system

Abstract

The system according to this embodiment aims to efficiently match unwanted items with other users based on the user's lifestyle and preferences. [Solution] The system according to the embodiment comprises an analysis unit, a matching unit, and a communication unit. The analysis unit analyzes the user's lifestyle and preferences. The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. The communication unit facilitates communication between users matched by the matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently match unnecessary items with other users based on the user's lifestyle and preferences.

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

Means for Solving the Problems

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The platform according to an embodiment of the present invention is a system that uses generative AI to analyze a user's lifestyle and preferences and matches unwanted items with other users. In this system, users register unwanted items, and the generative AI analyzes the user's lifestyle and preferences to perform the optimal match. The generative AI also acts as a proxy for communication between users, supporting smooth item exchange. For example, a user registers information about unwanted books, furniture, clothing, etc. This information is input into the generative AI. The generative AI analyzes the input information to analyze the user's lifestyle and preferences. The generative AI learns the user's behavior history and preferences to perform the optimal match. For example, if user A has an unwanted book and user B needs that book, the generative AI matches the two and facilitates the exchange of items. The generative AI also acts as a proxy for communication between users. For example, when user A sends a message to user B, the generative AI generates the message and sends it at the appropriate time. This allows users to exchange items without stress. This platform reduces waste and creates a community where users can share useful items with each other. For example, if user A has unwanted furniture and user B needs that furniture, the generating AI will match the two and facilitate the exchange of furniture. This will promote a sustainable lifestyle. Furthermore, the generating AI will continuously learn the user's lifestyle and preferences to make more accurate matches. For example, if user A frequently exchanges books, the generating AI will learn this trend and suggest more suitable book exchange partners. This will allow users to efficiently exchange items that suit their preferences. This platform promotes the sharing economy and encourages the efficient use of resources and a sustainable lifestyle. For example, if user A has unwanted clothing and user B needs that clothing, the generating AI will match the two and facilitate the exchange of clothing. This will reduce waste and lessen the burden on the global environment.This allows the platform to analyze users' lifestyles and preferences, match unwanted items with other users, and facilitate communication, thereby supporting smooth item exchanges.

[0029] The platform according to this embodiment comprises an analysis unit, a matching unit, and a communication unit. The analysis unit analyzes the user's lifestyle and preferences. The analysis unit learns the user's behavioral history and preferences using, for example, a generative AI, and performs optimal matching. The generative AI learns the user's behavioral history and preferences and performs optimal matching. For example, the generative AI can analyze the user's preferences based on the user's past behavioral history. The generative AI can also suggest optimal items based on the user's preferences. The matching unit matches unnecessary items with other users based on the information analyzed by the analysis unit. The matching unit performs optimal matching using, for example, a generative AI, based on the user's lifestyle and preferences. The generative AI can suggest optimal items based on the user's lifestyle and preferences. The generative AI can also suggest optimal items based on the user's preferences. The communication unit acts as an intermediary for communication between users matched by the matching unit. The communication unit generates messages between users using, for example, a generative AI, and sends them at the appropriate time. The generative AI can generate messages between users and send them at the appropriate time. Furthermore, the generating AI can also generate messages to facilitate communication between users. As a result, the platform according to the embodiment can analyze users' lifestyles and preferences, match unwanted items with other users, and facilitate communication, thereby supporting smooth item exchange.

[0030] The analytics department is responsible for analyzing users' lifestyles and preferences in detail. Specifically, it uses generative AI to learn users' behavioral history and preferences to make optimal matches. Generative AI can analyze users' preferences and tendencies based on their past behavioral history. For example, it collects data such as items users have purchased in the past, content they have viewed, and services they have used, and analyzes this data to understand users' tastes and lifestyle patterns. Furthermore, based on users' preferences, generative AI can predict and suggest items and services that users may be interested in in the future. This makes it possible to provide users with more personalized suggestions. In addition, generative AI can continuously learn from user feedback and improve the accuracy of its suggestions. For example, it records how users reacted to suggested items and adjusts the content of future suggestions based on that data. In this way, the analytics department can deeply understand users' lifestyles and preferences and support more appropriate matching.

[0031] The matching unit is responsible for matching unwanted items with other users based on information obtained by the analysis unit. Specifically, it uses generative AI to perform optimal matching based on the user's lifestyle and preferences. The generative AI can analyze the user's lifestyle and preferences in detail and suggest the most suitable items. For example, if an item that one user doesn't need is needed by another user, the system will use that information to perform a match. The generative AI can also find the best exchange partner based on the user's preferences and lifestyle. This allows users to efficiently find items that are valuable to them. Furthermore, the generative AI can learn from the user's past matching history and feedback to improve the accuracy of matching. For example, it can analyze patterns of successful matches in the past and improve the matching algorithm based on that data. As a result, the matching unit can provide users with more appropriate and satisfying matches.

[0032] The Communications Department is responsible for facilitating communication between users matched by the Matching Department. Specifically, it uses a Generative AI to generate messages between users and send them at the appropriate time. The Generative AI can generate messages between users and send them at the right time. For example, if a user requests detailed information about an item they wish to exchange, the Generative AI will automatically generate a message in response to that request and send it to the other user. The Generative AI can also generate messages to facilitate communication between users. For example, it can generate and send messages to users regarding the progress of the exchange and the next steps. Furthermore, the Generative AI can learn the user's communication style and past interactions to generate more natural and effective messages. This is expected to lead to smoother communication between users and a smoother exchange process. Based on user feedback, the Communications Department can adjust the content and timing of messages to always provide optimal communication. In this way, the Communications Department can support smooth interactions between users and increase the success rate of item exchanges.

[0033] The registration unit allows users to register unwanted items. The registration unit allows users to input detailed information about unwanted items. For example, users can register information about unwanted books, furniture, clothing, etc. This makes it clear which items are eligible for exchange by allowing users to register unwanted items. Unwanted items include, but are not limited to, items that are rarely used or are old. Some or all of the above processing in the registration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the registration unit can input the item information entered by the user into a generating AI, which can then analyze the item information and register it.

