system

Abstract

The system according to this embodiment aims to streamline the management of trading card collections and assist in obtaining missing cards. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a supply unit. The collection unit scans the cards owned by the collector and registers them in a database. The analysis unit analyzes the data collected by the collection unit and identifies missing cards. The supply unit advises on how to obtain the missing cards identified by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the collection management of trading cards is troublesome and it is difficult to efficiently identify and obtain the missing cards.

[0005] The system according to the embodiment aims to improve the collection management of trading cards and assist in obtaining the missing cards.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a supply unit. The collection unit scans the cards owned by the collector and registers them in a database. The analysis unit analyzes the data collected by the collection unit and identifies missing cards. The supply unit advises on how to obtain the missing cards identified by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the management of trading card collections and assist in obtaining missing cards. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment]<​​​​​​​​​​​​The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent system functioning as a cloud-based smartphone and personal computer application according to an embodiment of the present invention is a system that supports paper-based trading card collections. This system constantly monitors the collector's current card holdings, analyzes online card markets using AI, and provides advice on acquiring missing cards. This allows collectors to efficiently complete their collections. For example, to constantly monitor the collector's current card holdings, the system uses the smartphone camera to scan physical cards and automatically registers them in a database. The AI ​​analyzes and manages detailed card information (series, quantity, condition, etc.). This allows collectors to easily manage their collections and accurately track their current card holdings. Next, the AI ​​periodically scans online card markets to analyze the price and availability of missing cards. For example, by collecting and analyzing data from auction sites, online shops, forums, etc., the AI ​​can grasp market prices and availability in real time. This allows collectors to know the best timing for buying and trading. Furthermore, the system also includes a function to support card trading between app users. It matches users' wish lists with available lists, facilitating secure transactions. This enables safe and efficient card trading within a verified community. The app also provides information on trade shows and offline events for collectors, and assists with participant pre-registration and event support. This revitalizes community activity and promotes real-world interaction. Furthermore, it includes a feature that automatically notifies users when rare cards appear on the market and recommends immediate purchase or exchange action. This prevents users from missing out on rare cards and allows for quick action. Finally, it includes a feature that regularly evaluates the market value of owned cards, allowing users to understand their asset value. It also partners with insurance companies as needed to support card insurance, making asset management of card collections easier.In this way, AI agents can be used to help collectors efficiently complete their collections and provide a platform to connect with the community. The AI ​​agent system, functioning as a cloud-based smartphone and personal computer app, can efficiently manage the collector's card holdings and advise on how to acquire missing cards.

[0029] The AI ​​agent system, functioning as a cloud-based smartphone and personal computer application according to this embodiment, comprises a collection unit, an analysis unit, and a provision unit. The collection unit scans the collector's cards and registers them in a database. For example, the collection unit scans physical cards using a smartphone camera and automatically registers them in the database. The collection unit analyzes and manages detailed card information (series, number, condition, etc.). For example, the collection unit analyzes the card's series name, number, and condition (e.g., scratches or stains) and registers them in the database. The collection unit can use AI to analyze and manage detailed card information. The analysis unit analyzes the data collected by the collection unit and identifies missing cards. For example, the analysis unit periodically scans online card markets and analyzes the price and availability of missing cards. The analysis unit collects data from auction sites, online shops, forums, etc., and the AI ​​analyzes it to grasp market prices and availability in real time. For example, the analysis unit collects data from auction sites and the AI ​​analyzes it to grasp market prices and availability in real time. The analysis unit can use AI to scan online card marketplaces and analyze the price and availability of missing cards. The supply unit advises on how to obtain the missing cards identified by the analysis unit. The supply unit supports, for example, card exchanges between users. The supply unit matches users' wish lists with their offering lists to facilitate secure transactions. The supply unit supports secure and efficient card transactions within a verified community. For example, the supply unit matches users' wish lists with their offering lists to facilitate secure transactions. The supply unit can use AI to support card exchanges between users. As a result, the AI ​​agent system, functioning as a cloud-based smartphone and personal computer application according to the embodiment, can efficiently manage a collector's card holdings and advise on how to obtain missing cards.

[0030] The collection unit scans cards owned by collectors and registers them in a database. For example, the collection unit can use a smartphone camera to scan physical cards and automatically register them in the database. Specifically, it uses a smartphone camera to capture an image of the card and extracts detailed information using image recognition technology. This detailed information includes the series name, the number of cards, and the card's condition (e.g., scratches or stains). This information is analyzed using AI and registered in the database. For example, the card's series name is automatically extracted from the card's design and text information using image recognition technology. The number of cards is identified from the card's design and serial number. The card's condition is assessed by detecting scratches and stains on the card's surface using image analysis technology. This allows the collection unit to efficiently collect detailed information on collectors' cards and register it in the database. Furthermore, the collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and accessed by the analysis and provision departments. Additionally, adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes data collected by the collection department to identify missing cards. Specifically, based on the collected card data, it grasps the overall picture of a collector's collection and identifies which cards are missing. The analysis department uses AI to analyze the collected data and understand the patterns and trends of collectors' collections. For example, if a collector is collecting cards from a specific series, it can identify missing cards within that series. Furthermore, the analysis department regularly scans the online card market to analyze the price and availability of missing cards. Specifically, it collects data from auction sites, online shops, forums, etc., and uses AI to analyze it to understand market prices and availability in real time. For example, by collecting data from auction sites and using AI to analyze it, it can understand market prices and availability in real time. This allows the analysis department to efficiently identify cards that collectors are missing and suggest ways to acquire them. In addition, the analysis department can use historical data and statistical information to predict long-term market trends and price fluctuations. This provides collectors with information that will be useful when planning future card acquisitions.

