system

Abstract

The system according to this embodiment aims to alleviate feelings of loneliness among the elderly and provide psychological support. [Solution] The system according to this embodiment comprises an empathy response unit, an analysis unit, and a matching unit. The empathy response unit provides empathetic conversation. The analysis unit analyzes the user's personality and hobbies based on the conversation provided by the empathy response unit. The matching unit matches people with similar interests and common topics based on the personality and hobbies analyzed by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a lack of psychological support for the loneliness and mental health problems of the elderly, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the loneliness of the elderly and provide psychological support.

Means for Solving the Problems

[0006] The system according to the embodiment includes a sympathetic response unit, an analysis unit, and a matching unit. The sympathetic response unit provides a sympathetic conversation. The analysis unit analyzes the user's personality and hobbies based on the conversation provided by the sympathetic response unit. The matching unit matches people with similar interests and common topics based on the personality and hobbies analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can alleviate feelings of loneliness among the elderly and provide psychological support. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI ​​matching agent system according to an embodiment of the present invention is a system that reduces feelings of loneliness and improves the mental health of the elderly. This AI matching agent system provides empathetic conversations, analyzes the user's personality and hobbies, and matches them with people who have similar interests and common topics, thereby enabling the user to enjoy conversations with peace of mind. For example, the AI ​​matching agent system utilizes data from Q&A sites to provide empathetic and humane conversations. This allows the elderly to enjoy conversations with a sense of security. Next, the AI ​​matching agent system analyzes the user's personality and hobbies and matches them with people who have similar interests and common topics. This reduces tension during first encounters and creates a smooth flow of conversation. Furthermore, the AI ​​matching agent system learns real human worries and empathy patterns based on the abundant Q&A data from Q&A sites and provides empathetic responses. For example, the AI ​​matching agent system provides empathetic responses such as "That must have been tough" or "That's a wonderful experience," allowing the user to enjoy natural conversations with peace of mind. In addition, the AI ​​matching agent system analyzes the conversation content and identifies the user's personality and interests to understand more suitable conversation themes and styles, and suggests the optimal conversation partner with similar hobbies and interests. This facilitates smoother interactions and enables the building of lasting relationships. As a result, the AI ​​matching agent system can reduce feelings of loneliness among the elderly and improve their mental health.

[0029] The AI ​​matching agent system according to this embodiment comprises an empathy response unit, an analysis unit, and a matching unit. The empathy response unit provides empathetic conversation. For example, the empathy response unit provides empathetic responses to what the user says. For example, the empathy response unit can select empathetic words such as "That must have been tough" or "That sounds like a wonderful experience" and respond to the user. The empathy response unit can also recognize the user's emotions and respond accordingly. For example, if the user is sad, the empathy response unit can select words of comfort, and if the user is happy, it can select words of empathy. The analysis unit analyzes the user's personality and hobbies based on the conversation provided by the empathy response unit. For example, the analysis unit analyzes the content of the user's conversation to identify the user's personality and hobbies. For example, the analysis unit can extract keywords from the content of the user's conversation and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the content of the user's conversation to analyze the user's personality. The matching unit matches people with similar interests and common topics based on the personality and hobbies analyzed by the analysis unit. The matching unit identifies people with common hobbies and interests based on the user's personality and interests, and matches them with each other. The matching unit can also suggest people with common topics of conversation based on the user's hobbies and interests. Furthermore, the matching unit can match users with compatible partners based on their personalities. As a result, the AI ​​matching agent system according to this embodiment can reduce the user's feelings of loneliness and improve their mental health.

[0030] The empathy response unit provides empathetic conversation. For example, the empathy response unit responds empathetically to what the user says. For example, the empathy response unit can select empathetic words such as "That must have been tough" or "That sounds like a wonderful experience" and respond to the user. The empathy response unit can also recognize the user's emotions and respond accordingly. For example, if the user is sad, the empathy response unit can select words of comfort, and if the user is happy, it can select words of empathy. The empathy response unit uses natural language processing technology to analyze the user's statements and perform emotion analysis. Specifically, it extracts keywords and phrases that indicate emotions from the user's statements and classifies the emotions based on them. For example, if words such as "sad" or "painful" are included, it is determined that the user is sad, and if words such as "happy" or "joyful" are included, it is determined that the user is happy. Furthermore, the empathy response unit can improve the accuracy of emotion recognition by analyzing the user's tone of voice and facial expressions. For example, if the user's voice is trembling, it is determined that the user is nervous, and if the user's facial expression is smiling, it is determined that the user is happy. This allows the empathy response unit to provide appropriate responses tailored to the user's emotions. Furthermore, by referencing the user's past conversation history, the empathy response unit can provide more personalized responses. For example, by recording what the user has said and their emotions in the past, and responding based on this information, it can provide consistent and empathetic service to the user. This allows the empathy response unit to build trust with the user and improve user satisfaction.

[0031] The analysis unit analyzes the user's personality and interests based on the conversations provided by the empathy response unit. For example, the analysis unit analyzes the content of the user's conversations to identify the user's personality and interests. For example, the analysis unit can extract keywords from the content of the user's conversations and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the content of the user's conversations to analyze the user's personality. Specifically, it uses natural language processing technology to analyze the user's statements and extract keywords and phrases. For example, it extracts keywords related to hobbies such as "travel," "music," and "reading," and identifies the user's interests and concerns based on those. It can also infer the user's personality by analyzing the tone and context of the user's statements. For example, if the user makes many assertive statements, it is judged that the user is extroverted, and if the user makes many cautious statements, it is judged that the user is introverted. Furthermore, the analysis unit can conduct psychological tests based on the content of the user's conversations. For example, it asks the user a series of questions and analyzes their personality based on their answers. This allows the analysis unit to understand the user's personality and interests in detail and provide basic information for personalized matching. Furthermore, the analytics department can perform more accurate analyses by referring to the user's past conversation history. For example, it can record past conversation content and emotional changes, and use that to track changes in the user's personality and hobbies. This allows the analytics department to understand the user's latest interests and preferences, enabling more appropriate matching.