[0034] The analysis unit can learn users' behavioral history and preferences to perform optimal matching. For example, the analysis unit can use generative AI to learn users' behavioral history and preferences to perform optimal matching. Generative AI can learn users' behavioral history and preferences to perform optimal matching. For example, generative AI can analyze users' preferences based on their past behavioral history. Generative AI can also suggest optimal items based on users' preferences. This allows for more accurate matching by learning users' behavioral history and preferences. Behavioral history includes, but is not limited to, website browsing history and purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user behavioral history data into generative AI, which can then analyze the behavioral history data to learn user preferences.

[0035] The communication unit can generate messages between users and send them at the appropriate time. The communication unit can generate messages between users using, for example, a generation AI and send them at the appropriate time. The generation AI can generate messages between users and send them at the appropriate time. For example, when user A sends a message to user B, the generation AI can generate that message and send it at the appropriate time. This supports smooth communication by generating messages between users and sending them at the appropriate time. Appropriate timing includes, but is not limited to, the user's activity time and past message sending history. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input user message data into a generation AI, which can analyze the message data and send it at the appropriate time.

[0036] The matching unit can continuously learn the user's lifestyle and preferences to perform more accurate matching. The matching unit can continuously learn the user's lifestyle and preferences using, for example, generative AI to perform more accurate matching. The generative AI can continuously learn the user's lifestyle and preferences to perform more accurate matching. For example, the generative AI can continuously learn the user's preferences based on the user's past behavioral history. The generative AI can also suggest more appropriate items based on the user's preferences. In this way, more accurate matching becomes possible by continuously learning the user's lifestyle and preferences. Lifestyle includes, but is not limited to, daily behavioral patterns, hobbies, and consumption behavior. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can input the user's lifestyle data into the generative AI, and the generative AI can analyze the lifestyle data to continuously learn the user's preferences.

[0037] The communication unit can generate messages to facilitate communication between users. The communication unit generates messages to facilitate communication between users, for example, using a generation AI. The generation AI can generate messages to facilitate communication between users. For example, when user A sends a message to user B, the generation AI can generate that message and send it at the appropriate time. This allows for smooth item exchange by generating messages to facilitate communication between users. Messages to facilitate communication include, but are not limited to, greeting messages and messages regarding the progress of transactions. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input user message data into a generation AI, which can analyze the message data and send it at the appropriate time.

[0038] The analysis unit can improve the accuracy of its analysis by referring to the user's past exchange history during the analysis process. The analysis unit can improve the accuracy of its analysis by referring to the user's past exchange history, for example, using a generative AI. The generative AI can improve the accuracy of its analysis by referring to the user's past exchange history. For example, the generative AI can analyze the trends of items the user has exchanged in the past and preferentially suggest similar items. The generative AI can also suggest items that reflect the user's preferences based on their evaluations of items they have exchanged in the past. In this way, the accuracy of the analysis is improved by referring to the user's past exchange history. Past exchange history includes, but is not limited to, the types of items exchanged and the frequency of exchanges. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's exchange history data into a generative AI, and the generative AI can analyze the exchange history data to improve the accuracy of the analysis.

[0039] The analysis unit can analyze users' lifestyles and preferences in more detail based on their social media activity during the analysis process. For example, the analysis unit can use generative AI to analyze users' lifestyles and preferences in more detail based on their social media activity. The generative AI can analyze users' social media "likes" and comments and suggest items that reflect their preferences. The generative AI can also analyze the content of users' social media posts and suggest items that suit their lifestyle. This makes it possible to analyze users' lifestyles and preferences in more detail by analyzing their social media activity. Social media activity includes, but is not limited to, the content of posts, the history of likes, and follower information. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user social media data into the generative AI, which can then analyze the social media data to analyze lifestyles and preferences in more detail.

[0040] The analysis unit can analyze a user's lifestyle and preferences based on their geographical location information during the analysis process. For example, the analysis unit can use generative AI to analyze a user's lifestyle and preferences based on their geographical location information. The generative AI can analyze a user's lifestyle and preferences based on their geographical location information. For example, the generative AI can suggest items suitable for the user's area, taking into account the characteristics of the area where the user lives. The generative AI can also suggest items available nearby based on the user's current location. This allows for a more appropriate analysis of lifestyle and preferences by analyzing based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's geographical location data into the generative AI, which can then analyze the geographical location data to analyze the user's lifestyle and preferences.

[0041] The analysis unit can analyze a user's lifestyle and preferences based on their purchase history during the analysis process. For example, the analysis unit can use generative AI to analyze a user's lifestyle and preferences based on their purchase history. The generative AI can analyze a user's lifestyle and preferences based on their purchase history. For example, the generative AI can suggest similar items based on the user's past purchase history. Furthermore, the generative AI can suggest items that reflect the user's preferences based on their purchase history. This allows for a more appropriate analysis of lifestyle and preferences by analyzing the user's purchase history. Purchase history includes, but is not limited to, the types and frequency of purchases of purchased items. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user purchase history data into the generative AI, which can then analyze the purchase history data to analyze the user's lifestyle and preferences.

[0042] The matching unit can improve the accuracy of matching based on the user's past exchange history. The matching unit can improve the accuracy of matching based on the user's past exchange history, for example, by using a generative AI. The generative AI can improve the accuracy of matching based on the user's past exchange history. For example, the generative AI can prioritize matching users who have similar items based on the trends of items the user has exchanged in the past. The generative AI can also perform matching that reflects the user's preferences from the user's past exchange history. As a result, matching based on the user's past exchange history improves the accuracy of matching. Past exchange history includes, but is not limited to, the types of items exchanged and the frequency of exchanges. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's exchange history data into a generative AI, and the generative AI can analyze the exchange history data to improve the accuracy of matching.

[0043] The matching unit can perform optimal matching based on the user's social media activity during the matching process. The matching unit can perform optimal matching based on the user's social media activity, for example, by using a generative AI. The generative AI can perform optimal matching based on the user's social media activity. For example, the generative AI can perform matching that reflects the user's preferences based on the user's "likes" and comments on social media. The generative AI can also perform matching that suits the user's lifestyle based on the content of the user's social media posts. This makes it possible to perform more appropriate matching by matching based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the history of likes, and follower information. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the matching unit can input the user's social media data into a generative AI, and the generative AI can analyze the social media data to perform optimal matching.

