System

Abstract

An object of a system according to an embodiment is to evaluate an affinity between a user and a partner and to effectively assist communication after matching.SOLUTION: A system includes a scoring unit, an analysis unit, and an assist unit. The scoring unit scores a degree of compatibility between the user and the opponent. The analysis unit analyzes a past matching tendency on the basis of the degree of compatibility scored by the scoring unit. The assist unit assists communication after matching based on the matching tendency analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently evaluate the compatibility between users and other parties and to effectively assist communication after matching.

[0005] The system according to the embodiment aims to evaluate the compatibility between a user and a partner and to effectively assist communication after matching. [Means for solving the problem]

[0006] The system according to the embodiment includes a scoring unit, an analysis unit, and an assist unit. The scoring unit scores the compatibility between the user and the other party. The analysis unit analyzes past matching trends based on the compatibility scored by the scoring unit. The assist unit assists communication after matching based on the matching trends analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the compatibility between a user and a partner and effectively assist communication after matching. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A partner-finding system according to an embodiment of the present invention uses a generation AI to efficiently and effectively find a partner. In this system, the generation AI plays the role of a psychology professional or marriage counselor, scoring the compatibility between the user and a potential partner. It also analyzes past matching trends and suggests what kind of partner the user is likely to like. It also has a function to assist communication after matching. This allows the partner-finding system to efficiently and effectively find a partner. For example, finding a partner with high compatibility can help build a better relationship. Furthermore, incorporating self-evaluation allows users to better understand themselves and take an appropriate approach toward the partner. Furthermore, the communication assistance function facilitates smooth interactions after matching, deepening the relationship.

[0029] A partner search system according to an embodiment includes a scoring unit, an analysis unit, and an assist unit. The scoring unit scores the compatibility between a user and a partner. For example, when a user answers questions such as "What are your hobbies?" or "What are your dreams for the future?", the generation AI analyzes the answers and scores the compatibility. The generation AI uses a text generation AI (e.g., LLM) to score the compatibility based on the user's answers. The generation AI can also analyze the user's answers and score the compatibility using a multimodal generation AI. For example, the generation AI creates a psychological profile based on the content of the user's answers and scores the compatibility based on the profile. The analysis unit analyzes past matching trends based on the compatibility score scored by the scoring unit. For example, the generation AI analyzes data on partners to whom the user has previously sent "likes" and analyzes the characteristics of other users to whom the partners have sent "likes." The generation AI, for example, extracts characteristics of a partner that the user finds appealing based on the user's past matching data and makes suggestions based on those characteristics. The assistance unit assists communication after matching based on the matching tendencies analyzed by the analysis unit. For example, the generation AI provides automatic reply assistance even if the user is not good at communicating in writing or does not have time to reply to the other party. The generation AI automatically creates candidate sentences that will leave a good impression on the other party and follows up until the user is ready to send the message. For example, the generation AI analyzes the user's past message history and generates replies that are optimal for the user's communication style. This allows the partner search system according to the embodiment to efficiently and effectively find a partner. For example, by finding a partner with high compatibility, users can build better relationships. Furthermore, by incorporating self-evaluations, users can better understand themselves and take an appropriate approach toward the other party. Furthermore, the communication assistance function facilitates smoother post-matching interactions, deepening relationships.

[0030] The scoring unit analyzes a user's past SNS posts or message history to reflect more detailed personality and values ​​in the compatibility score. For example, the scoring unit uses a generation AI to analyze a user's past SNS posts and extract the user's personality and values ​​from the content of the posts. For example, it analyzes the themes the user frequently posts on and the tone of the words they use to identify personality traits. The scoring unit also uses a generation AI to analyze the user's message history and evaluate the user's communication style based on the content and frequency of messages. For example, it analyzes the topics the user prefers and how often they send messages. The scoring unit also uses a generation AI to analyze the user's friendships on SNS and extract the user's sociability and interpersonal characteristics from interactions with friends. For example, it evaluates the user's interpersonal skills based on the content and frequency of interactions with friends. This allows for a more accurate compatibility score by reflecting the user's personality and values ​​in detail.

[0031] The scoring unit analyzes the user's biometric data, evaluates their psychological state in real time, and reflects this in the compatibility score. For example, the scoring unit uses the generation AI to analyze the user's heart rate data and evaluate their stress level and relaxation state. For example, the scoring unit identifies their psychological state based on heart rate fluctuations when the user answers specific questions. The scoring unit also uses the generation AI to analyze the user's electrodermal response data and evaluate the intensity and type of their emotions. For example, it determines how the user feels about a specific topic from changes in electrodermal response. The scoring unit also monitors the user's biometric data in real time and continuously evaluates their psychological state. For example, it analyzes in real time what psychological changes the user experiences during a date. This allows the scoring unit to evaluate the user's psychological state in real time and reflect this in the compatibility score, resulting in a more accurate compatibility score.

