations on information provision. For example, the notification unit collects emotional data from users' social media posts and reviews, and the generation AI analyzes the data to identify emotional fluctuations. For example, it prioritizes the provision of information showing an increase in positive emotions. When analyzing the emotional data, the notification unit also evaluates the impact of specific events and campaigns on the user's emotions. For example, it analyzes how new product launches and sales events affect the user's emotions and uses this information to provide information. The notification unit also analyzes the user's emotional data over time to identify patterns of emotional fluctuation. For example, it predicts seasonal emotional fluctuations and emotional peaks at specific times and reflects this in the information provided. In this way, by analyzing the user's emotional data, it is possible to predict the impact of emotional fluctuations on information provision.

[0076] The notification unit can analyze a user's past purchase history and browsing history to provide optimal information. For example, the notification unit collects a user's past purchase history and browsing history, and the generation AI analyzes that data to provide optimal information. For example, related new product information is provided based on the brand and model of sneakers previously purchased. The notification unit also takes into account the user's preferences and interests when analyzing the purchase history and browsing history. For example, information on specific designs or materials is preferentially provided to users who prefer those designs or materials. The notification unit also analyzes purchase history and browsing history data along a time axis to identify areas for improvement in information provision. For example, it predicts seasonal fluctuations in purchase history and browsing history and provides optimal information. In this way, optimal information can be provided by analyzing a user's past purchase history and browsing history.

[0077] The notification unit can analyze a user's social media activity and provide information tailored to their interests. For example, the notification unit collects the user's social media posts and the accounts they follow, and the generation AI analyzes that data to provide the most appropriate information. For example, it can provide relevant new product information based on information about brands and influencers the user follows. The notification unit also takes into account the user's preferences and interests when analyzing social media activity. For example, it can provide relevant information based on posts that frequently use specific hashtags or keywords. The notification unit also analyzes social media activity data over time to identify areas for improvement in information provision. For example, it can predict how specific events or campaigns will affect the user's interests and provide the most appropriate information. In this way, it is possible to provide information tailored to the user's interests by analyzing the user's social media activity.

[0078] The notification unit can provide information on fashion items and accessories other than sneakers. For example, the notification unit collects data on fashion items and accessories other than sneakers, and the generation AI analyzes the data to provide optimal information. For example, it provides information on new apparel and bag products. The notification unit also takes into account the user's preferences and interests when providing information on fashion items and accessories. For example, it prioritizes providing information on specific designs and materials to users who prefer those designs and materials. The notification unit also analyzes information on fashion items and accessories along a time axis to identify areas for improvement in the information provided. For example, it predicts seasonal trends and the release dates of new products and provides optimal information. This makes it possible to provide information on fashion items and accessories other than sneakers.

[0079] The notification unit can combine information tailored to the user's interests with information on other hobbies and interests. For example, when providing information tailored to the user's interests, the notification unit collects data on other hobbies and interests, and the generation AI analyzes that data to provide comprehensive information. For example, information on new music and movie releases is provided. The notification unit also evaluates the relevance between different fields to combine information on hobbies and interests. For example, a user who likes a particular music genre or movie can be provided with information on fashion items related to that hobby. The notification unit also collects opinions from industry experts and influencers to combine information on hobbies and interests, and the generation AI analyzes that information. For example, music and movie reviews and interview articles are analyzed to provide comprehensive information. This allows comprehensive information to be provided by combining information tailored to the user's interests with information on other hobbies and interests.

[0080] The notification unit can use the emotion estimation function to provide the user with the information that most interests them in real time. The notification unit, for example, uses the emotion estimation function to identify the information that most interests the user. For example, emotion data is collected from the user's social media posts and reviews, and the generation AI analyzes the data to predict interests. In addition, when analyzing the user's emotion data, the notification unit evaluates the impact of specific information on the user's interests. For example, it identifies information that evokes strong positive emotions and provides that information preferentially. In addition, the notification unit uses the emotion estimation function to analyze the user's interests over time and identify patterns of emotional fluctuation. For example, it predicts seasonal interests or peaks in interest at specific times and reflects this in the information provided. In this way, the emotion estimation function can provide the user with the information that most interests them in real time.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The sneaker market support system can also analyze a user's health data to help them choose sneakers. For example, it can suggest the most suitable type of sneakers based on the user's number of steps and amount of exercise. It can also analyze the shape of the user's feet and walking patterns to recommend sneakers that put less strain on the feet. It can also suggest appropriate sneaker maintenance methods and replacement times based on the user's health condition and activity level. This allows users to choose sneakers that are best suited to their health condition and ensure a comfortable fit.

[0083] The sneaker market support system can further analyze user sentiment data and predict the impact of sentiment fluctuations on the market. For example, sentiment data is collected from users' social media posts and reviews, and the generative AI analyzes the data to identify sentiment fluctuations. It identifies brands and models with increasing positive sentiment and reflects their impact in market forecasts. It also evaluates the impact of specific events and campaigns on user sentiment, analyzing how new product launches and sales events affect user sentiment. This makes it possible to predict the impact of sentiment fluctuations on the market.

[0084] The sneaker market support system can also analyze a user's purchasing history and predict the sneakers they are likely to purchase next. For example, it can identify a user's preferred brands and designs based on past purchase data, and then use that information to suggest sneakers they are likely to purchase next. When analyzing a user's purchasing history, it can also take into account purchasing patterns according to seasons and events, predicting the sneakers they are likely to purchase at a particular time. This allows users to efficiently find sneakers that suit their preferences and lifestyle.