[0044] The matching unit can perform optimal matching based on the user's geographical location information during the matching process. The matching unit can perform optimal matching based on the user's geographical location information, for example, by using a generation AI. The generation AI can perform optimal matching based on the user's geographical location information. For example, the generation AI can consider the characteristics of the area where the user lives and match users with items that are suitable for that area. The generation AI can also match users with items that are available nearby based on the user's current location. This makes it possible to perform more appropriate matching by matching based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the matching unit may be performed using, for example, a generation AI, or without a generation AI. For example, the matching unit can input the user's geographical location data into a generation AI, and the generation AI can analyze the geographical location data to perform optimal matching.

[0045] The matching unit can perform optimal matching based on the user's purchase history during the matching process. The matching unit can perform optimal matching based on the user's purchase history, for example, by using a generative AI. The generative AI can perform optimal matching based on the user's purchase history. For example, the generative AI can prioritize matching users who have similar items based on the user's past purchase history. The generative AI can also perform matching that reflects the user's preferences based on their purchase history. This makes it possible to perform more appropriate matching by matching based on the user's purchase history. Purchase history includes, but is not limited to, the types of products purchased and the frequency of purchases. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the matching unit can input the user's purchase history data into a generative AI, and the generative AI can analyze the purchase history data to perform optimal matching.

[0046] The communication unit can generate the optimal message by referring to the user's past communication history when generating a message. For example, the communication unit can use a generation AI to refer to the user's past communication history and generate the optimal message. The generation AI can refer to the user's past communication history and generate the optimal message. For example, the generation AI can generate similar messages based on the trends of messages the user has sent in the past. The generation AI can also generate messages that reflect the user's preferences from the user's past communication history. In this way, more appropriate messages are generated by referring to the user's past communication history. Past communication history includes, but is not limited to, the content of messages sent and the frequency of replies. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input the user's communication history data into a generation AI, and the generation AI can analyze the communication history data to generate the optimal message.

[0047] The communication unit can generate appropriate messages based on the user's social media activity when generating messages. The communication unit can generate appropriate messages based on the user's social media activity using, for example, a generation AI. The generation AI can generate appropriate messages based on the user's social media activity. For example, the generation AI can generate messages that reflect the user's preferences based on the user's "likes" and comments on social media. The generation AI can also generate messages that match the user's lifestyle based on the content of the user's social media posts. As a result, more appropriate messages are generated by generating messages based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the history of likes, and follower information. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input the user's social media data into a generation AI, and the generation AI can analyze the social media data to generate an appropriate message.

[0048] The communication unit can generate appropriate messages based on the user's geographical location information when generating messages. The communication unit can generate appropriate messages based on the user's geographical location information using, for example, a generation AI. The generation AI can generate appropriate messages based on the user's geographical location information. For example, the generation AI can generate messages about nearby items based on the user's current location. The generation AI can also generate messages related to places visited based on the user's travel history. As a result, more appropriate messages are generated by generating messages based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input the user's geographical location data into a generation AI, and the generation AI can analyze the geographical location data to generate an appropriate message.

[0049] The communication unit can generate appropriate messages based on the user's purchase history when generating messages. The communication unit can generate appropriate messages based on the user's purchase history, for example, using a generation AI. The generation AI can generate appropriate messages based on the user's purchase history. For example, the generation AI can generate messages about similar items based on the user's past purchase history. The generation AI can also generate messages that reflect the user's preferences from the user's purchase history. As a result, more appropriate messages are generated by generating messages based on the user's purchase history. Purchase history includes, but is not limited to, the types of products purchased and the frequency of purchases. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input user purchase history data into a generation AI, and the generation AI can analyze the purchase history data to generate appropriate messages.

[0050] The registration unit can suggest the optimal registration method when registering an item by referring to the user's past registration history. The registration unit can suggest the optimal registration method by referring to the user's past registration history, for example, using a generation AI. The generation AI can suggest the optimal registration method by referring to the user's past registration history. For example, the generation AI can suggest a registration method for similar items based on the trends of items the user has registered in the past. The generation AI can also suggest a registration method that reflects the user's preferences from their past registration history. In this way, a more appropriate registration method is suggested by referring to the user's past registration history. Past registration history includes, but is not limited to, the types of items registered and the frequency of registration. Some or all of the above processing in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit can input the user's registration history data into a generation AI, and the generation AI can analyze the registration history data and suggest the optimal registration method.

[0051] The registration unit can prioritize registering highly relevant items based on the user's geographical location information when registering items. For example, the registration unit can use a generation AI to prioritize registering highly relevant items based on the user's geographical location information. The generation AI can prioritize registering highly relevant items based on the user's geographical location information. For example, the generation AI can consider the characteristics of the area where the user lives and prioritize registering items that are appropriate for that area. The generation AI can also prioritize registering items that are available nearby based on the user's current location. This makes it possible to register more appropriate items by registering items based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit can input the user's geographical location data into a generation AI, and the generation AI can analyze the geographical location data and prioritize registering highly relevant items.

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

[0053] The analysis unit can consider the user's health data when analyzing the user's lifestyle and preferences. For example, it can use data obtained from the user's fitness tracker or smartwatch to analyze the user's health status and exercise habits, and suggest health-related items. Furthermore, if the user has set specific health goals, it can prioritize suggesting items that align with those goals. This enables the suggestion of more appropriate items based on the user's health status. Health data includes, but is not limited to, heart rate, steps, and sleep data. Some or all of the processing described above in the analysis unit may be performed using generative AI, or not.

[0054] The matching unit can consider the user's family structure and pet information when analyzing the user's lifestyle and preferences. For example, if the user has children or pets, it can suggest items that suit that family structure. Also, if the user owns a pet, it can prioritize suggesting pet-related items. This makes it possible to suggest more appropriate items based on the user's family structure and pet information. Family structure and pet information includes, but is not limited to, the age of children and the type of pet. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0055] The communication unit can consider the user's language and cultural background when generating messages between users. For example, if users speak different languages, the generation AI can automatically translate and generate a message in the appropriate language. It can also generate messages that take into account appropriate expressions and etiquette based on the user's cultural background. This facilitates smooth communication between users. Language and cultural background include, but are not limited to, the language used, regional customs, and religious background. Some or all of the processing described above in the communication unit may be performed using the generation AI or not.