[0032] The scoring unit can score compatibility based on the user's occupation or lifestyle and suggest matches by occupation or lifestyle. In the scoring unit, for example, the generation AI analyzes the user's occupation data and scores compatibility by occupation. For example, the compatibility between users with the same occupation or related occupations is highly rated. In addition, the scoring unit can analyze the user's lifestyle data and score compatibility by lifestyle. For example, the compatibility between users who are outdoorsy is highly rated. In addition, the scoring unit can suggest optimal matches based on the user's occupation and lifestyle. For example, users with the same lifestyle are matched preferentially. This allows the generation AI to score compatibility based on the user's occupation and lifestyle and suggest optimal matches.

[0033] The scoring unit can analyze a user's music or movie preferences and reflect cultural similarity in the compatibility score. In the scoring unit, for example, the generation AI analyzes a user's music playlist and evaluates their music preferences. For example, it may highly evaluate the compatibility between users who like the same artist or genre. In the scoring unit, the generation AI analyzes a user's movie viewing history and evaluates their movie preferences. For example, it may highly evaluate the compatibility between users who like the same movie genre or director. In addition, the scoring unit reflects cultural similarity in the scoring based on the user's music or movie preferences. For example, it may preferentially match users with the same cultural background. In this way, by scoring compatibility based on the user's music or movie preferences and reflecting cultural similarity, it is possible to propose more accurate matches.

[0034] The analysis unit can analyze the content of the user's past messages and reflect the degree of similarity in communication styles in suggestions. In the analysis unit, for example, the generation AI analyzes the content of the user's past messages and evaluates the communication style. For example, it analyzes the user's preferred tone and language. The analysis unit also evaluates the degree of similarity with the other party's communication style based on the user's message history. For example, it may highly evaluate the compatibility with someone who prefers the same tone and language. The analysis unit also analyzes the content of the user's past messages and reflects the degree of similarity in communication styles in suggestions. For example, it makes suggestions based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's communication style and reflecting the degree of similarity in suggestions, more accurate matching can be proposed.

[0035] The analysis unit can analyze the user's past dating history and make suggestions based on the success rate and satisfaction of dates. In the analysis unit, for example, the generation AI analyzes the user's past dating history and evaluates the success rate of dates. For example, it analyzes what kind of date plans the user prefers. In addition, the analysis unit evaluates the satisfaction rate of dates based on the user's dating history. For example, it analyzes what kind of date plans the user was most satisfied with. In addition, the analysis unit analyzes the user's past dating history and makes suggestions based on the success rate and satisfaction of dates. For example, it makes suggestions based on date plans that were successful for the user in the past. In this way, by analyzing the user's dating history and making suggestions based on the success rate and satisfaction, more accurate matching can be suggested.

[0036] The analysis unit can analyze the user's friendships and make suggestions based on the friends' "Like" tendencies. In the analysis unit, for example, the generation AI analyzes the user's friendships and evaluates the friends' "Like" tendencies. For example, it analyzes the types of people the user's friends "Like." In addition, the analysis unit makes suggestions based on the user's friendships and the friends' "Like" tendencies. For example, it makes suggestions based on the characteristics of people the user's friends like. In addition, the analysis unit can analyze the user's friendships and make suggestions based on the friends' "Like" tendencies. For example, it makes suggestions based on the characteristics of successful matches that the user's friends have made in the past. In this way, by analyzing the user's friendships and making suggestions based on the friends' "Like" tendencies, more accurate matching can be suggested.

[0037] The analysis unit can analyze the user's work or school environment and make suggestions based on the "Like!" tendencies of people in the same environment. In the analysis unit, for example, the generation AI analyzes the user's work environment and evaluates the "Like!" tendencies of people in the same workplace. For example, it analyzes what kind of people people in the same workplace send "Like!" to. In addition, the analysis unit can analyze the user's school environment and evaluate the "Like!" tendencies of people in the same school. For example, it analyzes what kind of people people at the same school send "Like!" to. In addition, the analysis unit can make suggestions based on the user's work or school environment and on the "Like!" tendencies of people in the same environment. For example, it makes suggestions based on the characteristics of people preferred by people in the same workplace or school. In this way, by analyzing the user's work or school environment and making suggestions based on the "Like!" tendencies of people in the same environment, more accurate matching can be proposed.

[0038] The assisting unit can analyze the user's past message history and generate a reply that is optimal for the user's communication style. In the assisting unit, for example, the generation AI analyzes the user's past message history and evaluates the user's communication style. For example, it analyzes the user's preferred tone and wording. In addition, in the assisting unit, the generation AI generates an optimal reply based on the user's message history. For example, it generates a reply based on the communication style that has been successful for the user in the past. In addition, in the assisting unit, the generation AI analyzes the user's past message history and generates a reply that is optimal for the user's communication style. For example, it generates a reply based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's past message history and generating a reply that is optimal for the communication style, more effective communication can be supported.

[0039] The assist unit can analyze the content of the other party's message and generate a reply based on the other party's interests and concerns. In the assist unit, for example, the generation AI analyzes the content of the other party's message and evaluates the other party's interests and concerns. For example, it analyzes what topics the other party prefers. In addition, the assist unit generates a reply based on the other party's interests and concerns based on the content of the other party's message. For example, it generates a reply related to topics that interest the other party. In addition, the assist unit analyzes the content of the other party's message and generates a reply based on the other party's interests and concerns. For example, it generates a reply based on topics that the other party has shown interest in in the past. In this way, more effective communication can be supported by analyzing the content of the other party's message and generating a reply based on their interests and concerns.