[0032] The matching unit matches people with similar interests and common topics of conversation based on the personality and hobbies analyzed by the analysis unit. For example, the matching unit can identify people with common hobbies and interests based on the user's personality and hobbies, and match them together. For example, the matching unit can suggest people with common topics of conversation based on the user's hobbies and interests. The matching unit can also match users with compatible people based on their personality. Specifically, it uses an AI algorithm to analyze the user's personality and hobbies and perform the optimal matching. For example, if the user's personality is extroverted, it will match them with people with similar extroverted personalities, and if the user's hobby is music, it will match them with people who are interested in music. Furthermore, the matching unit can perform more accurate matching by referring to the user's past matching history. For example, it can analyze patterns of successful matches in the past and perform new matches based on them to improve the success rate. In addition, the matching unit can collect user feedback and continuously improve the accuracy of the matching algorithm. For example, it can evaluate whether the user is satisfied with the matching results and adjust the algorithm based on that evaluation. This allows the matching unit to provide users with the best possible matches and improve user satisfaction. Furthermore, the matching unit thoroughly implements data anonymization and security measures to protect user privacy. This ensures that users can use the system with peace of mind.

[0033] The empathy response unit can provide empathetic conversations by utilizing data from Q&A sites. For example, the empathy response unit can provide empathetic responses to users' worries and questions based on data from Q&A sites. For example, the empathy response unit can analyze data from Q&A sites and select appropriate empathetic responses to users' worries and questions. Furthermore, the empathy response unit can also provide responses that correspond to the user's emotions based on data from Q&A sites. For example, the empathy response unit can analyze data from Q&A sites and select words of comfort if the user is sad, and words of empathy if the user is happy. This improves the accuracy of empathetic conversations by utilizing data from Q&A sites. Data from Q&A sites includes, but is not limited to, user worries, questions, and answers. Some or all of the above processing in the empathy response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathy response unit can input data from Q&A sites into a generative AI and have the generative AI generate empathetic responses.

[0034] The analysis unit can analyze the user's personality and hobbies. For example, the analysis unit can analyze the user's conversation content to identify the user's personality and hobbies. For example, the analysis unit can extract keywords from the user's conversation content and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the user's conversation content to analyze the user's personality. This allows for more appropriate matching by analyzing the user's personality and hobbies. The analysis of personality and hobbies includes, but is not limited to, psychological tests and behavioral analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's conversation content into a generative AI and have the generative AI perform the analysis of personality and hobbies.

[0035] The matching unit can match people who have similar interests or common topics of conversation. For example, the matching unit can identify people with common hobbies or interests based on the user's personality and interests, and match them together. For example, the matching unit can suggest people with common topics of conversation based on the user's hobbies and interests. The matching unit can also match compatible people based on the user's personality. This promotes interaction between users by matching people with similar interests or common topics of conversation. Identifying similar interests or common topics includes, but is not limited to, common keywords or areas of interest. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input data on the user's personality and hobbies into a generative AI and have the generative AI execute the matching criteria.

[0036] The empathetic response unit can provide empathetic conversations that allow users to enjoy dialogue with peace of mind. For example, the empathetic response unit provides empathetic responses to what the user says. For example, the empathetic response unit can select empathetic words such as "That must have been tough" or "That sounds like a wonderful experience" and respond to the user. The empathetic response unit can also recognize the user's emotions and respond accordingly. For example, if the user is sad, the empathetic response unit can select words of comfort, and if the user is happy, it can select words of empathy. By providing empathetic conversations that allow users to enjoy dialogue with peace of mind, the user's feelings of loneliness are reduced. Methods for enjoying dialogue with peace of mind include, but are not limited to, privacy protection and the content of responses. Some or all of the above processing in the empathetic response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathetic response unit can input the user's conversation content into a generative AI and have the generative AI generate empathetic responses.

[0037] The analysis unit can analyze the content of a user's conversations to identify their personality and interests. For example, the analysis unit can analyze the content of a user's conversations to identify their personality and hobbies. For example, the analysis unit can extract keywords from the content of a user's conversations and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the content of a user's conversations to analyze their personality. This allows for more appropriate matching by identifying personality and interests through analysis of the content of a user's conversations. The analysis of conversation content includes, but is not limited to, keyword extraction and sentiment analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the content of a user's conversations into a generative AI and have the generative AI perform an analysis of personality and hobbies.

[0038] The matching unit can suggest the most suitable conversation partners who share common hobbies and interests. For example, the matching unit can identify people with common hobbies and interests based on the user's personality and hobbies, and match them with each other. For example, the matching unit can suggest people with common topics of conversation based on the user's hobbies and interests. The matching unit can also match users with compatible partners based on their personalities. This promotes interaction between users by suggesting the most suitable conversation partners who share common hobbies and interests. Identifying the most suitable conversation partner includes, but is not limited to, common hobbies and areas of interest. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input data on the user's personality and hobbies into a generative AI and have the generative AI identify the most suitable conversation partner.

[0039] The analysis unit can perform more accurate analyses of a user's personality and hobbies by referring to the user's past behavioral history. For example, the analysis unit can analyze a user's personality and hobbies based on events and activities the user has participated in in the past. For example, the analysis unit can analyze a user's personality and hobbies based on products and services the user has purchased in the past. For example, the analysis unit can analyze a user's personality and hobbies based on places the user has visited or traveled to in the past. This makes it possible to perform more accurate analyses of a user's personality and hobbies by referring to the user's past behavioral history. The use of past behavioral history includes, but is not limited to, data retention periods and privacy protection. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the user's past behavioral history into a generative AI and have the generative AI perform the analysis of personality and hobbies.

