System

Abstract

An object of a system according to an embodiment is to grasp deep information of a user and recommend an optimal partner after determining a true affinity.SOLUTION: A system includes an interactive AI, a biometric information analysis part, a compatibility determination part, and a recommendation part. An interactive AI collects biometric information through interaction with a user. The biometric information analyzer analyzes the biometric information collected by the interactive AI. The congeniality assessing section assesses congeniality based on the information analyzed by the biometrics information analyzing section. The recommendation unit recommends an optimal partner based on the compatibility determined by the compatibility determination 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 that it is difficult to fully grasp in-depth information about users and determine true compatibility when matching marriage partners.

[0005] The system according to the embodiment aims to understand in-depth information about the user, determine true compatibility, and then recommend the most suitable partner. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversational AI, a biometrics information analysis unit, a compatibility assessment unit, and a recommendation unit. The conversational AI collects biometrics information through dialogue with a user. The biometrics information analysis unit analyzes the biometrics information collected by the conversational AI. The compatibility assessment unit assesses compatibility based on the information analyzed by the biometrics information analysis unit. The recommendation unit recommends an optimal partner based on the compatibility assessed by the compatibility assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can understand in-depth information about the user, determine true compatibility, and then recommend the most suitable partner. [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) The marriage partner matching system according to the embodiment of the present invention is a system that collects biometric information through dialogue with a user, analyzes it using a generation AI, and recommends the most suitable partner. As a result, the marriage partner matching system can recommend the most suitable partner based on the user's in-depth information, enabling the user to realize a happy life after marriage.

[0029] A marriage partner matching system according to an embodiment includes a conversational AI, a biometrics information analysis unit, a compatibility assessment unit, and a recommendation unit. The conversational AI collects biometrics information through dialogue with a user. For example, if a user says, "I like traveling," the conversational AI analyzes the user's tone of voice and choice of words to understand the user's emotions and preferences. The conversational AI can also analyze the user's voiceprint and linguistic style to identify the user's personality and values. The biometrics information analysis unit analyzes the biometrics information collected by the conversational AI. For example, the biometrics information analysis unit infers the user's personality from the user's tone of voice and speaking style. The biometrics information analysis unit can also identify the user's values ​​and interests from the user's linguistic style. The compatibility assessment unit assesses compatibility based on the information analyzed by the biometrics information analysis unit. For example, if the user prefers outdoor activities, the compatibility assessment unit preferentially recommends candidates who also enjoy outdoor activities. Furthermore, by selecting candidates with similar communication styles, smooth communication can be expected. The recommendation unit recommends the most suitable partner based on the compatibility determined by the compatibility assessment unit. For example, the recommendation unit may suggest to the user, "The perfect partner for you is Mr. A, who loves traveling and is good at communication." In this way, the marriage partner matching system according to the embodiment can recommend the most suitable partner based on the user's in-depth information, enabling the user to realize a happy life after marriage.

[0030] Conversational AI can store a user's dialogue history over the long term and analyze the user's changes and growth based on the content of past dialogue. For example, conversational AI stores a user's dialogue history in a database and analyzes changes in the user's interests and concerns based on the content of past dialogue. For example, it extracts keywords that frequently appear in past dialogues. Conversational AI can also analyze long-term dialogue history to understand the user's growth and changes. For example, it can compare early dialogue with current dialogue to identify changes in the user's values ​​and preferences. Conversational AI can also make suggestions to support the user's growth based on the dialogue history. For example, it can refer to past dialogue content to suggest new topics that the user is likely to be interested in. This makes it possible to analyze the user's changes and growth and recommend more appropriate partners.

[0031] Conversational AI can analyze a user's living environment and provide more personalized dialogue based on background sounds and surrounding conditions. Conversational AI, for example, analyzes a user's living environment and understands background sounds and surrounding conditions. For example, it analyzes background music and noise and provides dialogue appropriate to the user's environment. Conversational AI also personalizes the content of the dialogue based on the user's living environment. For example, if the user is in a cafe, it will provide topics related to cafes. Conversational AI also infers the user's preferences and habits based on data from the living environment. For example, it analyzes the places and times the user frequently visits and adjusts the content of the dialogue. In this way, it analyzes the user's living environment and provides more personalized dialogue, thereby improving user satisfaction.

[0032] Conversational AI is multilingual and can handle users of different cultures and languages, and can conduct dialogue that takes cultural backgrounds into account. Conversational AI is multilingual and can handle users of different languages. For example, it can conduct dialogue in multiple languages, such as English, French, and Chinese. Conversational AI also provides dialogue that takes different cultural backgrounds into account. For example, it incorporates greetings and etiquette that are appropriate to the user's culture. Multilingual conversational AI also understands the user's cultural background and conducts appropriate dialogue. For example, it respects cultural customs and values. This allows it to handle users of different cultures and languages, and can provide services to a wider range of users.

[0033] The biometric information analysis unit can analyze the user's heart rate and sleep patterns in addition to biometric information to create a more comprehensive user profile. For example, the biometric information analysis unit collects the user's heart rate data and analyzes it in combination with the biometric information. For example, it estimates the user's stress level from heart rate fluctuations. The biometric information analysis unit also analyzes the user's sleep patterns and creates a comprehensive profile by combining them with the biometric information. For example, it evaluates the quality and quantity of sleep. The biometric information analysis unit also understands the user's lifestyle and health condition based on health data. For example, it analyzes the amount of exercise and eating patterns and reflects them in the user profile. In this way, by analyzing the user's health data, a more comprehensive user profile can be created, enabling more accurate partner recommendations.

