System

Abstract

An object of a system according to an embodiment is to propose an optimum relationship partner based on the personality of a user and to smoothly perform initial communication.SOLUTION: A system includes a personality analysis unit, a matching unit, a communication proxy unit, and a virtual middleman unit. The personality analysis unit analyzes personality of the user. The matching section proposes an optimum relationship partner based on the personality data analyzed by the personality analyzing section. The communication acting part acts for initial communication with the relationship partner proposed by the matching part. The virtual middleman unit provides support for the friendship based on the communication performed by the communication agent 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 suggest optimal dating partners based on the user's personality or to smoothly carry out initial communication.

[0005] The system according to the embodiment aims to propose the most suitable dating partner based on the user's personality and facilitate smooth initial communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality analysis unit, a matching unit, a communication agent unit, and a virtual matchmaker unit. The personality analysis unit analyzes the user's personality. The matching unit proposes an optimal dating partner based on the personality data analyzed by the personality analysis unit. The communication agent unit handles initial communication with the dating partner proposed by the matching unit. The virtual matchmaker unit provides support for dating based on the communication handled by the communication agent unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable dating partner based on the user's personality and facilitate initial communication. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 matching system according to an embodiment of the present invention uses a generation AI to analyze a user's personality, propose optimal dating partners, act as an agent for initial communication, and provide support for the relationship. This allows the matching system to accurately grasp the user's personality, propose optimal dating partners, facilitate smooth interactions between users, and provide support for the relationship.

[0029] A matching system according to an embodiment includes a personality analysis unit, a matching unit, a communication agent unit, and a virtual matchmaker unit. The personality analysis unit analyzes a user's personality. For example, the generation AI analyzes the user's personality, hobbies, and communication style through conversations with the user. The generation AI can also analyze the user's communication style based on the tone and frequency of the user's conversations. The generation AI can also analyze the user's personality based on prompts. The matching unit suggests optimal dating partners based on the personality data analyzed by the personality analysis unit. For example, for a user who likes movies, the generation AI can suggest other users who also like movies. The generation AI can also match users with similar communication styles. The generation AI can also analyze the user's personality data and perform optimal matching. The communication agent unit handles initial communication with the dating partner suggested by the matching unit. For example, when a user says, "Hello, you like movies, don't you?", the generation AI replies, "Yes, I especially like action movies." The generation AI can also generate appropriate responses based on the user's prompts. The generation AI can also facilitate smooth communication between users. The virtual matchmaker unit provides support for dating based on the communication handled by the communication agent unit. For example, when the user is planning a date, the generation AI may suggest, "How about this restaurant?" If the user is nervous, the generation AI may also provide advice such as, "Relax and have fun." Furthermore, the generation AI may provide appropriate support depending on the user's situation. As a result, the matching system according to the embodiment can analyze the user's personality, suggest the most suitable dating partner, handle initial communication on behalf of the user, and provide support for dating.

[0030] The personality analysis unit analyzes a user's past online activity to gain a deeper understanding of the user's personality and hobbies. For example, the personality analysis unit analyzes the user's social media posts to extract frequently used keywords and hashtags. For example, a user who posts a lot about travel may be determined to be a travel enthusiast. The personality analysis unit can also analyze the user's browsing history to identify topics of interest. For example, a user who frequently visits recipe sites may be determined to be a cooking enthusiast. The personality analysis unit can also analyze the user's online shopping history to understand trends in purchased products. For example, a user who purchases a lot of outdoor equipment may be determined to be an outdoor enthusiast. In this way, by analyzing the user's past online activity, a deeper understanding of the user's personality and hobbies can be gained.

[0031] The matching unit can perform more accurate matching by combining the results of a compatibility diagnostic test with the user's personality data. The matching unit, for example, integrates the user's personality data with the results of a compatibility diagnostic test to strengthen the matching algorithm. For example, it suggests an optimal partner based on personality traits and the results of a compatibility diagnostic test. The matching unit can also evaluate the compatibility between users based on the results of the compatibility diagnostic test. For example, it matches users who share common hobbies and values. The matching unit can also analyze the user's personality and behavioral patterns based on the results of the compatibility diagnostic test to suggest an optimal partner. For example, for an introverted user, it would suggest a partner who is also introverted. In this way, by combining the results of the compatibility diagnostic test, more accurate matching is possible.

