System

Abstract

An object of a system according to an embodiment is to perform authenticity determination, state determination, and value prediction of a trading card, and to allow a purchaser to automatically make a successful bid for a card satisfying desired conditions.SOLUTION: A system according to an embodiment includes an authenticity determination part, a state determination part, a value prediction part, and an automatic successful bidder. The authenticity determination unit analyzes the image of the trading card and determines authenticity. The state determination unit determines a state of the trading card. The value prediction unit predicts a market value of the trading card. The automatic successful bidder automatically makes a successful bid based on the desired conditions of the purchaser.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 makes it difficult to determine the authenticity, condition, and value of trading cards, making it difficult for buyers to find cards that meet their desired conditions.

[0005] The system according to the embodiment aims to determine the authenticity and condition of trading cards, as well as predict their value, and to automatically place a winning bid on cards that meet the buyer's desired conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes an authenticity determination unit, a condition determination unit, a value prediction unit, and an automatic bidding unit. The authenticity determination unit analyzes images of trading cards to determine their authenticity. The condition determination unit determines the condition of the trading card. The value prediction unit predicts the market value of the trading card. The automatic bidding unit automatically makes a successful bid based on the purchaser's desired conditions. [Effects of the Invention]

[0007] The system according to the embodiment determines the authenticity and condition of trading cards, and predicts their value, allowing purchasers to automatically bid on cards that meet their desired conditions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A trading card purchase support system according to an embodiment of the present invention is a system that solves the problems that arise when purchasing trading cards on a flea market app. This system utilizes AI technology to determine the authenticity and condition of cards at the time of listing and predict their expected value. It also provides an AI-based automatic bidding function at the time of purchase. This allows the trading card purchase support system to efficiently find cards that meet the user's desired conditions and purchase them at a fair price.

[0029] A trading card purchase support system according to an embodiment includes an authenticity determination unit, a condition determination unit, a value prediction unit, and an automatic bidding unit. The authenticity determination unit analyzes images of trading cards to determine their authenticity. For example, the generation AI analyzes images of cards to determine whether the cards are genuine or fake. The generation AI uses image analysis technology to detect features on the card's surface and fine patterns in the printing, and determines its authenticity based on the results. The condition determination unit determines the condition of the trading card. For example, the generation AI analyzes images of cards to determine their condition, such as scratches, stains, and creases. The generation AI uses image analysis technology to detect scratches and stains on the card's surface and evaluate its condition. The value prediction unit predicts the market value of the trading card. For example, the generation AI predicts the market value based on the results of the card's authenticity determination and condition determination. The generation AI refers to past transaction data and market trends to predict the card's value. The automatic bidding unit automatically makes a successful bid based on the buyer's desired conditions. For example, the generation AI searches a flea market app based on the buyer's desired conditions and automatically bids on cards that meet the conditions. The generation AI receives the buyer's desired conditions as prompts and automatically bids on cards based on those conditions. This allows the trading card purchase support system according to the embodiment to consistently perform authenticity determination, condition assessment, value prediction, and automatic bidding on trading cards. For example, users can efficiently find cards that meet their desired conditions and purchase them at a fair price. Furthermore, because the authenticity determination and condition assessment are performed by AI, the risk of purchasing a counterfeit product is reduced, allowing users to trade with peace of mind.

[0030] The authenticity determination unit can analyze the material or printing technology of the card to improve the accuracy of authenticity determination. For example, the authenticity determination unit not only has the generation AI analyze the image of the card, but also analyzes the material and printing technology of the card in detail. For example, the generation AI detects the texture of the card's surface and the fine patterns of the printing, and determines authenticity based on that. The generation AI improves the accuracy of authenticity determination by analyzing the material and printing technology of the card. In this way, the accuracy of authenticity determination is improved by analyzing the material and printing technology of the card.

[0031] The authenticity determination unit can use the transaction data as auxiliary information for authenticity determination by evaluating the reliability of specific sellers. In the authenticity determination unit, for example, the generation AI analyzes past transaction data and evaluates the reliability of specific sellers. For example, the generation AI may rate sellers who have a history of listing counterfeit items as less reliable. The generation AI refers to the transaction data and evaluates the reliability of sellers, thereby using the data as auxiliary information for authenticity determination. In this way, the reliability of authenticity determination is improved by evaluating the reliability of sellers.