[0034] The biometric information analysis unit can analyze the user's biometric information by time of day and season to understand fluctuations in emotions and preferences. The biometric information analysis unit, for example, analyzes the user's biometric information by time of day to understand fluctuations in emotions and preferences. For example, it evaluates the difference in emotional state between morning and evening. The biometric information analysis unit also analyzes the biometric information by season to identify fluctuations in emotions and preferences. For example, it analyzes the difference in stress levels between winter and summer. The biometric information analysis unit also predicts fluctuations in the user's emotions and preferences based on data by time of day and season. For example, it understands the user's activity patterns in specific seasons. This makes it possible to understand fluctuations in the user's emotions and preferences and recommend more appropriate partners.

[0035] The biometric information analysis unit can integrate the user's biometric information with other health data to evaluate their overall health condition. For example, the biometric information analysis unit integrates the user's biometric information with other health data (e.g., exercise amount and dietary data) to evaluate their overall health condition. For example, it analyzes the relationship between exercise amount and heart rate. The biometric information analysis unit also evaluates the user's lifestyle habits and health risks based on the health data. For example, it analyzes eating patterns and weight fluctuations. The biometric information analysis unit also integrates the biometric information with health data to monitor the user's health condition in real time. For example, it evaluates the speed at which the heart rate recovers after exercise. In this way, by integrating the user's biometric information with other health data, it is possible to evaluate their overall health condition and recommend more appropriate partners.

[0036] The compatibility assessment unit can consider the behavioral patterns and lifestyle habits of compatible partner candidates based on the user's deep information. The compatibility assessment unit, for example, analyzes the behavioral patterns of compatible partner candidates based on the user's deep information. For example, daily routines and hobbies are taken into consideration. The compatibility assessment unit also identifies compatible partner candidates based on lifestyle habits. For example, for a user who has the habit of going to bed early and getting up early, it suggests candidates with the same lifestyle. The compatibility assessment unit also comprehensively evaluates behavioral patterns and lifestyle habits to identify compatible partner candidates. For example, it takes into consideration food preferences and exercise habits. This makes it possible to recommend more appropriate partners by considering the behavioral patterns and lifestyle habits of compatible partner candidates based on the user's deep information.

[0037] The compatibility assessment unit can analyze the user's past romantic experiences and relationship history and reflect this in assessing compatibility. The compatibility assessment unit, for example, stores the user's past romantic experiences in a database and assesses compatibility based on that history. For example, it analyzes the factors that led to success and failure in past relationships. The compatibility assessment unit also analyzes the relationship history and reflects this in assessing compatibility. For example, it takes into account friendships and relationships at work. The compatibility assessment unit also identifies compatible partner candidates based on the user's past romantic experiences and relationship history. For example, it prioritizes recommending people who have worked well in the past. In this way, by analyzing the user's past romantic experiences and relationship history and reflecting this in assessing compatibility, it becomes possible to recommend more appropriate partners.

[0038] The compatibility assessment unit can find a compatible partner within a community with a common hobby based on the user's hobbies and interests. The compatibility assessment unit, for example, registers the user's hobbies and interests in a database and finds a compatible partner within a community with a common hobby. For example, it recommends users who participate in events related to the same hobby. The compatibility assessment unit also identifies compatible partner candidates based on the hobbies and interests. For example, it preferentially recommends users who are interested in the same sports or art. The compatibility assessment unit also analyzes data on hobbies and interests to find a compatible partner within a community with a common hobby. For example, it evaluates the degree of similarity of hobbies. This makes it possible to recommend more appropriate partners by finding a compatible partner within a community with a common hobby based on the user's hobbies and interests.

[0039] The compatibility assessment unit can evaluate compatibility with partner candidates of different cultures and nationalities based on the user's values ​​and lifestyle. The compatibility assessment unit, for example, registers the user's values ​​and lifestyle in a database and evaluates compatibility with partner candidates of different cultures and nationalities. For example, it takes into account cultural background and living habits. The compatibility assessment unit also identifies compatibility with partner candidates of different cultures and nationalities based on the user's values ​​and lifestyle. For example, it recommends users who have cross-cultural understanding or common values. The compatibility assessment unit also analyzes data on values ​​and lifestyles to evaluate compatibility with partner candidates of different cultures and nationalities. For example, it evaluates the degree of cultural compatibility. This makes it possible to recommend more appropriate partners by evaluating compatibility with partner candidates of different cultures and nationalities based on the user's values ​​and lifestyle.

[0040] The recommendation unit can provide a detailed profile of the recommended partner candidate, allowing the user to gain a deeper understanding. The recommendation unit, for example, provides a detailed profile of the recommended partner candidate. For example, it displays information such as hobbies, interests, values, and lifestyle habits. The recommendation unit also provides a detailed profile so that the user can gain a deeper understanding of the recommended partner candidate. For example, it displays past interaction history and common hobbies. The recommendation unit also supports the user in gaining a deeper understanding based on the detailed profile of the recommended partner candidate. For example, it suggests interactions based on the profile information. In this way, by providing a detailed profile of the recommended partner candidate, the user can gain a deeper understanding.

[0041] The recommendation unit can continuously improve the recommendation algorithm and increase accuracy based on user feedback. The recommendation unit, for example, collects user feedback and continuously improves the recommendation algorithm. For example, it adjusts algorithm parameters based on the feedback. The recommendation unit also analyzes user feedback to improve recommendation accuracy. For example, it identifies factors behind successful and unsuccessful matching. The recommendation unit also dynamically improves the recommendation algorithm based on user feedback. For example, it reflects feedback in real time. This allows the recommendation algorithm to be continuously improved and accuracy to be improved based on user feedback, making it possible to recommend more appropriate partners.

