System

Abstract

An object of a system according to an embodiment is to efficiently match an unnecessary item with another user on the basis of a lifestyle or preference of a user.SOLUTION: A system includes an analysis part, a matching part, and a communication part. The analysis unit analyzes the lifestyle or preference of the user. The matching unit matches the unnecessary item with another user based on the information analyzed by the analysis unit. The contact unit supports contact between the users matched by the matching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately match unwanted items with other users efficiently based on a user's lifestyle and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently match unwanted items with other users based on the user's lifestyle and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a matching unit, and a communication unit. The analysis unit analyzes a user's lifestyle or preferences. The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. The communication unit supports communication between users matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match unwanted items with other users based on the user's lifestyle and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A matching system according to an embodiment of the present invention analyzes a user's lifestyle and preferences and matches unwanted items with other users. The matching system promotes the sharing economy, promotes the efficient use of resources, and fosters sustainable lifestyles by allowing users to register unwanted items and use a generation AI to match users with other users based on that information. For example, the matching system allows users to offer furniture or clothing they no longer use to other users, allowing them to obtain needed items. For example, in the matching system, a user registers unwanted items on the platform. At this time, the user enters detailed information about the item (e.g., type, condition, and period of use). The matching system then uses a generation AI to analyze the user's lifestyle and preferences. The generation AI understands the user's preferences and lifestyle based on the user's past transaction history, registration information, and profile information. The matching system then uses the generation AI to match the registered unwanted items with the needs of other users. For example, if a user registers unwanted furniture, the generation AI identifies other users who need that furniture. Once a match is established, the matching system supports communication between users. For example, users can use a chat function to coordinate the method and date of item exchange. The matching system also provides functionality to ensure the security of transactions. For example, it has a transaction rating system and a photo upload function to check the condition of the item. This allows the matching system to make effective use of unwanted items and obtain the items users need. This allows the matching system to promote the sharing economy, making effective use of resources and realizing a sustainable lifestyle. For example, by offering furniture or clothing that users no longer use to other users, they can avoid having to purchase new items, leading to resource conservation.

[0029] A matching system according to an embodiment includes an analysis unit, a matching unit, and a communication unit. The analysis unit analyzes a user's lifestyle or preferences. The analysis unit analyzes the user's preferences and lifestyle based on, for example, the user's past transaction history, registration information, and profile information. The analysis unit can also analyze the user's behavioral patterns and consumption habits using a generation AI. For example, the analysis unit analyzes the user's past purchase history to identify preferences for specific items. The analysis unit can also analyze the user's hobbies and interests based on the user's profile information. For example, the analysis unit analyzes the user's self-introduction and hobby descriptions to understand the user's lifestyle. The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. For example, the matching unit matches registered unwanted items with the needs of other users. For example, the matching unit uses a generation AI to compare unwanted items with the needs of other users and perform optimal matching. The matching unit can also improve matching accuracy by taking the user's lifestyle and preferences into account. For example, the matching unit suggests optimal items based on the user's preferences and lifestyle. The communication unit supports communication between users matched by the matching unit. The communication unit supports users in coordinating the method and date / time of item delivery through, for example, a chat function. The communication unit can also provide a transaction rating system and a photo upload function for checking the condition of the item. For example, the communication unit provides a system for users to rate transactions and ensure the security of the transaction. The communication unit also provides a function for users to upload photos to check the condition of the item. In this way, the matching system according to the embodiment can promote the sharing economy and encourage effective use of resources and sustainable lifestyles by analyzing the lifestyles and preferences of users and matching unwanted items with other users.

[0030] The analysis unit can analyze a user's preferences or lifestyle based on the user's past transaction history, registration information, or profile information. The analysis unit, for example, analyzes the user's past transaction history to identify the user's preferences and lifestyle. For example, the analysis unit can analyze the types and frequency of items the user has purchased in the past to identify the user's consumption patterns. The analysis unit can also analyze the user's basic information and interests based on the user's registration information. For example, the analysis unit can analyze the user's basic information, such as age, gender, and address, to understand the user's lifestyle. The analysis unit can also analyze the user's hobbies and occupation based on the user's profile information. For example, the analysis unit can analyze the user's self-introduction and hobby descriptions to identify the user's preferences. This enables more accurate matching by analyzing the user's preferences and lifestyle based on the user's past transaction history, registration information, and profile information.

[0031] The matching unit can match registered unwanted items with the needs of other users. For example, the matching unit uses a generation AI to compare registered unwanted items with the needs of other users and perform optimal matching. For example, the matching unit identifies other users who need the unwanted furniture registered by the user. The matching unit can also improve the accuracy of matching by taking into account the user's lifestyle and preferences. For example, the matching unit suggests optimal items based on the user's preferences and lifestyle. The matching unit can also promote effective use of unwanted items based on the user's needs. For example, when a user registers unwanted clothing, the matching unit identifies other users who need that clothing and performs matching. In this way, by matching registered unwanted items with the needs of other users, effective use of unwanted items is promoted.

[0032] The communication unit can support users in coordinating the method or date and time of item delivery between each other through the chat function. The communication unit, for example, supports users in coordinating the method and date and time of item delivery between each other through the chat function. For example, the communication unit helps users decide the location and date and time of item delivery through chat. The communication unit can also provide a platform for users to confirm transaction details and exchange necessary information. For example, the communication unit supports users in asking questions about the condition or usage of an item through chat. This helps users coordinate the method and date and time of item delivery between each other through the chat function, thereby facilitating transactions.

