System

Abstract

An object of a system according to an embodiment is to analyze profile information and a desired condition of a user and perform optimal matching.SOLUTION: A system according to an embodiment includes a profile analysis unit, a matching unit, a communication support unit, a date plan suggestion unit, an advice providing unit, and a feedback collection unit. The profile analysis unit analyzes the profile information and the desired condition of the user using the generated AI. The matching unit performs optimum matching based on the profile information analyzed by the profile analysis unit and the desired condition. The communication support unit supports communication between the users matched by the matching unit. The date plan proposing unit proposes a date plan based on the desire or interest of the user. The advice providing unit provides the user with advice related to love and excitement. The feedback collection unit collects feedback from users to help improve the service.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 do not adequately perform optimal matching based on a user's profile information and desired conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the profile information and desired conditions of the user and perform optimal matching. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile analysis unit, a matching unit, a communication support unit, a date plan suggestion unit, an advice providing unit, and a feedback collection unit. The profile analysis unit uses a generation AI to analyze a user's profile information and desired conditions. The matching unit performs optimal matching based on the profile information and desired conditions analyzed by the profile analysis unit. The communication support unit supports communication between users matched by the matching unit. The date plan suggestion unit suggests date plans based on the user's wishes and interests. The advice providing unit provides users with advice on romance and matchmaking. The feedback collection unit collects feedback from users and uses it to improve the service. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the profile information and desired conditions of the user and perform optimal matching. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A dating and marriage system according to an embodiment of the present invention uses a generation AI to analyze a user's profile information and desired conditions, perform optimal matching, support communication, provide date plan suggestions and advice, and collect feedback to improve the service. This enables the dating and marriage system to analyze a user's profile information and desired conditions, perform optimal matching, support communication, provide date plan suggestions and advice, and collect feedback to improve the service.

[0029] A dating and marriage hunting system according to an embodiment includes a profile analysis unit, a matching unit, a communication support unit, a date plan suggestion unit, an advice providing unit, and a feedback collection unit. The profile analysis unit analyzes a user's profile information and desired conditions. For example, the profile analysis unit analyzes information such as age, gender, occupation, and hobbies entered by the user. The profile analysis unit can also analyze the user's desired conditions (e.g., the other person's age, hobbies, values, etc.). The matching unit performs optimal matching based on the profile information and desired conditions analyzed by the profile analysis unit. For example, the matching unit calculates a compatibility score and matches users with common hobbies and values. The communication support unit supports communication between users matched by the matching unit. For example, the communication support unit suggests appropriate message examples when sending a first message. It can also provide new topics of conversation if the conversation dies down. The date plan suggestion unit suggests date plans based on the user's preferences and interests. For example, if a user enters "I like nature," the date plan suggestion unit suggests date spots and activities where users can enjoy nature. It can also suggest the best date and time for a date based on the user's schedule. The advice providing unit provides the user with advice on romance and matchmaking. For example, if the user asks for advice saying, "I don't know what to talk about on a date," the advice providing unit can provide conversation tips and example topics. It can also provide individually customized advice based on the user's profile and feedback from past dates. The feedback collecting unit collects feedback from users and uses it to improve the service. For example, when a user enters their impressions after a date, the feedback collecting unit analyzes that feedback and reflects it in suggesting the next match or date plan.As a result, the dating and marriage system of the embodiment can analyze users' profile information and desired conditions, perform optimal matching, support communication, suggest date plans and provide advice, and collect feedback to improve the service.

[0030] The profile analysis unit analyzes a user's past behavioral history and message content to enable more accurate matching. For example, the profile analysis unit uses a generation AI to analyze the content of a user's past messages and identify people with common topics and interests. For example, it extracts characteristics of people with whom the user has exchanged many messages in the past and recommends new partners with similar characteristics. The profile analysis unit also analyzes a user's behavioral history to match users who are active during specific time periods. For example, it can prioritize matching between users who frequently log in at night. The profile analysis unit also analyzes past dating history to match users with dating patterns that have a high success rate. For example, it can match users who prefer specific date spots. This enables more accurate matching by analyzing a user's past behavioral history and message content.