[0042] The recommendation unit can provide a virtual dating simulation with the recommended partner candidate, allowing the user to experience actual compatibility. The recommendation unit, for example, provides a virtual dating simulation with the recommended partner candidate. For example, it uses virtual reality (VR) to experience a dating scenario. The recommendation unit also allows the user to experience actual compatibility through the virtual dating simulation. For example, it simulates conversations and collaboration. The recommendation unit also collects user feedback based on the virtual dating simulation and evaluates compatibility. For example, it analyzes emotion scores during the simulation. In this way, the virtual dating simulation with the recommended partner candidate is provided, allowing the user to experience actual compatibility.

[0043] The recommendation unit makes recommendations that incorporate the opinions of the user's friends and family, and can also consider compatibility from a social perspective. The recommendation unit, for example, collects the opinions of the user's friends and family and reflects them in the recommendations. For example, it prioritizes recommendations of candidates recommended by friends and family. The recommendation unit also makes recommendations that take compatibility from a social perspective into account. For example, it evaluates compatibility based on the opinions of friends and family. The recommendation unit also makes recommendations that incorporate the opinions of the user's friends and family, and evaluates compatibility from a social perspective. For example, it adjusts the recommendation algorithm based on feedback from friends and family. In this way, by making recommendations that incorporate the opinions of the user's friends and family and taking compatibility from a social perspective into account, it becomes possible to recommend a more appropriate partner.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] Conversational AI can store a user's dialogue history over the long term and analyze the user's changes and growth based on the content of past dialogue. For example, a user's dialogue history can be stored in a database and changes in the user's interests and concerns can be analyzed based on the content of past dialogue. For example, keywords that frequently appear in past dialogues can be extracted. Conversational AI can also analyze long-term dialogue history to understand the user's growth and changes. For example, it can compare early dialogue with current dialogue to identify changes in the user's values ​​and preferences. Conversational AI can also make suggestions to support the user's growth based on the dialogue history. For example, it can refer to past dialogue content to suggest new topics that the user is likely to be interested in. This makes it possible to analyze the user's changes and growth and recommend more appropriate partners.

[0046] Conversational AI can analyze a user's living environment and provide more personalized dialogue based on background sounds and surrounding conditions. For example, it can analyze a user's living environment and understand background sounds and surrounding conditions. For example, it can analyze background music and noise and provide dialogue appropriate to the user's environment. Conversational AI can also personalize the content of the dialogue based on the user's living environment. For example, if the user is in a cafe, it can provide topics related to cafes. Conversational AI can also infer the user's preferences and habits based on data from the living environment. For example, it can analyze the places and times the user frequently visits and adjust the content of the dialogue. In this way, it can analyze the user's living environment and provide more personalized dialogue, thereby improving user satisfaction.

[0047] Conversational AI is multilingual and can handle users of different cultures and languages, and can conduct dialogue that takes cultural backgrounds into account. For example, it can handle multilingual users and speak multiple languages. For example, it can conduct dialogue in multiple languages ​​such as English, French, and Chinese. Conversational AI also provides dialogue that takes different cultural backgrounds into account. For example, it can incorporate greetings and etiquette that are appropriate to the user's culture. Furthermore, multilingual conversational AI can understand the user's cultural background and conduct appropriate dialogue. For example, it can respect cultural customs and values. This allows it to handle users of different cultures and languages, thereby providing services to a wider range of users.

[0048] The biometric information analysis unit can analyze the user's heart rate and sleep patterns in addition to biometric information to create a more comprehensive user profile. For example, it collects the user's heart rate data and integrates it with biometric information for analysis. For example, it can estimate the user's stress level from heart rate fluctuations. The biometric information analysis unit also analyzes the user's sleep patterns and creates a comprehensive profile by combining it with biometric information. For example, it can evaluate the quality and quantity of sleep. The biometric information analysis unit also understands the user's lifestyle and health condition based on health data. For example, it can analyze the amount of exercise and eating patterns and reflect them in the user profile. In this way, by analyzing the user's health data, a more comprehensive user profile can be created, enabling more accurate partner recommendations.

[0049] The biometric information analysis unit can analyze the user's biometric information by time of day and season to understand fluctuations in emotions and preferences. For example, the biometric information of the user can be analyzed by time of day to understand fluctuations in emotions and preferences. For example, differences in emotional states between morning and evening can be evaluated. The biometric information analysis unit can also analyze the biometric information by season to identify fluctuations in emotions and preferences. For example, differences in stress levels between winter and summer can be analyzed. The biometric information analysis unit can also predict fluctuations in the user's emotions and preferences based on data by time of day and season. For example, it can understand the user's activity patterns in specific seasons. This allows for understanding fluctuations in the user's emotions and preferences, making it possible to recommend more appropriate partners.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: Conversational AI collects biometric information through conversations with users. For example, if a user says, "I like traveling," the conversational AI analyzes the user's tone of voice and word choice to understand the user's emotions and preferences. Conversational AI can also analyze the user's voiceprint and language style to identify the user's personality and values. Step 2: The biometric information analysis unit analyzes the biometric information collected by the conversational AI. For example, it can infer the user's personality from their tone of voice and speaking style. It can also identify the user's values ​​and interests from their language style. Step 3: The compatibility assessment unit determines compatibility based on the information analyzed by the biometric information analysis unit. For example, if the user enjoys outdoor activities, it will prioritize recommendations of candidates who also enjoy outdoor activities. In addition, selecting candidates with similar communication styles can be expected to facilitate smooth communication. Step 4: The recommendation unit recommends the most suitable partner based on the compatibility determined by the compatibility assessment unit. For example, it may suggest to the user, "The perfect partner for you is Person A, who loves traveling and is good at communication."

