system

Abstract

The system according to this embodiment aims to effectively utilize unwanted items and efficiently exchange them for desired items. [Solution] The system according to the embodiment comprises a collection unit, a matching unit, a negotiation unit, and an evaluation unit. The collection unit collects information on items that the user does not want and items that the user wants. The matching unit matches items based on the information collected by the collection unit. The negotiation unit allows users who have been matched by the matching unit to negotiate with each other. The evaluation unit evaluates the users.

Description

Technical Field

[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 prior art, there is a problem that the process of effectively utilizing unnecessary items and exchanging them for desired items is not efficiently carried out.

[0005] The system according to the embodiment aims to effectively utilize unnecessary items and efficiently exchange them for desired items.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a matching unit, a negotiation unit, and an evaluation unit. The collection unit collects information on items that users do not want and items they want. The matching unit matches items based on the information collected by the collection unit. The negotiation unit facilitates negotiations between users who have been matched by the matching unit. The evaluation unit evaluates the users. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize unwanted items and efficiently exchange them for desired items. [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, and the like. The communication I / F manages communication between multiple 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] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

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

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving 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) The item exchange system according to an embodiment of the present invention is a system that allows users to offer unwanted items to others and exchange them for items they want. In this item exchange system, users can register their unwanted items and request items they want. A generating AI analyzes this information and performs optimal matching. For example, if user A registers a bicycle they no longer use and user B requests furniture, the generating AI will match the two. After matching, the users can negotiate and agree on the exchange of items. The item exchange system also has an evaluation system, making it easier for users to find reliable trading partners. The generating AI can also create a profile of the user based on the items they have registered and provide product recommendations and usage instructions. This allows users to effectively utilize unwanted items while simultaneously obtaining desired items. It is an app that matches "what you want" with "what you don't want" while also contributing to the environment. As a result, the item exchange system can efficiently collect, match, negotiate, and evaluate information on users' unwanted and desired items.

[0029] The item exchange system according to this embodiment comprises a collection unit, a matching unit, a negotiation unit, and an evaluation unit. The collection unit collects information on items that a user does not want and items that they want. For example, the collection unit allows a user to log in to the app and register their unwanted items. The collection unit also allows a user to input a request for an item they want. For example, the collection unit allows a user to register an electronic device as an unwanted item and request furniture as an item they want. The collection unit stores the user's input information in a database and uses it later for matching. The matching unit uses a generation AI to match items based on the information collected by the collection unit. For example, the matching unit compares an unwanted item registered by user A with an item requested by user B and performs the optimal match. The matching unit can use a generation AI to perform matching while considering the similarity of items and the user's attribute information. For example, the matching unit matches a bicycle registered by user A with furniture requested by user B. The matching unit can use a generation AI to evaluate the value and condition of items and perform the optimal match. The Negotiation Unit facilitates negotiations between users matched by the Matching Unit. For example, the Negotiation Unit allows users A and B to exchange messages within the app and agree on the terms of item exchange. The Negotiation Unit supports negotiations until users reach an agreement. For example, the Negotiation Unit helps users agree that user A will provide a bicycle and user B will provide furniture. The Negotiation Unit can provide tools to facilitate negotiations between users. The Evaluation Unit evaluates users, making it easier to find reliable trading partners. For example, the Evaluation Unit allows users to input evaluations of their trading partners after a transaction. The Evaluation Unit stores user evaluation information in a database so that other users can refer to it. For example, the Evaluation Unit allows user A to input an evaluation of user B, rating them as a reliable trading partner. Based on user evaluation information, the Evaluation Unit can recommend reliable users. As a result, the item exchange system according to this embodiment can efficiently collect, match, negotiate, and evaluate information on unwanted and desired items from users.

[0030] The collection unit collects information on items users want and items they no longer need. For example, users can log in to the app and register their unwanted items. Specifically, users can enter detailed information about unwanted items into a dedicated form in the app and upload photos. This allows other users to visually check the condition and characteristics of the items. The collection unit can also receive requests for items users want. For example, users can register electronic devices as unwanted items and request furniture as desired items. The collection unit stores the user's input information in a database for later use in matching. Furthermore, the collection unit can also collect users' past transaction history and rating information, which can be used as data to assess user reliability. This allows the collection unit to accurately understand user needs and available items, supporting efficient matching. The collection unit automatically categorizes the information entered by users and stores it in a searchable format in the database. For example, it organizes data based on attribute information such as item category, condition, and region, allowing for quick searching later. This allows the collection unit to efficiently manage user input information and improve the overall system performance.

[0031] The matching unit uses a generation AI to match items based on information collected by the collection unit. For example, the matching unit compares unwanted items registered by user A with desired items requested by user B to achieve the optimal match. Specifically, the generation AI can perform matching by considering the similarity of items and user attribute information. For example, the matching unit matches a bicycle registered by user A with furniture requested by user B. The generation AI can evaluate the value and condition of items to achieve the optimal match. The generation AI uses natural language processing technology to analyze the item description entered by the user and automatically extracts the item's characteristics and condition. Furthermore, the generation AI uses image recognition technology to analyze uploaded item photos and evaluate the item's condition and appearance. This allows the matching unit to achieve the optimal match between items provided by users and items requested by users. Using the generation AI, the matching unit can also consider users' past transaction history and evaluation information to prioritize matching highly reliable users with each other. This allows the matching unit to improve user satisfaction and enhance the overall reliability of the system.