[0040] The assist unit can analyze the user's voice message and automatically generate a reply to the voice message. In the assist unit, for example, the generation AI analyzes the user's voice message and evaluates the tone and content of the voice. For example, it analyzes the tone in which the user speaks. In addition, in the assist unit, the generation AI generates an optimal reply voice message based on the user's voice message. For example, it generates a reply based on the style of voice messages that the user has used successfully in the past. In addition, in the assist unit, the generation AI analyzes the user's voice message and automatically generates a reply to the voice message. For example, it generates a reply based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's voice message and automatically generating a reply to the voice message, more effective communication can be supported.

[0041] The assisting unit can analyze a user's video message and automatically generate a reply to the video message. In the assisting unit, for example, the generation AI analyzes the user's video message and evaluates the content and facial expressions of the video. For example, it analyzes the facial expressions the user uses when speaking. In addition, the assisting unit generates an optimal video message reply based on the user's video message. For example, it generates a reply based on the style of video messages that the user has used successfully in the past. In addition, the assisting unit analyzes the user's video message and automatically generates a reply to the video message. For example, it generates a reply based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's video message and automatically generating a reply to the video message, more effective communication can be supported.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The scoring unit analyzes a user's hobbies and interests and can score the compatibility with people who share the same hobbies. For example, if a user is interested in a particular sport or art, the scoring unit will highly evaluate the compatibility with people who share that hobbies. The scoring unit also uses the generation AI to analyze posts and messages related to the user's hobbies and evaluate the degree of compatibility of hobbies. For example, if a user likes a particular music genre or movie, the scoring unit will highly evaluate the compatibility with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's hobbies and interests. For example, it will prioritize matching users who share the same hobbies. This allows the system to score compatibility based on the user's hobbies and interests and suggest optimal matches.

[0044] The scoring unit can analyze the user's health data and score compatibility based on their health status. For example, if the user uses a fitness app, the scoring unit analyzes that data and highly rates compatibility with partners who have a healthy lifestyle. The scoring unit also uses the generation AI to analyze the user's diet and exercise records and evaluate their health status. For example, if the user is following a specific diet or exercise plan, the scoring unit highly rates compatibility with partners who share that plan. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's health data. For example, it prioritizes matching users who have the same health goals. This allows the scoring unit to score compatibility based on the user's health data and suggest optimal matches.

[0045] The scoring unit can analyze a user's travel history and score compatibility based on travel preferences. For example, it can analyze the places a user has visited in the past and their travel style, and highly evaluate the compatibility with people who share the same travel preferences. The scoring unit also uses the generation AI to analyze the user's travel posts and photos and evaluate the degree of travel compatibility. For example, if a user likes a particular country or city, it will highly evaluate the compatibility with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's travel history. For example, it will prioritize matching users who like the same travel destinations. This allows the system to score compatibility based on the user's travel history and suggest optimal matches.

[0046] The scoring unit can analyze a user's reading history and score compatibility based on reading preferences. For example, it can analyze the books and authors the user has read in the past and highly evaluate the compatibility with people who share the same reading preferences. The scoring unit also uses the generation AI to analyze the user's reviews and comments about their reading and evaluate the degree of compatibility with their reading preferences. For example, if a user likes a particular genre or theme, it will highly evaluate the compatibility with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's reading history. For example, it will prioritize matching users who like the same books. This allows it to score compatibility based on the user's reading history and suggest optimal matches.

[0047] The scoring unit analyzes the user's cooking preferences and can reflect the degree of match of dishes in the compatibility score. For example, if a user likes a particular dish or recipe, the compatibility score will be highly rated with people who share that preference. In addition, the scoring unit uses the generation AI to analyze the user's cooking posts and photos and evaluate the degree of match of dishes. For example, if a user likes a particular cooking genre or ingredients, the compatibility score will be highly rated with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's cooking preferences. For example, users who like the same dishes are matched preferentially. This allows the compatibility score to be calculated based on the user's cooking preferences, and optimal matches to be suggested.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The scoring unit scores the compatibility between the user and the other party. For example, when the user answers questions such as "What are your hobbies?" or "What are your dreams for the future?", the generation AI analyzes the answers and scores the compatibility. The generation AI uses a text generation AI (e.g., LLM) to score the compatibility based on the user's answers. The generation AI can also use a multimodal generation AI to analyze the user's answers and score the compatibility. For example, the generation AI creates a psychological profile based on the user's answers and scores the compatibility based on that profile. Step 2: The analysis unit analyzes past matching trends based on the compatibility scores calculated by the scoring unit. For example, the generation AI analyzes data on people to whom the user has previously sent "likes" and analyzes the characteristics of other users to whom those people have sent "likes." For example, the generation AI extracts the characteristics of people that the user would "like" based on the user's past matching data and makes suggestions based on those characteristics. Step 3: The assisting unit assists with post-matching communication based on the matching tendencies analyzed by the analyzing unit. For example, the generating AI can provide automatic reply assistance even if the user is not good at communicating in writing or does not have time to reply to the other person. The generating AI automatically creates candidate sentences that will leave a good impression on the other person and follows up until the user is ready to send the message. For example, the generating AI can analyze the user's past message history and generate replies that are optimal for the user's communication style.

