System

Abstract

An object of a system according to an embodiment is to provide a high-quality spouse searching service while suppressing costs.SOLUTION: In one embodiment, a system includes a profile analyzer, a matching component, an online counseling component, an event planning component, a feedback analyzer, and a marketing optimizer. The profile analysis unit analyzes profile information of a user. The matching unit proposes an optimal partner to the user on the basis of the profile information analyzed by the profile analysis unit. The on-line counseling unit provides on-line counseling on the user's trouble or consultation. The event planning unit plans a marriage event that meets the needs of the user. The feedback analysis unit analyzes the feedback from the user and proposes improvement points of the service. The marketing optimization unit optimizes a marketing activity.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] With conventional technology, the cost of marriage agencies was high, making it difficult to provide high-quality matchmaking services.

[0005] The system according to the embodiment aims to provide high-quality matchmaking services while keeping costs down. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile analysis unit, a matching unit, an online counseling unit, an event planning unit, a feedback analysis unit, and a marketing optimization unit. The profile analysis unit analyzes the user's profile information. The matching unit suggests the most suitable partner for the user based on the profile information analyzed by the profile analysis unit. The online counseling unit provides online counseling for the user's concerns and questions. The event planning unit plans matchmaking events that meet the user's needs. The feedback analysis unit analyzes feedback from users and suggests areas for improving the service. The marketing optimization unit optimizes marketing activities. [Effects of the Invention]

[0007] The system according to the embodiment can provide high-quality matchmaking services while keeping costs down. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The matchmaking service system according to the embodiment of the present invention solves the problem of high costs associated with using marriage agencies, and utilizes generative AI to achieve efficient and effective matching and reduce costs. This allows the matchmaking service system to provide high-quality services at affordable prices to users.

[0029] A matchmaking service system according to an embodiment includes a profile analysis unit, a matching unit, an online counseling unit, an event planning unit, a feedback analysis unit, and a marketing optimization unit. The profile analysis unit analyzes a user's profile information. For example, the profile analysis unit analyzes information such as age, gender, hobbies, and occupation entered by the user. The profile analysis unit can also analyze the user's past message history and behavioral patterns. For example, the profile analysis unit can analyze the user's message history to identify the user's communication style and topics of interest. The profile analysis unit can analyze the user's behavioral patterns to determine what time of day the user is active and what activities the user prefers. The profile analysis unit can also analyze the user's psychological test results. For example, the profile analysis unit can suggest a compatible partner based on the results of a personality assessment test. The matching unit can suggest a suitable partner for the user based on the profile information analyzed by the profile analysis unit. For example, the profile analysis unit can suggest a partner who shares common hobbies and values. The online counseling unit provides online counseling to address the user's concerns and concerns. For example, the online counseling unit can provide counseling via video calls or chat. The online counseling unit can also analyze the user's past counseling history to provide more personalized advice. The event planning unit plans matchmaking events that meet the needs of users. For example, it analyzes feedback from past event participants to plan events that will provide high satisfaction. Furthermore, the event planning unit can analyze event data from different cultures and regions to plan events from an international perspective. The feedback analysis unit analyzes feedback from users and suggests areas for improving the service. For example, it analyzes surveys and reviews to identify areas for improvement in the service. The marketing optimization unit optimizes marketing activities. For example, it analyzes market data and user data to suggest optimal marketing strategies. As a result, the matchmaking service system according to the embodiment can solve the problem of high costs associated with using marriage agencies and provide higher quality matchmaking services.For example, efficient matching and online counseling allow users to receive high-quality services at affordable prices. Automated event planning and management also allows for high-quality events to be offered at affordable prices. Furthermore, service improvements and optimized marketing based on user feedback will increase user satisfaction.

[0030] The profile analysis unit analyzes a user's past message history and behavioral patterns to enable more accurate matching. In the profile analysis unit, for example, the generation AI analyzes a user's past message history to identify their communication style and topics of interest. For example, the generation AI analyzes the user's behavioral patterns to determine what time of day the user is active and what activities they prefer. For example, the generation AI extracts the user's values ​​and priorities from past message history and behavioral patterns and performs matching based on these. In this way, analyzing a user's past message history and behavioral patterns enables more accurate matching.