[0032] The Negotiation Department facilitates negotiations between users matched by the Matching Department. For example, the Negotiation Department allows users A and B to exchange messages within the app and agree on the terms of item exchange. Specifically, the Negotiation Department supports negotiations until users reach an agreement. For instance, the Negotiation Department might help users agree that user A will provide a bicycle and user B will provide furniture. The Negotiation Department can provide tools to facilitate smooth negotiations between users. For example, it can provide message templates and automated response functions to enable users to negotiate quickly and efficiently. Furthermore, the Negotiation Department can monitor the progress of negotiations in real time and issue alerts as needed. This allows the Negotiation Department to support communication between users and ensure smooth transactions. Additionally, the Negotiation Department can save users' negotiation history in a database for future reference. This allows the Negotiation Department to evaluate users' negotiation skills and transaction success rates, improving the overall system performance.

[0033] The rating system evaluates users, making it easier for them to find reliable trading partners. For example, users can input ratings for their trading partners after a transaction. Specifically, the rating system provides a form for users to rate their satisfaction with the transaction, the quality of the trading partner's responsiveness, the condition of the items, and so on. The rating system stores user rating information in a database, making it available for other users to refer to. For example, the rating system allows user A to input a rating for user B, rating them as a reliable trading partner. Based on user ratings, the rating system can recommend reliable users. Specifically, the rating system analyzes user rating scores and past trading history to list reliable users. This makes it easier for other users to find reliable trading partners. Furthermore, the rating system can provide feedback based on user ratings to improve the overall reliability of the system. For example, the rating system can notify users who receive low ratings of areas for improvement and provide advice to improve the quality of their trading. This allows the rating system to improve user reliability and enhance the overall performance of the system.

[0034] Generative AI can create a profile of a user based on their registered items and recommend products and provide instructions on how to use them. For example, the generative AI can analyze information about items registered by a user and estimate the user's hobbies and preferences. For instance, based on the sports equipment registered by a user, the generative AI can estimate that the user is interested in sports. Based on the user's hobbies and preferences, the generative AI can recommend related products. For example, the generative AI will recommend sports-related items to a user who has registered sports equipment. The generative AI can also provide instructions on how to use the items registered by the user. For example, the generative AI can explain how to use electronic devices registered by a user using videos or text. This improves user convenience by allowing the generative AI to create a profile of the user based on their registered items and provide product recommendations and instructions on how to use them.

[0035] The data collection unit can analyze the user's past transaction history and select the optimal information collection method. For example, the data collection unit can analyze patterns of successful transactions in the past and collect information using similar methods. The data collection unit can analyze the causes of unsuccessful transactions in the past and collect information in a way that avoids those causes. The data collection unit can increase the success rate by collecting information from the user's transaction history at specific times of day or on specific days of the week. This allows the optimal information collection method to be selected by analyzing past transaction history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's transaction history data into a generating AI and have the generating AI select the optimal information collection method.

[0036] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is planning to move, the data collection unit can prioritize collecting items related to moving. If the user has started a new hobby, the data collection unit can collect items related to that hobby. If the user is planning to attend a specific event, the data collection unit can collect items related to that event. By filtering information based on the user's living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant item information by considering the user's geographical location during data collection. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of item information that can be traded in that region. If the user is traveling, the data collection unit can collect information on items that can be traded at the travel destination. If the user is planning to move, the data collection unit can collect information on items related to the new address. In this way, by considering the user's geographical location, the data collection unit can prioritize the collection of highly relevant item information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant item information.

[0038] The data collection unit can analyze the user's social media activity and collect relevant item information during data collection. For example, the data collection unit can collect information related to items the user has shown interest in on social media. The data collection unit can analyze the content of posts from accounts the user follows on social media and collect relevant item information. The data collection unit can analyze the activities of groups the user participates in on social media and collect relevant item information. In this way, relevant item information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant item information.

[0039] The matching unit can improve the accuracy of matching by considering the interrelationships between items during the matching process. For example, the matching unit prioritizes matching items that are similar in category or use. The matching unit can improve the accuracy of matching when items have equivalent value. The matching unit can improve the accuracy of matching when items have similar usage frequency or condition. In this way, the accuracy of matching is improved by considering the interrelationships between items. Some or all of the above processes in the matching unit are performed using a generation AI. For example, the matching unit can input item interrelationship data into the generation AI and have the generation AI perform the matching accuracy improvement.

[0040] The matching unit can apply different matching algorithms to each item category during the matching process. For example, for home appliances, the matching unit can apply a matching algorithm that considers performance and brand. For clothing, the matching unit can apply a matching algorithm that considers size and design. For books, the matching unit can apply a matching algorithm that considers genre and author. By applying different matching algorithms to each item category, the accuracy of the matching is improved. Some or all of the above processing in the matching unit is performed using a generative AI. For example, the matching unit can input item category data into the generative AI and have the generative AI execute the application of different matching algorithms.

[0041] The matching unit can perform matching while considering the geographical distribution of items. For example, if a user wishes to trade in a nearby area, the matching unit will prioritize matching items in that nearby area. If a user wishes to trade in a distant area, the matching unit can match items in that distant area. If a user wishes to trade only in a specific area, the matching unit can match items in that area. This allows for more appropriate matching by considering the geographical distribution of items. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input geographical distribution data of items into the generation AI and have the generation AI perform the matching.