[0056] The analysis unit can consider the user's hobbies and interests when analyzing the user's lifestyle and preferences. For example, if a user has a particular hobby, it can prioritize suggesting items related to that hobby. Similarly, if a user wants to start a new hobby, it can suggest items related to that hobby. This allows for more appropriate item suggestions based on the user's hobbies and interests. Hobbies and interests include, but are not limited to, sports, music, and art. Some or all of the processing described above in the analysis unit may be performed using generative AI, or it may be performed without generative AI.

[0057] The matching unit can consider the user's occupation and work schedule when analyzing the user's lifestyle and preferences. For example, if the user is engaged in a specific occupation, it can suggest items related to that occupation. It can also suggest times when items can be exchanged based on the user's work schedule. This enables the suggestion of more appropriate items based on the user's occupation and work schedule. Occupation and work schedule include, but are not limited to, working hours and vacation plans. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without generative AI.

[0058] The communication unit can consider a user's past message history when generating messages between users. For example, it can generate similar messages based on the content and tone of messages a user has previously sent. It can also generate messages that reflect a user's preferences based on their past message history. This results in the generation of more appropriate messages based on the user's past message history. Past message history includes, but is not limited to, the content of messages sent and the frequency of replies. Some or all of the above processing in the communication unit may be performed using a generation AI, or it may be performed without a generation AI.

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

[0060] Step 1: The analysis unit analyzes the user's lifestyle and preferences. For example, it uses generative AI to learn the user's behavioral history and preferences and perform optimal matching. Generative AI can analyze the user's preferences based on the user's past behavioral history. It can also suggest the most suitable items based on the user's preferences. Step 2: The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. For example, it uses a generative AI to perform optimal matching based on the user's lifestyle and preferences. The generative AI can suggest the most suitable items based on the user's lifestyle and preferences. Step 3: The communication unit handles communication between users matched by the matching unit. For example, it generates messages between users using a generation AI and sends them at the appropriate time. The generation AI can generate messages between users and send them at the appropriate time. In addition, the generation AI can generate messages to facilitate communication between users.

[0061] (Example of form 2) The platform according to an embodiment of the present invention is a system that uses generative AI to analyze a user's lifestyle and preferences and matches unwanted items with other users. In this system, users register unwanted items, and the generative AI analyzes the user's lifestyle and preferences to perform the optimal match. The generative AI also acts as a proxy for communication between users, supporting smooth item exchange. For example, a user registers information about unwanted books, furniture, clothing, etc. This information is input into the generative AI. The generative AI analyzes the input information to analyze the user's lifestyle and preferences. The generative AI learns the user's behavior history and preferences to perform the optimal match. For example, if user A has an unwanted book and user B needs that book, the generative AI matches the two and facilitates the exchange of items. The generative AI also acts as a proxy for communication between users. For example, when user A sends a message to user B, the generative AI generates the message and sends it at the appropriate time. This allows users to exchange items without stress. This platform reduces waste and creates a community where users can share useful items with each other. For example, if user A has unwanted furniture and user B needs that furniture, the generating AI will match the two and facilitate the exchange of furniture. This will promote a sustainable lifestyle. Furthermore, the generating AI will continuously learn the user's lifestyle and preferences to make more accurate matches. For example, if user A frequently exchanges books, the generating AI will learn this trend and suggest more suitable book exchange partners. This will allow users to efficiently exchange items that suit their preferences. This platform promotes the sharing economy and encourages the efficient use of resources and a sustainable lifestyle. For example, if user A has unwanted clothing and user B needs that clothing, the generating AI will match the two and facilitate the exchange of clothing. This will reduce waste and lessen the burden on the global environment.This allows the platform to analyze users' lifestyles and preferences, match unwanted items with other users, and facilitate communication, thereby supporting smooth item exchanges.

[0062] The platform according to this embodiment comprises an analysis unit, a matching unit, and a communication unit. The analysis unit analyzes the user's lifestyle and preferences. The analysis unit learns the user's behavioral history and preferences using, for example, a generative AI, and performs optimal matching. The generative AI learns the user's behavioral history and preferences and performs optimal matching. For example, the generative AI can analyze the user's preferences based on the user's past behavioral history. The generative AI can also suggest optimal items based on the user's preferences. The matching unit matches unnecessary items with other users based on the information analyzed by the analysis unit. The matching unit performs optimal matching using, for example, a generative AI, based on the user's lifestyle and preferences. The generative AI can suggest optimal items based on the user's lifestyle and preferences. The generative AI can also suggest optimal items based on the user's preferences. The communication unit acts as an intermediary for communication between users matched by the matching unit. The communication unit generates messages between users using, for example, a generative AI, and sends them at the appropriate time. The generative AI can generate messages between users and send them at the appropriate time. Furthermore, the generating AI can also generate messages to facilitate communication between users. As a result, the platform according to the embodiment can analyze users' lifestyles and preferences, match unwanted items with other users, and facilitate communication, thereby supporting smooth item exchange.

[0063] The analytics department is responsible for analyzing users' lifestyles and preferences in detail. Specifically, it uses generative AI to learn users' behavioral history and preferences to make optimal matches. Generative AI can analyze users' preferences and tendencies based on their past behavioral history. For example, it collects data such as items users have purchased in the past, content they have viewed, and services they have used, and analyzes this data to understand users' tastes and lifestyle patterns. Furthermore, based on users' preferences, generative AI can predict and suggest items and services that users may be interested in in the future. This makes it possible to provide users with more personalized suggestions. In addition, generative AI can continuously learn from user feedback and improve the accuracy of its suggestions. For example, it records how users reacted to suggested items and adjusts the content of future suggestions based on that data. In this way, the analytics department can deeply understand users' lifestyles and preferences and support more appropriate matching.

[0064] The matching unit is responsible for matching unwanted items with other users based on information obtained by the analysis unit. Specifically, it uses generative AI to perform optimal matching based on the user's lifestyle and preferences. The generative AI can analyze the user's lifestyle and preferences in detail and suggest the most suitable items. For example, if an item that one user doesn't need is needed by another user, the system will use that information to perform a match. The generative AI can also find the best exchange partner based on the user's preferences and lifestyle. This allows users to efficiently find items that are valuable to them. Furthermore, the generative AI can learn from the user's past matching history and feedback to improve the accuracy of matching. For example, it can analyze patterns of successful matches in the past and improve the matching algorithm based on that data. As a result, the matching unit can provide users with more appropriate and satisfying matches.