[0031] The profile analysis unit analyzes users' lifestyles and values ​​in addition to their hobbies and interests, enabling more multifaceted matching. For example, the profile analysis unit uses a generation AI to analyze users' lifestyles and match users who are active at the same time. For example, it can match users who are early risers. The profile analysis unit also analyzes users' values ​​and recommends partners who share common values. For example, it can match users who are interested in environmental protection. The profile analysis unit also analyzes users' eating habits and exercise habits in addition to their hobbies and interests, and matches health-conscious users. For example, it can match vegetarian users. This enables more multifaceted matching by analyzing not only users' hobbies and interests, but also their lifestyles and values.

[0032] The communication support unit can analyze a user's past message history and suggest message examples that are individually customized. For example, the communication support unit uses a generation AI to analyze a user's past message history and suggest message examples that are effective for specific recipients. For example, it generates new messages based on message patterns that have been successful in the past. The communication support unit also analyzes a user's message history and suggests message examples that match specific topics or tones. For example, it can suggest new messages based on topics that have been popular in the past. The communication support unit also analyzes message history and suggests message examples that match the user's communication style. For example, it can suggest message examples that include humor for a user who prefers humorous messages. In this way, it is possible to suggest message examples that are individually customized by analyzing a user's past message history.

[0033] The communication support unit can analyze the user's conversation patterns, predict the flow of the conversation, and provide appropriate topics. For example, the communication support unit uses a generation AI to analyze the user's past conversation patterns, predict the flow of the conversation, and suggest the next topic to talk about. For example, it can provide new topics based on topics that were popular in past conversations. The communication support unit can also analyze the user's conversation patterns and suggest effective topics for specific people. For example, it can provide topics related to people who share common hobbies or interests. The communication support unit can also predict the flow of the conversation and suggest when the user should talk next. For example, it can provide a new topic when the conversation is about to stall. This makes it possible to analyze the user's conversation patterns, predict the flow of the conversation, and provide appropriate topics.

[0034] The date plan suggestion unit can analyze the user's past dating history and suggest date plans with a high success rate. For example, the date plan suggestion unit uses a generation AI to analyze the user's past dating history and suggest date plans with a high success rate. For example, it re-suggests date spots that were popular in the past. The date plan suggestion unit also analyzes the user's dating history and suggests similar activities if a particular activity was successful. For example, it can re-suggest outdoor activities that were enjoyed in the past. The date plan suggestion unit also analyzes the dating history and suggests date plans that were successful at specific times of day or seasons. For example, it can re-suggest seasonal events that were successful in the past. In this way, by analyzing the user's past dating history, it is possible to suggest date plans with a high success rate.

[0035] The date plan suggestion unit can analyze the user's interests and hobbies and propose individually customized date plans. For example, the generation AI in the date plan suggestion unit analyzes the user's hobbies and interests and proposes individually customized date plans. For example, a museum date can be proposed to a user who loves art. The date plan suggestion unit can also analyze the user's interests and propose date plans based on specific themes. For example, a live concert date can be proposed to a user who loves music. The date plan suggestion unit can also propose date plans that include activities that the user can enjoy based on their hobbies and interests. For example, a cooking class date can be proposed to a user who loves cooking. In this way, individually customized date plans can be proposed by analyzing the user's interests and hobbies.

[0036] The advice providing unit can analyze the user's past dating history and provide individually customized advice. For example, the advice providing unit uses a generation AI to analyze the user's past dating history and provide effective advice for a specific partner. For example, it provides new advice based on dating patterns that were successful in the past. The advice providing unit can also analyze the user's dating history and suggest similar activities if a specific activity was successful. For example, it can re-suggest outdoor activities that were enjoyed in the past. The advice providing unit can also analyze the dating history and suggest date plans that were successful at specific times of day or seasons. For example, it can re-suggest seasonal events that were successful in the past. In this way, individually customized advice can be provided by analyzing the user's past dating history.