[0050] (Example 2) A partner-finding system according to an embodiment of the present invention uses a generation AI to efficiently and effectively find a partner. In this system, the generation AI plays the role of a psychology professional or marriage counselor, scoring the compatibility between the user and a potential partner. It also analyzes past matching trends and suggests what kind of partner the user is likely to like. It also has a function to assist communication after matching. This allows the partner-finding system to efficiently and effectively find a partner. For example, finding a partner with high compatibility can help build a better relationship. Furthermore, incorporating self-evaluation allows users to better understand themselves and take an appropriate approach toward the partner. Furthermore, the communication assistance function facilitates smooth interactions after matching, deepening the relationship.

[0051] A partner search system according to an embodiment includes a scoring unit, an analysis unit, and an assist unit. The scoring unit scores the compatibility between a user and a partner. For example, when a user answers questions such as "What are your hobbies?" or "What are your dreams for the future?", the generation AI analyzes the answers and scores the compatibility. The generation AI uses a text generation AI (e.g., LLM) to score the compatibility based on the user's answers. The generation AI can also analyze the user's answers and score the compatibility using a multimodal generation AI. For example, the generation AI creates a psychological profile based on the content of the user's answers and scores the compatibility based on the profile. The analysis unit analyzes past matching trends based on the compatibility score scored by the scoring unit. For example, the generation AI analyzes data on partners to whom the user has previously sent "likes" and analyzes the characteristics of other users to whom the partners have sent "likes." The generation AI, for example, extracts characteristics of a partner that the user finds appealing based on the user's past matching data and makes suggestions based on those characteristics. The assistance unit assists communication after matching based on the matching tendencies analyzed by the analysis unit. For example, the generation AI provides automatic reply assistance even if the user is not good at communicating in writing or does not have time to reply to the other party. The generation AI automatically creates candidate sentences that will leave a good impression on the other party and follows up until the user is ready to send the message. For example, the generation AI analyzes the user's past message history and generates replies that are optimal for the user's communication style. This allows the partner search system according to the embodiment to efficiently and effectively find a partner. For example, by finding a partner with high compatibility, users can build better relationships. Furthermore, by incorporating self-evaluations, users can better understand themselves and take an appropriate approach toward the other party. Furthermore, the communication assistance function facilitates smoother post-matching interactions, deepening relationships.

[0052] The scoring unit analyzes a user's past SNS posts or message history to reflect more detailed personality and values ​​in the compatibility score. For example, the scoring unit uses a generation AI to analyze a user's past SNS posts and extract the user's personality and values ​​from the content of the posts. For example, it analyzes the themes the user frequently posts on and the tone of the words they use to identify personality traits. The scoring unit also uses a generation AI to analyze the user's message history and evaluate the user's communication style based on the content and frequency of messages. For example, it analyzes the topics the user prefers and how often they send messages. The scoring unit also uses a generation AI to analyze the user's friendships on SNS and extract the user's sociability and interpersonal characteristics from interactions with friends. For example, it evaluates the user's interpersonal skills based on the content and frequency of interactions with friends. This allows for a more accurate compatibility score by reflecting the user's personality and values ​​in detail.

[0053] The scoring unit analyzes the user's biometric data, evaluates their psychological state in real time, and reflects this in the compatibility score. For example, the scoring unit uses the generation AI to analyze the user's heart rate data and evaluate their stress level and relaxation state. For example, the scoring unit identifies their psychological state based on heart rate fluctuations when the user answers specific questions. The scoring unit also uses the generation AI to analyze the user's electrodermal response data and evaluate the intensity and type of their emotions. For example, it determines how the user feels about a specific topic from changes in electrodermal response. The scoring unit also monitors the user's biometric data in real time and continuously evaluates their psychological state. For example, it analyzes in real time what psychological changes the user experiences during a date. This allows the scoring unit to evaluate the user's psychological state in real time and reflect this in the compatibility score, resulting in a more accurate compatibility score.

[0054] The scoring unit uses the emotion estimation function to analyze the emotion a user expresses when answering a question and reflects the degree of emotional agreement in the compatibility score. For example, the scoring unit uses the generation AI to analyze the user's facial expression and estimate the emotion expressed when answering a question. For example, if the user answers with a smile, it determines that the emotion is positive. The scoring unit also uses the generation AI to analyze the user's tone of voice and evaluate the intensity and type of emotion. For example, if the user answers with an excited voice, it determines that the emotion is strong. The scoring unit also combines the content of the user's answer with the emotion estimation data and reflects the degree of emotional agreement in the scoring. For example, if the user expresses positive emotions, it evaluates the compatibility score highly. This allows for more accurate compatibility scoring by analyzing the user's emotions and reflecting the degree of emotional agreement in the compatibility score.