[0032] The condition assessment unit can analyze images of a card from multiple angles and make a three-dimensional condition assessment. For example, the generation AI takes images of a card from multiple angles and makes a three-dimensional condition assessment. For example, the generation AI analyzes the condition of not only the front of the card, but also the sides and back. The generation AI analyzes images of the card from multiple angles and makes a three-dimensional condition assessment. This allows for a more accurate condition assessment by assessing the card's condition three-dimensionally.

[0033] The condition assessment unit can analyze not only the front of the card, but also the back or sides, to perform a comprehensive condition assessment. For example, the generation AI analyzes images of not only the front of the card, but also the back and sides, to perform a comprehensive condition assessment. For example, the generation AI detects scratches and stains on the back of the card. The generation AI analyzes not only the front of the card, but also the back and sides, to perform a comprehensive condition assessment. This allows for a more accurate condition assessment by evaluating the overall condition of the card.

[0034] The value prediction unit can reflect not only transaction data but also market trends or demand in real time when predicting market value. For example, when the generation AI predicts the market value of a card, the value prediction unit not only references past transaction data but also analyzes current market trends in real time. For example, the generation AI predicts value based on the latest transaction prices and fluctuations in demand. When predicting the market value of a card, the generation AI reflects not only transaction data but also market trends and demand in real time. This enables more accurate value predictions by reflecting market trends and demand in real time.

[0035] The value prediction unit can take into account rarity or limited edition information when making a value prediction. For example, the value prediction unit takes into account the rarity of a card when the generation AI makes a value prediction for a card. For example, the generation AI predicts a high value for cards with a low number of copies issued or limited edition cards. The generation AI takes into account rarity and limited edition information when making a value prediction for a card. In this way, by taking into account the rarity and limited edition information of the card, more accurate value predictions are possible.

[0036] The automatic bidding unit can learn the purchase history and preferences of the buyer and automatically recommend the most suitable card. For example, the automatic bidding unit uses a generation AI to analyze the buyer's past purchase history and learn their preferences. For example, the generation AI will recommend new cards of a particular series or character to a buyer who likes cards from that series or character. The generation AI learns the buyer's purchase history and preferences and automatically recommends the most suitable card. This improves buyer satisfaction by recommending the most suitable card based on the buyer's preferences.

[0037] The automatic bidding unit can adjust the buyer's budget or desired conditions in real time and automatically place a winning bid at the optimal timing. In the automatic bidding unit, for example, the generation AI analyzes the buyer's budget and desired conditions in real time and automatically places a winning bid at the optimal timing. For example, the generation AI will automatically place a winning bid when it finds a card that best meets the buyer's conditions within the buyer's budget. The generation AI adjusts the buyer's budget and desired conditions in real time and automatically places a winning bid at the optimal timing. This improves buyer satisfaction by automatically placing a winning bid at the optimal timing based on the buyer's budget and desired conditions.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The trading card purchase support system can further include a preference learning unit that learns user preferences. The preference learning unit analyzes the user's past purchase history and browsing history to learn the user's preferences. For example, if a user likes cards from a particular series or character, new cards from that series or character can be recommended. Furthermore, by learning the user's preferences, it is possible to preferentially display cards that the user is likely to be interested in. This improves user satisfaction by recommending the most suitable cards based on the user's preferences.

[0040] The trading card purchase support system can further include a prediction unit that predicts future purchases based on the user's purchase history. The prediction unit analyzes the user's past purchase history and market trends to predict which cards the user is likely to purchase next. For example, a user who collects cards from a particular series can be notified when a new card from that series is released. The prediction unit can also monitor price fluctuations of cards the user is likely to purchase and encourage the user to purchase at the optimal time. This improves user satisfaction by predicting future purchases based on the user's purchase history and providing notifications and suggestions at the appropriate time.