[0037] The feedback collection unit can analyze user feedback and identify areas for improvement in the service. For example, the feedback collection unit uses a generation AI to analyze user feedback and suggest effective improvements for a specific partner. For example, it can suggest new improvements based on failures in past dates. The feedback collection unit can also analyze user feedback and suggest improvements when a specific activity fails. For example, it can suggest improvements for outdoor activities that failed in the past. The feedback collection unit can also analyze feedback and suggest improvements for date plans that failed during specific times or seasons. For example, it can suggest improvements for seasonal events that failed in the past. In this way, it is possible to identify areas for improvement in the service by analyzing user feedback.

[0038] The feedback collection unit can make individually customized improvement suggestions based on the user's feedback. For example, the generation AI analyzes the user's feedback and makes individually customized improvement suggestions. For example, the next date plan is adjusted based on feedback on a specific date plan. The feedback collection unit also makes improvement suggestions for a specific partner based on the user's feedback. For example, advice on the next conversation can be provided based on feedback on communication on past dates. The feedback collection unit also analyzes the feedback and makes improvement suggestions according to the user's individual needs. For example, improvement suggestions can be made for date plans based on specific hobbies or interests. This makes it possible to make individually customized improvement suggestions based on the user's feedback.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The profile analysis unit can analyze a user's past travel history and recommend people who love to travel. For example, based on information about past travel destinations, it can match users who have visited the same places. The profile analysis unit can also analyze a user's travel style (e.g., backpacking, resort enthusiast, etc.) and match users with the same style. Furthermore, the profile analysis unit can analyze a user's travel interests (e.g., historical places, natural scenery, etc.) and match users with common interests. This makes it possible to match users who love to travel.

[0041] The matching unit can analyze a user's musical preferences and recommend people who share a common musical genre. For example, based on information about the users' favorite artists and bands, it can match users who like the same artists. The matching unit can also analyze a user's music listening history and match users who share the same playlist. Furthermore, the matching unit can analyze a user's music event participation history and match users who have participated in the same event. This makes it possible to match users who share musical preferences.

[0042] The profile analysis unit can analyze the user's health data and recommend health-conscious partners. For example, it can match users with the same health consciousness based on the user's exercise habits and dietary habits. The profile analysis unit can also analyze the user's sleep patterns and match users with the same sleep patterns. Furthermore, the profile analysis unit can analyze the user's health goals (e.g., diet, strength training, etc.) and match users with the same goals. This makes it possible to match health-conscious users with each other.

[0043] The matching unit can analyze a user's reading history and recommend people who share the same reading hobby. For example, based on information about the books the user has read, it can match users who have read the same books. The matching unit can also analyze a user's reading genre preferences and match users who like the same genre. Furthermore, the matching unit can analyze a user's reviews of books and match users who have the same ratings. This makes it possible to match users who share a reading hobby.

[0044] The communication support unit can analyze the user's hobbies and interests and support conversations with people who share the same hobbies. For example, if the user likes movies, it can provide topics related to movies. If the user likes sports, it can also provide topics related to sports. Furthermore, if the user likes cooking, it can provide topics related to cooking. This allows conversations with people who share the same hobbies to proceed smoothly.

[0045] The date plan suggestion unit can analyze the user's past dating history and suggest date plans with a high success rate. For example, it can re-suggest date spots that were popular in the past. The date plan suggestion unit can also analyze the user's dating history and suggest similar activities if a specific activity was successful. Furthermore, the date plan suggestion unit can analyze the dating history and suggest date plans that were successful during specific times of the day or season. In this way, by analyzing the user's past dating history, it is possible to suggest date plans with a high success rate.