[0033] The communication unit may provide a transaction rating system or a photo upload function for checking the condition of an item. The communication unit, for example, provides a transaction rating system and supports users in rating transactions. For example, the communication unit provides a system that allows users to rate their satisfaction with a transaction and leave comments. The communication unit may also provide a photo upload function for checking the condition of an item. For example, the communication unit supports users in uploading photos of items and other users in checking the photos. In this way, providing a transaction rating system or a photo upload function for checking the condition of an item improves the safety of transactions.

[0034] The analysis unit can analyze the user's past transaction history in detail and extract specific patterns and trends. For example, the analysis unit can identify the types of items that the user has frequently traded in the past and suggest similar items. The analysis unit can also extract items that are traded more frequently in specific seasons from the user's transaction history and make seasonal suggestions. The analysis unit can also analyze the user's transaction history and extract preferences for specific brands and categories. This makes it possible to extract specific patterns and trends by analyzing the user's past transaction history in detail, enabling more accurate suggestions.

[0035] The analysis unit can predict lifestyle changes based on the user's registration information and profile information. The analysis unit predicts lifestyle changes based on, for example, the user's registration information and profile information. For example, the analysis unit can predict changes in life stages based on the user's age and occupation and suggest appropriate items. The analysis unit can also suggest items suitable for a new environment based on the user's address change information. The analysis unit can also predict changes in the user's family composition and suggest items suitable for the entire family. This makes it possible to suggest more appropriate items by predicting lifestyle changes based on the user's registration information and profile information.

[0036] The analysis unit can analyze the user's social media activity to more accurately understand the user's preferences or interests. The analysis unit, for example, analyzes the user's social media activity to more accurately understand the user's preferences and interests. For example, the analysis unit analyzes posts that the user has "liked" on social media and suggests related items. The analysis unit can also analyze the activities of the user's followers and friends on social media and suggest items of common interest. The analysis unit can also analyze the content of the user's social media posts and suggest items that the user may be interested in. In this way, by analyzing the user's social media activity, the user's preferences and interests can be more accurately understood, making it possible to suggest more appropriate items.

[0037] The analysis unit can analyze a region-specific lifestyle or preference based on the user's geographical location information. The analysis unit can, for example, analyze a region-specific lifestyle or preference by taking into account the user's geographical location information. For example, the analysis unit can suggest appropriate items based on the climate of the region in which the user lives. The analysis unit can also suggest related items based on the culture and customs of the user's region. The analysis unit can also suggest related items based on events and festivals in the user's region. This makes it possible to make suggestions based on a region-specific lifestyle or preference by taking into account the user's geographical location information.

[0038] The analysis unit can analyze the user's purchase history and make suggestions to encourage the user to register unnecessary items. The analysis unit, for example, analyzes the user's purchase history and makes suggestions to encourage the user to register unnecessary items. For example, the analysis unit can identify infrequently used items among items purchased by the user in the past and suggest that the user register the items as unnecessary items. The analysis unit can also suggest that the user register multiple items of the same category as unnecessary items based on the user's purchase history. The analysis unit can also analyze the user's purchase history to identify items that have not been used for a specific period of time and suggest that the user register the items as unnecessary items. In this way, analyzing the user's purchase history encourages the user to register unnecessary items and promotes the sharing economy.

[0039] The analysis unit can continuously improve the analysis algorithm by reflecting user feedback. For example, the analysis unit continuously improves the analysis algorithm by reflecting user feedback. For example, the analysis unit improves the accuracy of the analysis algorithm based on feedback provided by the user. The analysis unit can also adjust the frequency of suggesting specific items by reflecting user feedback. The analysis unit can also add new analysis parameters and improve the algorithm based on user feedback. In this way, by reflecting user feedback, the accuracy of the analysis algorithm can be improved, enabling more appropriate suggestions.

[0040] The matching unit can improve the accuracy of matching based on detailed information about the registered items. The matching unit improves the accuracy of matching by taking into account, for example, detailed information about the registered items (e.g., condition, period of use, etc.). For example, if the condition of the item is good, the matching unit matches it to more users. Also, if the item has only been used for a short period of time, the matching unit can match it as a nearly new item. Also, the matching unit can match items that meet the needs of a specific user based on the detailed information about the item. In this way, the accuracy of matching is improved by taking into account the detailed information about the registered items.

[0041] The matching unit can analyze the user's past matching history and identify a matching method with a high success rate. The matching unit, for example, analyzes the user's past matching history and identifies a matching method with a high success rate. For example, the matching unit analyzes patterns of successful matching by the user in the past and prioritizes similar patterns. The matching unit can also analyze the success rate of specific item categories from the user's past matching history. The matching unit can also identify an optimal matching method based on the user's past matching history. In this way, by analyzing the user's past matching history, a matching method with a high success rate can be identified, enabling more appropriate matching.