[0032] The matching unit can analyze the user's lifestyle rhythm or daily behavioral patterns and suggest partners with a compatible lifestyle. For example, the matching unit can analyze the user's lifestyle rhythm and suggest partners who are active during the same time period. For example, it can suggest partners who are night owls to a night owl user. The matching unit can also analyze the user's daily behavioral patterns and suggest partners who share common activity times. For example, it can suggest partners who also jog in the morning to a user who has a habit of jogging in the morning. The matching unit can also analyze the user's lifestyle and suggest partners who share similar values ​​and lifestyle habits. For example, it can suggest partners who are similarly health-conscious to a health-conscious user. In this way, by analyzing the user's lifestyle rhythm and behavioral patterns, it is possible to suggest partners with a compatible lifestyle.

[0033] The communication agent unit can learn the user's past conversation history and generate more personalized replies. For example, the communication agent unit analyzes the user's past conversation history and learns frequently used phrases and topics. For example, it prioritizes generating conversations related to specific hobbies and interests. The communication agent unit can also learn the user's conversation style and generate more natural replies. For example, it generates replies that incorporate phrases that the user frequently uses. The communication agent unit can also generate replies that match the user's preferences based on the user's past conversation history. For example, it generates replies related to topics that the user likes. In this way, by learning the user's past conversation history, more personalized replies become possible.

[0034] The virtual matchmaker can automatically generate a date plan for the user and suggest detailed schedules and locations. The virtual matchmaker can automatically generate a date plan based on the user's hobbies and interests, for example. For example, a movie and dinner plan is suggested for a user who likes movies. The virtual matchmaker can also suggest the optimal time for a date, taking the user's schedule into consideration. For example, if a user wants a date after work, a plan for the evening or night is suggested. The virtual matchmaker can also suggest date locations that suit the user's preferences. For example, a park or nature reserve is suggested for a user who likes nature. In this way, the burden on the user can be reduced by automatically generating a date plan and suggesting detailed schedules and locations.

[0035] The feedback collection unit can collect user feedback in real time and immediately reflect it in system improvements. The feedback collection unit, for example, collects user feedback in real time and reflects it in system improvements. For example, the matching algorithm can be adjusted based on user opinions. The feedback collection unit can also consider adding new functions or services based on user feedback. For example, a function suggested by a user can be added to the system. The feedback collection unit can also analyze user feedback, identify weaknesses in the system, and take measures to improve it. For example, areas that users find dissatisfying can be improved. In this way, user satisfaction can be improved by collecting feedback in real time and immediately reflecting it in system improvements.

[0036] The feedback collection unit can apply the feedback collection function to collecting and analyzing user reviews of products and services. The feedback collection unit, for example, applies the feedback collection function to user reviews of products and services and analyzes the content of the reviews. For example, it processes them by distinguishing between positive and negative reviews. The feedback collection unit can also take measures to improve products and services based on the user reviews. For example, it can enhance features that users have given high ratings to. The feedback collection unit can also collect user reviews and evaluate products and services. For example, it can add new features based on user opinions. In this way, applying the feedback collection function to collecting and analyzing user reviews of products and services can be useful for improving products and services.

[0037] The feedback collection unit can be applied to an innovation platform that develops new functions and services based on user feedback. The feedback collection unit, for example, builds an innovation platform that develops new functions and services based on user feedback. For example, new functions are added in response to user requests. The feedback collection unit can also analyze user feedback and determine the direction of innovation. For example, functions desired by users are developed as a priority. The feedback collection unit can also take measures to improve services based on user feedback. For example, points that users are dissatisfied with are improved. This makes it possible to develop new functions and services based on feedback, thereby enabling innovation that meets user needs.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The matching system may further include a health management unit. The health management unit collects the user's health data and can suggest optimal dating partners based on the user's health condition. For example, the health management unit may analyze the user's exercise habits and dietary habits to suggest health-conscious partners. The health management unit may also analyze the user's stress level to suggest partners with whom they can relax. Furthermore, the health management unit may analyze the user's sleep patterns to suggest partners who have the same sleep rhythm. This enables matching based on health data and supports healthier relationships.