[0032] The supply department advises users on how to obtain missing cards identified by the analysis department. Specifically, it supports card exchanges between users. The supply department matches users' wish lists with available lists to facilitate secure transactions. For example, the supply department registers lists of cards users want and cards they can offer in a database and uses AI to perform optimal matching. This allows users to exchange cards efficiently. Furthermore, the supply department supports safe and efficient card transactions within a verified community. Specifically, it verifies users' personal information and provides a secure transaction environment. For example, during transactions, users can choose reliable trading partners by referring to user ratings and transaction history. The supply department also monitors the progress of transactions in real time and can respond quickly if problems arise. In this way, the supply department can safely and efficiently support card exchanges between users and help collectors obtain missing cards. In addition, the supply department can collect user feedback and use it to improve the service. For example, based on post-transaction ratings and comments, the supply department considers measures to improve the quality of the service. In this way, the supply department can provide better service to users and increase satisfaction.

[0033] The data collection unit can scan physical cards using a smartphone camera and register them in a database. For example, the data collection unit can scan a physical card using a smartphone camera and save it as image data. Then, the data collection unit uses AI to analyze the image data and register the card's detailed information in a database. The data collection unit can scan physical cards using a smartphone camera, analyze the image data using AI, and register the card's detailed information in a database. This makes it easy to scan physical cards and register them in a database using a smartphone camera. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data acquired with a smartphone camera into a generating AI and have the generating AI generate detailed card information from the image data.

[0034] The analysis department can scan online card marketplaces and analyze the price and availability of missing cards. For example, the analysis department can periodically scan online card marketplaces, collect data from auction sites, online shops, forums, etc., and have AI analyze it to understand market prices and availability in real time. The analysis department can scan online card marketplaces and use AI to analyze the price and availability of missing cards. This allows the analysis department to understand the price and availability of missing cards by periodically scanning online card marketplaces. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input data from online card marketplaces into a generating AI, which can analyze the data to identify the price and availability of missing cards.

[0035] The service provider can assist users in exchanging cards with each other. For example, the service provider can match users' wish lists with their offering lists to facilitate secure transactions. The service provider can support secure and efficient card transactions within a verified community. The service provider can use AI to assist users in exchanging cards with each other. This enables secure and efficient card transactions by supporting card exchanges between users. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user wish list and offering list data into a generating AI, which can then analyze the data and perform matching.

[0036] The service provider can provide information on trade shows and offline events, and assist participants with pre-registration and during the events. For example, the service provider can provide information on trade shows and offline events and support participants with pre-registration. The service provider can assist participants during the events to ensure they can participate smoothly. The service provider can use AI to provide information on trade shows and offline events and assist participants with pre-registration and during the events. By providing information on trade shows and offline events, community activities will be revitalized and real-world interaction will be promoted. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information on trade shows and offline events into a generating AI, which can then analyze the information and provide it to participants.

[0037] The supply unit can notify when a rare card appears on the market and immediately recommend purchasing or trading. For example, the supply unit can automatically notify when a rare card appears on the market and recommend purchasing or trading. The supply unit can use AI to notify when a rare card appears on the market and immediately recommend purchasing or trading. This prevents rare cards from being missed and enables a quick response. Some or all of the above processing in the supply unit may be performed using AI or not. For example, the supply unit can input rare card market data into a generating AI, which can analyze the data and notify when a rare card appears on the market.

[0038] The supply unit can evaluate the market value of owned cards and understand their value as assets. For example, the supply unit can periodically evaluate the market value of owned cards and understand their value as assets. The supply unit can use AI to evaluate the market value of owned cards and understand their value as assets. This makes asset management of card collections easier by periodically evaluating the market value of owned cards. Some or all of the above processes in the supply unit may be performed using AI or not. For example, the supply unit can input market data of owned cards into a generating AI, and the generating AI can analyze the data and evaluate the market value.

[0039] The data collection unit can detect the condition of a card during scanning and register it in a database. For example, the data collection unit can use AI to detect whether there are scratches on the surface of the card during scanning and register it in the database. The data collection unit can use AI to detect dirt on the card during scanning and record its condition in the database. The data collection unit can use AI to detect wear on the edges of the card during scanning and register detailed condition information in the database. The data collection unit can use AI to detect the condition of a card during scanning and register it in the database. This makes card management easier by automatically detecting the condition of the card and registering it in the database. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to detect the condition of the card and register it in the database.

[0040] The data collection unit can identify the card series and edition during scanning and register them in the database. For example, the data collection unit can use AI to automatically identify the card series name during scanning and register it in the database. The data collection unit can use AI to automatically identify the card edition information during scanning and register it in the database. The data collection unit can use AI to automatically identify the card issue year during scanning and register it in the database. The data collection unit can use AI to identify the card series and edition during scanning and register it in the database. This makes card management easier by automatically identifying and registering the card series and edition in the database. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to identify the card series and edition and register it in the database.

[0041] The collection unit can acquire card owner information during scanning and register it in a database. For example, the collection unit can use AI to automatically acquire the card owner's name during scanning and register it in the database. The collection unit can use AI to automatically acquire the card owner's contact information during scanning and register it in the database. The collection unit can use AI to automatically acquire the card owner's address information during scanning and register it in the database. The collection unit can use AI to acquire card owner information during scanning and register it in the database. This makes card management easier by automatically acquiring and registering card owner information in the database. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the card owner information acquired during scanning into a generating AI, which can then analyze the data to acquire the owner information and register it in the database.

[0042] The data collection unit can acquire relevant information about a card (e.g., past transaction history) during scanning and register it in a database. For example, the data collection unit can use AI to automatically acquire the card's past transaction history during scanning and register it in a database. The data collection unit can use AI to automatically acquire the card's past owner information during scanning and register it in a database. The data collection unit can use AI to acquire relevant information about a card during scanning and register it in a database. This makes card management easier by automatically acquiring relevant information about a card and registering it in a database. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the relevant information about a card acquired during scanning into a generating AI, which can then analyze the data, acquire the relevant information, and register it in a database.