[0031] The profile analysis unit can analyze the user's psychological test results and perform matching that takes psychological compatibility into consideration. For example, the profile analysis unit administers a psychological test to the user and inputs the results into the generation AI. For example, it can suggest a compatible partner based on the results of a personality diagnostic test. For example, it can analyze the psychological test results to understand the user's personality traits and stress tolerance. For example, it can suggest a partner with a compatible communication style and problem-solving approach based on the psychological test results. In this way, by analyzing the user's psychological test results, it becomes possible to perform matching that takes psychological compatibility into consideration.

[0032] The online counseling unit can analyze the user's past counseling history and provide more personalized advice. For example, the online counseling unit uses a generation AI to analyze the user's past counseling history and provide advice for specific problems or concerns. For example, it proposes specific solutions based on the content of past consultations. For example, it analyzes the user's counseling history and evaluates the effectiveness of past advice. For example, it understands the user's tendencies and patterns based on the past counseling history and provides personalized advice. In this way, by analyzing the user's past counseling history, it is possible to provide more personalized advice.

[0033] The event planning department can analyze feedback from past event participants and plan events that will provide greater satisfaction. For example, the event planning department uses a generative AI to analyze feedback from past event participants and identify elements that provide greater satisfaction. For example, based on the feedback data, the department can identify activities and themes that participants prefer and plan events that reflect this. For example, the department can analyze past feedback and identify areas for improvement and factors that contribute to success. In this way, by analyzing feedback from past event participants, the department can plan events that will provide greater satisfaction.

[0034] The event planning department can analyze event data from different cultures and regions and plan events from an international perspective. For example, the generative AI analyzes event data from different cultures and regions and plans events from an international perspective. For example, it plans an event with a cross-cultural exchange theme. For example, it proposes activities that international participants can enjoy based on event data from different cultures and regions. In this way, it plans events that can be enjoyed by participants with diverse cultural backgrounds.

[0035] The event planning unit can automatically plan themed events based on the user's hobbies and interests. For example, the event planning unit analyzes the user's hobbies and interests, and the generation AI automatically plans themed events based on them. For example, a hiking event can be planned for users who love the outdoors. For example, planning a themed event based on hobbies and interests makes it easier for participants with common hobbies to interact with each other. For example, a cooking class can be planned for users who love cooking. In this way, themed events can be automatically planned based on the user's hobbies and interests.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The matchmaking service system may further include a health analysis unit that analyzes the user's health data. For example, the system may analyze the user's fitness data and dietary data to suggest partners with a healthy lifestyle. For example, the system may suggest partners with common health goals. For example, the health analysis unit may analyze the user's stress level and sleep patterns to match the user when the user's health is good. In this way, the system may analyze the user's health data to suggest partners with a healthy lifestyle.

[0038] The matchmaking service system can also be equipped with a recommendation function based on the user's hobbies and interests. For example, it can analyze information such as the user's favorite movies, music, and sports to suggest partners who share the same hobbies. For example, it can suggest events and activities related to the hobbies. For example, it can also suggest date plans based on the user's hobbies and interests. This allows the system to build closer relationships by suggesting partners who share the same hobbies and interests.

[0039] The matchmaking service system can further include a lifestyle analysis unit that analyzes the user's lifestyle data. For example, the system analyzes the user's life rhythm and daily activity patterns to suggest a partner with a compatible lifestyle. For example, a night-owl user can be suggested a partner with a night-owl personality. For example, an outdoor-oriented user can be suggested a partner with an outdoor-oriented personality. In this way, by analyzing the user's lifestyle data, it is possible to suggest a partner with a compatible lifestyle.

[0040] The matchmaking service system can further include a career analysis unit that analyzes the user's career data. For example, the system analyzes the user's occupation and career path to suggest partners with similar career aspirations. For example, it suggests partners in the same industry or occupation. For example, it suggests partners with similar career goals. In this way, by analyzing the user's career data, it is possible to suggest partners with similar career aspirations.