[0041] The trading card purchase support system can further include a value prediction unit that predicts future value based on the user's purchase history and market trends. The value prediction unit analyzes the user's past purchase history and market trends to predict future value. For example, if it is predicted that the value of a particular series of cards will increase in the future, this information can be provided to the user. The value prediction unit can also predict the future value of a card that the user is considering purchasing and provide this information as reference information for the purchase. This allows the user to consider future value when making a purchase.

[0042] The trading card purchase support system can further include a timing optimization unit that optimizes the timing of purchases based on the user's purchase history and market trends. The timing optimization unit analyzes the user's past purchase history and market trends to predict the optimal purchase timing. For example, it can predict when the price of a particular card will drop and provide that information to the user. The timing optimization unit can also predict the optimal purchase timing for a card that the user is planning to purchase and provide that information as reference for the purchase. This allows the user to make a purchase at the optimal time.

[0043] The trading card purchase support system can further include a risk assessment unit that assesses the risk of a purchase based on the user's purchase history and market trends. The risk assessment unit analyzes the user's past purchase history and market trends to assess the risk of a purchase. For example, if there is a high risk that a particular card is counterfeit, this information can be provided to the user. The risk assessment unit can also assess the risk of a card the user is about to purchase and provide this information as reference information for the purchase. This allows the user to consider the risk when making a purchase.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The authenticity determination unit analyzes the image of the trading card and determines its authenticity. The generation AI analyzes the image of the card and determines whether the card is genuine or fake. Image analysis technology is used to detect the surface features of the card and the fine patterns in the printing, and authenticity is determined based on this. Step 2: The condition assessment unit determines the condition of the trading card. The generation AI analyzes the image of the card and determines its condition, such as scratches, dirt, and creases. Using image analysis technology, it detects scratches and dirt on the surface of the card and evaluates its condition. Step 3: The value prediction unit predicts the market value of the trading card. The generation AI predicts the market value based on the results of the card's authenticity and condition assessment. It also references past transaction data and market trends to predict the card's value. Step 4: The automatic bidding unit automatically places a winning bid based on the buyer's desired conditions. The generation AI searches the flea market app based on the buyer's desired conditions and automatically places a winning bid for cards that match the conditions. It receives the buyer's desired conditions as a prompt and automatically places a winning bid based on those conditions.

[0046] (Example 2) A trading card purchase support system according to an embodiment of the present invention is a system that solves the problems that arise when purchasing trading cards on a flea market app. This system utilizes AI technology to determine the authenticity and condition of cards at the time of listing and predict their expected value. It also provides an AI-based automatic bidding function at the time of purchase. This allows the trading card purchase support system to efficiently find cards that meet the user's desired conditions and purchase them at a fair price.

[0047] A trading card purchase support system according to an embodiment includes an authenticity determination unit, a condition determination unit, a value prediction unit, and an automatic bidding unit. The authenticity determination unit analyzes images of trading cards to determine their authenticity. For example, the generation AI analyzes images of cards to determine whether the cards are genuine or fake. The generation AI uses image analysis technology to detect features on the card's surface and fine patterns in the printing, and determines its authenticity based on the results. The condition determination unit determines the condition of the trading card. For example, the generation AI analyzes images of cards to determine their condition, such as scratches, stains, and creases. The generation AI uses image analysis technology to detect scratches and stains on the card's surface and evaluate its condition. The value prediction unit predicts the market value of the trading card. For example, the generation AI predicts the market value based on the results of the card's authenticity determination and condition determination. The generation AI refers to past transaction data and market trends to predict the card's value. The automatic bidding unit automatically makes a successful bid based on the buyer's desired conditions. For example, the generation AI searches a flea market app based on the buyer's desired conditions and automatically bids on cards that meet the conditions. The generation AI receives the buyer's desired conditions as prompts and automatically bids on cards based on those conditions. This allows the trading card purchase support system according to the embodiment to consistently perform authenticity determination, condition assessment, value prediction, and automatic bidding on trading cards. For example, users can efficiently find cards that meet their desired conditions and purchase them at a fair price. Furthermore, because the authenticity determination and condition assessment are performed by AI, the risk of purchasing a counterfeit product is reduced, allowing users to trade with peace of mind.