[0052] (Example 2) The marriage partner matching system according to the embodiment of the present invention is a system that collects biometric information through dialogue with a user, analyzes it using a generation AI, and recommends the most suitable partner. As a result, the marriage partner matching system can recommend the most suitable partner based on the user's in-depth information, enabling the user to realize a happy life after marriage.

[0053] A marriage partner matching system according to an embodiment includes a conversational AI, a biometrics information analysis unit, a compatibility assessment unit, and a recommendation unit. The conversational AI collects biometrics information through dialogue with a user. For example, if a user says, "I like traveling," the conversational AI analyzes the user's tone of voice and choice of words to understand the user's emotions and preferences. The conversational AI can also analyze the user's voiceprint and linguistic style to identify the user's personality and values. The biometrics information analysis unit analyzes the biometrics information collected by the conversational AI. For example, the biometrics information analysis unit infers the user's personality from the user's tone of voice and speaking style. The biometrics information analysis unit can also identify the user's values ​​and interests from the user's linguistic style. The compatibility assessment unit assesses compatibility based on the information analyzed by the biometrics information analysis unit. For example, if the user prefers outdoor activities, the compatibility assessment unit preferentially recommends candidates who also enjoy outdoor activities. Furthermore, by selecting candidates with similar communication styles, smooth communication can be expected. The recommendation unit recommends the most suitable partner based on the compatibility determined by the compatibility assessment unit. For example, the recommendation unit may suggest to the user, "The perfect partner for you is Mr. A, who loves traveling and is good at communication." In this way, the marriage partner matching system according to the embodiment can recommend the most suitable partner based on the user's in-depth information, enabling the user to realize a happy life after marriage.

[0054] Conversational AI can analyze a user's voiceprint, language style, facial expressions, and gestures to understand more detailed emotions and preferences. For example, conversational AI can analyze a user's facial expressions in real time to understand changes in emotions. For example, it can analyze facial expressions such as smiles and frowns to identify the user's emotional state. Conversational AI can also capture a user's gestures with a camera and analyze their movements. For example, it can infer a user's interests and concerns based on changes in hand movements and posture. Conversational AI can also comprehensively analyze a user's voiceprint, language style, facial expressions, and gestures to understand more detailed emotions and preferences. For example, it can evaluate the degree of agreement between voice tone and facial expressions. This allows for a more detailed understanding of a user's emotions and preferences, enabling more accurate partner recommendations.

[0055] Conversational AI can store a user's dialogue history over the long term and analyze the user's changes and growth based on the content of past dialogue. For example, conversational AI stores a user's dialogue history in a database and analyzes changes in the user's interests and concerns based on the content of past dialogue. For example, it extracts keywords that frequently appear in past dialogues. Conversational AI can also analyze long-term dialogue history to understand the user's growth and changes. For example, it can compare early dialogue with current dialogue to identify changes in the user's values ​​and preferences. Conversational AI can also make suggestions to support the user's growth based on the dialogue history. For example, it can refer to past dialogue content to suggest new topics that the user is likely to be interested in. This makes it possible to analyze the user's changes and growth and recommend more appropriate partners.

[0056] Conversational AI can use emotion estimation functions to monitor changes in a user's emotions in real time and adjust the progress of the dialogue. Conversational AI, for example, uses emotion estimation functions to monitor changes in a user's emotions in real time. For example, if the user is excited, it will slow down the pace of the dialogue. Conversational AI also adjusts the content of the dialogue according to the user's emotional state. For example, if the user is feeling down, it will offer words of encouragement. Conversational AI also dynamically adjusts the progress of the dialogue based on emotion estimation data. For example, it will dig deeper into topics that the user is interested in. This allows for more appropriate dialogue by monitoring changes in a user's emotions in real time and adjusting the progress of the dialogue.

[0057] Conversational AI can analyze a user's living environment and provide more personalized dialogue based on background sounds and surrounding conditions. Conversational AI, for example, analyzes a user's living environment and understands background sounds and surrounding conditions. For example, it analyzes background music and noise and provides dialogue appropriate to the user's environment. Conversational AI also personalizes the content of the dialogue based on the user's living environment. For example, if the user is in a cafe, it will provide topics related to cafes. Conversational AI also infers the user's preferences and habits based on data from the living environment. For example, it analyzes the places and times the user frequently visits and adjusts the content of the dialogue. In this way, it analyzes the user's living environment and provides more personalized dialogue, thereby improving user satisfaction.

[0058] Conversational AI is multilingual and can handle users of different cultures and languages, and can conduct dialogue that takes cultural backgrounds into account. Conversational AI is multilingual and can handle users of different languages. For example, it can conduct dialogue in multiple languages, such as English, French, and Chinese. Conversational AI also provides dialogue that takes different cultural backgrounds into account. For example, it incorporates greetings and etiquette that are appropriate to the user's culture. Multilingual conversational AI also understands the user's cultural background and conducts appropriate dialogue. For example, it respects cultural customs and values. This allows it to handle users of different cultures and languages, and can provide services to a wider range of users.