[0055] The scoring unit can score compatibility based on the user's occupation or lifestyle and suggest matches by occupation or lifestyle. In the scoring unit, for example, the generation AI analyzes the user's occupation data and scores compatibility by occupation. For example, the compatibility between users with the same occupation or related occupations is highly rated. In addition, the scoring unit can analyze the user's lifestyle data and score compatibility by lifestyle. For example, the compatibility between users who are outdoorsy is highly rated. In addition, the scoring unit can suggest optimal matches based on the user's occupation and lifestyle. For example, users with the same lifestyle are matched preferentially. This allows the generation AI to score compatibility based on the user's occupation and lifestyle and suggest optimal matches.

[0056] The scoring unit can analyze a user's music or movie preferences and reflect cultural similarity in the compatibility score. In the scoring unit, for example, the generation AI analyzes a user's music playlist and evaluates their music preferences. For example, it may highly evaluate the compatibility between users who like the same artist or genre. In the scoring unit, the generation AI analyzes a user's movie viewing history and evaluates their movie preferences. For example, it may highly evaluate the compatibility between users who like the same movie genre or director. In addition, the scoring unit reflects cultural similarity in the scoring based on the user's music or movie preferences. For example, it may preferentially match users with the same cultural background. In this way, by scoring compatibility based on the user's music or movie preferences and reflecting cultural similarity, it is possible to propose more accurate matches.

[0057] The scoring unit uses the emotion estimation function to analyze how a user feels about a particular hobby or activity and can reflect the degree of similarity between hobbies and activities in the compatibility score. For example, the scoring unit uses the generation AI to analyze posts and messages about a user's hobby and evaluate their emotions using the emotion estimation function. For example, if a user has positive feelings about a particular hobby, the scoring unit highly evaluates the compatibility score with people who share that hobby. The scoring unit also analyzes the user's activity history and evaluates their emotions about the activity using the emotion estimation function. For example, if a user enjoys a particular activity, the scoring unit highly evaluates the compatibility score with people who share that activity. The scoring unit also reflects the degree of similarity between hobbies and activities in the scoring based on the generation AI's emotional data about the user's hobby or activity. For example, the scoring unit prioritizes matching between users who have positive feelings about the same hobby or activity. This allows the system to propose more accurate matches by analyzing users' emotions about hobbies and activities and reflecting the degree of similarity between hobbies and activities in the compatibility score.

[0058] The analysis unit can analyze the content of the user's past messages and reflect the degree of similarity in communication styles in suggestions. In the analysis unit, for example, the generation AI analyzes the content of the user's past messages and evaluates the communication style. For example, it analyzes the user's preferred tone and language. The analysis unit also evaluates the degree of similarity with the other party's communication style based on the user's message history. For example, it may highly evaluate the compatibility with someone who prefers the same tone and language. The analysis unit also analyzes the content of the user's past messages and reflects the degree of similarity in communication styles in suggestions. For example, it makes suggestions based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's communication style and reflecting the degree of similarity in suggestions, more accurate matching can be proposed.

[0059] The analysis unit can analyze the user's past dating history and make suggestions based on the success rate and satisfaction of dates. In the analysis unit, for example, the generation AI analyzes the user's past dating history and evaluates the success rate of dates. For example, it analyzes what kind of date plans the user prefers. In addition, the analysis unit evaluates the satisfaction rate of dates based on the user's dating history. For example, it analyzes what kind of date plans the user was most satisfied with. In addition, the analysis unit analyzes the user's past dating history and makes suggestions based on the success rate and satisfaction of dates. For example, it makes suggestions based on date plans that were successful for the user in the past. In this way, by analyzing the user's dating history and making suggestions based on the success rate and satisfaction, more accurate matching can be suggested.

[0060] The analysis unit uses the emotion estimation function to analyze the emotions the user has felt toward past matched partners and reflect the degree of emotional compatibility in the suggestions. For example, the analysis unit uses the generation AI to analyze the user's emotions toward past matched partners and evaluate the emotions using the emotion estimation function. For example, if the user has positive emotions toward a particular partner, the analysis unit makes suggestions based on the characteristics of that partner. The analysis unit also uses the generation AI to evaluate the degree of emotional compatibility based on the user's emotional data toward past matched partners. For example, the analysis unit highly evaluates the compatibility with partners who have the same emotions. The analysis unit also uses the generation AI to analyze the user's emotions toward past matched partners and reflect the degree of emotional compatibility in the suggestions. For example, the analysis unit makes suggestions based on the characteristics of partners about whom the user had positive emotions in the past. In this way, by analyzing the user's past emotions and reflecting the degree of emotional compatibility in the suggestions, more accurate matches can be suggested.