[0042] The matching unit can improve the accuracy of matching by referring to relevant literature for items during the matching process. For example, the matching unit can improve the accuracy of matching by referring to literature on how to use or maintain items. The matching unit can improve the accuracy of matching by referring to literature on the evaluation or review of items. The matching unit can improve the accuracy of matching by referring to literature on the market value of items. In this way, the accuracy of matching is improved by referring to relevant literature for items. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input relevant literature data for items into the generation AI and have the generation AI perform the matching accuracy improvement.

[0043] The negotiation unit can select the optimal negotiation method by referring to past negotiation history during negotiations. For example, the negotiation unit can refer to negotiation methods that the user has succeeded with in the past and apply similar methods. The negotiation unit can refer to negotiation methods that the user has failed with in the past and avoid those methods. The negotiation unit can select and apply specific negotiation methods from the user's negotiation history. In this way, the optimal negotiation method can be selected by referring to past negotiation history. Some or all of the above processes in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input the user's negotiation history data into a generating AI and have the generating AI perform the selection of the optimal negotiation method.

[0044] The negotiation department can conduct negotiations while considering the user's attribute information. For example, the negotiation department can select an appropriate negotiation method by considering the user's age and gender. The negotiation department can adjust the negotiation process by considering the user's occupation and hobbies. The negotiation department can adjust the negotiation process by considering the user's past transaction history. This makes it possible to conduct more appropriate negotiations by considering the user's attribute information. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input user attribute information data into a generating AI and have the generating AI perform adjustments to the negotiation process.

[0045] The negotiation unit can select the optimal negotiation method during negotiations, taking into account the user's geographical location information. For example, if the user wishes to conduct business in a nearby area, the negotiation unit can prioritize negotiations with users in that nearby area. If the user wishes to conduct business in a distant area, the negotiation unit can prioritize negotiations with users in that distant area. If the user wishes to conduct business only in a specific area, the negotiation unit can prioritize negotiations with users in that area. In this way, the optimal negotiation method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the negotiation unit may be performed using AI, or not. For example, the negotiation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of selecting the optimal negotiation method.

[0046] The negotiation department can analyze the user's social media activity during negotiations and propose negotiation strategies. For example, the negotiation department can propose negotiation strategies related to items the user has shown interest in on social media. The negotiation department can analyze the content of posts from accounts the user follows on social media and propose relevant negotiation strategies. The negotiation department can analyze the activities of groups the user participates in on social media and propose relevant negotiation strategies. In this way, by analyzing the user's social media activity, more appropriate negotiation strategies can be proposed. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input the user's social media data into a generating AI and have the generating AI execute the proposal of negotiation strategies.

[0047] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data during the evaluation process. For example, the evaluation unit can optimize its evaluation algorithm based on evaluation data previously provided by users. The evaluation unit can optimize its evaluation algorithm by referring to evaluation data from other users. The evaluation unit can optimize its evaluation algorithm by strengthening specific evaluation criteria based on past evaluation data. In this way, the evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0048] The evaluation unit can perform evaluations while considering the user's attribute information. For example, the evaluation unit can apply appropriate evaluation criteria by considering the user's age and gender. The evaluation unit can adjust the evaluation process by considering the user's occupation and hobbies. The evaluation unit can adjust the evaluation process by considering the user's past transaction history. This makes it possible to perform more appropriate evaluations by considering the user's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user attribute information data into a generating AI and have the generating AI perform adjustments to the evaluation process.

[0049] The evaluation unit can perform evaluations while considering the user's geographical location information. For example, if the user lives in a specific region, the evaluation unit can perform evaluations while considering the characteristics of that region. If the user is traveling, the evaluation unit can perform evaluations while considering the characteristics of the travel destination. If the user is planning to move, the evaluation unit can perform evaluations while considering the characteristics of the new address. This makes it possible to perform more appropriate evaluations by considering the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the evaluation.

[0050] The evaluation unit can improve the accuracy of its evaluations by analyzing the user's social media activity during the evaluation process. For example, the evaluation unit can perform evaluations related to items the user has shown interest in on social media. The evaluation unit can analyze the content of posts from accounts the user follows on social media and perform relevant evaluations. The evaluation unit can analyze the activities of groups the user participates in on social media and perform relevant evaluations. In this way, the accuracy of the evaluation is improved by analyzing the user's social media activity. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[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 analyze a user's past purchase history and automatically suggest registering unwanted items. For example, the collection unit can detect items that a user has purchased in the past but have not used for a certain period of time and suggest registering them as unwanted items. If a user has purchased a new item in the same category, the collection unit can suggest the old item as unwanted. The collection unit can also suggest items that other users have highly rated as unwanted items. This allows for the efficient discovery and registration of unwanted items that users may have forgotten about.

[0053] The data collection unit can analyze the user's past transaction history and select the optimal information collection method. For example, it can analyze patterns of successful transactions in the past and collect information using similar methods. It can also analyze the causes of unsuccessful transactions in the past and collect information in a way that avoids those causes. By collecting information from the user's transaction history at specific times or days of the week, the success rate can be increased. In this way, the optimal information collection method can be selected by analyzing past transaction history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's transaction history data into a generating AI and have the generating AI select the optimal information collection method.

[0054] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if a user is planning to move, it can prioritize collecting items related to moving. If a user has started a new hobby, it can collect items related to that hobby. If a user is planning to attend a specific event, it can collect items related to that event. By filtering information based on the user's living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0055] The data collection unit can prioritize the collection of highly relevant item information by considering the user's geographical location during data collection. For example, if a user lives in a specific region, it can prioritize the collection of item information that can be traded in that region. If a user is traveling, it can collect information on items that can be traded at their travel destination. If a user is planning to move, it can collect item information related to their new address. This allows for the priority collection of highly relevant item information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant item information.