[0048] The authenticity determination unit can analyze the material or printing technology of the card to improve the accuracy of authenticity determination. For example, the authenticity determination unit not only has the generation AI analyze the image of the card, but also analyzes the material and printing technology of the card in detail. For example, the generation AI detects the texture of the card's surface and the fine patterns of the printing, and determines authenticity based on that. The generation AI improves the accuracy of authenticity determination by analyzing the material and printing technology of the card. In this way, the accuracy of authenticity determination is improved by analyzing the material and printing technology of the card.

[0049] The authenticity determination unit can use the transaction data as auxiliary information for authenticity determination by evaluating the reliability of specific sellers. In the authenticity determination unit, for example, the generation AI analyzes past transaction data and evaluates the reliability of specific sellers. For example, the generation AI may rate sellers who have a history of listing counterfeit items as less reliable. The generation AI refers to the transaction data and evaluates the reliability of sellers, thereby using the data as auxiliary information for authenticity determination. In this way, the reliability of authenticity determination is improved by evaluating the reliability of sellers.

[0050] The authenticity determination unit can use the emotion estimation function to analyze emotions from the seller's description or comments, thereby complementing the reliability of the authenticity determination. In the authenticity determination unit, for example, the generation AI analyzes the seller's description and comments and analyzes emotions using the emotion estimation function. For example, if the seller is describing the card with confidence, the generation AI increases the reliability of the authenticity determination of the card. The generation AI uses the emotion estimation function to analyze emotions from the seller's description and comments, thereby complementing the reliability of the authenticity determination. In this way, by analyzing the seller's emotions, the reliability of the authenticity determination is improved.

[0051] The condition assessment unit can analyze images of a card from multiple angles and make a three-dimensional condition assessment. For example, the generation AI takes images of a card from multiple angles and makes a three-dimensional condition assessment. For example, the generation AI analyzes the condition of not only the front of the card, but also the sides and back. The generation AI analyzes images of the card from multiple angles and makes a three-dimensional condition assessment. This allows for a more accurate condition assessment by assessing the card's condition three-dimensionally.

[0052] The condition assessment unit can analyze not only the front of the card, but also the back or sides, to perform a comprehensive condition assessment. For example, the generation AI analyzes images of not only the front of the card, but also the back and sides, to perform a comprehensive condition assessment. For example, the generation AI detects scratches and stains on the back of the card. The generation AI analyzes not only the front of the card, but also the back and sides, to perform a comprehensive condition assessment. This allows for a more accurate condition assessment by evaluating the overall condition of the card.

[0053] The condition determination unit can use the emotion estimation function to analyze emotions from the seller's description and comments, thereby complementing the reliability of the condition determination. In the condition determination unit, for example, the generation AI analyzes the seller's description and comments, and analyzes emotions using the emotion estimation function. For example, if the seller is confident in their description, the generation AI increases the reliability of the condition determination of that card. The generation AI uses the emotion estimation function to analyze emotions from the seller's description and comments, thereby complementing the reliability of the condition determination. In this way, by analyzing the seller's emotions, the reliability of the condition determination is improved.

[0054] The value prediction unit can reflect not only transaction data but also market trends or demand in real time when predicting market value. For example, when the generation AI predicts the market value of a card, the value prediction unit not only references past transaction data but also analyzes current market trends in real time. For example, the generation AI predicts value based on the latest transaction prices and fluctuations in demand. When predicting the market value of a card, the generation AI reflects not only transaction data but also market trends and demand in real time. This enables more accurate value predictions by reflecting market trends and demand in real time.

[0055] The value prediction unit can take into account rarity or limited edition information when making a value prediction. For example, the value prediction unit takes into account the rarity of a card when the generation AI makes a value prediction for a card. For example, the generation AI predicts a high value for cards with a low number of copies issued or limited edition cards. The generation AI takes into account rarity and limited edition information when making a value prediction for a card. In this way, by taking into account the rarity and limited edition information of the card, more accurate value predictions are possible.

[0056] The value prediction unit can use the emotion estimation function to analyze emotions from buyer reviews or ratings and complement the reliability of the value prediction. In the value prediction unit, for example, the generation AI analyzes buyer reviews and ratings and analyzes emotions using the emotion estimation function. For example, if there are many positive reviews, the generation AI increases the reliability of the value prediction of that card. The generation AI uses the emotion estimation function to analyze emotions from buyer reviews and ratings and complement the reliability of the value prediction. In this way, by analyzing buyer emotions, the reliability of the value prediction is improved.