[0065] The Communications Department is responsible for facilitating communication between users matched by the Matching Department. Specifically, it uses a Generative AI to generate messages between users and send them at the appropriate time. The Generative AI can generate messages between users and send them at the right time. For example, if a user requests detailed information about an item they wish to exchange, the Generative AI will automatically generate a message in response to that request and send it to the other user. The Generative AI can also generate messages to facilitate communication between users. For example, it can generate and send messages to users regarding the progress of the exchange and the next steps. Furthermore, the Generative AI can learn the user's communication style and past interactions to generate more natural and effective messages. This is expected to lead to smoother communication between users and a smoother exchange process. Based on user feedback, the Communications Department can adjust the content and timing of messages to always provide optimal communication. In this way, the Communications Department can support smooth interactions between users and increase the success rate of item exchanges.

[0066] The registration unit allows users to register unwanted items. The registration unit allows users to input detailed information about unwanted items. For example, users can register information about unwanted books, furniture, clothing, etc. This makes it clear which items are eligible for exchange by allowing users to register unwanted items. Unwanted items include, but are not limited to, items that are rarely used or are old. Some or all of the above processing in the registration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the registration unit can input the item information entered by the user into a generating AI, which can then analyze the item information and register it.

[0067] The analysis unit can learn users' behavioral history and preferences to perform optimal matching. For example, the analysis unit can use generative AI to learn users' behavioral history and preferences to perform optimal matching. Generative AI can learn users' behavioral history and preferences to perform optimal matching. For example, generative AI can analyze users' preferences based on their past behavioral history. Generative AI can also suggest optimal items based on users' preferences. This allows for more accurate matching by learning users' behavioral history and preferences. Behavioral history includes, but is not limited to, website browsing history and purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user behavioral history data into generative AI, which can then analyze the behavioral history data to learn user preferences.

[0068] The communication unit can generate messages between users and send them at the appropriate time. The communication unit can generate messages between users using, for example, a generation AI and send them at the appropriate time. The generation AI can generate messages between users and send them at the appropriate time. For example, when user A sends a message to user B, the generation AI can generate that message and send it at the appropriate time. This supports smooth communication by generating messages between users and sending them at the appropriate time. Appropriate timing includes, but is not limited to, the user's activity time and past message sending history. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input user message data into a generation AI, which can analyze the message data and send it at the appropriate time.

[0069] The matching unit can continuously learn the user's lifestyle and preferences to perform more accurate matching. The matching unit can continuously learn the user's lifestyle and preferences using, for example, generative AI to perform more accurate matching. The generative AI can continuously learn the user's lifestyle and preferences to perform more accurate matching. For example, the generative AI can continuously learn the user's preferences based on the user's past behavioral history. The generative AI can also suggest more appropriate items based on the user's preferences. In this way, more accurate matching becomes possible by continuously learning the user's lifestyle and preferences. Lifestyle includes, but is not limited to, daily behavioral patterns, hobbies, and consumption behavior. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can input the user's lifestyle data into the generative AI, and the generative AI can analyze the lifestyle data to continuously learn the user's preferences.

[0070] The communication unit can generate messages to facilitate communication between users. The communication unit generates messages to facilitate communication between users, for example, using a generation AI. The generation AI can generate messages to facilitate communication between users. For example, when user A sends a message to user B, the generation AI can generate that message and send it at the appropriate time. This allows for smooth item exchange by generating messages to facilitate communication between users. Messages to facilitate communication include, but are not limited to, greeting messages and messages regarding the progress of transactions. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input user message data into a generation AI, which can analyze the message data and send it at the appropriate time.

[0071] The analysis unit can estimate the user's emotions and adjust the lifestyle and preference analysis methods based on the estimated emotions. The analysis unit can, for example, use generative AI to estimate the user's emotions and adjust the lifestyle and preference analysis methods based on the estimated emotions. The generative AI can estimate the user's emotions and adjust the lifestyle and preference analysis methods based on the estimated emotions. For example, if the user is feeling stressed, the generative AI can prioritize analyzing items that promote relaxation. Also, if the user is excited, the generative AI can analyze items that suit an active lifestyle. This allows for more appropriate analysis by adjusting the analysis methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can input user emotional data into a generating AI, which can then analyze the emotional data and adjust the analysis method based on lifestyle and preferences.

[0072] The analysis unit can improve the accuracy of its analysis by referring to the user's past exchange history during the analysis process. The analysis unit can improve the accuracy of its analysis by referring to the user's past exchange history, for example, using a generative AI. The generative AI can improve the accuracy of its analysis by referring to the user's past exchange history. For example, the generative AI can analyze the trends of items the user has exchanged in the past and preferentially suggest similar items. The generative AI can also suggest items that reflect the user's preferences based on their evaluations of items they have exchanged in the past. In this way, the accuracy of the analysis is improved by referring to the user's past exchange history. Past exchange history includes, but is not limited to, the types of items exchanged and the frequency of exchanges. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's exchange history data into a generative AI, and the generative AI can analyze the exchange history data to improve the accuracy of the analysis.

[0073] The analysis unit can analyze users' lifestyles and preferences in more detail based on their social media activity during the analysis process. For example, the analysis unit can use generative AI to analyze users' lifestyles and preferences in more detail based on their social media activity. The generative AI can analyze users' social media "likes" and comments and suggest items that reflect their preferences. The generative AI can also analyze the content of users' social media posts and suggest items that suit their lifestyle. This makes it possible to analyze users' lifestyles and preferences in more detail by analyzing their social media activity. Social media activity includes, but is not limited to, the content of posts, the history of likes, and follower information. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user social media data into the generative AI, which can then analyze the social media data to analyze lifestyles and preferences in more detail.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can, for example, use a generative AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The generative AI can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the generative AI can display detailed analysis results. Also, if the user is in a hurry, the generative AI can display concise analysis results. This allows for a more appropriate display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze the emotion data and adjust the display method of the analysis results.