[0043] The analysis unit can improve the accuracy of identifying cards that are in short supply by reflecting market trends during analysis. For example, the analysis unit can identify cards that are in short supply based on real-time market prices. The analysis unit can identify cards that are in short supply based on real-time transaction history. The analysis unit can identify cards that are in short supply based on real-time market demand. The analysis unit can improve the accuracy of identifying cards that are in short supply by using AI to reflect market trends during analysis. This improves the accuracy of identifying cards that are in short supply by reflecting market trends in real time. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input real-time market data into a generating AI, which can analyze the data to reflect market trends and identify cards that are in short supply.

[0044] The analysis unit can identify missing cards based on their rarity and popularity during analysis. For example, the analysis unit can identify missing cards based on their rarity. The analysis unit can identify missing cards based on their popularity. The analysis unit can identify missing cards based on their market value. The analysis unit can use AI to identify missing cards while considering their rarity and popularity during analysis. This improves the accuracy of identifying missing cards by considering their rarity and popularity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on card rarity and popularity into a generating AI, which can then analyze the data to identify missing cards.

[0045] The analysis unit can identify missing cards based on the geographical distribution of the cards during analysis. For example, the analysis unit can identify missing cards based on the geographical distribution of the cards. The analysis unit can identify missing cards based on the popularity of the cards in each region. The analysis unit can identify missing cards based on the market price of the cards in each region. The analysis unit can use AI to identify missing cards while considering the geographical distribution of the cards during analysis. This improves the accuracy of identifying missing cards by considering the geographical distribution of the cards. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the geographical distribution of cards into a generating AI, and the generating AI can analyze the data to identify missing cards.

[0046] The analysis department can identify missing cards during analysis based on relevant literature and databases for the cards. For example, the analysis department can identify missing cards based on relevant literature for the cards. The analysis department can identify missing cards based on the card database. The analysis department can identify missing cards based on the card's past transaction history. The analysis department can use AI to refer to relevant literature and databases for the cards during analysis to identify missing cards. This improves the accuracy of identifying missing cards by referring to relevant literature and databases for the cards. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input data from relevant literature and databases for the cards into a generating AI, which can then analyze the data to identify missing cards.

[0047] The distribution unit can set the level of detail required for obtaining the cards and provide appropriate advice to collectors at the time of distribution. For example, if the collector is a beginner, the distribution unit will provide advice that includes detailed steps. If the collector is experienced, the distribution unit will provide advice that includes concise steps. If the collector is in a hurry, the distribution unit will provide advice that includes steps that can be performed quickly. The distribution unit can use AI to adjust the level of detail required for obtaining the cards at the time of distribution and provide the collector with the most suitable advice. This allows the distribution unit to provide the collector with the most suitable advice by adjusting the level of detail required for obtaining the cards. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can have the generating AI set the level of detail of the advice according to the collector's experience level and situation.

[0048] The service provider can provide advice based on the availability and market price of the card at the time of provision. For example, the service provider can advise on the optimal timing for purchase based on the availability of the card. The service provider can advise on the optimal purchase price based on the market price of the card. The service provider can advise on the optimal purchase method by comprehensively considering the availability and market price of the card. The service provider can use AI to consider the availability and market price of the card when providing advice. This allows for the provision of more appropriate advice by considering the availability and market price of the card. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the availability and market price of the card into a generating AI, which can then analyze the data and provide optimal advice.

[0049] The service provider can provide advice at the time of service based on past success stories regarding how to obtain cards. For example, the service provider can advise on the optimal purchase method based on past success stories. The service provider can advise on the optimal exchange method based on past success stories. The service provider can advise on the optimal timing for transactions based on past success stories. The service provider can use AI to refer to past success stories regarding how to obtain cards and provide advice at the time of service. This allows for the provision of more appropriate advice by referring to past success stories. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input data on past success stories into a generating AI, which can then analyze the data and provide optimal advice.

[0050] The distribution unit can provide collectors with information on events and trade shows related to the cards at the time of distribution and offer appropriate advice. For example, the distribution unit can advise on the optimal timing for participation based on information on events related to the cards. The distribution unit can advise on the optimal timing for trading based on information on trade shows. The distribution unit can provide optimal advice by comprehensively considering information on events and trade shows related to the cards. The distribution unit can use AI to provide information on events and trade shows related to the cards at the time of distribution and offer optimal advice to collectors. This allows for more appropriate advice to be provided by providing information on events and trade shows related to the cards. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input information on events and trade shows related to the cards into a generating AI, which can then analyze the data and provide optimal advice.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The collection unit can detect the condition of a card during scanning and register it in a database. For example, during scanning, the AI ​​can detect whether there are scratches on the surface of the card and register it in the database. The AI ​​can detect dirt on the card and record its condition in the database. The AI ​​can detect wear on the edges of the card and register detailed condition information in the database. This makes card management easier by automatically detecting the condition of the card and registering it in the database. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to detect the condition of the card and register it in the database.

[0053] The collection unit can identify the card series and edition during scanning and register them in a database. For example, during scanning, the AI ​​can automatically identify the card series name and register it in the database. The AI ​​can also automatically identify the card edition information and register it in the database. The AI ​​can also automatically identify the card's year of issue and register it in the database. This makes card management easier by automatically identifying and registering the card series and edition in the database. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to identify the card series and edition and register it in the database.

[0054] The collection unit can acquire card owner information during scanning and register it in a database. For example, during scanning, the AI ​​can automatically acquire the card owner's name and register it in the database. The AI ​​can also automatically acquire the card owner's contact information and register it in the database. The AI ​​can also automatically acquire the card owner's address information and register it in the database. This makes card management easier by automatically acquiring and registering card owner information in the database. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the card owner information acquired during scanning into a generating AI, which can then analyze the data to acquire the owner information and register it in the database.