[0059] Conversational AI can use its emotion estimation function to make relaxation suggestions to reduce the stress and anxiety a user feels during a conversation. For example, conversational AI uses its emotion estimation function to detect the stress or anxiety a user feels during a conversation, for example by analyzing changes in voice tone and facial expressions. If the user feels stressed or anxious, the conversational AI can make relaxation suggestions, for example by suggesting deep breathing or relaxing music. Furthermore, based on the emotion estimation data, the conversational AI can provide dialogue content to reduce the user's stress and anxiety, for example by talking about relaxing topics or offering encouraging words. This reduces the stress and anxiety a user feels during a conversation, enabling a more comfortable conversation.

[0060] The biometric information analysis unit can analyze the user's heart rate and sleep patterns in addition to biometric information to create a more comprehensive user profile. For example, the biometric information analysis unit collects the user's heart rate data and analyzes it in combination with the biometric information. For example, it estimates the user's stress level from heart rate fluctuations. The biometric information analysis unit also analyzes the user's sleep patterns and creates a comprehensive profile by combining them with the biometric information. For example, it evaluates the quality and quantity of sleep. The biometric information analysis unit also understands the user's lifestyle and health condition based on health data. For example, it analyzes the amount of exercise and eating patterns and reflects them in the user profile. In this way, by analyzing the user's health data, a more comprehensive user profile can be created, enabling more accurate partner recommendations.

[0061] The biometric information analysis unit can analyze the user's biometric information by time of day and season to understand fluctuations in emotions and preferences. The biometric information analysis unit, for example, analyzes the user's biometric information by time of day to understand fluctuations in emotions and preferences. For example, it evaluates the difference in emotional state between morning and evening. The biometric information analysis unit also analyzes the biometric information by season to identify fluctuations in emotions and preferences. For example, it analyzes the difference in stress levels between winter and summer. The biometric information analysis unit also predicts fluctuations in the user's emotions and preferences based on data by time of day and season. For example, it understands the user's activity patterns in specific seasons. This makes it possible to understand fluctuations in the user's emotions and preferences and recommend more appropriate partners.

[0062] The biometric information analysis unit can use the emotion estimation function to predict emotional changes from the user's biometric information and promote dialogue at the appropriate time. The biometric information analysis unit, for example, uses the emotion estimation function to predict emotional changes from the user's biometric information. For example, it analyzes fluctuations in heart rate and breathing. The biometric information analysis unit also predicts emotional changes and promotes dialogue at the appropriate time. For example, it starts dialogue when the user is relaxed. The biometric information analysis unit also monitors the user's emotional state in real time based on the biometric information and adjusts the timing of the dialogue. For example, it provides important topics when stress is low. This makes it possible to predict changes in the user's emotions and promote dialogue at the appropriate time, enabling more effective dialogue.

[0063] The biometric information analysis unit can evaluate the user's stress level and relaxation level based on the biometric information and suggest an appropriate relaxation method. The biometric information analysis unit, for example, analyzes the user's biometric information and evaluates the stress level. For example, it calculates a stress score based on the heart rate and galvanic skin response. The biometric information analysis unit also evaluates the relaxation level and suggests an appropriate relaxation method. For example, it suggests breathing techniques or meditation. The biometric information analysis unit also monitors the user's stress and relaxation level in real time based on the biometric information and dynamically adjusts the relaxation method. For example, it suggests relaxing music when stress is high. In this way, the user's stress level and relaxation level can be evaluated and an appropriate relaxation method suggested, thereby reducing the user's stress and allowing them to relax.

[0064] The biometric information analysis unit can integrate the user's biometric information with other health data to evaluate their overall health condition. For example, the biometric information analysis unit integrates the user's biometric information with other health data (e.g., exercise amount and dietary data) to evaluate their overall health condition. For example, it analyzes the relationship between exercise amount and heart rate. The biometric information analysis unit also evaluates the user's lifestyle habits and health risks based on the health data. For example, it analyzes eating patterns and weight fluctuations. The biometric information analysis unit also integrates the biometric information with health data to monitor the user's health condition in real time. For example, it evaluates the speed at which the heart rate recovers after exercise. In this way, by integrating the user's biometric information with other health data, it is possible to evaluate their overall health condition and recommend more appropriate partners.

[0065] The biometric information analysis unit can use the emotion estimation function to monitor changes in emotion from the user's biometric information in real time and adjust the content of the dialogue. The biometric information analysis unit, for example, uses the emotion estimation function to monitor changes in emotion from the user's biometric information in real time. For example, it analyzes heart rate and galvanic skin response. The biometric information analysis unit also dynamically adjusts the content of the dialogue based on changes in emotion. For example, it can provide relaxing topics when the user is relaxed. The biometric information analysis unit also grasps the user's emotional state in real time based on the biometric information and adjusts the progress of the dialogue. For example, it can offer words of encouragement when the user is under high stress. In this way, by monitoring changes in the user's emotion in real time and adjusting the content of the dialogue, more appropriate dialogue is possible.

[0066] The compatibility assessment unit can consider the behavioral patterns and lifestyle habits of compatible partner candidates based on the user's deep information. The compatibility assessment unit, for example, analyzes the behavioral patterns of compatible partner candidates based on the user's deep information. For example, daily routines and hobbies are taken into consideration. The compatibility assessment unit also identifies compatible partner candidates based on lifestyle habits. For example, for a user who has the habit of going to bed early and getting up early, it suggests candidates with the same lifestyle. The compatibility assessment unit also comprehensively evaluates behavioral patterns and lifestyle habits to identify compatible partner candidates. For example, it takes into consideration food preferences and exercise habits. This makes it possible to recommend more appropriate partners by considering the behavioral patterns and lifestyle habits of compatible partner candidates based on the user's deep information.