[0075] The analysis unit can analyze a user's lifestyle and preferences based on their geographical location information during the analysis process. For example, the analysis unit can use generative AI to analyze a user's lifestyle and preferences based on their geographical location information. The generative AI can analyze a user's lifestyle and preferences based on their geographical location information. For example, the generative AI can suggest items suitable for the user's area, taking into account the characteristics of the area where the user lives. The generative AI can also suggest items available nearby based on the user's current location. This allows for a more appropriate analysis of lifestyle and preferences by analyzing based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's geographical location data into the generative AI, which can then analyze the geographical location data to analyze the user's lifestyle and preferences.

[0076] The analysis unit can analyze a user's lifestyle and preferences based on their purchase history during the analysis process. For example, the analysis unit can use generative AI to analyze a user's lifestyle and preferences based on their purchase history. The generative AI can analyze a user's lifestyle and preferences based on their purchase history. For example, the generative AI can suggest similar items based on the user's past purchase history. Furthermore, the generative AI can suggest items that reflect the user's preferences based on their purchase history. This allows for a more appropriate analysis of lifestyle and preferences by analyzing the user's purchase history. Purchase history includes, but is not limited to, the types and frequency of purchases of purchased items. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user purchase history data into the generative AI, which can then analyze the purchase history data to analyze the user's lifestyle and preferences.

[0077] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. The matching unit can, for example, use a generative AI to estimate the user's emotions and adjust the matching criteria based on the estimated emotions. The generative AI can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the generative AI can apply broad matching criteria. Also, if the user is in a hurry, the generative AI can apply criteria that prioritize quick matching. 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, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the matching unit may be performed using a generative AI, or not using a generative AI. For example, the matching unit can input user emotion data into a generative AI, which can analyze the emotion data and adjust the matching criteria.

[0078] The matching unit can improve the accuracy of matching based on the user's past exchange history. The matching unit can improve the accuracy of matching based on the user's past exchange history, for example, by using a generative AI. The generative AI can improve the accuracy of matching based on the user's past exchange history. For example, the generative AI can prioritize matching users who have similar items based on the trends of items the user has exchanged in the past. The generative AI can also perform matching that reflects the user's preferences from the user's past exchange history. As a result, matching based on the user's past exchange history improves the accuracy of matching. Past exchange history includes, but is not limited to, the types of items exchanged and the frequency of exchanges. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's exchange history data into a generative AI, and the generative AI can analyze the exchange history data to improve the accuracy of matching.

[0079] The matching unit can perform optimal matching based on the user's social media activity during the matching process. The matching unit can perform optimal matching based on the user's social media activity, for example, by using a generative AI. The generative AI can perform optimal matching based on the user's social media activity. For example, the generative AI can perform matching that reflects the user's preferences based on the user's "likes" and comments on social media. The generative AI can also perform matching that suits the user's lifestyle based on the content of the user's social media posts. This makes it possible to perform more appropriate matching by matching based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the history of likes, and follower information. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the matching unit can input the user's social media data into a generative AI, and the generative AI can analyze the social media data to perform optimal matching.

[0080] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated user emotions. The matching unit can, for example, use a generative AI to estimate the user's emotions and adjust the display method of the matching results based on the estimated user emotions. The generative AI can estimate the user's emotions and adjust the display method of the matching results based on the estimated user emotions. For example, if the user is relaxed, the generative AI can display detailed matching results. Also, if the user is in a hurry, the generative AI can display concise matching results. This allows for more appropriate display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the matching unit may be performed using a generative AI, for example, or without a generative AI. For example, the matching unit can input user emotion data into a generative AI, and the generative AI can analyze the emotion data and adjust the display method of the matching results.

[0081] The matching unit can perform optimal matching based on the user's geographical location information during the matching process. The matching unit can perform optimal matching based on the user's geographical location information, for example, by using a generation AI. The generation AI can perform optimal matching based on the user's geographical location information. For example, the generation AI can consider the characteristics of the area where the user lives and match users with items that are suitable for that area. The generation AI can also match users with items that are available nearby based on the user's current location. This makes it possible to perform more appropriate matching by matching based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the matching unit may be performed using, for example, a generation AI, or without a generation AI. For example, the matching unit can input the user's geographical location data into a generation AI, and the generation AI can analyze the geographical location data to perform optimal matching.

[0082] The matching unit can perform optimal matching based on the user's purchase history during the matching process. The matching unit can perform optimal matching based on the user's purchase history, for example, by using a generative AI. The generative AI can perform optimal matching based on the user's purchase history. For example, the generative AI can prioritize matching users who have similar items based on the user's past purchase history. The generative AI can also perform matching that reflects the user's preferences based on their purchase history. This makes it possible to perform more appropriate matching by matching based on the user's purchase history. Purchase history includes, but is not limited to, the types of products purchased and the frequency of purchases. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the matching unit can input the user's purchase history data into a generative AI, and the generative AI can analyze the purchase history data to perform optimal matching.

[0083] The communication unit can estimate the user's emotions and adjust the message content based on the estimated emotions. For example, the communication unit can use generative AI to estimate the user's emotions and adjust the message content based on the estimated emotions. The generative AI can estimate the user's emotions and adjust the message content based on the estimated emotions. For example, if the user is relaxed, the generative AI can generate a friendly message. Also, if the user is in a hurry, the generative AI can generate a concise and to-the-point message. This allows for the generation of more appropriate messages by adjusting the message content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communication unit may be performed using, for example, a generative AI, or not. For example, the communication unit can input user emotion data into a generative AI, which can then analyze the emotion data and adjust the message content.

[0084] The communication unit can generate the optimal message by referring to the user's past communication history when generating a message. For example, the communication unit can use a generation AI to refer to the user's past communication history and generate the optimal message. The generation AI can refer to the user's past communication history and generate the optimal message. For example, the generation AI can generate similar messages based on the trends of messages the user has sent in the past. The generation AI can also generate messages that reflect the user's preferences from the user's past communication history. In this way, more appropriate messages are generated by referring to the user's past communication history. Past communication history includes, but is not limited to, the content of messages sent and the frequency of replies. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input the user's communication history data into a generation AI, and the generation AI can analyze the communication history data to generate the optimal message.

[0085] The communication unit can generate appropriate messages based on the user's social media activity when generating messages. The communication unit can generate appropriate messages based on the user's social media activity using, for example, a generation AI. The generation AI can generate appropriate messages based on the user's social media activity. For example, the generation AI can generate messages that reflect the user's preferences based on the user's "likes" and comments on social media. The generation AI can also generate messages that match the user's lifestyle based on the content of the user's social media posts. As a result, more appropriate messages are generated by generating messages based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the history of likes, and follower information. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input the user's social media data into a generation AI, and the generation AI can analyze the social media data to generate an appropriate message.