[0042] The matching unit can apply an appropriate matching algorithm based on the user's lifestyle or preferences. The matching unit applies an optimal matching algorithm based on, for example, the user's lifestyle or preferences. For example, the matching unit prioritizes matching items that suit the user's lifestyle. The matching unit can also prioritize matching items of a specific brand or category based on the user's preferences. The matching unit can also apply an optimal matching algorithm taking the user's lifestyle and preferences into consideration. This enables more appropriate matching by applying an optimal matching algorithm based on the user's lifestyle and preferences.

[0043] The matching unit can prioritize matching with nearby users based on the user's geographical location information. The matching unit prioritizes matching with nearby users, for example, taking into account the user's geographical location information. For example, the matching unit prioritizes matching with nearby users based on the user's current location. The matching unit can also prioritize displaying nearby items based on the user's geographical location information. The matching unit can also perform optimal matching by taking into account the user's geographical location information. As a result, by taking into account the user's geographical location information, matching with nearby users is prioritized, improving the convenience of transactions.

[0044] The matching unit can analyze the user's social media activity and match users with common interests. The matching unit, for example, analyzes the user's social media activity and matches users with common interests. For example, the matching unit analyzes the user's "likes" and follows on social media and matches users with common interests. The matching unit can also analyze the content of the user's posts on social media and match users with common interests. The matching unit can also analyze the user's friendships on social media and match users with common interests. In this way, by analyzing the user's social media activity, it is possible to match users with common interests.

[0045] The matching unit can continuously improve the matching algorithm by reflecting user feedback. For example, the matching unit continuously improves the matching algorithm by reflecting user feedback. For example, the matching unit improves the accuracy of the matching algorithm based on feedback provided by the user. The matching unit can also adjust the matching frequency of a specific item by reflecting user feedback. The matching unit can also add new matching parameters and improve the algorithm based on user feedback. In this way, by reflecting user feedback, the accuracy of the matching algorithm can be improved, enabling more appropriate matching.

[0046] The communication unit can analyze the user's past communication history and suggest an appropriate means of communication. For example, the communication unit analyzes the user's past communication history and suggests the optimal means of communication. For example, the communication unit prioritizes and suggests communication means (chat, voice message, etc.) that the user has used in the past. The communication unit can also predict and suggest the communication means to be used during a specific time period based on the user's past communication history. The communication unit can also analyze the user's past communication history and suggest the most efficient means of communication. In this way, the optimal means of communication is suggested by analyzing the user's past communication history, improving communication efficiency.

[0047] The contact unit can appropriately adjust the timing of contact based on the user's lifestyle or preferences. The contact unit, for example, suggests the optimal time to contact based on the user's lifestyle. For example, if the user is a night owl, the contact unit contacts the user at night. The contact unit can also adjust the frequency of contact based on the user's preferences. For example, if the user prefers frequent contact, the contact unit increases the frequency of contact. The contact unit can also optimize the timing of contact taking the user's schedule into consideration. For example, the contact unit contacts the user during times when the user is not in a meeting. In this way, by optimizing the timing of contact based on the user's lifestyle and preferences, it becomes possible to contact the user at more appropriate times.

[0048] The communication unit can continuously improve the communication function by reflecting user feedback. The communication unit, for example, continuously improves the communication function by reflecting user feedback. For example, the communication unit improves the usability of the communication function based on feedback provided by the user. The communication unit can also adjust the priority of specific communication means by reflecting user feedback. The communication unit can also add new communication options and improve the function based on user feedback. In this way, by reflecting user feedback, the accuracy of the communication function can be improved, allowing for more appropriate communication.

[0049] The contact unit can suggest an appropriate means of contact based on the user's geographical location information. The contact unit, for example, considers the user's geographical location information to suggest the optimal means of contact. For example, the contact unit may preferentially suggest a voice message when the user is on the move. The contact unit may also preferentially suggest a chat function when the user is at home. The contact unit may also suggest a quiet means of contact (such as a text message) when the user is in a public place. In this way, the optimal means of contact is suggested by considering the user's geographical location information, improving communication efficiency.

[0050] The contact unit can analyze the user's social media activity and promote contact between users who share common interests. The contact unit can, for example, analyze the user's social media activity and promote contact between users who share common interests. For example, the contact unit can analyze the user's "likes" and follows on social media and promote contact between users who share common interests. The contact unit can also analyze the content of the user's posts on social media and promote contact between users who share common interests. The contact unit can also analyze the user's friendships on social media and promote contact between users who share common interests. In this way, by analyzing the user's social media activity, contact between users who share common interests can be promoted.

[0051] The communication unit can customize the communication function by reflecting user feedback. The communication unit, for example, customizes the communication function by reflecting user feedback. For example, the communication unit adds customization options for the communication function based on feedback provided by the user. The communication unit can also customize specific communication methods by reflecting user feedback. The communication unit can also add new customization options and improve the communication function based on user feedback. In this way, the communication function is customized by reflecting user feedback, enabling more appropriate communication.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The matching system may further include a health management unit that acquires the user's health data and suggests items based on the user's health condition. For example, the health management unit may analyze data acquired from the user's fitness tracker or smartwatch and suggest items according to the user's health condition. For example, if the user is not getting enough exercise, the health management unit may suggest fitness equipment or sports goods. Also, if the user is feeling stressed, the health management unit may suggest relaxation items or stress relief goods. Furthermore, if the user has set a specific health goal, the health management unit may suggest items that match that goal. This makes it possible to suggest more appropriate items based on the user's health condition.