[0067] The compatibility assessment unit can analyze the user's past romantic experiences and relationship history and reflect this in assessing compatibility. The compatibility assessment unit, for example, stores the user's past romantic experiences in a database and assesses compatibility based on that history. For example, it analyzes the factors that led to success and failure in past relationships. The compatibility assessment unit also analyzes the relationship history and reflects this in assessing compatibility. For example, it takes into account friendships and relationships at work. The compatibility assessment unit also identifies compatible partner candidates based on the user's past romantic experiences and relationship history. For example, it prioritizes recommending people who have worked well in the past. In this way, by analyzing the user's past romantic experiences and relationship history and reflecting this in assessing compatibility, it becomes possible to recommend more appropriate partners.

[0068] The compatibility assessment unit uses an emotion estimation function to evaluate the emotional compatibility between the user and candidate in real time and perform optimal matching. The compatibility assessment unit, for example, uses the emotion estimation function to evaluate the emotional compatibility between the user and candidate in real time. For example, it analyzes the emotion score during the conversation. The compatibility assessment unit also performs optimal matching based on the emotional compatibility. For example, it preferentially recommends candidates with high emotion scores. The compatibility assessment unit also monitors the emotional compatibility between the user and candidate in real time to improve the accuracy of matching. For example, it evaluates the degree of agreement of emotions. This allows the emotional compatibility between the user and candidate to be evaluated in real time and optimally matched, making it possible to recommend a more appropriate partner.

[0069] The compatibility assessment unit can find a compatible partner within a community with a common hobby based on the user's hobbies and interests. The compatibility assessment unit, for example, registers the user's hobbies and interests in a database and finds a compatible partner within a community with a common hobby. For example, it recommends users who participate in events related to the same hobby. The compatibility assessment unit also identifies compatible partner candidates based on the hobbies and interests. For example, it preferentially recommends users who are interested in the same sports or art. The compatibility assessment unit also analyzes data on hobbies and interests to find a compatible partner within a community with a common hobby. For example, it evaluates the degree of similarity of hobbies. This makes it possible to recommend more appropriate partners by finding a compatible partner within a community with a common hobby based on the user's hobbies and interests.

[0070] The compatibility assessment unit can evaluate compatibility with partner candidates of different cultures and nationalities based on the user's values ​​and lifestyle. The compatibility assessment unit, for example, registers the user's values ​​and lifestyle in a database and evaluates compatibility with partner candidates of different cultures and nationalities. For example, it takes into account cultural background and living habits. The compatibility assessment unit also identifies compatibility with partner candidates of different cultures and nationalities based on the user's values ​​and lifestyle. For example, it recommends users who have cross-cultural understanding or common values. The compatibility assessment unit also analyzes data on values ​​and lifestyles to evaluate compatibility with partner candidates of different cultures and nationalities. For example, it evaluates the degree of cultural compatibility. This makes it possible to recommend more appropriate partners by evaluating compatibility with partner candidates of different cultures and nationalities based on the user's values ​​and lifestyle.

[0071] The compatibility assessment unit can use the emotion estimation function to monitor the emotional compatibility between the user and candidate over the long term and support the development of the relationship. The compatibility assessment unit, for example, uses the emotion estimation function to monitor the emotional compatibility between the user and candidate over the long term. For example, it analyzes the emotion scores of regular conversations. The compatibility assessment unit also supports the development of the relationship based on the emotional compatibility. For example, it suggests a date when the emotion score is high. The compatibility assessment unit also monitors the emotional compatibility between the user and candidate over the long term and provides feedback to support the development of the relationship. For example, it provides advice according to changes in emotions. This makes it possible to recommend more suitable partners by monitoring the emotional compatibility between the user and candidate over the long term and supporting the development of the relationship.

[0072] The recommendation unit can provide a detailed profile of the recommended partner candidate, allowing the user to gain a deeper understanding. The recommendation unit, for example, provides a detailed profile of the recommended partner candidate. For example, it displays information such as hobbies, interests, values, and lifestyle habits. The recommendation unit also provides a detailed profile so that the user can gain a deeper understanding of the recommended partner candidate. For example, it displays past interaction history and common hobbies. The recommendation unit also supports the user in gaining a deeper understanding based on the detailed profile of the recommended partner candidate. For example, it suggests interactions based on the profile information. In this way, by providing a detailed profile of the recommended partner candidate, the user can gain a deeper understanding.

[0073] The recommendation unit can continuously improve the recommendation algorithm and increase accuracy based on user feedback. The recommendation unit, for example, collects user feedback and continuously improves the recommendation algorithm. For example, it adjusts algorithm parameters based on the feedback. The recommendation unit also analyzes user feedback to improve recommendation accuracy. For example, it identifies factors behind successful and unsuccessful matching. The recommendation unit also dynamically improves the recommendation algorithm based on user feedback. For example, it reflects feedback in real time. This allows the recommendation algorithm to be continuously improved and accuracy to be improved based on user feedback, making it possible to recommend more appropriate partners.

[0074] The recommendation unit uses an emotion estimation function to evaluate the emotions the user feels toward the recommended partner in real time and make an optimal suggestion. The recommendation unit, for example, uses the emotion estimation function to evaluate the emotions the user feels toward the recommended partner in real time. For example, it analyzes the emotion score during the conversation. The recommendation unit also suggests an optimal partner based on the emotion evaluation. For example, it preferentially recommends candidates with high emotion scores. The recommendation unit also monitors the emotions the user feels toward the recommended partner in real time and makes an optimal suggestion. For example, it evaluates the degree of agreement of emotions. This allows the user to evaluate the emotions the user feels toward the recommended partner in real time and make an optimal suggestion, making it possible to recommend a more appropriate partner.