[0041] The matchmaking service system can further include an event planning unit that automatically plans themed events based on the user's hobbies and interests. For example, a generation AI analyzes the user's hobbies and interests and automatically plans themed events based on them. For example, a hiking event can be planned for users who love the outdoors. For example, planning a themed event based on hobbies and interests makes it easier for participants with common hobbies to interact with each other. For example, a cooking class can be planned for users who love cooking. In this way, themed events based on the user's hobbies and interests can be automatically planned.

[0042] The matchmaking service system can further include a lifestyle analysis unit that analyzes the user's lifestyle data and suggests a partner with a compatible lifestyle. For example, the system analyzes the user's life rhythm and daily activity patterns and suggests a partner with a compatible lifestyle. For example, a partner with a night owl personality is suggested for a night owl user. For example, a partner with an outdoor lover is suggested for an outdoor lover user. In this way, by analyzing the user's lifestyle data, it is possible to suggest a partner with a compatible lifestyle.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The profile analysis unit analyzes the user's profile information. For example, it analyzes information entered by the user, such as age, gender, hobbies, and occupation. It also analyzes past message history and behavioral patterns to understand the user's communication style, topics of interest, active times, and preferred activities. It also analyzes the results of a psychological test to suggest compatible partners. Step 2: The matching unit proposes the most suitable partner to the user based on the profile information analyzed by the profile analysis unit, for example, by proposing partners who share common interests and values. Step 3: The online counseling department provides online counseling for the user's concerns or questions. For example, counseling can be conducted via video calls or chats. It can also analyze past counseling history to provide more personalized advice. Step 4: The event planning department plans matchmaking events that meet the user's needs. For example, it analyzes feedback from past event participants to plan events that will generate high levels of satisfaction. It can also analyze event data from different cultures and regions to plan events from an international perspective. Step 5: The feedback analysis unit analyzes user feedback and proposes ways to improve the service. For example, it analyzes surveys and reviews to identify areas for improvement in the service. Step 6: The Marketing Optimization Department optimizes marketing activities, for example by analyzing market data and user data and proposing optimal marketing strategies.

[0045] (Example 2) The matchmaking service system according to the embodiment of the present invention solves the problem of high costs associated with using marriage agencies, and utilizes generative AI to achieve efficient and effective matching and reduce costs. This allows the matchmaking service system to provide high-quality services at affordable prices to users.

[0046] A matchmaking service system according to an embodiment includes a profile analysis unit, a matching unit, an online counseling unit, an event planning unit, a feedback analysis unit, and a marketing optimization unit. The profile analysis unit analyzes a user's profile information. For example, the profile analysis unit analyzes information such as age, gender, hobbies, and occupation entered by the user. The profile analysis unit can also analyze the user's past message history and behavioral patterns. For example, the profile analysis unit can analyze the user's message history to identify the user's communication style and topics of interest. The profile analysis unit can analyze the user's behavioral patterns to determine what time of day the user is active and what activities the user prefers. The profile analysis unit can also analyze the user's psychological test results. For example, the profile analysis unit can suggest a compatible partner based on the results of a personality assessment test. The matching unit can suggest a suitable partner for the user based on the profile information analyzed by the profile analysis unit. For example, the profile analysis unit can suggest a partner who shares common hobbies and values. The online counseling unit provides online counseling to address the user's concerns and concerns. For example, the online counseling unit can provide counseling via video calls or chat. The online counseling unit can also analyze the user's past counseling history to provide more personalized advice. The event planning unit plans matchmaking events that meet the needs of users. For example, it analyzes feedback from past event participants to plan events that will provide high satisfaction. Furthermore, the event planning unit can analyze event data from different cultures and regions to plan events from an international perspective. The feedback analysis unit analyzes feedback from users and suggests areas for improving the service. For example, it analyzes surveys and reviews to identify areas for improvement in the service. The marketing optimization unit optimizes marketing activities. For example, it analyzes market data and user data to suggest optimal marketing strategies. As a result, the matchmaking service system according to the embodiment can solve the problem of high costs associated with using marriage agencies and provide higher quality matchmaking services.For example, efficient matching and online counseling allow users to receive high-quality services at affordable prices. Automated event planning and management also allows for high-quality events to be offered at affordable prices. Furthermore, service improvements and optimized marketing based on user feedback will increase user satisfaction.