[0085] The sneaker market support system can further analyze user sentiment data and predict the impact of sentiment fluctuations on prices. For example, sentiment data is collected from users' social media posts and reviews, and the generative AI analyzes the data to identify sentiment fluctuations. It predicts the likelihood of price increases for brands and models with increasing positive sentiment. It also evaluates the impact of specific events and campaigns on user sentiment, analyzing how new product launches and sales events affect user sentiment. This makes it possible to predict the impact of sentiment fluctuations on prices.

[0086] The sneaker market support system can also analyze users' social media activity and provide information tailored to their interests. For example, it can collect users' social media posts and the accounts they follow, and the generation AI can analyze that data to provide the most appropriate information. It can provide relevant new product information based on information about the brands and influencers the user follows. It can also provide related information based on posts that frequently use specific hashtags or keywords. This makes it possible to provide information tailored to a user's interests by analyzing their social media activity.

[0087] The sneaker market support system can further analyze users' emotional data and predict the impact of emotional fluctuations on the value of a collection. For example, emotional data is collected from users' social media posts and reviews, and the generative AI analyzes the data to identify emotional fluctuations. It predicts the likelihood that the value of sneakers with increasing positive emotions will increase. It also evaluates the impact of specific events and campaigns on users' emotions, analyzing how new product launches and sales events affect users' emotions. This makes it possible to predict the impact of emotional fluctuations on the value of a collection.

[0088] The sneaker market support system can also analyze a user's health data to help them choose sneakers. For example, it can suggest the most suitable type of sneakers based on the user's number of steps and amount of exercise. It can also analyze the shape of the user's feet and walking patterns to recommend sneakers that put less strain on the feet. It can also suggest appropriate sneaker maintenance methods and replacement times based on the user's health condition and activity level. This allows users to choose sneakers that are best suited to their health condition and ensure a comfortable fit.

[0089] The sneaker market support system can further analyze user emotional data and predict the impact of emotional fluctuations on information provision. For example, emotional data is collected from users' social media posts and reviews, and the generative AI analyzes the data to identify emotional fluctuations. Information with increasing positive emotions is prioritized for provision. The system also evaluates the impact of specific events and campaigns on user emotions, analyzing how new product launches and sales events affect user emotions. This makes it possible to predict the impact of emotional fluctuations on information provision.

[0090] The sneaker market support system can also analyze a user's purchasing history and predict the sneakers they are likely to purchase next. For example, it can identify a user's preferred brands and designs based on past purchase data, and then use that information to suggest sneakers they are likely to purchase next. When analyzing a user's purchasing history, it can also take into account purchasing patterns according to seasons and events, predicting the sneakers they are likely to purchase at a particular time. This allows users to efficiently find sneakers that suit their preferences and lifestyle.

[0091] The sneaker market support system can also analyze users' social media activity and provide information tailored to their interests. For example, it can collect users' social media posts and the accounts they follow, and the generation AI can analyze that data to provide the most appropriate information. It can provide relevant new product information based on information about the brands and influencers the user follows. It can also provide related information based on posts that frequently use specific hashtags or keywords. This makes it possible to provide information tailored to a user's interests by analyzing their social media activity.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The market analysis department uses the generative AI to analyze trends in the sneaker market. For example, the generative AI analyzes past sales data and social media trends to predict the next trending sneakers. The market analysis department also uses the generative AI to conduct market analysis based on data and trend information on the sneaker market. Step 2: The trend forecasting unit predicts the future potential of specific models and brands based on the sneaker market trends analyzed by the market analysis unit. For example, the generative AI uses time series analysis and machine learning models to make trend predictions. Step 3: The price estimation unit uses the generation AI to estimate the current market value of the rare sneakers. For example, the generation AI analyzes auction data and transaction history to estimate the sneakers' market value. Step 4: The investment advice unit suggests buying and selling times to investors based on the market value assessed by the price estimation unit. For example, the generation AI provides investment advice based on risk assessment and investment strategy. Step 5: The Portfolio Management department uses Generative AI to track the collector's sneaker collection and manage the value of the entire portfolio. For example, Generative AI records the purchase date, condition, and current market value of each sneaker, and updates the portfolio value in real time. Step 6: The advice provider stores the information on the sneaker collection managed by the portfolio manager and provides advice on selling and storing the sneakers. For example, the generator AI suggests the best time to sell and how to store the sneakers based on their condition and market value. Step 7: The notification unit uses the generation AI to provide information tailored to the user's interests. For example, the generation AI notifies the user of sneaker release information or auction information that interests the user.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 market analysis department that uses generative AI to analyze trends in the sneaker market, A trend prediction unit that predicts that a specific model or brand will become popular in the future based on the trends of the sneaker market analyzed by the market analysis unit; a price estimation unit that uses the generating AI to evaluate the current market value of rare sneakers; an investment advice unit that suggests buying and selling times to investors based on the market value evaluated by the price estimation unit; a portfolio management unit that uses the generating AI to track the collector's sneaker collection and manage the value of the entire portfolio; an advice providing unit that stores information about the sneaker collection managed by the portfolio management unit and provides advice regarding selling or storing the sneaker collection; a notification unit that uses the generation AI to provide information tailored to the user's interests. A system characterized by:

2. The market analysis department Analyzing the emotional data of the users and predicting the impact of emotional fluctuations on the market.

2. The system of claim 1.

3. The market analysis department Analyzing data related to the design and materials of the sneakers and predicting the next popular trend of the design or material 2. The system of claim 1.

4. The market analysis department Analyzing data on the sales of the sneakers by region and predicting trends by region 2. The system of claim 1.

5. The market analysis department Analyzing data from fashion markets other than the sneaker market and predicting crossover trends 2. The system of claim 1.

Images