[0054] The matching unit can prioritize matching with nearby users based on the user's geographical location information. For example, matching with nearby users can be prioritized based on the user's current location. Also, nearby items can be preferentially displayed based on the user's geographical location information. Furthermore, optimal matching can be performed taking into account the user's geographical location information. As a result, by taking into account the user's geographical location information, matching with nearby users is prioritized, improving the convenience of transactions.

[0055] The contact unit can analyze the user's social media activity and promote contact between users who share common interests. For example, it can analyze the user's "likes" and follows on social media to promote contact between users who share common interests. It can also analyze the content of the user's posts on social media to promote contact between users who share common interests. It can also analyze the user's friendships on social media to promote contact between users who share common interests. In this way, analyzing the user's social media activity promotes contact between users who share common interests.

[0056] The analysis unit can analyze a user's purchasing history and make suggestions to encourage the registration of unnecessary items. For example, it can identify infrequently used items among items purchased by the user in the past and suggest that the user register them as unnecessary items. It can also suggest that if there are multiple items in the same category, the user register them as unnecessary items. It can also identify items that have not been used for a specific period of time and suggest that the user register them as unnecessary items. In this way, analyzing a user's purchasing history encourages the registration of unnecessary items and promotes the sharing economy.

[0057] The contact unit can suggest an appropriate means of contact based on the user's geographical location information. For example, if the user is on the move, it can preferentially suggest a voice message. Also, if the user is at home, it can preferentially suggest a chat function. Furthermore, if the user is in a public place, it can suggest a quiet means of contact (such as a text message). In this way, by taking the user's geographical location information into consideration, the optimal means of contact is suggested, improving communication efficiency.

[0058] The analysis unit can continuously improve the analysis algorithm by reflecting user feedback. For example, it can improve the accuracy of the analysis algorithm based on feedback provided by the user. It can also adjust the frequency of suggesting specific items. It can also add new analysis parameters and improve the algorithm. In this way, by reflecting user feedback, the accuracy of the analysis algorithm can be improved, allowing for more appropriate suggestions.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The analysis unit analyzes the user's lifestyle or preferences. Specifically, it analyzes the user's preferences and lifestyle based on the user's past transaction history, registration information, and profile information. Generative AI can also be used to analyze the user's behavioral patterns and consumption habits. For example, it can analyze the user's past purchase history to identify preferences for specific items. Furthermore, it analyzes the user's hobbies and interests based on the user's profile information to understand the user's lifestyle. Step 2: The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. Specifically, it matches registered unwanted items with the needs of other users. Using generation AI, it compares unwanted items with the needs of other users to achieve optimal matching. It also takes into account the user's lifestyle and preferences to improve the accuracy of matching. For example, it suggests optimal items based on the user's preferences and lifestyle. Step 3: The communication unit supports communication between users matched by the matching unit. Specifically, it supports users in coordinating the method and date of item delivery through a chat function. It also provides a transaction rating system and a photo upload function for checking the item's condition. For example, it provides a system for users to rate transactions, ensuring the safety of transactions. It also provides a function for users to upload photos for checking the item's condition.

[0061] (Example 2) A matching system according to an embodiment of the present invention analyzes a user's lifestyle and preferences and matches unwanted items with other users. The matching system promotes the sharing economy, promotes the efficient use of resources, and fosters sustainable lifestyles by allowing users to register unwanted items and use a generation AI to match users with other users based on that information. For example, the matching system allows users to offer furniture or clothing they no longer use to other users, allowing them to obtain needed items. For example, in the matching system, a user registers unwanted items on the platform. At this time, the user enters detailed information about the item (e.g., type, condition, and period of use). The matching system then uses a generation AI to analyze the user's lifestyle and preferences. The generation AI understands the user's preferences and lifestyle based on the user's past transaction history, registration information, and profile information. The matching system then uses the generation AI to match the registered unwanted items with the needs of other users. For example, if a user registers unwanted furniture, the generation AI identifies other users who need that furniture. Once a match is established, the matching system supports communication between users. For example, users can use a chat function to coordinate the method and date of item exchange. The matching system also provides functionality to ensure the security of transactions. For example, it has a transaction rating system and a photo upload function to check the condition of the item. This allows the matching system to make effective use of unwanted items and obtain the items users need. This allows the matching system to promote the sharing economy, making effective use of resources and realizing a sustainable lifestyle. For example, by offering furniture or clothing that users no longer use to other users, they can avoid having to purchase new items, leading to resource conservation.

[0062] A matching system according to an embodiment includes an analysis unit, a matching unit, and a communication unit. The analysis unit analyzes a user's lifestyle or preferences. The analysis unit analyzes the user's preferences and lifestyle based on, for example, the user's past transaction history, registration information, and profile information. The analysis unit can also analyze the user's behavioral patterns and consumption habits using a generation AI. For example, the analysis unit analyzes the user's past purchase history to identify preferences for specific items. The analysis unit can also analyze the user's hobbies and interests based on the user's profile information. For example, the analysis unit analyzes the user's self-introduction and hobby descriptions to understand the user's lifestyle. The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. For example, the matching unit matches registered unwanted items with the needs of other users. For example, the matching unit uses a generation AI to compare unwanted items with the needs of other users and perform optimal matching. The matching unit can also improve matching accuracy by taking the user's lifestyle and preferences into account. For example, the matching unit suggests optimal items based on the user's preferences and lifestyle. The communication unit supports communication between users matched by the matching unit. The communication unit supports users in coordinating the method and date / time of item delivery through, for example, a chat function. The communication unit can also provide a transaction rating system and a photo upload function for checking the condition of the item. For example, the communication unit provides a system for users to rate transactions and ensure the security of the transaction. The communication unit also provides a function for users to upload photos to check the condition of the item. In this way, the matching system according to the embodiment can promote the sharing economy and encourage effective use of resources and sustainable lifestyles by analyzing the lifestyles and preferences of users and matching unwanted items with other users.