[0047] The profile analysis unit analyzes a user's past message history and behavioral patterns to enable more accurate matching. In the profile analysis unit, for example, the generation AI analyzes a user's past message history to identify their communication style and topics of interest. For example, the generation AI analyzes the user's behavioral patterns to determine what time of day the user is active and what activities they prefer. For example, the generation AI extracts the user's values ​​and priorities from past message history and behavioral patterns and performs matching based on these. In this way, analyzing a user's past message history and behavioral patterns enables more accurate matching.

[0048] The profile analysis unit can analyze the user's psychological test results and perform matching that takes psychological compatibility into consideration. For example, the profile analysis unit administers a psychological test to the user and inputs the results into the generation AI. For example, it can suggest a compatible partner based on the results of a personality diagnostic test. For example, it can analyze the psychological test results to understand the user's personality traits and stress tolerance. For example, it can suggest a partner with a compatible communication style and problem-solving approach based on the psychological test results. In this way, by analyzing the user's psychological test results, it becomes possible to perform matching that takes psychological compatibility into consideration.

[0049] The profile analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest emotionally stable partners. The profile analysis unit, for example, analyzes the user's emotional state in real time and suggests emotionally stable partners. For example, matching is performed during periods of low stress. For example, the emotion estimation function is used to analyze the emotions a user has when entering their profile, and matching is performed when positive emotions are strong. For example, the user's emotional state is analyzed and an emotionally stable partner is suggested. In this way, by analyzing the user's emotional state in real time, an emotionally stable partner can be suggested.

[0050] The online counseling unit can analyze the user's past counseling history and provide more personalized advice. For example, the online counseling unit uses a generation AI to analyze the user's past counseling history and provide advice for specific problems or concerns. For example, it proposes specific solutions based on the content of past consultations. For example, it analyzes the user's counseling history and evaluates the effectiveness of past advice. For example, it understands the user's tendencies and patterns based on the past counseling history and provides personalized advice. In this way, by analyzing the user's past counseling history, it is possible to provide more personalized advice.

[0051] The online counseling unit can analyze the user's voice tone and facial expression and provide advice according to their emotional state. The online counseling unit, for example, analyzes the user's voice tone to understand their emotional state. For example, it suggests ways to relax when stress is high. For example, it analyzes the user's facial expression to understand their emotional state in real time. For example, it provides words of encouragement when the user looks sad. For example, it analyzes the voice tone and facial expression and provides advice according to their emotional state. In this way, by analyzing the user's voice tone and facial expression, it is possible to provide advice according to their emotional state.

[0052] The online counseling unit can use the emotion estimation function to analyze the user's emotional state in real time and provide emotionally stable advice. The online counseling unit can, for example, use the emotion estimation function to analyze the user's emotional state in real time and provide emotionally stable advice. For example, advice is provided when stress is low. For example, the online counseling unit can analyze the user's emotional state in real time and provide emotionally stable advice. For example, advice is provided when emotional fluctuations are low. In this way, emotionally stable advice can be provided by analyzing the user's emotional state in real time.

[0053] The event planning department can analyze feedback from past event participants and plan events that will provide greater satisfaction. For example, the event planning department uses a generative AI to analyze feedback from past event participants and identify elements that provide greater satisfaction. For example, based on the feedback data, the department can identify activities and themes that participants prefer and plan events that reflect this. For example, the department can analyze past feedback and identify areas for improvement and factors that contribute to success. In this way, by analyzing feedback from past event participants, the department can plan events that will provide greater satisfaction.

[0054] The event planning department can use the emotion estimation function to analyze the emotional state of event participants in real time and manage an event that provides high emotional satisfaction. The event planning department, for example, uses the emotion estimation function to analyze the emotional state of event participants in real time and manage an event that provides high emotional satisfaction. For example, the event planning department analyzes the facial expressions and voices of participants and adjusts the progress of the event. For example, the emotional state of participants can be monitored during the event and activities that evoke strong positive emotions can be prioritized. For example, an activity can be extended when participants are enjoying themselves. In this way, by analyzing the emotional state of event participants in real time, an event that provides high emotional satisfaction can be managed.