[0075] The recommendation unit can provide a virtual dating simulation with the recommended partner candidate, allowing the user to experience actual compatibility. The recommendation unit, for example, provides a virtual dating simulation with the recommended partner candidate. For example, it uses virtual reality (VR) to experience a dating scenario. The recommendation unit also allows the user to experience actual compatibility through the virtual dating simulation. For example, it simulates conversations and collaboration. The recommendation unit also collects user feedback based on the virtual dating simulation and evaluates compatibility. For example, it analyzes emotion scores during the simulation. In this way, the virtual dating simulation with the recommended partner candidate is provided, allowing the user to experience actual compatibility.

[0076] The recommendation unit makes recommendations that incorporate the opinions of the user's friends and family, and can also consider compatibility from a social perspective. The recommendation unit, for example, collects the opinions of the user's friends and family and reflects them in the recommendations. For example, it prioritizes recommendations of candidates recommended by friends and family. The recommendation unit also makes recommendations that take compatibility from a social perspective into account. For example, it evaluates compatibility based on the opinions of friends and family. The recommendation unit also makes recommendations that incorporate the opinions of the user's friends and family, and evaluates compatibility from a social perspective. For example, it adjusts the recommendation algorithm based on feedback from friends and family. In this way, by making recommendations that incorporate the opinions of the user's friends and family and taking compatibility from a social perspective into account, it becomes possible to recommend a more appropriate partner.

[0077] The recommendation unit can use the emotion estimation function to monitor the emotions the user feels toward the recommended partner over the long term and support the development of the relationship. The recommendation unit, for example, uses the emotion estimation function to monitor the emotions the user feels toward the recommended partner over the long term. For example, it analyzes the emotion score of regular conversations. The recommendation unit also supports the development of the relationship based on the emotion monitoring. For example, it suggests a date when the emotion score is high. The recommendation unit also monitors the emotions the user feels toward the recommended partner over the long term and provides feedback to support the development of the relationship. For example, it provides advice according to changes in emotion. In this way, by monitoring the emotions the user feels toward the recommended partner over the long term and supporting the development of the relationship, it becomes possible to recommend a more appropriate partner.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] Conversational AI can analyze a user's voiceprint, language style, facial expressions, and gestures to understand more detailed emotions and preferences. For example, it can analyze a user's facial expressions in real time to understand changes in emotions. For example, it can analyze facial expressions such as smiles and frowns to identify the user's emotional state. Conversational AI can also capture a user's gestures with a camera and analyze their movements. For example, it can infer a user's interests and concerns based on changes in hand movements and posture. Conversational AI can also comprehensively analyze a user's voiceprint, language style, facial expressions, and gestures to understand more detailed emotions and preferences. For example, it can evaluate the degree of agreement between voice tone and facial expressions. This allows for a more detailed understanding of a user's emotions and preferences, enabling more accurate partner recommendations.

[0080] Conversational AI can store a user's dialogue history over the long term and analyze the user's changes and growth based on the content of past dialogue. For example, a user's dialogue history can be stored in a database and changes in the user's interests and concerns can be analyzed based on the content of past dialogue. For example, keywords that frequently appear in past dialogues can be extracted. Conversational AI can also analyze long-term dialogue history to understand the user's growth and changes. For example, it can compare early dialogue with current dialogue to identify changes in the user's values ​​and preferences. Conversational AI can also make suggestions to support the user's growth based on the dialogue history. For example, it can refer to past dialogue content to suggest new topics that the user is likely to be interested in. This makes it possible to analyze the user's changes and growth and recommend more appropriate partners.

[0081] Conversational AI can use emotion estimation functions to monitor changes in a user's emotions in real time and adjust the progress of the dialogue. For example, the emotion estimation function can be used to monitor changes in a user's emotions in real time. For example, if the user is excited, the pace of the dialogue can be slowed down. Conversational AI can also adjust the content of the dialogue according to the user's emotional state. For example, if the user is feeling down, the AI ​​can offer words of encouragement. Conversational AI can also dynamically adjust the progress of the dialogue based on emotion estimation data. For example, it can dig deeper into topics that the user is interested in. This allows for more appropriate dialogue by monitoring changes in a user's emotions in real time and adjusting the progress of the dialogue.

[0082] Conversational AI can analyze a user's living environment and provide more personalized dialogue based on background sounds and surrounding conditions. For example, it can analyze a user's living environment and understand background sounds and surrounding conditions. For example, it can analyze background music and noise and provide dialogue appropriate to the user's environment. Conversational AI can also personalize the content of the dialogue based on the user's living environment. For example, if the user is in a cafe, it can provide topics related to cafes. Conversational AI can also infer the user's preferences and habits based on data from the living environment. For example, it can analyze the places and times the user frequently visits and adjust the content of the dialogue. In this way, it can analyze the user's living environment and provide more personalized dialogue, thereby improving user satisfaction.

[0083] Conversational AI is multilingual and can handle users of different cultures and languages, and can conduct dialogue that takes cultural backgrounds into account. For example, it can handle multilingual users and speak multiple languages. For example, it can conduct dialogue in multiple languages ​​such as English, French, and Chinese. Conversational AI also provides dialogue that takes different cultural backgrounds into account. For example, it can incorporate greetings and etiquette that are appropriate to the user's culture. Furthermore, multilingual conversational AI can understand the user's cultural background and conduct appropriate dialogue. For example, it can respect cultural customs and values. This allows it to handle users of different cultures and languages, thereby providing services to a wider range of users.