[0063] The analysis unit can analyze a user's preferences or lifestyle based on the user's past transaction history, registration information, or profile information. The analysis unit, for example, analyzes the user's past transaction history to identify the user's preferences and lifestyle. For example, the analysis unit can analyze the types and frequency of items the user has purchased in the past to identify the user's consumption patterns. The analysis unit can also analyze the user's basic information and interests based on the user's registration information. For example, the analysis unit can analyze the user's basic information, such as age, gender, and address, to understand the user's lifestyle. The analysis unit can also analyze the user's hobbies and occupation based on the user's profile information. For example, the analysis unit can analyze the user's self-introduction and hobby descriptions to identify the user's preferences. This enables more accurate matching by analyzing the user's preferences and lifestyle based on the user's past transaction history, registration information, and profile information.

[0064] The matching unit can match registered unwanted items with the needs of other users. For example, the matching unit uses a generation AI to compare registered unwanted items with the needs of other users and perform optimal matching. For example, the matching unit identifies other users who need the unwanted furniture registered by the user. The matching unit can also improve the accuracy of matching by taking into account the user's lifestyle and preferences. For example, the matching unit suggests optimal items based on the user's preferences and lifestyle. The matching unit can also promote effective use of unwanted items based on the user's needs. For example, when a user registers unwanted clothing, the matching unit identifies other users who need that clothing and performs matching. In this way, by matching registered unwanted items with the needs of other users, effective use of unwanted items is promoted.

[0065] The communication unit can support users in coordinating the method or date and time of item delivery between each other through the chat function. The communication unit, for example, supports users in coordinating the method and date and time of item delivery between each other through the chat function. For example, the communication unit helps users decide the location and date and time of item delivery through chat. The communication unit can also provide a platform for users to confirm transaction details and exchange necessary information. For example, the communication unit supports users in asking questions about the condition or usage of an item through chat. This helps users coordinate the method and date and time of item delivery between each other through the chat function, thereby facilitating transactions.

[0066] The communication unit may provide a transaction rating system or a photo upload function for checking the condition of an item. The communication unit, for example, provides a transaction rating system and supports users in rating transactions. For example, the communication unit provides a system that allows users to rate their satisfaction with a transaction and leave comments. The communication unit may also provide a photo upload function for checking the condition of an item. For example, the communication unit supports users in uploading photos of items and other users in checking the photos. In this way, providing a transaction rating system or a photo upload function for checking the condition of an item improves the safety of transactions.

[0067] The analysis unit can estimate the user's emotions and adjust the lifestyle and preference analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the lifestyle and preference analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can have the generation AI prioritize suggesting items that will help them relax. Also, if the user is excited, the analysis unit can have the generation AI suggest items that are suited to an active lifestyle. Also, if the user is feeling depressed, the analysis unit can have the generation AI suggest items that will lift their spirits. This allows for more appropriate item suggestions by adjusting the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The analysis unit can analyze the user's past transaction history in detail and extract specific patterns and trends. For example, the analysis unit can identify the types of items that the user has frequently traded in the past and suggest similar items. The analysis unit can also extract items that are traded more frequently in specific seasons from the user's transaction history and make seasonal suggestions. The analysis unit can also analyze the user's transaction history and extract preferences for specific brands and categories. This makes it possible to extract specific patterns and trends by analyzing the user's past transaction history in detail, enabling more accurate suggestions.

[0069] The analysis unit can predict lifestyle changes based on the user's registration information and profile information. The analysis unit predicts lifestyle changes based on, for example, the user's registration information and profile information. For example, the analysis unit can predict changes in life stages based on the user's age and occupation and suggest appropriate items. The analysis unit can also suggest items suitable for a new environment based on the user's address change information. The analysis unit can also predict changes in the user's family composition and suggest items suitable for the entire family. This makes it possible to suggest more appropriate items by predicting lifestyle changes based on the user's registration information and profile information.

[0070] The analysis unit can analyze the user's social media activity to more accurately understand the user's preferences or interests. The analysis unit, for example, analyzes the user's social media activity to more accurately understand the user's preferences and interests. For example, the analysis unit analyzes posts that the user has "liked" on social media and suggests related items. The analysis unit can also analyze the activities of the user's followers and friends on social media and suggest items of common interest. The analysis unit can also analyze the content of the user's social media posts and suggest items that the user may be interested in. In this way, by analyzing the user's social media activity, the user's preferences and interests can be more accurately understood, making it possible to suggest more appropriate items.