[0040] The analysis unit can customize the results of personality and hobby analysis based on the user's current living situation and environment. For example, the analysis unit can analyze personality and hobby based on the user's current occupation and work situation. For example, the analysis unit can analyze personality and hobby based on the user's current home environment and family structure. For example, the analysis unit can analyze personality and hobby based on the user's current health status and lifestyle. By customizing the analysis results based on the user's current living situation and environment, a more appropriate analysis becomes possible. Identifying the current living situation and environment includes, but is not limited to, income and family structure. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data on the user's current living situation and environment into a generative AI and have the generative AI perform the customization of the personality and hobby analysis results.

[0041] The analysis unit can analyze region-specific personality traits and hobbies by taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit will analyze personality traits and hobbies based on the culture and customs of that region. For example, if the user is traveling, the analysis unit will analyze personality traits and hobbies based on the culture and customs of the travel destination. For example, if the user is in their hometown, the analysis unit will analyze personality traits and hobbies based on the local culture and customs. This makes it possible to analyze region-specific personality traits and hobbies by taking into account the user's geographical location information. The use of geographical location information includes, but is not limited to, methods for acquiring location information and privacy protection. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform an analysis of region-specific personality traits and hobbies.

[0042] The analysis unit can analyze a user's social media activity and perform related personality and hobby analyses. For example, the analysis unit can analyze personality and hobby based on content shared by the user on social media. For example, the analysis unit can analyze personality and hobby based on posts from accounts followed by the user on social media. For example, the analysis unit can analyze personality and hobby based on groups and communities the user participates in on social media. This allows for a more accurate analysis of personality and hobby by analyzing the user's social media activity. Analysis of social media activity includes, but is not limited to, analysis of posted content and followers. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input data on the user's social media activity into a generative AI and have the generative AI perform related personality and hobby analyses.

[0043] The matching unit can refer to the user's past matching history to provide more appropriate matches. For example, the matching unit can provide appropriate matches based on the content of conversations the user has had with people they have previously matched with. For example, the matching unit can provide appropriate matches based on the user's compatibility with people they have previously matched with. For example, the matching unit can provide appropriate matches based on shared hobbies and interests with people the user has previously matched with. This makes it possible to provide more appropriate matches by referring to the user's past matching history. The use of past matching history includes, but is not limited to, data retention periods and privacy protection. 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 past matching history into a generative AI and have the generative AI perform the task of providing appropriate matches.

[0044] The matching unit can perform optimal matching based on the user's current situation and environment. For example, the matching unit can perform optimal matching based on the user's current occupation and work situation. For example, the matching unit can perform optimal matching based on the user's current home environment and family structure. For example, the matching unit can perform optimal matching based on the user's current health status and lifestyle. This makes it possible to perform more appropriate matching by performing matching based on the user's current situation and environment. Identifying the current situation and environment includes, but is not limited to, location information and time of day. 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 data on the user's current situation and environment into a generative AI and have the generative AI perform the provision of optimal matching.

[0045] The matching unit can perform region-specific matching by taking into account the user's geographical location information. For example, if the user lives in a specific region, the matching unit will perform matching based on the culture and customs of that region. For example, if the user is traveling, the matching unit will perform matching based on the culture and customs of the travel destination. For example, if the user is in their hometown, the matching unit will perform matching based on the local culture and customs. This makes region-specific matching possible by taking into account the user's geographical location information. The use of geographical location information includes, but is not limited to, methods for acquiring location information and privacy protection. 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 geographical location information into a generative AI and have the generative AI perform the provision of region-specific matching.

[0046] The matching unit can analyze a user's social media activity and provide relevant matches. For example, the matching unit can provide relevant matches based on content shared by the user on social media. For example, the matching unit can provide relevant matches based on posts from accounts followed by the user on social media. For example, the matching unit can provide relevant matches based on groups and communities the user participates in on social media. This allows for more appropriate matching by analyzing the user's social media activity. Analysis of social media activity includes, but is not limited to, analysis of posts and followers. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input data on the user's social media activity into a generative AI and have the generative AI perform the provision of relevant matches.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The analytics department can acquire users' health data and use it to analyze their personality and hobbies. For example, based on a user's step count data, it can analyze whether they are likely to have active hobbies. It can also suggest relaxing hobbies based on a user's sleep data. Furthermore, it can estimate a user's stress level based on their heart rate data and suggest hobbies that help relieve stress. This allows for more accurate personality and hobby analysis by utilizing users' health data.

[0049] The matching function can analyze a user's past conversations and suggest conversation topics. For example, it can suggest conversation topics with people who share common hobbies or interests based on what the user has said in the past. It can also analyze the user's level of interest in a specific topic based on what they have said in the past and match them with people who are interested in that topic. Furthermore, it can analyze the user's feelings towards a specific topic based on what they have said in the past and suggest conversation topics based on those feelings. In this way, by utilizing the user's past conversations, it becomes possible to suggest more appropriate conversation topics.

[0050] The analytics department can analyze users' purchase history and use that information to analyze their personality and hobbies. For example, if a user frequently purchases outdoor equipment, it can be analyzed that they are interested in outdoor activities. Similarly, if a user purchases many books, it can be analyzed that they enjoy reading. Furthermore, if a user purchases health foods, it can be analyzed that they are health-conscious. By utilizing users' purchase history, it becomes possible to perform more accurate analyses of their personality and hobbies.

[0051] The empathy response unit can provide relevant news and information based on the user's hobbies and interests. For example, if the user is interested in sports, it can provide the latest sports news. If the user is interested in music, it can provide the latest music release information. Furthermore, if the user is interested in travel, it can provide tourist information and event information for travel destinations. This allows for more fulfilling conversations by providing relevant news and information based on the user's hobbies and interests.

[0052] The analysis department can analyze a user's personality and hobbies by taking their life events into consideration. For example, if a user gets married, it can suggest hobbies related to family life. If a user changes jobs, it can suggest hobbies that will help them adapt to their new work environment. Furthermore, if a user moves, it can suggest hobbies related to activities in their new area. This allows for a more appropriate analysis of a user's personality and hobbies by considering their life events.