[0055] The data collection unit can acquire relevant information about a card (e.g., past transaction history) during scanning and register it in a database. For example, during scanning, the AI ​​can automatically acquire the card's past transaction history and register it in the database. The AI ​​can also automatically acquire past owner information of the card and register it in the database. The AI ​​can also automatically acquire past market prices of the card and register them in the database. This makes card management easier by automatically acquiring relevant information about the card and registering it in the database. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the relevant information about the card acquired during scanning into a generating AI, which can then analyze the data, acquire the relevant information, and register it in the database.

[0056] The analysis unit can improve the accuracy of identifying cards that are in short supply by reflecting market trends during analysis. For example, it can identify cards that are in short supply based on real-time market prices, real-time transaction history, or real-time market demand. This improves the accuracy of identifying cards that are in short supply by reflecting market trends in real time. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input real-time market data into a generating AI, which can then analyze the data to reflect market trends and identify cards that are in short supply.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit scans the collector's cards and registers them in the database. For example, it uses a smartphone camera to scan physical cards and automatically registers them in the database. The collection unit analyzes and manages detailed card information (series, number, condition, etc.). AI can be used to analyze and manage detailed card information. Step 2: The analysis department analyzes the data collected by the collection department to identify missing cards. For example, it regularly scans online card markets to analyze the price and availability of missing cards. By collecting data from auction sites, online shops, forums, etc., and having AI analyze it, it is possible to understand market prices and availability in real time. Step 3: The supply team advises on how to obtain the missing cards identified by the analysis team. For example, they support card exchanges between users, match users' wish lists with available lists, and facilitate secure transactions. They support safe and efficient card transactions within a verified community. AI can be used to support card exchanges between users.

[0059] (Example of form 2)An AI agent system functioning as a cloud-based smartphone and personal computer application according to an embodiment of the present invention is a system that supports paper-based trading card collections. This system constantly monitors the collector's current card holdings, analyzes online card markets using AI, and provides advice on acquiring missing cards. This allows collectors to efficiently complete their collections. For example, to constantly monitor the collector's current card holdings, the system uses the smartphone camera to scan physical cards and automatically registers them in a database. The AI ​​analyzes and manages detailed card information (series, quantity, condition, etc.). This allows collectors to easily manage their collections and accurately track their current card holdings. Next, the AI ​​periodically scans online card markets to analyze the price and availability of missing cards. For example, by collecting and analyzing data from auction sites, online shops, forums, etc., the AI ​​can grasp market prices and availability in real time. This allows collectors to know the best timing for buying and trading. Furthermore, the system also includes a function to support card trading between app users. It matches users' wish lists with available lists, facilitating secure transactions. This enables safe and efficient card trading within a verified community. The app also provides information on trade shows and offline events for collectors, and assists with participant pre-registration and event support. This revitalizes community activity and promotes real-world interaction. Furthermore, it includes a feature that automatically notifies users when rare cards appear on the market and recommends immediate purchase or exchange action. This prevents users from missing out on rare cards and allows for quick action. Finally, it includes a feature that regularly evaluates the market value of owned cards, allowing users to understand their asset value. It also partners with insurance companies as needed to support card insurance, making asset management of card collections easier.In this way, AI agents can be used to help collectors efficiently complete their collections and provide a platform to connect with the community. The AI ​​agent system, functioning as a cloud-based smartphone and personal computer app, can efficiently manage the collector's card holdings and advise on how to acquire missing cards.

[0060] The AI ​​agent system, functioning as a cloud-based smartphone and personal computer application according to this embodiment, comprises a collection unit, an analysis unit, and a provision unit. The collection unit scans the collector's cards and registers them in a database. For example, the collection unit scans physical cards using a smartphone camera and automatically registers them in the database. The collection unit analyzes and manages detailed card information (series, number, condition, etc.). For example, the collection unit analyzes the card's series name, number, and condition (e.g., scratches or stains) and registers them in the database. The collection unit can use AI to analyze and manage detailed card information. The analysis unit analyzes the data collected by the collection unit and identifies missing cards. For example, the analysis unit periodically scans online card markets and analyzes the price and availability of missing cards. The analysis unit collects data from auction sites, online shops, forums, etc., and the AI ​​analyzes it to grasp market prices and availability in real time. For example, the analysis unit collects data from auction sites and the AI ​​analyzes it to grasp market prices and availability in real time. The analysis unit can use AI to scan online card marketplaces and analyze the price and availability of missing cards. The supply unit advises on how to obtain the missing cards identified by the analysis unit. The supply unit supports, for example, card exchanges between users. The supply unit matches users' wish lists with their offering lists to facilitate secure transactions. The supply unit supports secure and efficient card transactions within a verified community. For example, the supply unit matches users' wish lists with their offering lists to facilitate secure transactions. The supply unit can use AI to support card exchanges between users. As a result, the AI ​​agent system, functioning as a cloud-based smartphone and personal computer application according to the embodiment, can efficiently manage a collector's card holdings and advise on how to obtain missing cards.

[0061] The collection unit scans cards owned by collectors and registers them in a database. For example, the collection unit can use a smartphone camera to scan physical cards and automatically register them in the database. Specifically, it uses a smartphone camera to capture an image of the card and extracts detailed information using image recognition technology. This detailed information includes the series name, the number of cards, and the card's condition (e.g., scratches or stains). This information is analyzed using AI and registered in the database. For example, the card's series name is automatically extracted from the card's design and text information using image recognition technology. The number of cards is identified from the card's design and serial number. The card's condition is assessed by detecting scratches and stains on the card's surface using image analysis technology. This allows the collection unit to efficiently collect detailed information on collectors' cards and register it in the database. Furthermore, the collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and accessed by the analysis and provision departments. Additionally, adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0062] The analysis department analyzes data collected by the collection department to identify missing cards. Specifically, based on the collected card data, it grasps the overall picture of a collector's collection and identifies which cards are missing. The analysis department uses AI to analyze the collected data and understand the patterns and trends of collectors' collections. For example, if a collector is collecting cards from a specific series, it can identify missing cards within that series. Furthermore, the analysis department regularly scans the online card market to analyze the price and availability of missing cards. Specifically, it collects data from auction sites, online shops, forums, etc., and uses AI to analyze it to understand market prices and availability in real time. For example, by collecting data from auction sites and using AI to analyze it, it can understand market prices and availability in real time. This allows the analysis department to efficiently identify cards that collectors are missing and suggest ways to acquire them. In addition, the analysis department can use historical data and statistical information to predict long-term market trends and price fluctuations. This provides collectors with information that will be useful when planning future card acquisitions.