[0071] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying items that help the user relax. Also, if the user is excited, the analysis unit can prioritize displaying items that suit an active lifestyle. Also, if the user is feeling depressed, the analysis unit can prioritize displaying items that will lift the user's spirits. This enables more appropriate items to be suggested by prioritizing the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The analysis unit can analyze a region-specific lifestyle or preference based on the user's geographical location information. The analysis unit can, for example, analyze a region-specific lifestyle or preference by taking into account the user's geographical location information. For example, the analysis unit can suggest appropriate items based on the climate of the region in which the user lives. The analysis unit can also suggest related items based on the culture and customs of the user's region. The analysis unit can also suggest related items based on events and festivals in the user's region. This makes it possible to make suggestions based on a region-specific lifestyle or preference by taking into account the user's geographical location information.

[0073] The analysis unit can analyze the user's purchase history and make suggestions to encourage the user to register unnecessary items. The analysis unit, for example, analyzes the user's purchase history and makes suggestions to encourage the user to register unnecessary items. For example, the analysis unit can identify infrequently used items among items purchased by the user in the past and suggest that the user register the items as unnecessary items. The analysis unit can also suggest that the user register multiple items of the same category as unnecessary items based on the user's purchase history. The analysis unit can also analyze the user's purchase history to identify items that have not been used for a specific period of time and suggest that the user register the items as unnecessary items. In this way, analyzing the user's purchase history encourages the user to register unnecessary items and promotes the sharing economy.

[0074] The analysis unit can continuously improve the analysis algorithm by reflecting user feedback. For example, the analysis unit continuously improves the analysis algorithm by reflecting user feedback. For example, the analysis unit improves the accuracy of the analysis algorithm based on feedback provided by the user. The analysis unit can also adjust the frequency of suggesting specific items by reflecting user feedback. The analysis unit can also add new analysis parameters and improve the algorithm based on user feedback. In this way, by reflecting user feedback, the accuracy of the analysis algorithm can be improved, enabling more appropriate suggestions.

[0075] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, the matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, if the user is feeling stressed, the matching unit can prioritize matching items that help them relax. Also, if the user is excited, the matching unit can prioritize matching items that suit an active lifestyle. Also, if the user is feeling depressed, the matching unit can prioritize matching items that will lift their spirits. This allows for more appropriate matching by adjusting the matching criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The matching unit can improve the accuracy of matching based on detailed information about the registered items. The matching unit improves the accuracy of matching by taking into account, for example, detailed information about the registered items (e.g., condition, period of use, etc.). For example, if the condition of the item is good, the matching unit matches it to more users. Also, if the item has only been used for a short period of time, the matching unit can match it as a nearly new item. Also, the matching unit can match items that meet the needs of a specific user based on the detailed information about the item. In this way, the accuracy of matching is improved by taking into account the detailed information about the registered items.

[0077] The matching unit can analyze the user's past matching history and identify a matching method with a high success rate. The matching unit, for example, analyzes the user's past matching history and identifies a matching method with a high success rate. For example, the matching unit analyzes patterns of successful matching by the user in the past and prioritizes similar patterns. The matching unit can also analyze the success rate of specific item categories from the user's past matching history. The matching unit can also identify an optimal matching method based on the user's past matching history. In this way, by analyzing the user's past matching history, a matching method with a high success rate can be identified, enabling more appropriate matching.

[0078] The matching unit can apply an appropriate matching algorithm based on the user's lifestyle or preferences. The matching unit applies an optimal matching algorithm based on, for example, the user's lifestyle or preferences. For example, the matching unit prioritizes matching items that suit the user's lifestyle. The matching unit can also prioritize matching items of a specific brand or category based on the user's preferences. The matching unit can also apply an optimal matching algorithm taking the user's lifestyle and preferences into consideration. This enables more appropriate matching by applying an optimal matching algorithm based on the user's lifestyle and preferences.

[0079] The matching unit can estimate the user's emotion and adjust the display method of the matching results based on the estimated user emotion. For example, the matching unit can estimate the user's emotion and adjust the display method of the matching results based on the estimated user emotion. For example, if the user is feeling stressed, the matching unit can provide a simple, highly visible display method. If the user is relaxed, the matching unit can also provide a display method that includes detailed information. If the user is in a hurry, the matching unit can also provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the matching results based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The matching unit can prioritize matching with nearby users based on the user's geographical location information. The matching unit prioritizes matching with nearby users, for example, taking into account the user's geographical location information. For example, the matching unit prioritizes matching with nearby users based on the user's current location. The matching unit can also prioritize displaying nearby items based on the user's geographical location information. The matching unit can also perform optimal matching by taking into account the user's geographical location information. As a result, by taking into account the user's geographical location information, matching with nearby users is prioritized, improving the convenience of transactions.

[0081] The matching unit can analyze the user's social media activity and match users with common interests. The matching unit, for example, analyzes the user's social media activity and matches users with common interests. For example, the matching unit analyzes the user's "likes" and follows on social media and matches users with common interests. The matching unit can also analyze the content of the user's posts on social media and match users with common interests. The matching unit can also analyze the user's friendships on social media and match users with common interests. In this way, by analyzing the user's social media activity, it is possible to match users with common interests.