[0061] The analysis unit can analyze the user's friendships and make suggestions based on the friends' "Like" tendencies. In the analysis unit, for example, the generation AI analyzes the user's friendships and evaluates the friends' "Like" tendencies. For example, it analyzes the types of people the user's friends "Like." In addition, the analysis unit makes suggestions based on the user's friendships and the friends' "Like" tendencies. For example, it makes suggestions based on the characteristics of people the user's friends like. In addition, the analysis unit can analyze the user's friendships and make suggestions based on the friends' "Like" tendencies. For example, it makes suggestions based on the characteristics of successful matches that the user's friends have made in the past. In this way, by analyzing the user's friendships and making suggestions based on the friends' "Like" tendencies, more accurate matching can be suggested.

[0062] The analysis unit can analyze the user's work or school environment and make suggestions based on the "Like!" tendencies of people in the same environment. In the analysis unit, for example, the generation AI analyzes the user's work environment and evaluates the "Like!" tendencies of people in the same workplace. For example, it analyzes what kind of people people in the same workplace send "Like!" to. In addition, the analysis unit can analyze the user's school environment and evaluate the "Like!" tendencies of people in the same school. For example, it analyzes what kind of people people at the same school send "Like!" to. In addition, the analysis unit can make suggestions based on the user's work or school environment and on the "Like!" tendencies of people in the same environment. For example, it makes suggestions based on the characteristics of people preferred by people in the same workplace or school. In this way, by analyzing the user's work or school environment and making suggestions based on the "Like!" tendencies of people in the same environment, more accurate matching can be proposed.

[0063] The analysis unit uses the emotion estimation function to analyze the emotions a user feels at a specific location or event, and can suggest partners related to that location or event. For example, the generation AI in the analysis unit analyzes the user's past event participation history and evaluates their emotions using the emotion estimation function. For example, if the user has positive emotions at a specific event, the analysis unit suggests partners related to that event. The analysis unit also uses the generation AI to analyze the user's emotional data at a specific location and suggest partners related to that location. For example, if the user is relaxing at a specific cafe, the analysis unit suggests partners related to that cafe. The analysis unit also uses the emotion estimation function to evaluate the emotional match based on the user's event participation history and location data, and suggests partners related to that location or event. For example, the analysis unit suggests partners who participated in the same event and have positive emotions. This allows the analysis of the user's emotions at a specific location or event and suggests partners related to that location or event, making it possible to suggest more accurate matches.

[0064] The assisting unit can analyze the user's past message history and generate a reply that is optimal for the user's communication style. In the assisting unit, for example, the generation AI analyzes the user's past message history and evaluates the user's communication style. For example, it analyzes the user's preferred tone and wording. In addition, in the assisting unit, the generation AI generates an optimal reply based on the user's message history. For example, it generates a reply based on the communication style that has been successful for the user in the past. In addition, in the assisting unit, the generation AI analyzes the user's past message history and generates a reply that is optimal for the user's communication style. For example, it generates a reply based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's past message history and generating a reply that is optimal for the communication style, more effective communication can be supported.

[0065] The assist unit can analyze the content of the other party's message and generate a reply based on the other party's interests and concerns. In the assist unit, for example, the generation AI analyzes the content of the other party's message and evaluates the other party's interests and concerns. For example, it analyzes what topics the other party prefers. In addition, the assist unit generates a reply based on the other party's interests and concerns based on the content of the other party's message. For example, it generates a reply related to topics that interest the other party. In addition, the assist unit analyzes the content of the other party's message and generates a reply based on the other party's interests and concerns. For example, it generates a reply based on topics that the other party has shown interest in in the past. In this way, more effective communication can be supported by analyzing the content of the other party's message and generating a reply based on their interests and concerns.

[0066] The assisting unit can use the emotion estimation function to analyze the user's emotion toward the other person's message and generate an emotionally appropriate reply. In the assisting unit, for example, the generation AI analyzes the user's emotion toward the other person's message and evaluates the emotion using the emotion estimation function. For example, if the user has positive emotions toward a particular message, the assisting unit generates a reply based on that emotion. In addition, the assisting unit generates an emotionally appropriate reply based on the user's emotion data. For example, if the user has negative emotions toward a particular message, the assisting unit generates a reply that alleviates those emotions. In addition, the assisting unit analyzes the user's emotion toward the other person's message and generates an emotionally appropriate reply. For example, if the user has positive emotions toward a particular message, the assisting unit generates a reply that emphasizes those emotions. In this way, by analyzing the user's emotions toward the other person's message and generating an emotionally appropriate reply, more effective communication can be supported.

[0067] The assist unit can analyze the user's voice message and automatically generate a reply to the voice message. In the assist unit, for example, the generation AI analyzes the user's voice message and evaluates the tone and content of the voice. For example, it analyzes the tone in which the user speaks. In addition, in the assist unit, the generation AI generates an optimal reply voice message based on the user's voice message. For example, it generates a reply based on the style of voice messages that the user has used successfully in the past. In addition, in the assist unit, the generation AI analyzes the user's voice message and automatically generates a reply to the voice message. For example, it generates a reply based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's voice message and automatically generating a reply to the voice message, more effective communication can be supported.