[0056] The data collection unit can collect relevant item information by analyzing the user's social media activity during data collection. For example, it can collect information related to items the user has shown interest in on social media. It can also collect relevant item information by analyzing the content of posts from accounts the user follows on social media. It can also collect relevant item information by analyzing the activities of groups the user participates in on social media. In this way, relevant item information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant item information.

[0057] The matching unit can improve the accuracy of matching by considering the interrelationships between items during the matching process. For example, if items have similar categories or uses, matching is prioritized. If items have equivalent values, matching accuracy can be improved. If items have similar usage frequency or conditions, matching accuracy can be improved. In this way, considering the interrelationships between items improves the accuracy of matching. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input item interrelationship data into the generation AI and have the generation AI perform the matching accuracy improvement.

[0058] The matching unit can apply different matching algorithms to each item category during the matching process. For example, for home appliances, a matching algorithm that considers performance and brand can be applied. For clothing, a matching algorithm that considers size and design can be applied. For books, a matching algorithm that considers genre and author can be applied. By applying different matching algorithms to each item category, the accuracy of the matching is improved. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input item category data into the generation AI and have the generation AI execute the application of different matching algorithms.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The collection unit collects information about the user's unwanted items and desired items. Users can log in to the app, register their unwanted items, and enter requests for desired items. For example, a user can register electronic devices as unwanted items and request furniture as desired items. The collection unit stores this information in a database and uses it for matching later. Step 2: The matching unit matches items based on the information collected by the collection unit. Using a generation AI, it considers item similarity and user attribute information to perform the optimal match. For example, it matches a bicycle registered by user A with furniture requested by user B. The matching unit can evaluate the value and condition of items to perform the optimal match. Step 3: The Negotiation Department facilitates negotiations between users matched by the Matching Department. User A and User B can exchange messages within the app and agree on the terms of the item exchange. The Negotiation Department supports the negotiations until the users reach an agreement and provides tools to facilitate the negotiation process. Step 4: The evaluation department evaluates users to make it easier to find reliable trading partners. Users enter their evaluation of their trading partners after a trade, and this evaluation information is stored in the database. Based on user evaluation information, the evaluation department can recommend reliable users.

[0061] (Example of form 2) The item exchange system according to an embodiment of the present invention is a system that allows users to offer unwanted items to others and exchange them for items they want. In this item exchange system, users can register their unwanted items and request items they want. A generating AI analyzes this information and performs optimal matching. For example, if user A registers a bicycle they no longer use and user B requests furniture, the generating AI will match the two. After matching, the users can negotiate and agree on the exchange of items. The item exchange system also has an evaluation system, making it easier for users to find reliable trading partners. The generating AI can also create a profile of the user based on the items they have registered and provide product recommendations and usage instructions. This allows users to effectively utilize unwanted items while simultaneously obtaining desired items. It is an app that matches "what you want" with "what you don't want" while also contributing to the environment. As a result, the item exchange system can efficiently collect, match, negotiate, and evaluate information on users' unwanted and desired items.

[0062] The item exchange system according to this embodiment comprises a collection unit, a matching unit, a negotiation unit, and an evaluation unit. The collection unit collects information on items that a user does not want and items that they want. For example, the collection unit allows a user to log in to the app and register their unwanted items. The collection unit also allows a user to input a request for an item they want. For example, the collection unit allows a user to register an electronic device as an unwanted item and request furniture as an item they want. The collection unit stores the user's input information in a database and uses it later for matching. The matching unit uses a generation AI to match items based on the information collected by the collection unit. For example, the matching unit compares an unwanted item registered by user A with an item requested by user B and performs the optimal match. The matching unit can use a generation AI to perform matching while considering the similarity of items and the user's attribute information. For example, the matching unit matches a bicycle registered by user A with furniture requested by user B. The matching unit can use a generation AI to evaluate the value and condition of items and perform the optimal match. The Negotiation Unit facilitates negotiations between users matched by the Matching Unit. For example, the Negotiation Unit allows users A and B to exchange messages within the app and agree on the terms of item exchange. The Negotiation Unit supports negotiations until users reach an agreement. For example, the Negotiation Unit helps users agree that user A will provide a bicycle and user B will provide furniture. The Negotiation Unit can provide tools to facilitate negotiations between users. The Evaluation Unit evaluates users, making it easier to find reliable trading partners. For example, the Evaluation Unit allows users to input evaluations of their trading partners after a transaction. The Evaluation Unit stores user evaluation information in a database so that other users can refer to it. For example, the Evaluation Unit allows user A to input an evaluation of user B, rating them as a reliable trading partner. Based on user evaluation information, the Evaluation Unit can recommend reliable users. As a result, the item exchange system according to this embodiment can efficiently collect, match, negotiate, and evaluate information on unwanted and desired items from users.

[0063] The collection unit collects information on items users want and items they no longer need. For example, users can log in to the app and register their unwanted items. Specifically, users can enter detailed information about unwanted items into a dedicated form in the app and upload photos. This allows other users to visually check the condition and characteristics of the items. The collection unit can also receive requests for items users want. For example, users can register electronic devices as unwanted items and request furniture as desired items. The collection unit stores the user's input information in a database for later use in matching. Furthermore, the collection unit can also collect users' past transaction history and rating information, which can be used as data to assess user reliability. This allows the collection unit to accurately understand user needs and available items, supporting efficient matching. The collection unit automatically categorizes the information entered by users and stores it in a searchable format in the database. For example, it organizes data based on attribute information such as item category, condition, and region, allowing for quick searching later. This allows the collection unit to efficiently manage user input information and improve the overall system performance.