[0055] The event planning department can analyze event data from different cultures and regions and plan events from an international perspective. For example, the generative AI analyzes event data from different cultures and regions and plans events from an international perspective. For example, it plans an event with a cross-cultural exchange theme. For example, it proposes activities that international participants can enjoy based on event data from different cultures and regions. In this way, it plans events that can be enjoyed by participants with diverse cultural backgrounds.

[0056] The event planning unit can automatically plan themed events based on the user's hobbies and interests. For example, the event planning unit analyzes the user's hobbies and interests, and the generation AI automatically plans themed events based on them. For example, a hiking event can be planned for users who love the outdoors. For example, planning a themed event based on hobbies and interests makes it easier for participants with common hobbies to interact with each other. For example, a cooking class can be planned for users who love cooking. In this way, themed events can be automatically planned based on the user's hobbies and interests.

[0057] The event planning department can use the emotion estimation function to analyze the emotions felt by event participants during the event in real time and make suggestions to elicit positive emotions. The event planning department, for example, uses the emotion estimation function to analyze the emotions felt by event participants during the event in real time and make suggestions to elicit positive emotions. For example, extending an activity when participants are enjoying themselves. For example, by monitoring the emotional state of participants during the event and prioritizing activities that elicit strong positive emotions, for example, extending an activity when participants are enjoying themselves. In this way, the emotions felt by event participants during the event can be analyzed in real time and suggestions to elicit positive emotions can be made, thereby improving event satisfaction.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The matchmaking service system may further include a health analysis unit that analyzes the user's health data. For example, the system may analyze the user's fitness data and dietary data to suggest partners with a healthy lifestyle. For example, the system may suggest partners with common health goals. For example, the health analysis unit may analyze the user's stress level and sleep patterns to match the user when the user's health is good. In this way, the system may analyze the user's health data to suggest partners with a healthy lifestyle.

[0060] The matchmaking service system can also be equipped with a recommendation function based on the user's hobbies and interests. For example, it can analyze information such as the user's favorite movies, music, and sports to suggest partners who share the same hobbies. For example, it can suggest events and activities related to the hobbies. For example, it can also suggest date plans based on the user's hobbies and interests. This allows the system to build closer relationships by suggesting partners who share the same hobbies and interests.

[0061] The matchmaking service system can further include a lifestyle analysis unit that analyzes the user's lifestyle data. For example, the system analyzes the user's life rhythm and daily activity patterns to suggest a partner with a compatible lifestyle. For example, a night-owl user can be suggested a partner with a night-owl personality. For example, an outdoor-oriented user can be suggested a partner with an outdoor-oriented personality. In this way, by analyzing the user's lifestyle data, it is possible to suggest a partner with a compatible lifestyle.

[0062] The matchmaking service system can further include a career analysis unit that analyzes the user's career data. For example, the system analyzes the user's occupation and career path to suggest partners with similar career aspirations. For example, it suggests partners in the same industry or occupation. For example, it suggests partners with similar career goals. In this way, by analyzing the user's career data, it is possible to suggest partners with similar career aspirations.

[0063] The matchmaking service system can further include an emotion analysis unit that analyzes the user's emotional state. For example, it can analyze the user's emotional fluctuations from their past message history and behavioral patterns, and suggest an emotionally stable partner. For example, it can perform matching during periods of low stress, suggesting a partner with fewer emotional fluctuations. In this way, it can suggest an emotionally stable partner by analyzing the user's emotional state.

[0064] The matchmaking service system can further include a counseling unit that analyzes the user's emotional state in real time and provides emotionally stable advice. For example, the system analyzes the user's tone of voice and facial expressions to understand the user's emotional state. For example, when the user is under high stress, the system suggests ways to relax. For example, when the user looks sad, the system provides words of encouragement. In this way, the system can provide emotionally stable advice by analyzing the user's emotional state in real time.