[0063] The supply department advises users on how to obtain missing cards identified by the analysis department. Specifically, it supports card exchanges between users. The supply department matches users' wish lists with available lists to facilitate secure transactions. For example, the supply department registers lists of cards users want and cards they can offer in a database and uses AI to perform optimal matching. This allows users to exchange cards efficiently. Furthermore, the supply department supports safe and efficient card transactions within a verified community. Specifically, it verifies users' personal information and provides a secure transaction environment. For example, during transactions, users can choose reliable trading partners by referring to user ratings and transaction history. The supply department also monitors the progress of transactions in real time and can respond quickly if problems arise. In this way, the supply department can safely and efficiently support card exchanges between users and help collectors obtain missing cards. In addition, the supply department can collect user feedback and use it to improve the service. For example, based on post-transaction ratings and comments, the supply department considers measures to improve the quality of the service. In this way, the supply department can provide better service to users and increase satisfaction.

[0064] The data collection unit can scan physical cards using a smartphone camera and register them in a database. For example, the data collection unit can scan a physical card using a smartphone camera and save it as image data. Then, the data collection unit uses AI to analyze the image data and register the card's detailed information in a database. The data collection unit can scan physical cards using a smartphone camera, analyze the image data using AI, and register the card's detailed information in a database. This makes it easy to scan physical cards and register them in a database using a smartphone camera. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data acquired with a smartphone camera into a generating AI and have the generating AI generate detailed card information from the image data.

[0065] The analysis department can scan online card marketplaces and analyze the price and availability of missing cards. For example, the analysis department can periodically scan online card marketplaces, collect data from auction sites, online shops, forums, etc., and have AI analyze it to understand market prices and availability in real time. The analysis department can scan online card marketplaces and use AI to analyze the price and availability of missing cards. This allows the analysis department to understand the price and availability of missing cards by periodically scanning online card marketplaces. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input data from online card marketplaces into a generating AI, which can analyze the data to identify the price and availability of missing cards.

[0066] The service provider can assist users in exchanging cards with each other. For example, the service provider can match users' wish lists with their offering lists to facilitate secure transactions. The service provider can support secure and efficient card transactions within a verified community. The service provider can use AI to assist users in exchanging cards with each other. This enables secure and efficient card transactions by supporting card exchanges between users. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user wish list and offering list data into a generating AI, which can then analyze the data and perform matching.

[0067] The service provider can provide information on trade shows and offline events, and assist participants with pre-registration and during the events. For example, the service provider can provide information on trade shows and offline events and support participants with pre-registration. The service provider can assist participants during the events to ensure they can participate smoothly. The service provider can use AI to provide information on trade shows and offline events and assist participants with pre-registration and during the events. By providing information on trade shows and offline events, community activities will be revitalized and real-world interaction will be promoted. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information on trade shows and offline events into a generating AI, which can then analyze the information and provide it to participants.

[0068] The supply unit can notify when a rare card appears on the market and immediately recommend purchasing or trading. For example, the supply unit can automatically notify when a rare card appears on the market and recommend purchasing or trading. The supply unit can use AI to notify when a rare card appears on the market and immediately recommend purchasing or trading. This prevents rare cards from being missed and enables a quick response. Some or all of the above processing in the supply unit may be performed using AI or not. For example, the supply unit can input rare card market data into a generating AI, which can analyze the data and notify when a rare card appears on the market.

[0069] The supply unit can evaluate the market value of owned cards and understand their value as assets. For example, the supply unit can periodically evaluate the market value of owned cards and understand their value as assets. The supply unit can use AI to evaluate the market value of owned cards and understand their value as assets. This makes asset management of card collections easier by periodically evaluating the market value of owned cards. Some or all of the above processes in the supply unit may be performed using AI or not. For example, the supply unit can input market data of owned cards into a generating AI, and the generating AI can analyze the data and evaluate the market value.

[0070] The collection unit can estimate the collector's emotions and set the timing for scanning cards based on the estimated emotions. For example, if the collector is excited, the collection unit speeds up the scanning timing to quickly register the cards. If the collector is relaxed, the collection unit sets the scanning timing as usual to perform accurate scans. If the collector is tired, the collection unit delays the scanning timing to reduce the scanning burden. The collection unit can use AI to estimate the collector's emotions and set the timing for scanning cards based on the estimated emotions. This improves scanning efficiency by adjusting the scanning timing according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the collector's facial expression data into a generative AI, which can analyze the data to estimate emotions and set the scanning timing.

[0071] The data collection unit can detect the condition of a card during scanning and register it in a database. For example, the data collection unit can use AI to detect whether there are scratches on the surface of the card during scanning and register it in the database. The data collection unit can use AI to detect dirt on the card during scanning and record its condition in the database. The data collection unit can use AI to detect wear on the edges of the card during scanning and register detailed condition information in the database. The data collection unit can use AI to detect the condition of a card during scanning and register it in the database. This makes card management easier by automatically detecting the condition of the card and registering it in the database. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to detect the condition of the card and register it in the database.

[0072] The data collection unit can identify the card series and edition during scanning and register them in the database. For example, the data collection unit can use AI to automatically identify the card series name during scanning and register it in the database. The data collection unit can use AI to automatically identify the card edition information during scanning and register it in the database. The data collection unit can use AI to automatically identify the card issue year during scanning and register it in the database. The data collection unit can use AI to identify the card series and edition during scanning and register it in the database. This makes card management easier by automatically identifying and registering the card series and edition in the database. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to identify the card series and edition and register it in the database.