[0064] The matching unit uses a generation AI to match items based on information collected by the collection unit. For example, the matching unit compares unwanted items registered by user A with desired items requested by user B to achieve the optimal match. Specifically, the generation AI can perform matching by considering the similarity of items and user attribute information. For example, the matching unit matches a bicycle registered by user A with furniture requested by user B. The generation AI can evaluate the value and condition of items to achieve the optimal match. The generation AI uses natural language processing technology to analyze the item description entered by the user and automatically extracts the item's characteristics and condition. Furthermore, the generation AI uses image recognition technology to analyze uploaded item photos and evaluate the item's condition and appearance. This allows the matching unit to achieve the optimal match between items provided by users and items requested by users. Using the generation AI, the matching unit can also consider users' past transaction history and evaluation information to prioritize matching highly reliable users with each other. This allows the matching unit to improve user satisfaction and enhance the overall reliability of the system.

[0065] The Negotiation Department facilitates negotiations between users matched by the Matching Department. For example, the Negotiation Department allows users A and B to exchange messages within the app and agree on the terms of item exchange. Specifically, the Negotiation Department supports negotiations until users reach an agreement. For instance, the Negotiation Department might help users agree that user A will provide a bicycle and user B will provide furniture. The Negotiation Department can provide tools to facilitate smooth negotiations between users. For example, it can provide message templates and automated response functions to enable users to negotiate quickly and efficiently. Furthermore, the Negotiation Department can monitor the progress of negotiations in real time and issue alerts as needed. This allows the Negotiation Department to support communication between users and ensure smooth transactions. Additionally, the Negotiation Department can save users' negotiation history in a database for future reference. This allows the Negotiation Department to evaluate users' negotiation skills and transaction success rates, improving the overall system performance.

[0066] The rating system evaluates users, making it easier for them to find reliable trading partners. For example, users can input ratings for their trading partners after a transaction. Specifically, the rating system provides a form for users to rate their satisfaction with the transaction, the quality of the trading partner's responsiveness, the condition of the items, and so on. The rating system stores user rating information in a database, making it available for other users to refer to. For example, the rating system allows user A to input a rating for user B, rating them as a reliable trading partner. Based on user ratings, the rating system can recommend reliable users. Specifically, the rating system analyzes user rating scores and past trading history to list reliable users. This makes it easier for other users to find reliable trading partners. Furthermore, the rating system can provide feedback based on user ratings to improve the overall reliability of the system. For example, the rating system can notify users who receive low ratings of areas for improvement and provide advice to improve the quality of their trading. This allows the rating system to improve user reliability and enhance the overall performance of the system.

[0067] Generative AI can create a profile of a user based on their registered items and recommend products and provide instructions on how to use them. For example, the generative AI can analyze information about items registered by a user and estimate the user's hobbies and preferences. For instance, based on the sports equipment registered by a user, the generative AI can estimate that the user is interested in sports. Based on the user's hobbies and preferences, the generative AI can recommend related products. For example, the generative AI will recommend sports-related items to a user who has registered sports equipment. The generative AI can also provide instructions on how to use the items registered by the user. For example, the generative AI can explain how to use electronic devices registered by a user using videos or text. This improves user convenience by allowing the generative AI to create a profile of the user based on their registered items and provide product recommendations and instructions on how to use them.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of collecting information on unwanted and desired items based on the estimated emotions. For example, if the user is stressed, the data collection unit will refrain from collecting information and try again when the user is relaxed. If the user is excited, the data collection unit will immediately collect information and proceed with matching quickly. If the user is tired, the data collection unit will postpone information collection until the next day, allowing the user to collect information when they are refreshed. By adjusting the timing of information collection according to the user's emotions, information can be collected at a more appropriate time. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0069] The data collection unit can analyze the user's past transaction history and select the optimal information collection method. For example, the data collection unit can analyze patterns of successful transactions in the past and collect information using similar methods. The data collection unit can analyze the causes of unsuccessful transactions in the past and collect information in a way that avoids those causes. The data collection unit can increase the success rate by collecting information from the user's transaction history at specific times of day or on specific days of the week. This allows the optimal information collection method to be selected by analyzing past transaction history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's transaction history data into a generating AI and have the generating AI select the optimal information collection method.

[0070] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is planning to move, the data collection unit can prioritize collecting items related to moving. If the user has started a new hobby, the data collection unit can collect items related to that hobby. If the user is planning to attend a specific event, the data collection unit can collect items related to that event. By filtering information based on the user's living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of item information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting information on desired items. If the user is relaxed, the data collection unit can prioritize collecting information on unwanted items. If the user is stressed, the data collection unit can temporarily lower the priority of item information to collect. This allows for the collection of more appropriate information by prioritizing item information according to the user'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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The data collection unit can prioritize the collection of highly relevant item information by considering the user's geographical location during data collection. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of item information that can be traded in that region. If the user is traveling, the data collection unit can collect information on items that can be traded at the travel destination. If the user is planning to move, the data collection unit can collect information on items related to the new address. In this way, by considering the user's geographical location, the data collection unit can prioritize the collection of highly relevant item information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant item information.