[0040] The personality analysis unit can form groups with common hobbies based on users' hobbies and interests, and promote interaction within the groups. For example, the personality analysis unit can form a movie-watching group for users who love movies. The personality analysis unit can also form a hiking group for users who love the outdoors. Furthermore, the personality analysis unit can form a cooking class group for users who love cooking. This promotes interaction between users with common hobbies, enabling more natural matching.

[0041] The personality analysis unit can analyze the user's past travel history and suggest partners who love to travel. For example, the personality analysis unit can analyze the countries and regions the user has visited and suggest partners who have visited the same places. The personality analysis unit can also analyze the user's travel style and suggest partners who have the same travel style. Furthermore, the personality analysis unit can analyze the user's travel frequency and suggest partners who enjoy traveling with the same frequency. This makes it possible to match users who love to travel and have fun sharing common topics.

[0042] The matching unit can analyze information about the user's pet and suggest people who have pets. For example, if the user has a dog, the matching unit can suggest people who also have dogs. If the user has a cat, the matching unit can also suggest people who also have cats. Furthermore, if the user does not have a pet, the matching unit can also suggest people who do not have pets. This allows people to have fun talking about common topics related to pets, making for a more natural match.

[0043] The matching unit can analyze the user's food preferences and suggest partners who share the same food preferences. For example, if the user is vegetarian, the matching unit can suggest partners who are also vegetarian. Also, if the user likes spicy food, the matching unit can suggest partners who also like spicy food. Furthermore, if the user likes a particular dish, the matching unit can suggest partners who like that dish. This makes it possible to match based on food preferences, allowing users to have fun talking about common food topics.

[0044] The communication agent unit can learn the tones of the user's past messages and generate replies that match the user's tone. For example, the communication agent unit can learn the friendly tone that the user often uses and generate replies in the same tone. The communication agent unit can also learn the formal tone that the user often uses and generate replies in the same tone. Furthermore, the communication agent unit can learn the casual tone that the user often uses and generate replies in the same tone. This makes it possible to generate replies that match the user's tone, resulting in more natural communication.

[0045] The virtual matchmaker not only automatically generates date plans based on the user's hobbies and interests, but also proposes plans that fit the user's budget. For example, the virtual matchmaker can propose reasonable restaurants and cafes taking into account the user's budget. The virtual matchmaker can also propose free or low-cost date spots according to the user's budget. Furthermore, the virtual matchmaker can reduce the financial burden by proposing date plans that fit the user's budget. This allows for more realistic date plans to be realized by proposing date plans that fit the user's budget.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The personality analysis unit analyzes the user's personality. For example, the generation AI analyzes the user's personality, hobbies, and communication style through conversations with the user. The generation AI can also analyze the user's communication style from the tone and frequency of their conversations. Furthermore, the generation AI can analyze the user's personality based on user prompts. Step 2: The matching unit suggests the most suitable partner based on the personality data analyzed by the personality analysis unit. For example, if a user likes movies, the generation AI will suggest other users who also like movies. The generation AI can also match users with similar communication styles. Furthermore, the generation AI can analyze the user's personality data and make the best match. Step 3: The communication agent handles the initial communication with the potential partner proposed by the matching agent. For example, if the user says, "Hello, I see you like movies," the generation AI will respond with, "Yes, I especially like action movies." The generation AI can also generate appropriate responses based on the user's prompts. Furthermore, the generation AI can facilitate smooth communication between users. Step 4: The virtual matchmaker section provides support for the relationship based on the communication handled by the communication agent section. For example, when the user is planning a date, the generation AI may suggest, "How about this restaurant?" If the user is nervous, the generation AI may also give advice such as, "Relax and have fun." Furthermore, the generation AI can provide appropriate support depending on the user's situation.