[0057] The automatic bidding unit can learn the purchase history and preferences of the buyer and automatically recommend the most suitable card. For example, the automatic bidding unit uses a generation AI to analyze the buyer's past purchase history and learn their preferences. For example, the generation AI will recommend new cards of a particular series or character to a buyer who likes cards from that series or character. The generation AI learns the buyer's purchase history and preferences and automatically recommends the most suitable card. This improves buyer satisfaction by recommending the most suitable card based on the buyer's preferences.

[0058] The automatic bidding unit can adjust the buyer's budget or desired conditions in real time and automatically place a winning bid at the optimal timing. In the automatic bidding unit, for example, the generation AI analyzes the buyer's budget and desired conditions in real time and automatically places a winning bid at the optimal timing. For example, the generation AI will automatically place a winning bid when it finds a card that best meets the buyer's conditions within the buyer's budget. The generation AI adjusts the buyer's budget and desired conditions in real time and automatically places a winning bid at the optimal timing. This improves buyer satisfaction by automatically placing a winning bid at the optimal timing based on the buyer's budget and desired conditions.

[0059] The automatic bidding unit can use the emotion estimation function to analyze the emotions of the buyer and automatically place a winning bid when their desire to purchase is at its peak. In the automatic bidding unit, for example, the generation AI analyzes the emotions of the buyer in real time and automatically places a winning bid when their desire to purchase is at its peak. For example, the generation AI automatically places a winning bid when the buyer shows excitement or anticipation. The generation AI uses the emotion estimation function to analyze the emotions of the buyer and automatically place a winning bid when their desire to purchase is at its peak. In this way, by analyzing the emotions of the buyer, it is possible to automatically place a winning bid when their desire to purchase is at its peak.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The trading card purchase support system can further include a preference learning unit that learns user preferences. The preference learning unit analyzes the user's past purchase history and browsing history to learn the user's preferences. For example, if a user likes cards from a particular series or character, new cards from that series or character can be recommended. Furthermore, by learning the user's preferences, it is possible to preferentially display cards that the user is likely to be interested in. This improves user satisfaction by recommending the most suitable cards based on the user's preferences.

[0062] The trading card purchase support system may further include a suggestion unit that estimates the user's emotions and makes suggestions to increase the user's willingness to purchase based on the estimated emotions. The suggestion unit analyzes the user's emotions and, for example, when the user shows excitement or anticipation, can suggest related cards or limited edition cards. Also, when the user is depressed, it can provide special discount or campaign information. In this way, appropriate suggestions based on the user's emotions can increase the user's willingness to purchase.

[0063] The trading card purchase support system can further include a prediction unit that predicts future purchases based on the user's purchase history. The prediction unit analyzes the user's past purchase history and market trends to predict which cards the user is likely to purchase next. For example, a user who collects cards from a particular series can be notified when a new card from that series is released. The prediction unit can also monitor price fluctuations of cards the user is likely to purchase and encourage the user to purchase at the optimal time. This improves user satisfaction by predicting future purchases based on the user's purchase history and providing notifications and suggestions at the appropriate time.

[0064] The trading card purchase support system may further include a follow-up unit that estimates the user's emotions and performs post-purchase follow-up based on the estimated emotions. The follow-up unit analyzes the user's emotions and, for example, if the user is satisfied with a purchase, can suggest related cards or accessories. Also, if the user is dissatisfied with a purchase, it can guide the user through return or exchange procedures. In this way, appropriate follow-up based on the user's emotions can improve user satisfaction.

[0065] The trading card purchase support system can further include a value prediction unit that predicts future value based on the user's purchase history and market trends. The value prediction unit analyzes the user's past purchase history and market trends to predict future value. For example, if it is predicted that the value of a particular series of cards will increase in the future, this information can be provided to the user. The value prediction unit can also predict the future value of a card that the user is considering purchasing and provide this information as reference information for the purchase. This allows the user to consider future value when making a purchase.