[0084] Conversational AI can use its emotion estimation function to make relaxation suggestions to reduce the stress and anxiety a user feels during a conversation. For example, the emotion estimation function can be used to detect the stress or anxiety a user feels during a conversation, for example by analyzing changes in voice tone and facial expressions. If the user feels stressed or anxious, the conversational AI can make relaxation suggestions, for example by suggesting deep breathing or relaxing music. Furthermore, based on the emotion estimation data, the conversational AI can provide dialogue content to reduce the user's stress and anxiety, for example by talking about relaxing topics or offering encouraging words. This reduces the stress and anxiety a user feels during a conversation, enabling a more comfortable conversation.

[0085] The biometric information analysis unit can analyze the user's heart rate and sleep patterns in addition to biometric information to create a more comprehensive user profile. For example, it collects the user's heart rate data and integrates it with biometric information for analysis. For example, it can estimate the user's stress level from heart rate fluctuations. The biometric information analysis unit also analyzes the user's sleep patterns and creates a comprehensive profile by combining it with biometric information. For example, it can evaluate the quality and quantity of sleep. The biometric information analysis unit also understands the user's lifestyle and health condition based on health data. For example, it can analyze the amount of exercise and eating patterns and reflect them in the user profile. In this way, by analyzing the user's health data, a more comprehensive user profile can be created, enabling more accurate partner recommendations.

[0086] The biometric information analysis unit can analyze the user's biometric information by time of day and season to understand fluctuations in emotions and preferences. For example, the biometric information of the user can be analyzed by time of day to understand fluctuations in emotions and preferences. For example, differences in emotional states between morning and evening can be evaluated. The biometric information analysis unit can also analyze the biometric information by season to identify fluctuations in emotions and preferences. For example, differences in stress levels between winter and summer can be analyzed. The biometric information analysis unit can also predict fluctuations in the user's emotions and preferences based on data by time of day and season. For example, it can understand the user's activity patterns in specific seasons. This allows for understanding fluctuations in the user's emotions and preferences, making it possible to recommend more appropriate partners.

[0087] The biometric information analysis unit can use the emotion estimation function to predict emotional changes from the user's biometric information and promote dialogue at the appropriate time. For example, the emotion estimation function is used to predict emotional changes from the user's biometric information. For example, fluctuations in heart rate and breathing are analyzed. The biometric information analysis unit also predicts emotional changes and promotes dialogue at the appropriate time. For example, it starts dialogue when the user is relaxed. The biometric information analysis unit also monitors the user's emotional state in real time based on the biometric information and adjusts the timing of the dialogue. For example, it provides important topics when stress is low. This makes it possible to predict changes in the user's emotions and promote dialogue at the appropriate time, enabling more effective dialogue.

[0088] The biometric information analysis unit can evaluate the user's stress level and relaxation level based on the biometric information and suggest appropriate relaxation methods. For example, it analyzes the user's biometric information and evaluates the stress level. For example, it calculates a stress score based on the heart rate and galvanic skin response. The biometric information analysis unit also evaluates the relaxation level and suggests appropriate relaxation methods. For example, it suggests breathing techniques or meditation. The biometric information analysis unit also monitors the user's stress and relaxation level in real time based on the biometric information and dynamically adjusts the relaxation method. For example, it suggests relaxing music when stress is high. In this way, the user's stress level and relaxation level can be evaluated and appropriate relaxation methods suggested, thereby reducing stress and helping the user to relax.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: Conversational AI collects biometric information through conversations with users. For example, if a user says, "I like traveling," the conversational AI analyzes the user's tone of voice and word choice to understand the user's emotions and preferences. Conversational AI can also analyze the user's voiceprint and language style to identify the user's personality and values. Step 2: The biometric information analysis unit analyzes the biometric information collected by the conversational AI. For example, it can infer the user's personality from their tone of voice and speaking style. It can also identify the user's values ​​and interests from their language style. Step 3: The compatibility assessment unit determines compatibility based on the information analyzed by the biometric information analysis unit. For example, if the user enjoys outdoor activities, it will prioritize recommendations of candidates who also enjoy outdoor activities. In addition, selecting candidates with similar communication styles can be expected to facilitate smooth communication. Step 4: The recommendation unit recommends the most suitable partner based on the compatibility determined by the compatibility assessment unit. For example, it may suggest to the user, "The perfect partner for you is Person A, who loves traveling and is good at communication."

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0127] The 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.

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

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] 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]

[0158] 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. Equipped with conversational AI, The conversational AI collects biometric information through dialogue with the user, Equipped with a biometrics information analysis unit, the biometrics information analysis unit analyzes the biometrics information collected by the interactive AI, Equipped with a compatibility assessment department, the compatibility determining unit determines compatibility based on the information analyzed by the biometrics information analyzing unit, Equipped with a recommendation section, The recommendation unit recommends an optimal partner based on the compatibility determined by the compatibility determination unit. A system characterized by:

2. The conversational AI is analyzing the user's voiceprint, language style, facial expressions, and gestures; Capture more detailed emotions and preferences 2. The system of claim 1.

3. The conversational AI is storing the user's interaction history over a long period of time; Analyze the user's changes and growth based on past conversations 2. The system of claim 1.

4. The conversational AI is monitoring changes in the user's emotions in real time; Coordinate the progress of the dialogue 2. The system of claim 1.

5. The conversational AI is Analyzing the user's living environment; Providing more personalized interactions based on background sounds and surroundings 2. The system of claim 1.

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