[0048] (Example 2) The matching system according to an embodiment of the present invention uses a generation AI to analyze a user's personality, propose optimal dating partners, act as an agent for initial communication, and provide support for the relationship. This allows the matching system to accurately grasp the user's personality, propose optimal dating partners, facilitate smooth interactions between users, and provide support for the relationship.

[0049] A matching system according to an embodiment includes a personality analysis unit, a matching unit, a communication agent unit, and a virtual matchmaker unit. The personality analysis unit analyzes a user's personality. For example, the generation AI analyzes the user's personality, hobbies, and communication style through conversations with the user. The generation AI can also analyze the user's communication style based on the tone and frequency of the user's conversations. The generation AI can also analyze the user's personality based on prompts. The matching unit suggests optimal dating partners based on the personality data analyzed by the personality analysis unit. For example, for a user who likes movies, the generation AI can suggest other users who also like movies. The generation AI can also match users with similar communication styles. The generation AI can also analyze the user's personality data and perform optimal matching. The communication agent unit handles initial communication with the dating partner suggested by the matching unit. For example, when a user says, "Hello, you like movies, don't you?", the generation AI replies, "Yes, I especially like action movies." The generation AI can also generate appropriate responses based on the user's prompts. The generation AI can also facilitate smooth communication between users. The virtual matchmaker unit provides support for dating based on the communication handled by the communication agent unit. For example, when the user is planning a date, the generation AI may suggest, "How about this restaurant?" If the user is nervous, the generation AI may also provide advice such as, "Relax and have fun." Furthermore, the generation AI may provide appropriate support depending on the user's situation. As a result, the matching system according to the embodiment can analyze the user's personality, suggest the most suitable dating partner, handle initial communication on behalf of the user, and provide support for dating.

[0050] The personality analysis unit can analyze not only the content of a user's conversation but also at least one non-verbal element, such as facial expression, tone of voice, or gesture, to perform a more detailed personality analysis. For example, when a user is conversing through a camera, the personality analysis unit uses facial expression recognition technology to analyze facial expressions such as smiles and surprise, and reflects this in the personality analysis. For example, a user who smiles frequently may be determined to be sociable. The personality analysis unit can also use voice tone analysis technology to analyze the user's tone of voice and estimate their emotions and personality. For example, a user who speaks in a calm tone may be determined to have a calm personality. The personality analysis unit can also use gesture analysis technology to analyze the user's gestures and reflect this in the personality analysis. For example, a user who frequently moves their hands may be determined to have an active personality. This allows for a more detailed personality analysis by analyzing non-verbal elements as well.

[0051] The personality analysis unit analyzes a user's past online activity to gain a deeper understanding of the user's personality and hobbies. For example, the personality analysis unit analyzes the user's social media posts to extract frequently used keywords and hashtags. For example, a user who posts a lot about travel may be determined to be a travel enthusiast. The personality analysis unit can also analyze the user's browsing history to identify topics of interest. For example, a user who frequently visits recipe sites may be determined to be a cooking enthusiast. The personality analysis unit can also analyze the user's online shopping history to understand trends in purchased products. For example, a user who purchases a lot of outdoor equipment may be determined to be an outdoor enthusiast. In this way, by analyzing the user's past online activity, a deeper understanding of the user's personality and hobbies can be gained.

[0052] The personality analysis unit can use the emotion estimation function to track changes in the user's emotions in real time and reflect the emotion data in the personality analysis. The personality analysis unit can use the emotion estimation function to track changes in emotions in real time while the user is talking. For example, it can identify moments in the conversation when emotions such as joy and sadness are strongly expressed. The personality analysis unit can also use facial expression recognition technology to analyze changes in the user's facial expressions and collect emotion data. For example, it can detect smiling and tearful expressions to understand changes in emotion. The personality analysis unit can also use voice analysis technology to analyze changes in the tone and speed of the user's voice and collect emotion data. For example, it can determine that a higher voice tone indicates excitement. This allows for more accurate personality analysis by tracking changes in emotions in real time and reflecting them in the personality analysis.