[0053] The empathy response unit can provide empathetic responses by taking into account the user's cultural background. For example, if the user belongs to a specific cultural sphere, it can provide responses appropriate to that culture. Furthermore, if the user grew up in a multicultural environment, it can provide responses that demonstrate an understanding of different cultures. Additionally, if the user practices a specific religion, it can provide responses related to that religion. This allows for more appropriate and empathetic responses by considering the user's cultural background.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The empathy response unit provides empathetic conversation. For example, it selects empathetic words to respond to what the user says, such as "That must have been tough" or "That sounds like a wonderful experience." It can also recognize the user's emotions and respond accordingly. For example, if the user is sad, it can choose words of comfort, and if the user is happy, it can choose words of empathy. Step 2: The analysis unit analyzes the user's personality and interests based on the conversation provided by the empathy response unit. For example, it analyzes the content of the user's conversation, extracts keywords, and identifies the user's interests and concerns. It can also conduct psychological tests based on the conversation content to analyze the user's personality. Step 3: The matching unit matches people with similar interests and common topics of conversation based on the personality and hobbies analyzed by the analysis unit. For example, it identifies people with common hobbies and interests based on the user's personality and hobbies, and matches them with each other. It can also match users with compatible partners based on their personality.

[0056] (Example of form 2) The AI ​​matching agent system according to an embodiment of the present invention is a system that reduces feelings of loneliness and improves the mental health of the elderly. This AI matching agent system provides empathetic conversations, analyzes the user's personality and hobbies, and matches them with people who have similar interests and common topics, thereby enabling the user to enjoy conversations with peace of mind. For example, the AI ​​matching agent system utilizes data from Q&A sites to provide empathetic and humane conversations. This allows the elderly to enjoy conversations with a sense of security. Next, the AI ​​matching agent system analyzes the user's personality and hobbies and matches them with people who have similar interests and common topics. This reduces tension during first encounters and creates a smooth flow of conversation. Furthermore, the AI ​​matching agent system learns real human worries and empathy patterns based on the abundant Q&A data from Q&A sites and provides empathetic responses. For example, the AI ​​matching agent system provides empathetic responses such as "That must have been tough" or "That's a wonderful experience," allowing the user to enjoy natural conversations with peace of mind. In addition, the AI ​​matching agent system analyzes the conversation content and identifies the user's personality and interests to understand more suitable conversation themes and styles, and suggests the optimal conversation partner with similar hobbies and interests. This facilitates smoother interactions and enables the building of lasting relationships. As a result, the AI ​​matching agent system can reduce feelings of loneliness among the elderly and improve their mental health.

[0057] The AI ​​matching agent system according to this embodiment comprises an empathy response unit, an analysis unit, and a matching unit. The empathy response unit provides empathetic conversation. For example, the empathy response unit provides empathetic responses to what the user says. For example, the empathy response unit can select empathetic words such as "That must have been tough" or "That sounds like a wonderful experience" and respond to the user. The empathy response unit can also recognize the user's emotions and respond accordingly. For example, if the user is sad, the empathy response unit can select words of comfort, and if the user is happy, it can select words of empathy. The analysis unit analyzes the user's personality and hobbies based on the conversation provided by the empathy response unit. For example, the analysis unit analyzes the content of the user's conversation to identify the user's personality and hobbies. For example, the analysis unit can extract keywords from the content of the user's conversation and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the content of the user's conversation to analyze the user's personality. The matching unit matches people with similar interests and common topics based on the personality and hobbies analyzed by the analysis unit. The matching unit identifies people with common hobbies and interests based on the user's personality and interests, and matches them with each other. The matching unit can also suggest people with common topics of conversation based on the user's hobbies and interests. Furthermore, the matching unit can match users with compatible partners based on their personalities. As a result, the AI ​​matching agent system according to this embodiment can reduce the user's feelings of loneliness and improve their mental health.

[0058] The empathy response unit provides empathetic conversation. For example, the empathy response unit responds empathetically to what the user says. For example, the empathy response unit can select empathetic words such as "That must have been tough" or "That sounds like a wonderful experience" and respond to the user. The empathy response unit can also recognize the user's emotions and respond accordingly. For example, if the user is sad, the empathy response unit can select words of comfort, and if the user is happy, it can select words of empathy. The empathy response unit uses natural language processing technology to analyze the user's statements and perform emotion analysis. Specifically, it extracts keywords and phrases that indicate emotions from the user's statements and classifies the emotions based on them. For example, if words such as "sad" or "painful" are included, it is determined that the user is sad, and if words such as "happy" or "joyful" are included, it is determined that the user is happy. Furthermore, the empathy response unit can improve the accuracy of emotion recognition by analyzing the user's tone of voice and facial expressions. For example, if the user's voice is trembling, it is determined that the user is nervous, and if the user's facial expression is smiling, it is determined that the user is happy. This allows the empathy response unit to provide appropriate responses tailored to the user's emotions. Furthermore, by referencing the user's past conversation history, the empathy response unit can provide more personalized responses. For example, by recording what the user has said and their emotions in the past, and responding based on this information, it can provide consistent and empathetic service to the user. This allows the empathy response unit to build trust with the user and improve user satisfaction.

[0059] The analysis unit analyzes the user's personality and interests based on the conversations provided by the empathy response unit. For example, the analysis unit analyzes the content of the user's conversations to identify the user's personality and interests. For example, the analysis unit can extract keywords from the content of the user's conversations and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the content of the user's conversations to analyze the user's personality. Specifically, it uses natural language processing technology to analyze the user's statements and extract keywords and phrases. For example, it extracts keywords related to hobbies such as "travel," "music," and "reading," and identifies the user's interests and concerns based on those. It can also infer the user's personality by analyzing the tone and context of the user's statements. For example, if the user makes many assertive statements, it is judged that the user is extroverted, and if the user makes many cautious statements, it is judged that the user is introverted. Furthermore, the analysis unit can conduct psychological tests based on the content of the user's conversations. For example, it asks the user a series of questions and analyzes their personality based on their answers. This allows the analysis unit to understand the user's personality and interests in detail and provide basic information for personalized matching. Furthermore, the analytics department can perform more accurate analyses by referring to the user's past conversation history. For example, it can record past conversation content and emotional changes, and use that to track changes in the user's personality and hobbies. This allows the analytics department to understand the user's latest interests and preferences, enabling more appropriate matching.