[0073] The data collection unit can analyze the user's social media activity and collect relevant item information during data collection. For example, the data collection unit can collect information related to items the user has shown interest in on social media. The data collection unit can analyze the content of posts from accounts the user follows on social media and collect relevant item information. The data collection unit can analyze the activities of groups the user participates in on social media and collect relevant item information. In this way, relevant item information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant item information.

[0074] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is excited, the matching unit prioritizes quick matching. If the user is relaxed, the matching unit can apply detailed matching criteria. If the user is stressed, the matching unit can relax the matching criteria and perform a simpler match. This allows for more appropriate matching by adjusting the matching criteria according to the user'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 matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI adjust the matching criteria.

[0075] The matching unit can improve the accuracy of matching by considering the interrelationships between items during the matching process. For example, the matching unit prioritizes matching items that are similar in category or use. The matching unit can improve the accuracy of matching when items have equivalent value. The matching unit can improve the accuracy of matching when items have similar usage frequency or condition. In this way, the accuracy of matching is improved by considering the interrelationships between items. Some or all of the above processes in the matching unit are performed using a generation AI. For example, the matching unit can input item interrelationship data into the generation AI and have the generation AI perform the matching accuracy improvement.

[0076] The matching unit can apply different matching algorithms to each item category during the matching process. For example, for home appliances, the matching unit can apply a matching algorithm that considers performance and brand. For clothing, the matching unit can apply a matching algorithm that considers size and design. For books, the matching unit can apply a matching algorithm that considers genre and author. By applying different matching algorithms to each item category, the accuracy of the matching is improved. Some or all of the above processing in the matching unit is performed using a generative AI. For example, the matching unit can input item category data into the generative AI and have the generative AI execute the application of different matching algorithms.

[0077] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated emotions. For example, if the user is excited, the matching unit can provide a visually stimulating display method. If the user is relaxed, the matching unit can provide a calm display method. If the user is stressed, the matching unit can provide a simple and easy-to-read display method. By adjusting the display method of the matching results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the matching unit is performed using the generative AI. For example, the matching unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0078] The matching unit can perform matching while considering the geographical distribution of items. For example, if a user wishes to trade in a nearby area, the matching unit will prioritize matching items in that nearby area. If a user wishes to trade in a distant area, the matching unit can match items in that distant area. If a user wishes to trade only in a specific area, the matching unit can match items in that area. This allows for more appropriate matching by considering the geographical distribution of items. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input geographical distribution data of items into the generation AI and have the generation AI perform the matching.

[0079] The matching unit can improve the accuracy of matching by referring to relevant literature for items during the matching process. For example, the matching unit can improve the accuracy of matching by referring to literature on how to use or maintain items. The matching unit can improve the accuracy of matching by referring to literature on the evaluation or review of items. The matching unit can improve the accuracy of matching by referring to literature on the market value of items. In this way, the accuracy of matching is improved by referring to relevant literature for items. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input relevant literature data for items into the generation AI and have the generation AI perform the matching accuracy improvement.

[0080] The negotiation unit can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is tense, the negotiation unit can proceed slowly to create a sense of security. If the user is relaxed, the negotiation unit can proceed quickly. If the user is excited, the negotiation unit can proceed quickly to aim for an immediate agreement. This allows for more appropriate negotiations by adjusting the negotiation process according to the user'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 negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the negotiation process.

[0081] The negotiation unit can select the optimal negotiation method by referring to past negotiation history during negotiations. For example, the negotiation unit can refer to negotiation methods that the user has succeeded with in the past and apply similar methods. The negotiation unit can refer to negotiation methods that the user has failed with in the past and avoid those methods. The negotiation unit can select and apply specific negotiation methods from the user's negotiation history. In this way, the optimal negotiation method can be selected by referring to past negotiation history. Some or all of the above processes in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input the user's negotiation history data into a generating AI and have the generating AI perform the selection of the optimal negotiation method.

[0082] The negotiation department can conduct negotiations while considering the user's attribute information. For example, the negotiation department can select an appropriate negotiation method by considering the user's age and gender. The negotiation department can adjust the negotiation process by considering the user's occupation and hobbies. The negotiation department can adjust the negotiation process by considering the user's past transaction history. This makes it possible to conduct more appropriate negotiations by considering the user's attribute information. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input user attribute information data into a generating AI and have the generating AI perform adjustments to the negotiation process.

[0083] The negotiation unit can estimate the user's emotions and determine negotiation priorities based on those estimated emotions. For example, if the user is excited, the negotiation unit will prioritize that negotiation. If the user is relaxed, the negotiation unit may lower the priority of that negotiation. If the user is stressed, the negotiation unit may temporarily lower the priority of that negotiation. This allows for more appropriate negotiations by determining negotiation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI determine the negotiation priorities.

[0084] The negotiation unit can select the optimal negotiation method during negotiations, taking into account the user's geographical location information. For example, if the user wishes to conduct business in a nearby area, the negotiation unit can prioritize negotiations with users in that nearby area. If the user wishes to conduct business in a distant area, the negotiation unit can prioritize negotiations with users in that distant area. If the user wishes to conduct business only in a specific area, the negotiation unit can prioritize negotiations with users in that area. In this way, the optimal negotiation method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the negotiation unit may be performed using AI, or not. For example, the negotiation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of selecting the optimal negotiation method.