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

[0047] Step 1: The profile analysis unit analyzes the user's profile information and desired conditions. For example, it analyzes information entered by the user, such as age, gender, occupation, and hobbies, and also analyzes the desired conditions of the other person, such as age, hobbies, and values. Step 2: The matching unit performs optimal matching based on the profile information and desired conditions analyzed by the profile analysis unit. For example, it calculates a compatibility score and matches users with common hobbies and values. Step 3: The communication support unit supports communication between users matched by the matching unit. For example, it suggests examples of appropriate messages when sending a first message, and provides new topics of conversation if the conversation dies down. Step 4: The date plan suggestion unit suggests date plans based on the user's preferences and interests. For example, if the user inputs "I like nature," the unit suggests date spots and activities where you can enjoy nature, and suggests the best date and time to suit the user's schedule. Step 5: The advice provider provides the user with advice on romance and marriage. For example, if a user says, "I don't know what to talk about on a date," the advice provider will provide conversation tips and example topics, as well as provide individually customized advice based on the user's profile and feedback from past dates. Step 6: The feedback collection unit collects user feedback and uses it to improve the service. For example, if a user enters their impressions after a date, the feedback is analyzed and reflected in the next match and date plan suggestions.

[0048] (Example 2) A dating and marriage system according to an embodiment of the present invention uses a generation AI to analyze a user's profile information and desired conditions, perform optimal matching, support communication, provide date plan suggestions and advice, and collect feedback to improve the service. This enables the dating and marriage system to analyze a user's profile information and desired conditions, perform optimal matching, support communication, provide date plan suggestions and advice, and collect feedback to improve the service.

[0049] A dating and marriage hunting system according to an embodiment includes a profile analysis unit, a matching unit, a communication support unit, a date plan suggestion unit, an advice providing unit, and a feedback collection unit. The profile analysis unit analyzes a user's profile information and desired conditions. For example, the profile analysis unit analyzes information such as age, gender, occupation, and hobbies entered by the user. The profile analysis unit can also analyze the user's desired conditions (e.g., the other person's age, hobbies, values, etc.). The matching unit performs optimal matching based on the profile information and desired conditions analyzed by the profile analysis unit. For example, the matching unit calculates a compatibility score and matches users with common hobbies and values. The communication support unit supports communication between users matched by the matching unit. For example, the communication support unit suggests appropriate message examples when sending a first message. It can also provide new topics of conversation if the conversation dies down. The date plan suggestion unit suggests date plans based on the user's preferences and interests. For example, if a user enters "I like nature," the date plan suggestion unit suggests date spots and activities where users can enjoy nature. It can also suggest the best date and time for a date based on the user's schedule. The advice providing unit provides the user with advice on romance and matchmaking. For example, if the user asks for advice saying, "I don't know what to talk about on a date," the advice providing unit can provide conversation tips and example topics. It can also provide individually customized advice based on the user's profile and feedback from past dates. The feedback collecting unit collects feedback from users and uses it to improve the service. For example, when a user enters their impressions after a date, the feedback collecting unit analyzes that feedback and reflects it in suggesting the next match or date plan.As a result, the dating and marriage system of the embodiment can analyze users' profile information and desired conditions, perform optimal matching, support communication, suggest date plans and provide advice, and collect feedback to improve the service.

[0050] The profile analysis unit analyzes a user's past behavioral history and message content to enable more accurate matching. For example, the profile analysis unit uses a generation AI to analyze the content of a user's past messages and identify people with common topics and interests. For example, it extracts characteristics of people with whom the user has exchanged many messages in the past and recommends new partners with similar characteristics. The profile analysis unit also analyzes a user's behavioral history to match users who are active during specific time periods. For example, it can prioritize matching between users who frequently log in at night. The profile analysis unit also analyzes past dating history to match users with dating patterns that have a high success rate. For example, it can match users who prefer specific date spots. This enables more accurate matching by analyzing a user's past behavioral history and message content.

[0051] The profile analysis unit can analyze the user's psychological state and emotions and recommend compatible partners. For example, the profile analysis unit uses a generation AI to analyze the user's psychological state from the content of the user's messages and recommend partners who are less stressed. For example, it can match users who often have relaxed conversations with each other. The profile analysis unit can also analyze the user's emotions and recommend partners who elicit positive emotions. For example, it can match users who have many photos of them smiling with each other. The profile analysis unit can also analyze the user's psychological test results and recommend compatible partners. For example, it can match users with similar personality test results with each other. In this way, it can recommend compatible partners by analyzing the user's psychological state and emotions.