[0082] The matching unit can continuously improve the matching algorithm by reflecting user feedback. For example, the matching unit continuously improves the matching algorithm by reflecting user feedback. For example, the matching unit improves the accuracy of the matching algorithm based on feedback provided by the user. The matching unit can also adjust the matching frequency of a specific item by reflecting user feedback. The matching unit can also add new matching parameters and improve the algorithm based on user feedback. In this way, by reflecting user feedback, the accuracy of the matching algorithm can be improved, enabling more appropriate matching.

[0083] The communication unit can estimate the user's emotions and adjust the contact method based on the estimated user emotions. For example, the communication unit can estimate the user's emotions and adjust the contact method based on the estimated user emotions. For example, if the user is feeling stressed, the communication unit can provide a simple and quick contact method. Alternatively, if the user is relaxed, the communication unit can provide detailed contact options. Alternatively, if the user is in a hurry, the communication unit can prioritize voice messages or chat functions. In this way, by adjusting the contact method based on the user's emotions, a more appropriate contact method can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The communication unit can analyze the user's past communication history and suggest an appropriate means of communication. For example, the communication unit analyzes the user's past communication history and suggests the optimal means of communication. For example, the communication unit prioritizes and suggests communication means (chat, voice message, etc.) that the user has used in the past. The communication unit can also predict and suggest the communication means to be used during a specific time period based on the user's past communication history. The communication unit can also analyze the user's past communication history and suggest the most efficient means of communication. In this way, the optimal means of communication is suggested by analyzing the user's past communication history, improving communication efficiency.

[0085] The contact unit can appropriately adjust the timing of contact based on the user's lifestyle or preferences. The contact unit, for example, suggests the optimal time to contact based on the user's lifestyle. For example, if the user is a night owl, the contact unit contacts the user at night. The contact unit can also adjust the frequency of contact based on the user's preferences. For example, if the user prefers frequent contact, the contact unit increases the frequency of contact. The contact unit can also optimize the timing of contact taking the user's schedule into consideration. For example, the contact unit contacts the user during times when the user is not in a meeting. In this way, by optimizing the timing of contact based on the user's lifestyle and preferences, it becomes possible to contact the user at more appropriate times.

[0086] The communication unit can continuously improve the communication function by reflecting user feedback. The communication unit, for example, continuously improves the communication function by reflecting user feedback. For example, the communication unit improves the usability of the communication function based on feedback provided by the user. The communication unit can also adjust the priority of specific communication means by reflecting user feedback. The communication unit can also add new communication options and improve the function based on user feedback. In this way, by reflecting user feedback, the accuracy of the communication function can be improved, allowing for more appropriate communication.

[0087] The communication unit can estimate the user's emotions and adjust the message content based on the estimated user emotions. The communication unit, for example, estimates the user's emotions and adjusts the message content based on the estimated user emotions. For example, if the user is feeling stressed, the communication unit can provide concise and clear message content. Also, if the user is relaxed, the communication unit can provide detailed message content. Also, if the user is in a hurry, the communication unit can provide quick and to-the-point message content. In this way, by adjusting the message content based on the user's emotions, more appropriate message content is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The contact unit can suggest an appropriate means of contact based on the user's geographical location information. The contact unit, for example, considers the user's geographical location information to suggest the optimal means of contact. For example, the contact unit may preferentially suggest a voice message when the user is on the move. The contact unit may also preferentially suggest a chat function when the user is at home. The contact unit may also suggest a quiet means of contact (such as a text message) when the user is in a public place. In this way, the optimal means of contact is suggested by considering the user's geographical location information, improving communication efficiency.

[0089] The contact unit can analyze the user's social media activity and promote contact between users who share common interests. The contact unit can, for example, analyze the user's social media activity and promote contact between users who share common interests. For example, the contact unit can analyze the user's "likes" and follows on social media and promote contact between users who share common interests. The contact unit can also analyze the content of the user's posts on social media and promote contact between users who share common interests. The contact unit can also analyze the user's friendships on social media and promote contact between users who share common interests. In this way, by analyzing the user's social media activity, contact between users who share common interests can be promoted.

[0090] The communication unit can customize the communication function by reflecting user feedback. The communication unit, for example, customizes the communication function by reflecting user feedback. For example, the communication unit adds customization options for the communication function based on feedback provided by the user. The communication unit can also customize specific communication methods by reflecting user feedback. The communication unit can also add new customization options and improve the communication function based on user feedback. In this way, the communication function is customized by reflecting user feedback, enabling more appropriate communication. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, matching unit, and communication unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the communication unit is realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, matching unit, and communication unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the communication unit is realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, matching unit, and communication unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the communication unit is realized by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, matching unit, and communication unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the communication unit is realized by the control unit 46A of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The matching system may further include a health management unit that acquires the user's health data and suggests items based on the user's health condition. For example, the health management unit may analyze data acquired from the user's fitness tracker or smartwatch and suggest items according to the user's health condition. For example, if the user is not getting enough exercise, the health management unit may suggest fitness equipment or sports goods. Also, if the user is feeling stressed, the health management unit may suggest relaxation items or stress relief goods. Furthermore, if the user has set a specific health goal, the health management unit may suggest items that match that goal. This makes it possible to suggest more appropriate items based on the user's health condition.