[0068] The assisting unit can analyze a user's video message and automatically generate a reply to the video message. In the assisting unit, for example, the generation AI analyzes the user's video message and evaluates the content and facial expressions of the video. For example, it analyzes the facial expressions the user uses when speaking. In addition, the assisting unit generates an optimal video message reply based on the user's video message. For example, it generates a reply based on the style of video messages that the user has used successfully in the past. In addition, the assisting unit analyzes the user's video message and automatically generates a reply to the video message. For example, it generates a reply based on the characteristics of people with whom the user has had good communication in the past. In this way, by analyzing the user's video message and automatically generating a reply to the video message, more effective communication can be supported.

[0069] The assisting unit can use the emotion estimation function to analyze the user's emotions toward a specific topic and generate a reply related to that topic. In the assisting unit, for example, the generation AI analyzes the user's emotions toward a specific topic and evaluates the emotions using the emotion estimation function. For example, if the user has positive emotions toward a specific topic, the assisting unit generates a reply based on those emotions. In addition, the assisting unit generates a reply related to a specific topic based on the user's emotion data. For example, if the user has negative emotions toward a specific topic, the assisting unit generates a reply that alleviates those emotions. In addition, the assisting unit generates a reply related to a specific topic using the generation AI to analyze the user's emotions toward a specific topic. For example, if the user has positive emotions toward a specific topic, the assisting unit generates a reply that emphasizes those emotions. In this way, by analyzing the user's emotions toward a specific topic and generating a reply related to that topic, more effective communication can be supported.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The scoring unit analyzes a user's hobbies and interests and can score the compatibility with people who share the same hobbies. For example, if a user is interested in a particular sport or art, the scoring unit will highly evaluate the compatibility with people who share that hobbies. The scoring unit also uses the generation AI to analyze posts and messages related to the user's hobbies and evaluate the degree of compatibility of hobbies. For example, if a user likes a particular music genre or movie, the scoring unit will highly evaluate the compatibility with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's hobbies and interests. For example, it will prioritize matching users who share the same hobbies. This allows the system to score compatibility based on the user's hobbies and interests and suggest optimal matches.

[0072] The scoring unit can analyze the user's health data and score compatibility based on their health status. For example, if the user uses a fitness app, the scoring unit analyzes that data and highly rates compatibility with partners who have a healthy lifestyle. The scoring unit also uses the generation AI to analyze the user's diet and exercise records and evaluate their health status. For example, if the user is following a specific diet or exercise plan, the scoring unit highly rates compatibility with partners who share that plan. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's health data. For example, it prioritizes matching users who have the same health goals. This allows the scoring unit to score compatibility based on the user's health data and suggest optimal matches.

[0073] The scoring unit can analyze a user's travel history and score compatibility based on travel preferences. For example, it can analyze the places a user has visited in the past and their travel style, and highly evaluate the compatibility with people who share the same travel preferences. The scoring unit also uses the generation AI to analyze the user's travel posts and photos and evaluate the degree of travel compatibility. For example, if a user likes a particular country or city, it will highly evaluate the compatibility with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's travel history. For example, it will prioritize matching users who like the same travel destinations. This allows the system to score compatibility based on the user's travel history and suggest optimal matches.

[0074] The scoring unit can analyze a user's reading history and score compatibility based on reading preferences. For example, it can analyze the books and authors the user has read in the past and highly evaluate the compatibility with people who share the same reading preferences. The scoring unit also uses the generation AI to analyze the user's reviews and comments about their reading and evaluate the degree of compatibility with their reading preferences. For example, if a user likes a particular genre or theme, it will highly evaluate the compatibility with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's reading history. For example, it will prioritize matching users who like the same books. This allows it to score compatibility based on the user's reading history and suggest optimal matches.

[0075] The scoring unit analyzes the user's cooking preferences and can reflect the degree of match of dishes in the compatibility score. For example, if a user likes a particular dish or recipe, the compatibility score will be highly rated with people who share that preference. In addition, the scoring unit uses the generation AI to analyze the user's cooking posts and photos and evaluate the degree of match of dishes. For example, if a user likes a particular cooking genre or ingredients, the compatibility score will be highly rated with people who share that preference. Furthermore, the scoring unit uses the generation AI to suggest optimal matches based on the user's cooking preferences. For example, users who like the same dishes are matched preferentially. This allows the compatibility score to be calculated based on the user's cooking preferences, and optimal matches to be suggested.

[0076] The scoring unit uses the emotion estimation function to analyze the emotions a user feels when listening to specific music, and can reflect the degree of musical similarity in the compatibility score. For example, if a user feels positive emotions when listening to specific music, the scoring unit will highly evaluate the compatibility score with people who share that music. The scoring unit also uses the generation AI to analyze the user's music playlist and evaluate emotions using the emotion estimation function. For example, if a user feels positive emotions when listening to a specific artist or genre, the scoring unit will highly evaluate the compatibility score with people who share that emotion. The scoring unit also uses the generation AI to reflect the degree of emotional similarity in the scoring score based on the user's musical preferences. This allows the system to analyze the user's emotions toward music and reflect the degree of musical similarity in the compatibility score, thereby proposing more accurate matches.