[0060] The matching unit matches people with similar interests and common topics of conversation based on the personality and hobbies analyzed by the analysis unit. For example, the matching unit can identify people with common hobbies and interests based on the user's personality and hobbies, and match them together. For example, the matching unit can suggest people with common topics of conversation based on the user's hobbies and interests. The matching unit can also match users with compatible people based on their personality. Specifically, it uses an AI algorithm to analyze the user's personality and hobbies and perform the optimal matching. For example, if the user's personality is extroverted, it will match them with people with similar extroverted personalities, and if the user's hobby is music, it will match them with people who are interested in music. Furthermore, the matching unit can perform more accurate matching by referring to the user's past matching history. For example, it can analyze patterns of successful matches in the past and perform new matches based on them to improve the success rate. In addition, the matching unit can collect user feedback and continuously improve the accuracy of the matching algorithm. For example, it can evaluate whether the user is satisfied with the matching results and adjust the algorithm based on that evaluation. This allows the matching unit to provide users with the best possible matches and improve user satisfaction. Furthermore, the matching unit thoroughly implements data anonymization and security measures to protect user privacy. This ensures that users can use the system with peace of mind.

[0061] The empathy response unit can provide empathetic conversations by utilizing data from Q&A sites. For example, the empathy response unit can provide empathetic responses to users' worries and questions based on data from Q&A sites. For example, the empathy response unit can analyze data from Q&A sites and select appropriate empathetic responses to users' worries and questions. Furthermore, the empathy response unit can also provide responses that correspond to the user's emotions based on data from Q&A sites. For example, the empathy response unit can analyze data from Q&A sites and select words of comfort if the user is sad, and words of empathy if the user is happy. This improves the accuracy of empathetic conversations by utilizing data from Q&A sites. Data from Q&A sites includes, but is not limited to, user worries, questions, and answers. Some or all of the above processing in the empathy response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathy response unit can input data from Q&A sites into a generative AI and have the generative AI generate empathetic responses.

[0062] The analysis unit can analyze the user's personality and hobbies. For example, the analysis unit can analyze the user's conversation content to identify the user's personality and hobbies. For example, the analysis unit can extract keywords from the user's conversation content and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the user's conversation content to analyze the user's personality. This allows for more appropriate matching by analyzing the user's personality and hobbies. The analysis of personality and hobbies includes, but is not limited to, psychological tests and behavioral analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's conversation content into a generative AI and have the generative AI perform the analysis of personality and hobbies.

[0063] The matching unit can match people who have similar interests or common topics of conversation. For example, the matching unit can identify people with common hobbies or interests based on the user's personality and interests, and match them together. For example, the matching unit can suggest people with common topics of conversation based on the user's hobbies and interests. The matching unit can also match compatible people based on the user's personality. This promotes interaction between users by matching people with similar interests or common topics of conversation. Identifying similar interests or common topics includes, but is not limited to, common keywords or areas of interest. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input data on the user's personality and hobbies into a generative AI and have the generative AI execute the matching criteria.

[0064] The empathetic response unit can provide empathetic conversations that allow users to enjoy dialogue with peace of mind. For example, the empathetic response unit provides empathetic responses to what the user says. For example, the empathetic response unit can select empathetic words such as "That must have been tough" or "That sounds like a wonderful experience" and respond to the user. The empathetic response unit can also recognize the user's emotions and respond accordingly. For example, if the user is sad, the empathetic response unit can select words of comfort, and if the user is happy, it can select words of empathy. By providing empathetic conversations that allow users to enjoy dialogue with peace of mind, the user's feelings of loneliness are reduced. Methods for enjoying dialogue with peace of mind include, but are not limited to, privacy protection and the content of responses. Some or all of the above processing in the empathetic response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathetic response unit can input the user's conversation content into a generative AI and have the generative AI generate empathetic responses.

[0065] The analysis unit can analyze the content of a user's conversations to identify their personality and interests. For example, the analysis unit can analyze the content of a user's conversations to identify their personality and hobbies. For example, the analysis unit can extract keywords from the content of a user's conversations and identify the user's interests and concerns based on those keywords. The analysis unit can also conduct psychological tests based on the content of a user's conversations to analyze their personality. This allows for more appropriate matching by identifying personality and interests through analysis of the content of a user's conversations. The analysis of conversation content includes, but is not limited to, keyword extraction and sentiment analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the content of a user's conversations into a generative AI and have the generative AI perform an analysis of personality and hobbies.

[0066] The matching unit can suggest the most suitable conversation partners who share common hobbies and interests. For example, the matching unit can identify people with common hobbies and interests based on the user's personality and hobbies, and match them with each other. For example, the matching unit can suggest people with common topics of conversation based on the user's hobbies and interests. The matching unit can also match users with compatible partners based on their personalities. This promotes interaction between users by suggesting the most suitable conversation partners who share common hobbies and interests. Identifying the most suitable conversation partner includes, but is not limited to, common hobbies and areas of interest. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input data on the user's personality and hobbies into a generative AI and have the generative AI identify the most suitable conversation partner.

[0067] The empathy response unit can estimate the user's emotions and adjust the content of the conversation empathetically based on the estimated emotions. For example, if the user is sad, the empathy response unit will select comforting words and proceed with the conversation in a gentle tone. For example, if the user is happy, the empathy response unit will empathize and offer positive topics. For example, if the user is feeling anxious, the empathy response unit will select reassuring words and proceed with the conversation in a calm tone. This allows for a more appropriate response by adjusting the content of the conversation empathetically 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-described processes in the empathy response unit may be performed using a generative AI, or not. For example, the empathy response unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the content of the empathetic conversation.