[0053] The matching unit can perform more accurate matching by combining the results of a compatibility diagnostic test with the user's personality data. The matching unit, for example, integrates the user's personality data with the results of a compatibility diagnostic test to strengthen the matching algorithm. For example, it suggests an optimal partner based on personality traits and the results of a compatibility diagnostic test. The matching unit can also evaluate the compatibility between users based on the results of the compatibility diagnostic test. For example, it matches users who share common hobbies and values. The matching unit can also analyze the user's personality and behavioral patterns based on the results of the compatibility diagnostic test to suggest an optimal partner. For example, for an introverted user, it would suggest a partner who is also introverted. In this way, by combining the results of the compatibility diagnostic test, more accurate matching is possible.

[0054] The matching unit can analyze the user's lifestyle rhythm or daily behavioral patterns and suggest partners with a compatible lifestyle. For example, the matching unit can analyze the user's lifestyle rhythm and suggest partners who are active during the same time period. For example, it can suggest partners who are night owls to a night owl user. The matching unit can also analyze the user's daily behavioral patterns and suggest partners who share common activity times. For example, it can suggest partners who also jog in the morning to a user who has a habit of jogging in the morning. The matching unit can also analyze the user's lifestyle and suggest partners who share similar values ​​and lifestyle habits. For example, it can suggest partners who are similarly health-conscious to a health-conscious user. In this way, by analyzing the user's lifestyle rhythm and behavioral patterns, it is possible to suggest partners with a compatible lifestyle.

[0055] The matching unit can use the emotion estimation function to identify a partner from whom the user feels the most positive emotion and preferentially suggest that partner. The matching unit can, for example, identify a partner who elicits positive emotions based on the user's emotion data. For example, it can preferentially suggest a partner who smiles a lot during conversation. The matching unit can also use the emotion estimation function to identify a partner from whom the user feels the most positive emotion. For example, it can suggest a partner with whom the user enjoys conversation when the user is relaxed. The matching unit can also analyze the emotion data and preferentially suggest a partner from whom the user feels the most positive emotion. For example, it can suggest a partner with whom the user enjoys conversation when the user is excited. In this way, better matching can be achieved by identifying a partner from whom the user feels positive emotion and preferentially suggesting them.

[0056] The communication agent unit can learn the user's past conversation history and generate more personalized replies. For example, the communication agent unit analyzes the user's past conversation history and learns frequently used phrases and topics. For example, it prioritizes generating conversations related to specific hobbies and interests. The communication agent unit can also learn the user's conversation style and generate more natural replies. For example, it generates replies that incorporate phrases that the user frequently uses. The communication agent unit can also generate replies that match the user's preferences based on the user's past conversation history. For example, it generates replies related to topics that the user likes. In this way, by learning the user's past conversation history, more personalized replies become possible.

[0057] The communication agent unit can select an appropriate tone or wording depending on the emotional state of the user. The communication agent unit, for example, analyzes the emotional state of the user in real time and selects an appropriate tone or wording. For example, if the user is nervous, it replies in a gentle tone that relaxes the user. The communication agent unit can also select appropriate wording depending on the emotional state of the user. For example, if the user is angry, it selects wording that responds calmly. The communication agent unit can also achieve more natural communication by analyzing the emotional state of the user and selecting an appropriate tone or wording. For example, if the user is happy, it replies in a tone that shows empathy. In this way, more natural communication is possible by selecting an appropriate tone or wording depending on the emotional state.

[0058] The virtual matchmaker can automatically generate a date plan for the user and suggest detailed schedules and locations. The virtual matchmaker can automatically generate a date plan based on the user's hobbies and interests, for example. For example, a movie and dinner plan is suggested for a user who likes movies. The virtual matchmaker can also suggest the optimal time for a date, taking the user's schedule into consideration. For example, if a user wants a date after work, a plan for the evening or night is suggested. The virtual matchmaker can also suggest date locations that suit the user's preferences. For example, a park or nature reserve is suggested for a user who likes nature. In this way, the burden on the user can be reduced by automatically generating a date plan and suggesting detailed schedules and locations.