[0086] The communication unit can estimate the user's emotions and adjust the timing of message delivery based on the estimated emotions. For example, the communication unit can use generative AI to estimate the user's emotions and adjust the timing of message delivery based on the estimated emotions. The generative AI can estimate the user's emotions and adjust the timing of message delivery based on the estimated emotions. For example, if the user is relaxed, the generative AI can send a message at an appropriate time. Also, if the user is in a hurry, the generative AI can send a message quickly. By adjusting the delivery timing based on the user's emotions, messages are sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communication unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication unit can input user emotion data into a generative AI, which can analyze the emotion data and adjust the timing of message delivery.

[0087] The communication unit can generate appropriate messages based on the user's geographical location information when generating messages. The communication unit can generate appropriate messages based on the user's geographical location information using, for example, a generation AI. The generation AI can generate appropriate messages based on the user's geographical location information. For example, the generation AI can generate messages about nearby items based on the user's current location. The generation AI can also generate messages related to places visited based on the user's travel history. As a result, more appropriate messages are generated by generating messages based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input the user's geographical location data into a generation AI, and the generation AI can analyze the geographical location data to generate an appropriate message.

[0088] The communication unit can generate appropriate messages based on the user's purchase history when generating messages. The communication unit can generate appropriate messages based on the user's purchase history, for example, using a generation AI. The generation AI can generate appropriate messages based on the user's purchase history. For example, the generation AI can generate messages about similar items based on the user's past purchase history. The generation AI can also generate messages that reflect the user's preferences from the user's purchase history. As a result, more appropriate messages are generated by generating messages based on the user's purchase history. Purchase history includes, but is not limited to, the types of products purchased and the frequency of purchases. Some or all of the above processing in the communication unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communication unit can input user purchase history data into a generation AI, and the generation AI can analyze the purchase history data to generate appropriate messages.

[0089] The registration unit can estimate the user's emotions and adjust the item registration procedure based on the estimated emotions. The registration unit can estimate the user's emotions using, for example, a generative AI and adjust the item registration procedure based on the estimated emotions. The generative AI can estimate the user's emotions and adjust the item registration procedure based on the estimated emotions. For example, if the user is relaxed, the generative AI can provide a detailed registration procedure. Also, if the user is in a hurry, the generative AI can provide a concise registration procedure. This ensures that a more appropriate registration procedure is provided by adjusting the registration procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the registration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the registration unit can input user emotion data into a generative AI, which can analyze the emotion data and adjust the item registration procedure.

[0090] The registration unit can suggest the optimal registration method when registering an item by referring to the user's past registration history. The registration unit can suggest the optimal registration method by referring to the user's past registration history, for example, using a generation AI. The generation AI can suggest the optimal registration method by referring to the user's past registration history. For example, the generation AI can suggest a registration method for similar items based on the trends of items the user has registered in the past. The generation AI can also suggest a registration method that reflects the user's preferences from their past registration history. In this way, a more appropriate registration method is suggested by referring to the user's past registration history. Past registration history includes, but is not limited to, the types of items registered and the frequency of registration. Some or all of the above processing in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit can input the user's registration history data into a generation AI, and the generation AI can analyze the registration history data and suggest the optimal registration method.

[0091] The registration unit can estimate the user's emotions and determine the priority of items to register based on the estimated emotions. The registration unit can, for example, use a generative AI to estimate the user's emotions and determine the priority of items to register based on the estimated emotions. The generative AI can estimate the user's emotions and determine the priority of items to register based on the estimated emotions. For example, if the user is relaxed, the generative AI can provide detailed priorities. Also, if the user is in a hurry, the generative AI can provide concise priorities. This makes it possible to register more appropriate items by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the registration unit may be performed using a generative AI, or not using a generative AI. For example, the registration unit can input user emotion data into a generative AI, and the generative AI can analyze the emotion data to determine the priority of items to register.

[0092] The registration unit can prioritize registering highly relevant items based on the user's geographical location information when registering items. For example, the registration unit can use a generation AI to prioritize registering highly relevant items based on the user's geographical location information. The generation AI can prioritize registering highly relevant items based on the user's geographical location information. For example, the generation AI can consider the characteristics of the area where the user lives and prioritize registering items that are appropriate for that area. The generation AI can also prioritize registering items that are available nearby based on the user's current location. This makes it possible to register more appropriate items by registering items based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit can input the user's geographical location data into a generation AI, and the generation AI can analyze the geographical location data and prioritize registering highly relevant items.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The analysis unit can consider the user's health data when analyzing the user's lifestyle and preferences. For example, it can use data obtained from the user's fitness tracker or smartwatch to analyze the user's health status and exercise habits, and suggest health-related items. Furthermore, if the user has set specific health goals, it can prioritize suggesting items that align with those goals. This enables the suggestion of more appropriate items based on the user's health status. Health data includes, but is not limited to, heart rate, steps, and sleep data. Some or all of the processing described above in the analysis unit may be performed using generative AI, or not.

[0095] The matching unit can consider the user's family structure and pet information when analyzing the user's lifestyle and preferences. For example, if the user has children or pets, it can suggest items that suit that family structure. Also, if the user owns a pet, it can prioritize suggesting pet-related items. This makes it possible to suggest more appropriate items based on the user's family structure and pet information. Family structure and pet information includes, but is not limited to, the age of children and the type of pet. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0096] The communication unit can consider the user's language and cultural background when generating messages between users. For example, if users speak different languages, the generation AI can automatically translate and generate a message in the appropriate language. It can also generate messages that take into account appropriate expressions and etiquette based on the user's cultural background. This facilitates smooth communication between users. Language and cultural background include, but are not limited to, the language used, regional customs, and religious background. Some or all of the processing described above in the communication unit may be performed using the generation AI or not.

[0097] The analysis unit can estimate the user's emotions and adjust the lifestyle and preference analysis methods based on the estimated emotions. For example, if the user is feeling stressed, the generative AI can prioritize analyzing items that promote relaxation. Similarly, if the user is excited, the generative AI can analyze items that suit an active lifestyle. This allows for more appropriate analysis by adjusting the analysis methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the analysis unit may be performed using or without generative AI.