[0068] The empathy response unit can refer to the user's past conversation history to provide more personalized and empathetic responses. For example, the empathy response unit can revisit relevant topics based on what the user has said in the past to advance the conversation. For example, the empathy response unit can check whether there has been any progress on the worries or problems the user has previously mentioned and provide an appropriate response. For example, the empathy response unit can revisit topics the user has shown interest in in the past to deepen the conversation. This makes it possible to provide more personalized and empathetic responses by referring to the user's past conversation history. The use of past conversation history includes, but is not limited to, data retention periods and privacy protection. Some or all of the above processing in the empathy response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathy response unit can input the user's past conversation history into a generative AI and have the generative AI generate personalized and empathetic responses.

[0069] The empathy response unit can generate appropriate empathetic responses based on the user's current situation and environment. For example, if the user is out, the empathy response unit will provide topics related to the outside environment. For example, if the user is at home, the empathy response unit will provide topics related to events within the home. For example, if the user is participating in a specific event, the empathy response unit will provide topics related to that event. This enables more appropriate empathetic responses by generating responses based on the user's current situation and environment. Identifying the current situation and environment includes, but is not limited to, location information and time of day. Some or all of the above processing in the empathy response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the empathy response unit can input data on the user's current situation and environment into a generative AI and have the generative AI generate an empathetic response.

[0070] The empathy response unit can estimate the user's emotions and adjust the tone of the conversation empathetically based on the estimated emotions. For example, if the user is depressed, the empathy response unit will speak in a gentle tone. If the user is excited, the empathy response unit will speak in an energetic tone. If the user is tired, the empathy response unit will speak in a calm tone. This allows for a more appropriate response by adjusting the tone of the conversation 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 empathy response unit may be performed using a generative AI, or not using a generative AI. For example, the empathy response unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the tone of the conversation empathetically.

[0071] The empathy response unit can provide region-specific empathetic responses by taking into account the user's geographical location information. For example, if the user is in a specific region, the empathy response unit can provide topics related to the weather or events in that region. For example, if the user is traveling, the empathy response unit can provide topics related to tourist information or local specialties of the travel destination. For example, if the user is in their hometown, the empathy response unit can provide topics related to local news or events. This makes region-specific empathetic responses possible by taking into account the user's geographical location information. The use of geographical location information includes, but is not limited to, methods for acquiring location information and privacy protection. Some or all of the processing described above in the empathy response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathy response unit can input the user's geographical location information into a generative AI and have the generative AI generate region-specific empathetic responses.

[0072] The empathy response unit can analyze a user's social media activity and provide relevant empathetic responses. For example, the empathy response unit can provide relevant topics based on what the user has shared on social media. For example, the empathy response unit can provide empathetic responses based on posts from accounts the user follows on social media. For example, the empathy response unit can provide relevant topics based on groups and communities the user participates in on social media. This allows for more appropriate empathetic responses by analyzing the user's social media activity. Analysis of social media activity includes, but is not limited to, analysis of posts and followers. Some or all of the above processing in the empathy response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathy response unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant empathetic responses.

[0073] The analysis unit can estimate the user's emotions and adjust the personality and hobby analysis results based on the estimated emotions. For example, if the user is depressed, the analysis unit will emphasize positive hobbies and interests. For example, if the user is excited, the analysis unit will emphasize active hobbies and interests. For example, if the user is relaxed, the analysis unit will emphasize relaxing hobbies and interests. This allows for a more appropriate analysis by adjusting the personality and hobby analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI adjust the personality and hobby analysis results.

[0074] The analysis unit can perform more accurate analyses of a user's personality and hobbies by referring to the user's past behavioral history. For example, the analysis unit can analyze a user's personality and hobbies based on events and activities the user has participated in in the past. For example, the analysis unit can analyze a user's personality and hobbies based on products and services the user has purchased in the past. For example, the analysis unit can analyze a user's personality and hobbies based on places the user has visited or traveled to in the past. This makes it possible to perform more accurate analyses of a user's personality and hobbies by referring to the user's past behavioral history. The use of past behavioral history includes, but is not limited to, data retention periods and privacy protection. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the user's past behavioral history into a generative AI and have the generative AI perform the analysis of personality and hobbies.

[0075] The analysis unit can customize the results of personality and hobby analysis based on the user's current living situation and environment. For example, the analysis unit can analyze personality and hobby based on the user's current occupation and work situation. For example, the analysis unit can analyze personality and hobby based on the user's current home environment and family structure. For example, the analysis unit can analyze personality and hobby based on the user's current health status and lifestyle. By customizing the analysis results based on the user's current living situation and environment, a more appropriate analysis becomes possible. Identifying the current living situation and environment includes, but is not limited to, income and family structure. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data on the user's current living situation and environment into a generative AI and have the generative AI perform the customization of the personality and hobby analysis results.

[0076] The analysis unit can estimate the user's emotions and determine the priority of personality and hobby analyses based on the estimated emotions. For example, if the user is depressed, the analysis unit will prioritize positive hobbies and interests in its analysis. For example, if the user is excited, the analysis unit will prioritize active hobbies and interests in its analysis. For example, if the user is relaxed, the analysis unit will prioritize relaxing hobbies and interests in its analysis. This allows for more appropriate analysis by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of personality and hobby analyses.

[0077] The analysis unit can analyze region-specific personality traits and hobbies by taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit will analyze personality traits and hobbies based on the culture and customs of that region. For example, if the user is traveling, the analysis unit will analyze personality traits and hobbies based on the culture and customs of the travel destination. For example, if the user is in their hometown, the analysis unit will analyze personality traits and hobbies based on the local culture and customs. This makes it possible to analyze region-specific personality traits and hobbies by taking into account the user's geographical location information. The use of geographical location information includes, but is not limited to, methods for acquiring location information and privacy protection. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform an analysis of region-specific personality traits and hobbies.