[0073] The collection unit can estimate the collector's emotions and set priorities for scanning cards based on the estimated emotions. For example, if the collector is excited, the collection unit will prioritize scanning cards of high rarity. If the collector is relaxed, the collection unit will scan cards in the normal order. If the collector is tired, the collection unit will prioritize scanning a small number of cards to reduce the scanning burden. The collection unit can use AI to estimate the collector's emotions and set priorities for scanning cards based on the estimated emotions. This improves scanning efficiency by determining the priority of cards to scan according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the collector's facial expression data into a generating AI, which then analyzes the data to estimate emotions and set priorities for scanning the cards.

[0074] The collection unit can acquire card owner information during scanning and register it in a database. For example, the collection unit can use AI to automatically acquire the card owner's name during scanning and register it in the database. The collection unit can use AI to automatically acquire the card owner's contact information during scanning and register it in the database. The collection unit can use AI to automatically acquire the card owner's address information during scanning and register it in the database. The collection unit can use AI to acquire card owner information during scanning and register it in the database. This makes card management easier by automatically acquiring and registering card owner information in the database. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the card owner information acquired during scanning into a generating AI, which can then analyze the data to acquire the owner information and register it in the database.

[0075] The data collection unit can acquire relevant information about a card (e.g., past transaction history) during scanning and register it in a database. For example, the data collection unit can use AI to automatically acquire the card's past transaction history during scanning and register it in a database. The data collection unit can use AI to automatically acquire the card's past owner information during scanning and register it in a database. The data collection unit can use AI to acquire relevant information about a card during scanning and register it in a database. This makes card management easier by automatically acquiring relevant information about a card and registering it in a database. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the relevant information about a card acquired during scanning into a generating AI, which can then analyze the data, acquire the relevant information, and register it in a database.

[0076] The analysis unit can estimate the collector's emotions and set up a method for identifying missing cards based on the estimated emotions. For example, if the collector is excited, the analysis unit will prioritize identifying cards of high rarity. If the collector is relaxed, the analysis unit will identify missing cards in the normal order. If the collector is tired, the analysis unit will prioritize identifying a small number of cards to alleviate the burden of identifying them. The analysis unit can use AI to estimate the collector's emotions and set up a method for identifying missing cards based on the estimated emotions. This improves the accuracy of identification by adjusting the method for identifying missing cards according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collector's facial expression data into a generating AI, which then analyzes the data to estimate emotions and set a method for identifying missing cards.

[0077] The analysis unit can improve the accuracy of identifying cards that are in short supply by reflecting market trends during analysis. For example, the analysis unit can identify cards that are in short supply based on real-time market prices. The analysis unit can identify cards that are in short supply based on real-time transaction history. The analysis unit can identify cards that are in short supply based on real-time market demand. The analysis unit can improve the accuracy of identifying cards that are in short supply by using AI to reflect market trends during analysis. This improves the accuracy of identifying cards that are in short supply by reflecting market trends in real time. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input real-time market data into a generating AI, which can analyze the data to reflect market trends and identify cards that are in short supply.

[0078] The analysis unit can identify missing cards based on their rarity and popularity during analysis. For example, the analysis unit can identify missing cards based on their rarity. The analysis unit can identify missing cards based on their popularity. The analysis unit can identify missing cards based on their market value. The analysis unit can use AI to identify missing cards while considering their rarity and popularity during analysis. This improves the accuracy of identifying missing cards by considering their rarity and popularity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on card rarity and popularity into a generating AI, which can then analyze the data to identify missing cards.

[0079] The analysis unit can estimate the collector's emotions and prioritize missing cards based on the estimated emotions. For example, if the collector is excited, the analysis unit will prioritize identifying cards of high rarity. If the collector is relaxed, the analysis unit will identify missing cards in the usual order. If the collector is tired, the analysis unit will prioritize identifying a small number of cards to alleviate certain burdens. The analysis unit can use AI to estimate the collector's emotions and prioritize missing cards based on the estimated emotions. This improves certain efficiencies by determining the priority of missing cards according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collector's facial expression data into a generating AI, which then analyzes the data to estimate emotions and set priorities for the missing cards.

[0080] The analysis unit can identify missing cards based on the geographical distribution of the cards during analysis. For example, the analysis unit can identify missing cards based on the geographical distribution of the cards. The analysis unit can identify missing cards based on the popularity of the cards in each region. The analysis unit can identify missing cards based on the market price of the cards in each region. The analysis unit can use AI to identify missing cards while considering the geographical distribution of the cards during analysis. This improves the accuracy of identifying missing cards by considering the geographical distribution of the cards. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the geographical distribution of cards into a generating AI, and the generating AI can analyze the data to identify missing cards.

[0081] The analysis department can identify missing cards during analysis based on relevant literature and databases for the cards. For example, the analysis department can identify missing cards based on relevant literature for the cards. The analysis department can identify missing cards based on the card database. The analysis department can identify missing cards based on the card's past transaction history. The analysis department can use AI to refer to relevant literature and databases for the cards during analysis to identify missing cards. This improves the accuracy of identifying missing cards by referring to relevant literature and databases for the cards. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input data from relevant literature and databases for the cards into a generating AI, which can then analyze the data to identify missing cards.

[0082] The service provider can estimate the collector's emotions and set the expression of advice based on the estimated emotions. For example, if the collector is excited, the service provider will provide advice in an encouraging tone. If the collector is relaxed, the service provider will provide advice in a calm tone. If the collector is tired, the service provider will provide advice in a concise and easy-to-understand tone. The service provider can use AI to estimate the collector's emotions and set the expression of advice based on the estimated emotions. This allows for more appropriate advice to be provided by adjusting the expression of advice according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the collector's facial expression data into a generative AI, which can analyze the data to estimate emotions and set the expression of advice.