[0085] The negotiation department can analyze the user's social media activity during negotiations and propose negotiation strategies. For example, the negotiation department can propose negotiation strategies related to items the user has shown interest in on social media. The negotiation department can analyze the content of posts from accounts the user follows on social media and propose relevant negotiation strategies. The negotiation department can analyze the activities of groups the user participates in on social media and propose relevant negotiation strategies. In this way, by analyzing the user's social media activity, more appropriate negotiation strategies can be proposed. Some or all of the above processes in the negotiation department may be performed using AI or not. For example, the negotiation department can input the user's social media data into a generating AI and have the generating AI execute the proposal of negotiation strategies.

[0086] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is excited, the evaluation unit can apply strict evaluation criteria. If the user is relaxed, the evaluation unit can apply flexible evaluation criteria. If the user is stressed, the evaluation unit can relax the evaluation criteria and perform a simpler evaluation. This allows for a more appropriate evaluation by adjusting the evaluation criteria according to the user'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 evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0087] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data during the evaluation process. For example, the evaluation unit can optimize its evaluation algorithm based on evaluation data previously provided by users. The evaluation unit can optimize its evaluation algorithm by referring to evaluation data from other users. The evaluation unit can optimize its evaluation algorithm by strengthening specific evaluation criteria based on past evaluation data. In this way, the evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0088] The evaluation unit can perform evaluations while considering the user's attribute information. For example, the evaluation unit can apply appropriate evaluation criteria by considering the user's age and gender. The evaluation unit can adjust the evaluation process by considering the user's occupation and hobbies. The evaluation unit can adjust the evaluation process by considering the user's past transaction history. This makes it possible to perform more appropriate evaluations by considering the user's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user attribute information data into a generating AI and have the generating AI perform adjustments to the evaluation process.

[0089] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is excited, the evaluation unit can provide a visually stimulating display method. If the user is relaxed, the evaluation unit can provide a calm display method. If the user is stressed, the evaluation unit can provide a simple and highly visible display method. By adjusting the display method of the evaluation results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0090] The evaluation unit can perform evaluations while considering the user's geographical location information. For example, if the user lives in a specific region, the evaluation unit can perform evaluations while considering the characteristics of that region. If the user is traveling, the evaluation unit can perform evaluations while considering the characteristics of the travel destination. If the user is planning to move, the evaluation unit can perform evaluations while considering the characteristics of the new address. This makes it possible to perform more appropriate evaluations by considering the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the evaluation.

[0091] The evaluation unit can improve the accuracy of its evaluations by analyzing the user's social media activity during the evaluation process. For example, the evaluation unit can perform evaluations related to items the user has shown interest in on social media. The evaluation unit can analyze the content of posts from accounts the user follows on social media and perform relevant evaluations. The evaluation unit can analyze the activities of groups the user participates in on social media and perform relevant evaluations. In this way, the accuracy of the evaluation is improved by analyzing the user's social media activity. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The collection unit can analyze a user's past purchase history and automatically suggest registering unwanted items. For example, the collection unit can detect items that a user has purchased in the past but have not used for a certain period of time and suggest registering them as unwanted items. If a user has purchased a new item in the same category, the collection unit can suggest the old item as unwanted. The collection unit can also suggest items that other users have highly rated as unwanted items. This allows for the efficient discovery and registration of unwanted items that users may have forgotten about.

[0094] The matching unit can estimate the user's emotions and adjust the matching priority based on the estimated emotions. For example, if the user is excited, it prioritizes quick matching. If the user is relaxed, detailed matching criteria can be applied. If the user is stressed, the matching criteria can be relaxed, and a simpler match can be performed. This allows for more appropriate matching by adjusting the matching priority according to the user'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 matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI adjust the matching criteria.

[0095] The negotiation unit can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is nervous, the negotiation can proceed slowly to create a sense of security. If the user is relaxed, the negotiation can proceed quickly. If the user is excited, the negotiation can proceed quickly to aim for an immediate agreement. This allows for more appropriate negotiations by adjusting the negotiation process according to the user'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-described processes in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the negotiation process.

[0096] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is excited, strict evaluation criteria can be applied. If the user is relaxed, flexible evaluation criteria can be applied. If the user is stressed, the evaluation criteria can be relaxed and a simple evaluation can be performed. This allows for a more appropriate evaluation by adjusting the evaluation criteria according to the user'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 evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0097] The data collection unit can analyze the user's past transaction history and select the optimal information collection method. For example, it can analyze patterns of successful transactions in the past and collect information using similar methods. It can also analyze the causes of unsuccessful transactions in the past and collect information in a way that avoids those causes. By collecting information from the user's transaction history at specific times or days of the week, the success rate can be increased. In this way, the optimal information collection method can be selected by analyzing past transaction history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's transaction history data into a generating AI and have the generating AI select the optimal information collection method.

[0098] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if a user is planning to move, it can prioritize collecting items related to moving. If a user has started a new hobby, it can collect items related to that hobby. If a user is planning to attend a specific event, it can collect items related to that event. By filtering information based on the user's living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0099] The data collection unit can prioritize the collection of highly relevant item information by considering the user's geographical location during data collection. For example, if a user lives in a specific region, it can prioritize the collection of item information that can be traded in that region. If a user is traveling, it can collect information on items that can be traded at their travel destination. If a user is planning to move, it can collect item information related to their new address. This allows for the priority collection of highly relevant item information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant item information.

[0100] The data collection unit can collect relevant item information by analyzing the user's social media activity during data collection. For example, it can collect information related to items the user has shown interest in on social media. It can also collect relevant item information by analyzing the content of posts from accounts the user follows on social media. It can also collect relevant item information by analyzing the activities of groups the user participates in on social media. In this way, relevant item information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant item information.