[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 relaxed, the generative AI can apply broad matching criteria. If the user is in a hurry, the generative AI can also apply criteria that prioritize quick matching. By adjusting the matching criteria based on the user's emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using the generative AI or not.

[0099] The communication unit can estimate the user's emotions and adjust the message content based on the estimated emotions. For example, a generative AI can generate a friendly message if the user is relaxed. It can also generate a concise and to-the-point message if the user is in a hurry. By adjusting the message content based on the user's emotions, a more appropriate message is generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the communication unit may be performed using a generative AI, or they may not.

[0100] The registration unit can estimate the user's emotions and adjust the item registration procedure based on the estimated emotions. For example, if the user is relaxed, the generating AI can provide detailed registration instructions. If the user is in a hurry, the generating AI can also provide concise registration instructions. By adjusting the registration procedure based on the user's emotions, a more appropriate registration procedure is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the registration unit may be performed using a generating AI or not.

[0101] The analysis unit can consider the user's hobbies and interests when analyzing the user's lifestyle and preferences. For example, if a user has a particular hobby, it can prioritize suggesting items related to that hobby. Similarly, if a user wants to start a new hobby, it can suggest items related to that hobby. This allows for more appropriate item suggestions based on the user's hobbies and interests. Hobbies and interests include, but are not limited to, sports, music, and art. Some or all of the processing described above in the analysis unit may be performed using generative AI, or it may be performed without generative AI.

[0102] The matching unit can consider the user's occupation and work schedule when analyzing the user's lifestyle and preferences. For example, if the user is engaged in a specific occupation, it can suggest items related to that occupation. It can also suggest times when items can be exchanged based on the user's work schedule. This enables the suggestion of more appropriate items based on the user's occupation and work schedule. Occupation and work schedule include, but are not limited to, working hours and vacation plans. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without generative AI.

[0103] The communication unit can consider a user's past message history when generating messages between users. For example, it can generate similar messages based on the content and tone of messages a user has previously sent. It can also generate messages that reflect a user's preferences based on their past message history. This results in the generation of more appropriate messages based on the user's past message history. Past message history includes, but is not limited to, the content of messages sent and the frequency of replies. Some or all of the above processing in the communication unit may be performed using a generation AI, or it may be performed without a generation AI.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The analysis unit analyzes the user's lifestyle and preferences. For example, it uses generative AI to learn the user's behavioral history and preferences and perform optimal matching. Generative AI can analyze the user's preferences based on the user's past behavioral history. It can also suggest the most suitable items based on the user's preferences. Step 2: The matching unit matches unwanted items with other users based on the information analyzed by the analysis unit. For example, it uses a generative AI to perform optimal matching based on the user's lifestyle and preferences. The generative AI can suggest the most suitable items based on the user's lifestyle and preferences. Step 3: The communication unit handles communication between users matched by the matching unit. For example, it generates messages between users using a generation AI and sends them at the appropriate time. The generation AI can generate messages between users and send them at the appropriate time. In addition, the generation AI can generate messages to facilitate communication between users.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements described above, including the analysis unit, matching unit, communication unit, and registration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12. The communication unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The registration unit is implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the analysis unit, matching unit, communication unit, and registration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The communication unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The registration unit is implemented, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the analysis unit, matching unit, communication unit, and registration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12. The communication unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The registration unit is implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the analysis unit, matching unit, communication unit, and registration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12. The communication unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The registration unit is implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) The analysis department analyzes users' lifestyles and preferences, A matching unit that matches unwanted items with other users based on the information analyzed by the aforementioned analysis unit, The system includes a communication unit that handles communication between users matched by the matching unit. A system characterized by the following features. (Note 2) It includes a registration section where users can register items they no longer need. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It learns the user's behavior history and preferences to perform optimal matching. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned communications department, Generate messages between users and send them at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The matching unit is By continuously learning users' lifestyles and preferences, we can achieve more accurate matching. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned communications department, Generate messages to facilitate communication between users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis methods for lifestyle and preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by referring to the user's past exchange history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During the analysis, lifestyles and preferences are analyzed in more detail based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, lifestyles and preferences are analyzed based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, lifestyles and preferences are analyzed based on the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is During the matching process, the accuracy of matching is improved based on the user's past exchange history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is During the matching process, the system optimizes matching based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is The system estimates the user's emotions and adjusts how matching results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is During the matching process, the system uses the user's geographical location information to perform optimal matching. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is During the matching process, the system optimizes the matching based on the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned communications department, It estimates the user's emotions and adjusts the message content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned communications department, When generating a message, the system refers to the user's past communication history to generate the most suitable message. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned communications department, When generating messages, appropriate messages are generated based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned communications department, It estimates the user's emotions and adjusts the timing of message delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned communications department, When generating messages, appropriate messages are generated based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned communications department, When generating messages, appropriate messages are generated based on the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned registration unit is The system estimates the user's emotions and adjusts the item registration process based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned registration unit is When registering an item, the system refers to the user's past registration history to suggest the most suitable registration method. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned registration unit is The system estimates the user's emotions and determines the priority of items to register based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned registration unit is When registering items, the system prioritizes registering highly relevant items based on the user's geographical location. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes users' lifestyles and preferences, A matching unit that matches unwanted items with other users based on the information analyzed by the aforementioned analysis unit, The system includes a communication unit that handles communication between users matched by the matching unit. A system characterized by the following features.

2. It includes a registration section where users can register items they no longer need. The system according to feature 1.

3. The aforementioned analysis unit is It learns the user's behavior history and preferences to perform optimal matching. The system according to feature 1.

4. The aforementioned communications department, Generate messages between users and send them at the appropriate time. The system according to feature 1.

5. The matching unit is By continuously learning users' lifestyles and preferences, we can achieve more accurate matching. The system according to feature 1.

6. The aforementioned communications department, Generate messages to facilitate communication between users. The system according to feature 1.

7. The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis methods for lifestyle and preferences based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by referring to the user's past exchange history. The system according to feature 1.

9. The aforementioned analysis unit is During the analysis, lifestyles and preferences are analyzed in more detail based on the user's social media activity. The system according to feature 1.

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