[0078] The analysis unit can analyze a user's social media activity and perform related personality and hobby analyses. For example, the analysis unit can analyze personality and hobby based on content shared by the user on social media. For example, the analysis unit can analyze personality and hobby based on posts from accounts followed by the user on social media. For example, the analysis unit can analyze personality and hobby based on groups and communities the user participates in on social media. This allows for a more accurate analysis of personality and hobby by analyzing the user's social media activity. Analysis of social media activity includes, but is not limited to, analysis of posted content and followers. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input data on the user's social media activity into a generative AI and have the generative AI perform related personality and hobby analyses.

[0079] 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 depressed, the matching unit will match them with someone with a positive personality. If the user is excited, the matching unit will match them with someone with an active personality. If the user is relaxed, the matching unit will match them with someone with a relaxed personality. 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, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 and have the generative AI adjust the matching criteria.

[0080] The matching unit can refer to the user's past matching history to provide more appropriate matches. For example, the matching unit can provide appropriate matches based on the content of conversations the user has had with people they have previously matched with. For example, the matching unit can provide appropriate matches based on the user's compatibility with people they have previously matched with. For example, the matching unit can provide appropriate matches based on shared hobbies and interests with people the user has previously matched with. This makes it possible to provide more appropriate matches by referring to the user's past matching history. The use of past matching history includes, but is not limited to, data retention periods and privacy protection. 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 past matching history into a generative AI and have the generative AI perform the task of providing appropriate matches.

[0081] The matching unit can perform optimal matching based on the user's current situation and environment. For example, the matching unit can perform optimal matching based on the user's current occupation and work situation. For example, the matching unit can perform optimal matching based on the user's current home environment and family structure. For example, the matching unit can perform optimal matching based on the user's current health status and lifestyle. This makes it possible to perform more appropriate matching by performing matching based on the user's current situation and environment. Identifying the current situation and environment includes, but is not limited to, location information and time of day. 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 data on the user's current situation and environment into a generative AI and have the generative AI perform the provision of optimal matching.

[0082] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is depressed, the matching unit will prioritize displaying people with positive personalities. If the user is excited, the matching unit will prioritize displaying people with active personalities. If the user is relaxed, the matching unit will prioritize displaying people with relaxing personalities. By adjusting the order in which matching results are displayed based on the user's emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 and have the generative AI adjust the display order of the matching results.

[0083] The matching unit can perform region-specific matching by taking into account the user's geographical location information. For example, if the user lives in a specific region, the matching unit will perform matching based on the culture and customs of that region. For example, if the user is traveling, the matching unit will perform matching based on the culture and customs of the travel destination. For example, if the user is in their hometown, the matching unit will perform matching based on the local culture and customs. This makes region-specific matching possible by taking into account the user's geographical location information. The use of geographical location information includes, but is not limited to, methods for acquiring location information and privacy protection. 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 geographical location information into a generative AI and have the generative AI perform the provision of region-specific matching.

[0084] The matching unit can analyze a user's social media activity and provide relevant matches. For example, the matching unit can provide relevant matches based on content shared by the user on social media. For example, the matching unit can provide relevant matches based on posts from accounts followed by the user on social media. For example, the matching unit can provide relevant matches based on groups and communities the user participates in on social media. This allows for more appropriate matching by analyzing the user's social media activity. Analysis of social media activity includes, but is not limited to, analysis of posts and followers. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input data on the user's social media activity into a generative AI and have the generative AI perform the provision of relevant matches.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The empathy response unit can analyze the user's tone of voice and speaking speed to estimate the user's emotions. For example, if the user speaks slowly, the empathy response unit can estimate that the user is calm and provide a gentle response. Conversely, if the user speaks quickly, the empathy response unit can estimate that the user is excited and provide a lively response. Furthermore, if the user's tone of voice is low, the empathy response unit can estimate that the user is sad and choose words of comfort. This enables more appropriate and empathetic responses based on the user's tone of voice and speaking speed.

[0087] The analytics department can acquire users' health data and use it to analyze their personality and hobbies. For example, based on a user's step count data, it can analyze whether they are likely to have active hobbies. It can also suggest relaxing hobbies based on a user's sleep data. Furthermore, it can estimate a user's stress level based on their heart rate data and suggest hobbies that help relieve stress. This allows for more accurate personality and hobby analysis by utilizing users' health data.

[0088] The matching function can analyze a user's past conversations and suggest conversation topics. For example, it can suggest conversation topics with people who share common hobbies or interests based on what the user has said in the past. It can also analyze the user's level of interest in a specific topic based on what they have said in the past and match them with people who are interested in that topic. Furthermore, it can analyze the user's feelings towards a specific topic based on what they have said in the past and suggest conversation topics based on those feelings. In this way, by utilizing the user's past conversations, it becomes possible to suggest more appropriate conversation topics.

[0089] The empathy response unit can recognize the user's facial expressions and provide empathetic responses based on those expressions. For example, if the user smiles, the empathy response unit can assume the user is happy and provide a positive response. If the user frowns, the empathy response unit can assume the user is confused and choose words of advice or support. Furthermore, if the user is crying, the empathy response unit can assume the user is sad and choose words of comfort. This enables more appropriate empathetic responses based on the user's facial expressions.

[0090] The analytics department can analyze users' purchase history and use that information to analyze their personality and hobbies. For example, if a user frequently purchases outdoor equipment, it can be analyzed that they are interested in outdoor activities. Similarly, if a user purchases many books, it can be analyzed that they enjoy reading. Furthermore, if a user purchases health foods, it can be analyzed that they are health-conscious. By utilizing users' purchase history, it becomes possible to perform more accurate analyses of their personality and hobbies.

[0091] The matching unit can estimate the user's real-time emotions and adjust the timing of matching based on those emotions. For example, if a user is feeling down, it can wait until the user is relaxed before attempting to match them. Conversely, if a user is excited, it can immediately attempt to match them to facilitate active conversation. Furthermore, if a user is relaxed, it can attempt to match them to enjoy a calm conversation. This allows for more appropriate timing of matching based on the user's real-time emotions.