[0066] The trading card purchasing support system may further include an optimization unit that estimates the user's emotions and optimizes the purchasing process based on the estimated emotions. The optimization unit analyzes the user's emotions and, for example, if the user is feeling stressed, may make suggestions to simplify the purchasing process. Also, if the user is enjoying themselves, it may make suggestions for related cards and accessories. In this way, optimizing the purchasing process based on the user's emotions can improve user satisfaction.

[0067] The trading card purchase support system can further include a timing optimization unit that optimizes the timing of purchases based on the user's purchase history and market trends. The timing optimization unit analyzes the user's past purchase history and market trends to predict the optimal purchase timing. For example, it can predict when the price of a particular card will drop and provide that information to the user. The timing optimization unit can also predict the optimal purchase timing for a card that the user is planning to purchase and provide that information as reference for the purchase. This allows the user to make a purchase at the optimal time.

[0068] The trading card purchase support system may further include a support unit that estimates the user's emotions and provides post-purchase support based on the estimated emotions. The support unit analyzes the user's emotions and, for example, if the user is satisfied with a purchase, can suggest related cards or accessories. Furthermore, if the user is dissatisfied with a purchase, the support unit can guide the user through return or exchange procedures. This allows for appropriate support based on the user's emotions, thereby improving user satisfaction.

[0069] The trading card purchase support system can further include a risk assessment unit that assesses the risk of a purchase based on the user's purchase history and market trends. The risk assessment unit analyzes the user's past purchase history and market trends to assess the risk of a purchase. For example, if there is a high risk that a particular card is counterfeit, this information can be provided to the user. The risk assessment unit can also assess the risk of a card the user is about to purchase and provide this information as reference information for the purchase. This allows the user to consider the risk when making a purchase.

[0070] The trading card purchase support system can further include a risk assessment unit that estimates the user's emotions and assesses the risk of the purchase based on the estimated emotions. The risk assessment unit analyzes the user's emotions, and, for example, if the user feels anxious, can provide a detailed explanation of the risks of the purchase. Alternatively, if the user feels confident, the risk can be explained in a simplified manner. In this way, by assessing the risks of the purchase based on the user's emotions, user satisfaction can be improved.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The authenticity determination unit analyzes the image of the trading card and determines its authenticity. The generation AI analyzes the image of the card and determines whether the card is genuine or fake. Image analysis technology is used to detect the surface features of the card and the fine patterns in the printing, and authenticity is determined based on this. Step 2: The condition assessment unit determines the condition of the trading card. The generation AI analyzes the image of the card and determines its condition, such as scratches, dirt, and creases. Using image analysis technology, it detects scratches and dirt on the surface of the card and evaluates its condition. Step 3: The value prediction unit predicts the market value of the trading card. The generation AI predicts the market value based on the results of the card's authenticity and condition assessment. It also references past transaction data and market trends to predict the card's value. Step 4: The automatic bidding unit automatically places a winning bid based on the buyer's desired conditions. The generation AI searches the flea market app based on the buyer's desired conditions and automatically places a winning bid for cards that match the conditions. It receives the buyer's desired conditions as a prompt and automatically places a winning bid based on those conditions.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. It is a system equipped with generative AI, The generated AI is an authenticity determination unit that analyzes images of trading cards and determines their authenticity; a state determination unit that determines the state of the trading card; a value prediction unit that predicts the market value of the trading card; an automatic bidding unit that automatically makes a successful bid based on the purchaser's desired conditions; A system characterized by:

2. The authenticity determination unit Analyzing card materials or printing techniques to improve the accuracy of authenticity determination 2. The system of claim 1.

3. The state determination unit Analyzes card images from multiple angles to determine their condition in three dimensions 2. The system of claim 1.

4. The value prediction unit Reflecting real-time market trends or demand, not just transaction data, when forecasting said market value.

2. The system of claim 1.

5. The automatic bidding unit Learn the purchase history and preferences of the buyer and automatically recommend the most suitable card 2. The system of claim 1.

6. The authenticity determination unit Analyzing sentiment from seller descriptions or comments to enhance the reliability of authenticity assessment 2. The system of claim 1.

Images