[0083] The distribution unit can set the level of detail required for obtaining the cards and provide appropriate advice to collectors at the time of distribution. For example, if the collector is a beginner, the distribution unit will provide advice that includes detailed steps. If the collector is experienced, the distribution unit will provide advice that includes concise steps. If the collector is in a hurry, the distribution unit will provide advice that includes steps that can be performed quickly. The distribution unit can use AI to adjust the level of detail required for obtaining the cards at the time of distribution and provide the collector with the most suitable advice. This allows the distribution unit to provide the collector with the most suitable advice by adjusting the level of detail required for obtaining the cards. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can have the generating AI set the level of detail of the advice according to the collector's experience level and situation.

[0084] The service provider can provide advice based on the availability and market price of the card at the time of provision. For example, the service provider can advise on the optimal timing for purchase based on the availability of the card. The service provider can advise on the optimal purchase price based on the market price of the card. The service provider can advise on the optimal purchase method by comprehensively considering the availability and market price of the card. The service provider can use AI to consider the availability and market price of the card when providing advice. This allows for the provision of more appropriate advice by considering the availability and market price of the card. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the availability and market price of the card into a generating AI, which can then analyze the data and provide optimal advice.

[0085] The service provider can estimate the collector's emotions and prioritize advice based on those emotions. For example, if the collector is excited, the service provider will prioritize advice on rare cards. If the collector is relaxed, the service provider will provide advice in the normal order. If the collector is tired, the service provider will prioritize advice on fewer cards to reduce the burden of advice. The service provider can use AI to estimate the collector's emotions and prioritize advice based on those emotions. This allows for more appropriate advice to be provided by determining the priority of advice according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the collector's facial expression data into a generative AI, which can analyze the data to estimate emotions and set the priority of advice.

[0086] The service provider can provide advice at the time of service based on past success stories regarding how to obtain cards. For example, the service provider can advise on the optimal purchase method based on past success stories. The service provider can advise on the optimal exchange method based on past success stories. The service provider can advise on the optimal timing for transactions based on past success stories. The service provider can use AI to refer to past success stories regarding how to obtain cards and provide advice at the time of service. This allows for the provision of more appropriate advice by referring to past success stories. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input data on past success stories into a generating AI, which can then analyze the data and provide optimal advice.

[0087] The distribution unit can provide collectors with information on events and trade shows related to the cards at the time of distribution and offer appropriate advice. For example, the distribution unit can advise on the optimal timing for participation based on information on events related to the cards. The distribution unit can advise on the optimal timing for trading based on information on trade shows. The distribution unit can provide optimal advice by comprehensively considering information on events and trade shows related to the cards. The distribution unit can use AI to provide information on events and trade shows related to the cards at the time of distribution and offer optimal advice to collectors. This allows for more appropriate advice to be provided by providing information on events and trade shows related to the cards. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input information on events and trade shows related to the cards into a generating AI, which can then analyze the data and provide optimal advice.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The collection unit can estimate the collector's emotions and set the timing for scanning cards based on the estimated emotions. For example, if the collector is excited, the scanning timing is accelerated to quickly register the cards. If the collector is relaxed, the scanning timing is set to normal for accurate scanning. If the collector is tired, the scanning timing is delayed to reduce the burden of scanning. This improves scanning efficiency by adjusting the scanning timing according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the collector's facial expression data into a generative AI, which can analyze the data to estimate emotions and set the scanning timing.

[0090] The analysis unit can estimate the collector's emotions and set a method for identifying missing cards based on the estimated emotions. For example, if the collector is excited, it will prioritize identifying cards of high rarity. If the collector is relaxed, it will identify missing cards in the usual order. If the collector is tired, it will prioritize identifying a small number of cards to alleviate the burden of identifying them. This improves the accuracy of identification by adjusting the method for identifying missing cards according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collector's facial expression data into a generative AI, which can analyze the data to estimate emotions and set a method for identifying missing cards.

[0091] The service provider can estimate the collector's emotions and set the expression of advice based on the estimated emotions. For example, if the collector is excited, advice is provided in an encouraging tone. If the collector is relaxed, advice is provided in a calm tone. If the collector is tired, advice is provided in a concise and easy-to-understand tone. This allows for more appropriate advice to be provided by adjusting the expression of advice according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the collector's facial expression data into the generative AI, which can analyze the data to estimate emotions and set the expression of advice.

[0092] The service provider can estimate the collector's emotions and prioritize advice based on those emotions. For example, if the collector is excited, it will prioritize advice for rarer cards. If the collector is relaxed, it will provide advice in the normal order. If the collector is tired, it will prioritize advice for fewer cards to reduce the burden of advice. This allows for more appropriate advice to be provided by determining the priority of advice according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the collector's facial expression data into a generative AI, which can analyze the data to estimate emotions and set the priority of advice.

[0093] The collection unit can estimate the collector's emotions and set a priority for scanning cards based on the estimated emotions. For example, if the collector is excited, rare cards will be scanned first. If the collector is relaxed, cards will be scanned in the normal order. If the collector is tired, fewer cards will be scanned first to reduce the scanning burden. This improves scanning efficiency by determining the priority of cards to scan according to the collector's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the collector's facial expression data into a generative AI, which can analyze the data to estimate emotions and set a priority for scanning cards.

[0094] The collection unit can detect the condition of a card during scanning and register it in a database. For example, during scanning, the AI ​​can detect whether there are scratches on the surface of the card and register it in the database. The AI ​​can detect dirt on the card and record its condition in the database. The AI ​​can detect wear on the edges of the card and register detailed condition information in the database. This makes card management easier by automatically detecting the condition of the card and registering it in the database. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to detect the condition of the card and register it in the database.

[0095] The collection unit can identify the card series and edition during scanning and register them in a database. For example, during scanning, the AI ​​can automatically identify the card series name and register it in the database. The AI ​​can also automatically identify the card edition information and register it in the database. The AI ​​can also automatically identify the card's year of issue and register it in the database. This makes card management easier by automatically identifying and registering the card series and edition in the database. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the image data of the card acquired during scanning into a generating AI, which can then analyze the data to identify the card series and edition and register it in the database.