[0092] The empathy response unit can provide relevant news and information based on the user's hobbies and interests. For example, if the user is interested in sports, it can provide the latest sports news. If the user is interested in music, it can provide the latest music release information. Furthermore, if the user is interested in travel, it can provide tourist information and event information for travel destinations. This allows for more fulfilling conversations by providing relevant news and information based on the user's hobbies and interests.

[0093] The analysis department can analyze a user's personality and hobbies by taking their life events into consideration. For example, if a user gets married, it can suggest hobbies related to family life. If a user changes jobs, it can suggest hobbies that will help them adapt to their new work environment. Furthermore, if a user moves, it can suggest hobbies related to activities in their new area. This allows for a more appropriate analysis of a user's personality and hobbies by considering their life events.

[0094] The matching unit can estimate the user's emotions and adjust the matching frequency based on those emotions. For example, if the user is feeling down, the matching frequency can be reduced, and the system can wait until the user is relaxed. Conversely, if the user is excited, the matching frequency can be increased to encourage active conversation. Furthermore, if the user is relaxed, matching can be performed at an appropriate frequency to allow for calm conversation. This enables more appropriate matching based on the user's emotions.

[0095] The empathy response unit can provide empathetic responses by taking into account the user's cultural background. For example, if the user belongs to a specific cultural sphere, it can provide responses appropriate to that culture. Furthermore, if the user grew up in a multicultural environment, it can provide responses that demonstrate an understanding of different cultures. Additionally, if the user practices a specific religion, it can provide responses related to that religion. This allows for more appropriate and empathetic responses by considering the user's cultural background.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The empathy response unit provides empathetic conversation. For example, it selects empathetic words to respond to what the user says, such as "That must have been tough" or "That sounds like a wonderful experience." It can also recognize the user's emotions and respond accordingly. For example, if the user is sad, it can choose words of comfort, and if the user is happy, it can choose words of empathy. Step 2: The analysis unit analyzes the user's personality and interests based on the conversation provided by the empathy response unit. For example, it analyzes the content of the user's conversation, extracts keywords, and identifies the user's interests and concerns. It can also conduct psychological tests based on the conversation content to analyze the user's personality. Step 3: The matching unit matches people with similar interests and common topics of conversation based on the personality and hobbies analyzed by the analysis unit. For example, it identifies people with common hobbies and interests based on the user's personality and hobbies, and matches them with each other. It can also match users with compatible partners based on their personality.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the empathy response unit, analysis unit, and matching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the empathy response unit is implemented by the control unit 46A of the smart device 14 and provides empathetic responses to the user's conversation. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's personality and hobbies. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches people with similar interests or common topics. 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.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the empathy response unit, analysis unit, and matching unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the empathy response unit is implemented by the control unit 46A of the smart glasses 214 and provides empathetic responses to the user's conversation. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's personality and hobbies. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches people with similar interests or common topics. 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.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the empathy response unit, analysis unit, and matching unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the empathy response unit is implemented by the control unit 46A of the headset terminal 314 and provides empathetic responses to the user's conversation. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's personality and hobbies. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches people with similar interests or common topics. 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.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the empathy response unit, analysis unit, and matching unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the empathy response unit is implemented by the control unit 46A of the robot 414 and provides empathetic responses to the user's conversation. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's personality and hobbies. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches people with similar interests or common topics. 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) An empathetic response unit that provides empathetic conversation, An analysis unit analyzes the user's personality and hobbies based on the conversation provided by the empathy response unit, The system includes a matching unit that matches people with similar interests and common topics of conversation based on the personality and hobbies analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The empathy response unit is Utilizing data from Q&A sites to provide empathetic conversations The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the user's personality and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Matching people with similar interests or common topics of conversation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The empathy response unit is To provide empathetic conversations that allow users to enjoy dialogue with peace of mind. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyze user conversations to identify personality and interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The matching unit is We suggest the perfect conversation partner who shares your hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The empathy response unit is It estimates the user's emotions and adjusts the content of the conversation to be more empathetic based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The empathy response unit is Referencing the user's past conversation history provides more personalized and empathetic responses. The system described in Appendix 1, characterized by the features described herein. (Note 10) The empathy response unit is It generates appropriate and empathetic responses based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The empathy response unit is It estimates the user's emotions and adjusts the tone of the conversation to be more empathetic based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The empathy response unit is Providing region-specific, empathetic responses that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The empathy response unit is Analyze users' social media activity and provide relevant, empathetic responses. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis results of personality and hobbies based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is By referring to the user's past behavioral history, we can perform a more accurate analysis of their personality and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Customize the analysis results of personality and hobbies based on the user's current living situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and prioritizes personality and hobby analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We analyze regional characteristics and hobbies, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is Analyze users' social media activity to understand their related personality traits and interests. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The matching unit is Referencing the user's past matching history provides more appropriate matches. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is We perform optimal matching based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is The system performs region-specific matching, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is Analyzes users' social media activity and provides relevant matching. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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. An empathetic response unit that provides empathetic conversation, An analysis unit analyzes the user's personality and hobbies based on the conversation provided by the empathy response unit, The system includes a matching unit that matches people with similar interests and common topics of conversation based on the personality and hobbies analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The empathy response unit is Utilizing data from Q&A sites to provide empathetic conversations The system according to feature 1.

3. The aforementioned analysis unit is Analyze the user's personality and hobbies. The system according to feature 1.

4. The matching unit is Matching people with similar interests or common topics of conversation. The system according to feature 1.

5. The empathy response unit is To provide empathetic conversations that allow users to enjoy dialogue with peace of mind. The system according to feature 1.

6. The aforementioned analysis unit is Analyze user conversations to identify personality and interests. The system according to feature 1.

7. The matching unit is We suggest the perfect conversation partner who shares your hobbies and interests. The system according to feature 1.

8. The empathy response unit is It estimates the user's emotions and adjusts the content of the conversation to be more empathetic based on those estimated emotions. The system according to feature 1.

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