[0065] The matchmaking service system can further include an event planning unit that analyzes the emotional state of users in real time and manages events that are emotionally satisfying. For example, the event progress can be adjusted by analyzing the participants' facial expressions and voices. For example, the emotional state of participants can be monitored during the event, and activities that evoke strong positive emotions can be prioritized. For example, an activity can be extended when participants are enjoying themselves. In this way, by analyzing the emotional state of event participants in real time, an event that is emotionally satisfying can be managed.

[0066] The matchmaking service system can further include an event planning unit that analyzes the emotional state of users in real time and makes suggestions to elicit positive emotions. For example, an activity can be extended when participants are enjoying themselves. For example, the emotional state of participants can be monitored during an event, and activities that elicit strong positive emotions can be prioritized. For example, an activity can be extended when participants are enjoying themselves. In this way, the emotions felt by event participants during the event can be analyzed in real time and suggestions to elicit positive emotions can be made, thereby improving event satisfaction.

[0067] The matchmaking service system can further include an event planning unit that automatically plans themed events based on the user's hobbies and interests. For example, a generation AI analyzes the user's hobbies and interests and automatically plans themed events based on them. For example, a hiking event can be planned for users who love the outdoors. For example, planning a themed event based on hobbies and interests makes it easier for participants with common hobbies to interact with each other. For example, a cooking class can be planned for users who love cooking. In this way, themed events based on the user's hobbies and interests can be automatically planned.

[0068] The matchmaking service system can further include a lifestyle analysis unit that analyzes the user's lifestyle data and suggests a partner with a compatible lifestyle. For example, the system analyzes the user's life rhythm and daily activity patterns and suggests a partner with a compatible lifestyle. For example, a partner with a night owl personality is suggested for a night owl user. For example, a partner with an outdoor lover is suggested for an outdoor lover user. In this way, by analyzing the user's lifestyle data, it is possible to suggest a partner with a compatible lifestyle.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The profile analysis unit analyzes the user's profile information. For example, it analyzes information entered by the user, such as age, gender, hobbies, and occupation. It also analyzes past message history and behavioral patterns to understand the user's communication style, topics of interest, active times, and preferred activities. It also analyzes the results of a psychological test to suggest compatible partners. Step 2: The matching unit proposes the most suitable partner to the user based on the profile information analyzed by the profile analysis unit, for example, by proposing partners who share common interests and values. Step 3: The online counseling department provides online counseling for the user's concerns or questions. For example, counseling can be conducted via video calls or chats. It can also analyze past counseling history to provide more personalized advice. Step 4: The event planning department plans matchmaking events that meet the user's needs. For example, it analyzes feedback from past event participants to plan events that will generate high levels of satisfaction. It can also analyze event data from different cultures and regions to plan events from an international perspective. Step 5: The feedback analysis unit analyzes user feedback and proposes ways to improve the service. For example, it analyzes surveys and reviews to identify areas for improvement in the service. Step 6: The Marketing Optimization Department optimizes marketing activities, for example by analyzing market data and user data and proposing optimal marketing strategies.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, The generated AI is a profile analysis unit that analyzes user profile information; a matching unit that proposes a suitable partner to the user based on the profile information analyzed by the profile analysis unit; an online counseling unit that provides online counseling for the user's worries and concerns; An event planning department that plans a matchmaking event that meets the needs of the user; a feedback analysis unit that analyzes the feedback from the users and proposes improvements to the service; A marketing optimization unit that optimizes marketing activities. A system characterized by:

2. The profile analysis unit Analyze the user's past message history and behavioral patterns to perform more accurate matching.

2. The system of claim 1.

3. The profile analysis unit Analyze the results of the user's psychological test and perform matching taking psychological compatibility into consideration.

2. The system of claim 1.

4. The profile analysis unit Analyzing the user's emotional state in real time and suggesting emotionally stable partners 2. The system of claim 1.

5. The online counseling department: Analyzing the user's past counseling history to provide more personalized advice 2. The system of claim 1.

6. The online counseling department: Analyzing the user's tone of voice and facial expressions to provide advice according to their emotional state 2. The system of claim 1.

7. The online counseling department: Analyzing the user's emotional state in real time and providing emotionally stable advice 2. The system of claim 1.

8. The event planning department Analyze the feedback from past event participants and plan events that will provide greater satisfaction.

2. The system of claim 1.

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