[0059] The virtual matchmaker can use the emotion estimation function to provide real-time advice to reduce the user's tension and anxiety. For example, the virtual matchmaker can provide real-time advice to reduce tension and anxiety based on the user's emotion data. For example, the virtual matchmaker can suggest deep breathing or other relaxation techniques. If the user is nervous before a date, the virtual matchmaker can also suggest music or videos to help them relax. For example, it can play music that has a relaxing effect. If the user feels anxious during the date, the virtual matchmaker can also send a reassuring message. For example, it can send a message such as "It's okay, have fun." By providing real-time advice to reduce tension and anxiety, the virtual matchmaker can allow the user to proceed with the relationship with peace of mind.

[0060] The feedback collection unit can collect user feedback in real time and immediately reflect it in system improvements. The feedback collection unit, for example, collects user feedback in real time and reflects it in system improvements. For example, the matching algorithm can be adjusted based on user opinions. The feedback collection unit can also consider adding new functions or services based on user feedback. For example, a function suggested by a user can be added to the system. The feedback collection unit can also analyze user feedback, identify weaknesses in the system, and take measures to improve it. For example, areas that users find dissatisfying can be improved. In this way, user satisfaction can be improved by collecting feedback in real time and immediately reflecting it in system improvements.

[0061] The feedback collection unit can perform sentiment analysis on the content of the feedback and process it by distinguishing between positive and negative feedback. For example, the feedback collection unit can perform sentiment analysis on the user's feedback and distinguish between positive and negative feedback. For example, it can prioritize the reflection of positive feedback. The feedback collection unit can also analyze negative feedback and identify areas for improvement. For example, it can improve areas that the user felt dissatisfied with. The feedback collection unit can also analyze the emotional aspects of the feedback using sentiment analysis technology. For example, it can identify the intensity and type of the user's emotion. This enables more effective system improvement by analyzing the content of the feedback and process it by distinguishing between positive and negative feedback.

[0062] The feedback collection unit can use the emotion estimation function to analyze the emotional aspects of the user's feedback and make improvements that are more in line with the emotions. The feedback collection unit, for example, uses the emotion estimation function to analyze the emotional aspects of the user's feedback. For example, it identifies points that the user feels dissatisfied with and makes improvements that are more in line with the emotions. The feedback collection unit can also take measures to improve the system based on the user's emotion data. For example, it can strengthen points that the user is pleased with. The feedback collection unit can also use the emotion estimation function to track changes in the user's emotions and analyze the emotional aspects of the feedback. For example, it can identify situations in which the user feels positive emotions. As a result, it is possible to use the emotion estimation function to analyze the emotional aspects of the feedback and make improvements that are more in line with the emotions, thereby improving user satisfaction.

[0063] The feedback collection unit can apply the feedback collection function to collecting and analyzing user reviews of products and services. The feedback collection unit, for example, applies the feedback collection function to user reviews of products and services and analyzes the content of the reviews. For example, it processes them by distinguishing between positive and negative reviews. The feedback collection unit can also take measures to improve products and services based on the user reviews. For example, it can enhance features that users have given high ratings to. The feedback collection unit can also collect user reviews and evaluate products and services. For example, it can add new features based on user opinions. In this way, applying the feedback collection function to collecting and analyzing user reviews of products and services can be useful for improving products and services.

[0064] The feedback collection unit can be applied to an innovation platform that develops new functions and services based on user feedback. The feedback collection unit, for example, builds an innovation platform that develops new functions and services based on user feedback. For example, new functions are added in response to user requests. The feedback collection unit can also analyze user feedback and determine the direction of innovation. For example, functions desired by users are developed as a priority. The feedback collection unit can also take measures to improve services based on user feedback. For example, points that users are dissatisfied with are improved. This makes it possible to develop new functions and services based on feedback, thereby enabling innovation that meets user needs.