[0093] The analysis unit can estimate the user's emotions and adjust the analysis results of the user's preferences and lifestyle based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can prioritize suggesting items that will help them relax. Also, if the user is excited, the analysis unit can suggest items that suit an active lifestyle. Furthermore, if the user is feeling down, the analysis unit can suggest items that will lift their spirits. In this way, by adjusting the analysis results based on the user's emotions, it becomes possible to suggest more appropriate items.

[0094] The matching unit can prioritize matching with nearby users based on the user's geographical location information. For example, matching with nearby users can be prioritized based on the user's current location. Also, nearby items can be preferentially displayed based on the user's geographical location information. Furthermore, optimal matching can be performed taking into account the user's geographical location information. As a result, by taking into account the user's geographical location information, matching with nearby users is prioritized, improving the convenience of transactions.

[0095] The contact unit can estimate the user's emotions and adjust the contact method based on the estimated emotions. For example, if the user is feeling stressed, a simple and quick contact method can be provided. Alternatively, if the user is relaxed, detailed contact options can be provided. Furthermore, if the user is in a hurry, voice messages or chat functions can be prioritized. In this way, by adjusting the contact method based on the user's emotions, more appropriate contact means can be provided.

[0096] The contact unit can analyze the user's social media activity and promote contact between users who share common interests. For example, it can analyze the user's "likes" and follows on social media to promote contact between users who share common interests. It can also analyze the content of the user's posts on social media to promote contact between users who share common interests. It can also analyze the user's friendships on social media to promote contact between users who share common interests. In this way, analyzing the user's social media activity promotes contact between users who share common interests.

[0097] The analysis unit can analyze a user's purchasing history and make suggestions to encourage the registration of unnecessary items. For example, it can identify infrequently used items among items purchased by the user in the past and suggest that the user register them as unnecessary items. It can also suggest that if there are multiple items in the same category, the user register them as unnecessary items. It can also identify items that have not been used for a specific period of time and suggest that the user register them as unnecessary items. In this way, analyzing a user's purchasing history encourages the registration of unnecessary items and promotes the sharing economy.

[0098] 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 feeling stressed, items that help them relax can be prioritized for matching. Also, if the user is excited, items that suit an active lifestyle can be prioritized for matching. Furthermore, if the user is feeling depressed, items that will lift their spirits can be prioritized for matching. In this way, adjusting the matching criteria based on the user's emotions enables more appropriate matching.

[0099] The contact unit can suggest an appropriate means of contact based on the user's geographical location information. For example, if the user is on the move, it can preferentially suggest a voice message. Also, if the user is at home, it can preferentially suggest a chat function. Furthermore, if the user is in a public place, it can suggest a quiet means of contact (such as a text message). In this way, by taking the user's geographical location information into consideration, the optimal means of contact is suggested, improving communication efficiency.

[0100] The analysis unit can continuously improve the analysis algorithm by reflecting user feedback. For example, it can improve the accuracy of the analysis algorithm based on feedback provided by the user. It can also adjust the frequency of suggesting specific items. It can also add new analysis parameters and improve the algorithm. In this way, by reflecting user feedback, the accuracy of the analysis algorithm can be improved, allowing for more appropriate suggestions.

[0101] 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 feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the matching results based on the user's emotions, more appropriate display is possible.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The analysis unit analyzes the user's lifestyle or preferences. Specifically, it analyzes the user's preferences and lifestyle based on the user's past transaction history, registration information, and profile information. Generative AI can also be used to analyze the user's behavioral patterns and consumption habits. For example, it can analyze the user's past purchase history to identify preferences for specific items. Furthermore, it analyzes the user's hobbies and interests based on the user's profile information to understand the user's lifestyle. Step 2: The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. Specifically, it matches registered unwanted items with the needs of other users. Using generation AI, it compares unwanted items with the needs of other users to achieve optimal matching. It also takes into account the user's lifestyle and preferences to improve the accuracy of matching. For example, it suggests optimal items based on the user's preferences and lifestyle. Step 3: The communication unit supports communication between users matched by the matching unit. Specifically, it supports users in coordinating the method and date of item delivery through a chat function. It also provides a transaction rating system and a photo upload function for checking the item's condition. For example, it provides a system for users to rate transactions, ensuring the safety of transactions. It also provides a function for users to upload photos for checking the item's condition.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: an analysis unit that analyzes a user's lifestyle or preferences; a matching unit that matches unnecessary items with other users based on the information analyzed by the analysis unit; and a communication unit that supports communication between users matched by the matching unit.

2. The system according to claim 1, wherein the analysis unit analyzes the user's preferences or lifestyle based on the user's past transaction history, registration information, or profile information.

3. The matching unit Matching registered unwanted items with other users' needs The system of claim 1 .

4. The system according to claim 1 , wherein the communication unit supports users in coordinating a method or date and time for transferring an item through a chat function.

5. The system of claim 1 , wherein the communication unit provides a transaction rating system or a photo upload function for verifying the condition of an item.

6. The analysis unit Inferring user emotions and adjusting lifestyle and preference analysis results based on the inferred user emotions The system of claim 1 .

7. The analysis unit Conduct detailed analysis of users' past trading history to identify specific patterns and trends The system of claim 1 .

8. The system according to claim 1 , wherein the analysis unit predicts changes in a user's lifestyle based on registration information or profile information of the user.

9. The system of claim 1 , wherein the analysis unit analyzes a user's social media activity to more accurately understand their preferences or interests.

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