[0096] The collection unit can acquire card owner information during scanning and register it in a database. For example, during scanning, the AI ​​can automatically acquire the card owner's name and register it in the database. The AI ​​can also automatically acquire the card owner's contact information and register it in the database. The AI ​​can also automatically acquire the card owner's address information and register it in the database. This makes card management easier by automatically acquiring and registering card owner information in the database. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the card owner information acquired during scanning into a generating AI, which can then analyze the data to acquire the owner information and register it in the database.

[0097] The data collection unit can acquire relevant information about a card (e.g., past transaction history) during scanning and register it in a database. For example, during scanning, the AI ​​can automatically acquire the card's past transaction history and register it in the database. The AI ​​can also automatically acquire past owner information of the card and register it in the database. The AI ​​can also automatically acquire past market prices of the card and register them in the database. This makes card management easier by automatically acquiring relevant information about the card and registering it in the database. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the relevant information about the card acquired during scanning into a generating AI, which can then analyze the data, acquire the relevant information, and register it in the database.

[0098] The analysis unit can improve the accuracy of identifying cards that are in short supply by reflecting market trends during analysis. For example, it can identify cards that are in short supply based on real-time market prices, real-time transaction history, or real-time market demand. This improves the accuracy of identifying cards that are in short supply by reflecting market trends in real time. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input real-time market data into a generating AI, which can then analyze the data to reflect market trends and identify cards that are in short supply.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection unit scans the collector's cards and registers them in the database. For example, it uses a smartphone camera to scan physical cards and automatically registers them in the database. The collection unit analyzes and manages detailed card information (series, number, condition, etc.). AI can be used to analyze and manage detailed card information. Step 2: The analysis department analyzes the data collected by the collection department to identify missing cards. For example, it regularly scans online card markets to analyze the price and availability of missing cards. By collecting data from auction sites, online shops, forums, etc., and having AI analyze it, it is possible to understand market prices and availability in real time. Step 3: The supply team advises on how to obtain the missing cards identified by the analysis team. For example, they support card exchanges between users, match users' wish lists with available lists, and facilitate secure transactions. They support safe and efficient card transactions within a verified community. AI can be used to support card exchanges between users.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit scans a real card using the camera 42 of the smart device 14, analyzes the card's detailed information using the control unit 46A, and registers it in the database 24. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scans the online card market to analyze the price and availability of missing cards. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and supports card exchange between users, facilitating secure transactions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

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

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit scans a real card using the camera 42 of the smart glasses 214, analyzes the card's detailed information using the control unit 46A, and registers it in the database 24. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scans the online card marketplace to analyze the price and availability of missing cards. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and supports card exchange between users, facilitating secure transactions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit scans a real card using the camera 42 of the headset terminal 314, analyzes the card's detailed information using the control unit 46A, and registers it in the database 24. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scans the online card market to analyze the price and availability of missing cards. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and supports card exchange between users, facilitating secure transactions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, and supply unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit scans real cards using the camera 42 of the robot 414, analyzes the detailed information of the cards by the control unit 46A, and registers it in the database 24. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scans the online card marketplace to analyze the price and availability of missing cards. The supply unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and supports card exchange between users and promotes secure transactions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The collection department scans the cards owned by collectors and registers them in a database, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies the missing cards, A system comprising: a provision unit that advises on how to obtain the missing cards identified by the aforementioned analysis unit. (Note 2) The aforementioned collection unit is Use your smartphone camera to scan the physical card and register it in the database. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Scan online card marketplaces to analyze the price and availability of missing cards. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Supports the exchange of cards between users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide information on trade shows and offline events, and offer support for participant pre-registration and during events. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Notify us when rare cards appear on the market and recommend immediate purchase or trade action. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Evaluate the market value of your cards and understand their value as assets. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the collector's emotions and sets the timing for scanning cards based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During scanning, the card's status is detected and registered in the database. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During scanning, the card's series and edition are identified and registered in the database. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the collector's sentiment and prioritizes which cards to scan based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During scanning, the card owner's information is retrieved and registered in the database. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During scanning, the system retrieves relevant information about the card and registers it in the database. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Estimate the collector's sentiment and set up a method for identifying missing cards based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, we aim to reflect market trends in cards and improve the accuracy of identifying cards that are lacking. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, we identify missing cards based on their rarity and popularity. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is The system estimates the collector's sentiment and prioritizes the missing cards based on that estimate. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, we identify missing cards based on their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, we identify missing cards based on relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the collector's emotions and sets the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing cards, set the level of detail required to obtain them and offer appropriate advice to collectors. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, we offer advice based on the availability and market price of the cards. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Estimate the collector's emotions and prioritize advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the cards, we will offer advice based on past success stories regarding how to obtain them. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing cards, we offer information on related events and trade shows, and provide appropriate advice to collectors. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department scans the cards owned by collectors and registers them in a database, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies the missing cards, A system comprising: a provision unit that advises on how to obtain the missing cards identified by the aforementioned analysis unit.

2. The aforementioned collection unit is Use your smartphone camera to scan the physical card and register it in the database. The system according to feature 1.

3. The aforementioned analysis unit is Scan online card marketplaces to analyze the price and availability of missing cards. The system according to feature 1.

4. The aforementioned supply unit is, Supports the exchange of cards between users. The system according to feature 1.

5. The aforementioned supply unit is, We provide information on trade shows and offline events, and offer support for participant pre-registration and during events. The system according to feature 1.

6. The aforementioned supply unit is, Notify us when rare cards appear on the market and recommend immediate purchase or trade action. The system according to feature 1.

7. The aforementioned supply unit is, Evaluate the market value of your cards and understand their value as assets. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the collector's emotions and sets the timing for scanning cards based on those estimated emotions. The system according to feature 1.

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