[0077] The scoring unit uses the emotion estimation function to analyze the emotions a user feels when watching a particular movie, and can reflect the degree of match in the compatibility score. For example, if a user feels positive emotions when watching a particular movie, the scoring unit will highly evaluate the compatibility score with someone who shares that movie. The scoring unit also uses the generation AI to analyze the user's movie viewing history and evaluate emotions using the emotion estimation function. For example, if a user feels positive emotions when watching a particular movie genre or director, the scoring unit will highly evaluate the compatibility score with someone who shares that emotion. The scoring unit also reflects the degree of emotional match in the scoring score based on the user's movie preferences. This allows the generation AI to analyze the user's emotions toward movies and reflect the degree of match in the compatibility score, making it possible to propose more accurate matches.

[0078] The scoring unit uses the emotion estimation function to analyze the emotions a user feels when playing a specific sport and can reflect the degree of sport match in the compatibility score. For example, if a user feels positive emotions when playing a specific sport, the scoring unit can highly evaluate the compatibility score with a person who shares that sport. The generation AI also analyzes the user's sports activity history and evaluates emotions using the emotion estimation function. For example, if a user feels enjoyment when playing a specific sport, the scoring unit can highly evaluate the compatibility score with a person who shares that emotion. The generation AI also reflects the degree of emotional match in the scoring based on the user's sports preferences. This allows the system to analyze the user's emotions toward sports and reflect the degree of sport match in the compatibility score, thereby proposing more accurate matches.

[0079] The scoring unit uses the emotion estimation function to analyze the emotions a user feels when participating in a specific event and can reflect the degree of event match in the compatibility score. For example, if a user feels positive emotions when participating in a specific event, the scoring unit can highly evaluate the compatibility score with people who share that event. The generation AI also analyzes the user's event participation history and evaluates emotions using the emotion estimation function. For example, if a user feels happy when participating in a specific event, the scoring unit can highly evaluate the compatibility score with people who share that emotion. The generation AI also reflects the degree of emotional match in the scoring based on the user's event preferences. This allows the generation AI to analyze the user's emotions toward an event and reflect the degree of event match in the compatibility score, thereby proposing more accurate matches.

[0080] The scoring unit uses the emotion estimation function to analyze the emotions a user feels when visiting a specific place and can reflect the degree of location match in the compatibility score. For example, if a user feels positive emotions when visiting a specific place, the scoring unit will highly evaluate the compatibility score with a person who shares that place. The scoring unit also uses the generation AI to analyze the user's visit history and evaluate emotions using the emotion estimation function. For example, if a user feels relaxed when visiting a specific cafe or park, the scoring unit will highly evaluate the compatibility score with a person who shares that emotion. The scoring unit also uses the generation AI to reflect the degree of emotional match in the scoring based on the user's visit history. This allows the system to analyze the user's emotions toward the places they visit and reflect the degree of location match in the compatibility score, thereby proposing more accurate matches.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The scoring unit scores the compatibility between the user and the other party. For example, when the user answers questions such as "What are your hobbies?" or "What are your dreams for the future?", the generation AI analyzes the answers and scores the compatibility. The generation AI uses a text generation AI (e.g., LLM) to score the compatibility based on the user's answers. The generation AI can also use a multimodal generation AI to analyze the user's answers and score the compatibility. For example, the generation AI creates a psychological profile based on the user's answers and scores the compatibility based on that profile. Step 2: The analysis unit analyzes past matching trends based on the compatibility scores calculated by the scoring unit. For example, the generation AI analyzes data on people to whom the user has previously sent "likes" and analyzes the characteristics of other users to whom those people have sent "likes." For example, the generation AI extracts the characteristics of people that the user would "like" based on the user's past matching data and makes suggestions based on those characteristics. Step 3: The assisting unit assists with post-matching communication based on the matching tendencies analyzed by the analyzing unit. For example, the generating AI can provide automatic reply assistance even if the user is not good at communicating in writing or does not have time to reply to the other person. The generating AI automatically creates candidate sentences that will leave a good impression on the other person and follows up until the user is ready to send the message. For example, the generating AI can analyze the user's past message history and generate replies that are optimal for the user's communication style.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, The generated AI is a scoring unit that scores the compatibility between the user and the other party; an analysis unit that analyzes past matching trends based on the compatibility score obtained by the scoring unit; an assist unit that assists communication after matching based on the matching tendency analyzed by the analysis unit; A system characterized by:

2. The scoring unit Analyzing the user's past SNS posts or message history, and reflecting more detailed personality and values ​​in the compatibility score The system of claim 1 .

3. The scoring unit Analyzing the user's biological data, evaluating the user's psychological state in real time, and reflecting the evaluation result in the compatibility degree. The system of claim 1 .

4. The scoring unit The emotions of the user when answering the question are analyzed, and the degree of emotional agreement is reflected in the compatibility degree. The system of claim 1 .

5. The scoring unit The compatibility degree is scored based on the user's occupation or lifestyle, and matching by occupation or lifestyle is proposed. The system of claim 1 .

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