[0065] The feedback collection unit can be applied to a customization system that uses the emotion estimation function to suggest products and services that will most satisfy a user. The feedback collection unit, for example, uses the emotion estimation function to build a customization system that suggests products and services that will most satisfy a user. For example, it suggests the most suitable product based on the user's emotion data. The feedback collection unit can also analyze the user's emotion data and suggest products and services that match the user's preferences. For example, it preferentially suggests products that the user is pleased with. The feedback collection unit can also use the emotion estimation function to track changes in the user's emotions and identify products and services that will most satisfy the user. For example, it suggests products that the user is satisfied with. In this way, it is possible to improve user satisfaction by using the emotion estimation function to suggest products and services that will most satisfy the user.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The matching system may further include a health management unit. The health management unit collects the user's health data and can suggest optimal dating partners based on the user's health condition. For example, the health management unit may analyze the user's exercise habits and dietary habits to suggest health-conscious partners. The health management unit may also analyze the user's stress level to suggest partners with whom they can relax. Furthermore, the health management unit may analyze the user's sleep patterns to suggest partners who have the same sleep rhythm. This enables matching based on health data and supports healthier relationships.

[0068] The personality analysis unit can form groups with common hobbies based on users' hobbies and interests, and promote interaction within the groups. For example, the personality analysis unit can form a movie-watching group for users who love movies. The personality analysis unit can also form a hiking group for users who love the outdoors. Furthermore, the personality analysis unit can form a cooking class group for users who love cooking. This promotes interaction between users with common hobbies, enabling more natural matching.

[0069] The personality analysis unit can analyze the user's past travel history and suggest partners who love to travel. For example, the personality analysis unit can analyze the countries and regions the user has visited and suggest partners who have visited the same places. The personality analysis unit can also analyze the user's travel style and suggest partners who have the same travel style. Furthermore, the personality analysis unit can analyze the user's travel frequency and suggest partners who enjoy traveling with the same frequency. This makes it possible to match users who love to travel and have fun sharing common topics.

[0070] The personality analysis unit can use the emotion estimation function to identify the environment in which the user is most relaxed and suggest a date plan based on that environment. For example, if the user often relaxes in nature, the personality analysis unit can suggest a date at a nature park. If the user often relaxes at a cafe, the personality analysis unit can also suggest a date at a cafe. Furthermore, if the user often relaxes by listening to music, the personality analysis unit can also suggest a date at a music event. This allows for a more natural relationship by suggesting a date in an environment where the user can relax.

[0071] The matching unit can analyze information about the user's pet and suggest people who have pets. For example, if the user has a dog, the matching unit can suggest people who also have dogs. If the user has a cat, the matching unit can also suggest people who also have cats. Furthermore, if the user does not have a pet, the matching unit can also suggest people who do not have pets. This allows people to have fun talking about common topics related to pets, making for a more natural match.

[0072] The matching unit can analyze the user's food preferences and suggest partners who share the same food preferences. For example, if the user is vegetarian, the matching unit can suggest partners who are also vegetarian. Also, if the user likes spicy food, the matching unit can suggest partners who also like spicy food. Furthermore, if the user likes a particular dish, the matching unit can suggest partners who like that dish. This makes it possible to match based on food preferences, allowing users to have fun talking about common food topics.

[0073] The matching unit can use the emotion estimation function to identify a partner with whom the user feels most at ease and preferentially suggest that partner. For example, the matching unit can identify a partner who evokes a sense of ease based on the user's emotion data. For example, it can preferentially suggest a partner who appears relaxed during a conversation. The matching unit can also use the emotion estimation function to identify a partner with whom the user feels most at ease. For example, it can suggest a partner with whom the user enjoys a conversation when the user is calm. The matching unit can also analyze the emotion data and preferentially suggest a partner with whom the user feels most at ease. For example, it can suggest a partner with whom the user enjoys a conversation when the user is relaxed. In this way, better matching can be achieved by identifying a partner with whom the user feels at ease and preferentially suggesting them.