[0101] The matching unit can improve the accuracy of matching by considering the interrelationships between items during the matching process. For example, if items have similar categories or uses, matching is prioritized. If items have equivalent values, matching accuracy can be improved. If items have similar usage frequency or conditions, matching accuracy can be improved. In this way, considering the interrelationships between items improves the accuracy of matching. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input item interrelationship data into the generation AI and have the generation AI perform the matching accuracy improvement.

[0102] The matching unit can apply different matching algorithms to each item category during the matching process. For example, for home appliances, a matching algorithm that considers performance and brand can be applied. For clothing, a matching algorithm that considers size and design can be applied. For books, a matching algorithm that considers genre and author can be applied. By applying different matching algorithms to each item category, the accuracy of the matching is improved. Some or all of the above processing in the matching unit is performed using a generation AI. For example, the matching unit can input item category data into the generation AI and have the generation AI execute the application of different matching algorithms.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The collection unit collects information about the user's unwanted items and desired items. Users can log in to the app, register their unwanted items, and enter requests for desired items. For example, a user can register electronic devices as unwanted items and request furniture as desired items. The collection unit stores this information in a database and uses it for matching later. Step 2: The matching unit matches items based on the information collected by the collection unit. Using a generation AI, it considers item similarity and user attribute information to perform the optimal match. For example, it matches a bicycle registered by user A with furniture requested by user B. The matching unit can evaluate the value and condition of items to perform the optimal match. Step 3: The Negotiation Department facilitates negotiations between users matched by the Matching Department. User A and User B can exchange messages within the app and agree on the terms of the item exchange. The Negotiation Department supports the negotiations until the users reach an agreement and provides tools to facilitate the negotiation process. Step 4: The evaluation department evaluates users to make it easier to find reliable trading partners. Users enter their evaluation of their trading partners after a trade, and this evaluation information is stored in the database. Based on user evaluation information, the evaluation department can recommend reliable users.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the collection unit, matching unit, negotiation unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14, allowing users to log in to the app, register unwanted items, and input requests for desired items. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12, performing item matching based on collected information using a generation AI. The negotiation unit is implemented by the control unit 46A of the smart device 14, allowing users to exchange messages within the app and determine the terms of item exchange. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12, storing user evaluation information in a database for other users to refer to. 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.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, matching unit, negotiation unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214, allowing users to log in to the app, register unwanted items, and input requests for desired items. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12, performing item matching based on collected information using a generating AI. The negotiation unit is implemented by the control unit 46A of the smart glasses 214, allowing users to exchange messages within the app and determine the terms of item exchange. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12, storing user evaluation information in a database for other users to refer to. 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.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, matching unit, negotiation unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, allowing users to log in to the app, register unwanted items, and input requests for desired items. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12, performing item matching based on collected information using a generation AI. The negotiation unit is implemented by the control unit 46A of the headset terminal 314, allowing users to exchange messages within the app and determine the terms of item exchange. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12, storing user evaluation information in a database for other users to refer to. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the collection unit, matching unit, negotiation unit, and evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414, allowing users to log in to the app, register unwanted items, and input requests for desired items. The matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which matches items based on information collected using a generating AI. The negotiation unit is implemented by, for example, the control unit 46A of the robot 414, allowing users to exchange messages within the app and determine the terms of item exchange. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which stores user evaluation information in a database so that other users can refer to it. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A collection unit that collects information on items that users do not want and items they want, Based on the information collected by the aforementioned collection unit, a matching unit performs item matching, The matching unit conducts negotiations with the users who have been matched by the matching unit, It comprises an evaluation unit that performs user evaluations. A system characterized by the following features. (Note 2) The generating AI is, Based on the items registered by the user, a profile of the user is assumed, and product recommendations and usage instructions are provided. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of information gathering on unwanted and desired items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past transaction history and select the optimal method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and determines the priority of item information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant item information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant item information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The matching unit is During matchmaking, the system improves the accuracy of matching by considering the interrelationships between items. The system described in Appendix 1, characterized by the features described herein. (Note 11) The matching unit is When matching items, different matching algorithms are applied to each item category. The system described in Appendix 1, characterized by the features described herein. (Note 12) The matching unit is The system estimates the user's emotions and adjusts how matching results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is During matchmaking, the geographical distribution of items is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is During the matching process, we improve the accuracy of the matching by referring to related literature for the items. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned negotiating body said, During negotiations, the optimal negotiation method is selected by referring to past negotiation history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned negotiating body said, When negotiating, we take user attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned negotiating body said, The system estimates the user's emotions and determines negotiation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned negotiating body said, During negotiations, the optimal negotiation method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned negotiating body said, During negotiations, we analyze users' social media activity and propose negotiation strategies. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation process, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, user attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During the evaluation process, the user's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, During the evaluation process, we analyze users' social media activity to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 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. A collection unit that collects information on items that users do not want and items they want, Based on the information collected by the aforementioned collection unit, a matching unit performs item matching, The matching unit conducts negotiations with the users who have been matched by the matching unit, It comprises an evaluation unit that performs user evaluations. A system characterized by the following features.

2. Generative AI is, Based on the items registered by the user, a profile of the user is assumed, and product recommendations and usage instructions are provided. The system according to feature 1.

3. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of information gathering on unwanted and desired items based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past transaction history and select the optimal method for gathering information. The system according to feature 1.

5. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and determines the priority of item information to collect based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant item information, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant item information. The system according to feature 1.

9. The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system according to feature 1.

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