[0074] The communication agent unit can learn the tones of the user's past messages and generate replies that match the user's tone. For example, the communication agent unit can learn the friendly tone that the user often uses and generate replies in the same tone. The communication agent unit can also learn the formal tone that the user often uses and generate replies in the same tone. Furthermore, the communication agent unit can learn the casual tone that the user often uses and generate replies in the same tone. This makes it possible to generate replies that match the user's tone, resulting in more natural communication.

[0075] The communication agent unit can generate a response incorporating appropriate humor depending on the emotional state of the user. For example, if the user is relaxed, the communication agent unit can generate a response incorporating light humor. If the user is nervous, the communication agent unit can also generate a response incorporating humor to relieve the tension. Furthermore, if the user is sad, the communication agent unit can also generate a response incorporating humor to lighten the mood. This makes it possible to generate a response incorporating appropriate humor depending on the emotional state, thereby achieving more natural communication.

[0076] The virtual matchmaker not only automatically generates date plans based on the user's hobbies and interests, but also proposes plans that fit the user's budget. For example, the virtual matchmaker can propose reasonable restaurants and cafes taking into account the user's budget. The virtual matchmaker can also propose free or low-cost date spots according to the user's budget. Furthermore, the virtual matchmaker can reduce the financial burden by proposing date plans that fit the user's budget. This allows for more realistic date plans to be realized by proposing date plans that fit the user's budget.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The personality analysis unit analyzes the user's personality. For example, the generation AI analyzes the user's personality, hobbies, and communication style through conversations with the user. The generation AI can also analyze the user's communication style from the tone and frequency of their conversations. Furthermore, the generation AI can analyze the user's personality based on user prompts. Step 2: The matching unit suggests the most suitable partner based on the personality data analyzed by the personality analysis unit. For example, if a user likes movies, the generation AI will suggest other users who also like movies. The generation AI can also match users with similar communication styles. Furthermore, the generation AI can analyze the user's personality data and make the best match. Step 3: The communication agent handles the initial communication with the potential partner proposed by the matching agent. For example, if the user says, "Hello, I see you like movies," the generation AI will respond with, "Yes, I especially like action movies." The generation AI can also generate appropriate responses based on the user's prompts. Furthermore, the generation AI can facilitate smooth communication between users. Step 4: The virtual matchmaker section provides support for the relationship based on the communication handled by the communication agent section. For example, when the user is planning a date, the generation AI may suggest, "How about this restaurant?" If the user is nervous, the generation AI may also give advice such as, "Relax and have fun." Furthermore, the generation AI can provide appropriate support depending on the user's situation.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a personality analysis unit that analyzes the personality of the user; a matching unit that proposes an optimal partner based on the personality data analyzed by the personality analysis unit; a communication agent unit that acts as an agent for initial communication with a dating partner proposed by the matching unit; a virtual matchmaker unit that provides support for dating based on the communication performed by the communication agent unit. A system characterized by:

2. The personality analysis unit Analyzing not only the content of the user's conversation but also at least one non-verbal element among facial expressions, tone of voice, and gestures to perform a more detailed personality analysis.

2. The system of claim 1.

3. The matching unit In addition to the user's personality data, the results of a compatibility diagnostic test are combined to perform more accurate matching.

2. The system of claim 1.

4. The communication agent unit Learn from the user's past conversation history to generate more personalized responses 2. The system of claim 1.

5. The virtual matchmaker department: Automatically generate a date plan for the user and suggest detailed schedules and locations 2. The system of claim 1.

6. The feedback collection unit: Analyze the sentiment of feedback and differentiate between positive and negative feedback 2. The system of claim 1.

7. The personality analysis unit Tracking the user's emotional changes in real time and reflecting the emotional data in personality analysis 2. The system of claim 1.

8. The matching unit Identifying the person for whom the user has the most positive feelings and preferentially suggesting the person 2. The system of claim 1.

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