[0052] The profile analysis unit analyzes users' lifestyles and values ​​in addition to their hobbies and interests, enabling more multifaceted matching. For example, the profile analysis unit uses a generation AI to analyze users' lifestyles and match users who are active at the same time. For example, it can match users who are early risers. The profile analysis unit also analyzes users' values ​​and recommends partners who share common values. For example, it can match users who are interested in environmental protection. The profile analysis unit also analyzes users' eating habits and exercise habits in addition to their hobbies and interests, and matches health-conscious users. For example, it can match vegetarian users. This enables more multifaceted matching by analyzing not only users' hobbies and interests, but also their lifestyles and values.

[0053] The communication support unit can analyze a user's past message history and suggest message examples that are individually customized. For example, the communication support unit uses a generation AI to analyze a user's past message history and suggest message examples that are effective for specific recipients. For example, it generates new messages based on message patterns that have been successful in the past. The communication support unit also analyzes a user's message history and suggests message examples that match specific topics or tones. For example, it can suggest new messages based on topics that have been popular in the past. The communication support unit also analyzes message history and suggests message examples that match the user's communication style. For example, it can suggest message examples that include humor for a user who prefers humorous messages. In this way, it is possible to suggest message examples that are individually customized by analyzing a user's past message history.

[0054] The communication support unit can analyze the user's conversation patterns, predict the flow of the conversation, and provide appropriate topics. For example, the communication support unit uses a generation AI to analyze the user's past conversation patterns, predict the flow of the conversation, and suggest the next topic to talk about. For example, it can provide new topics based on topics that were popular in past conversations. The communication support unit can also analyze the user's conversation patterns and suggest effective topics for specific people. For example, it can provide topics related to people who share common hobbies or interests. The communication support unit can also predict the flow of the conversation and suggest when the user should talk next. For example, it can provide a new topic when the conversation is about to stall. This makes it possible to analyze the user's conversation patterns, predict the flow of the conversation, and provide appropriate topics.

[0055] The date plan suggestion unit can analyze the user's past dating history and suggest date plans with a high success rate. For example, the date plan suggestion unit uses a generation AI to analyze the user's past dating history and suggest date plans with a high success rate. For example, it re-suggests date spots that were popular in the past. The date plan suggestion unit also analyzes the user's dating history and suggests similar activities if a particular activity was successful. For example, it can re-suggest outdoor activities that were enjoyed in the past. The date plan suggestion unit also analyzes the dating history and suggests date plans that were successful at specific times of day or seasons. For example, it can re-suggest seasonal events that were successful in the past. In this way, by analyzing the user's past dating history, it is possible to suggest date plans with a high success rate.

[0056] The date plan suggestion unit can analyze the user's interests and hobbies and propose individually customized date plans. For example, the generation AI in the date plan suggestion unit analyzes the user's hobbies and interests and proposes individually customized date plans. For example, a museum date can be proposed to a user who loves art. The date plan suggestion unit can also analyze the user's interests and propose date plans based on specific themes. For example, a live concert date can be proposed to a user who loves music. The date plan suggestion unit can also propose date plans that include activities that the user can enjoy based on their hobbies and interests. For example, a cooking class date can be proposed to a user who loves cooking. In this way, individually customized date plans can be proposed by analyzing the user's interests and hobbies.

[0057] The advice providing unit can analyze the user's past dating history and provide individually customized advice. For example, the advice providing unit uses a generation AI to analyze the user's past dating history and provide effective advice for a specific partner. For example, it provides new advice based on dating patterns that were successful in the past. The advice providing unit can also analyze the user's dating history and suggest similar activities if a specific activity was successful. For example, it can re-suggest outdoor activities that were enjoyed in the past. The advice providing unit can also analyze the dating history and suggest date plans that were successful at specific times of day or seasons. For example, it can re-suggest seasonal events that were successful in the past. In this way, individually customized advice can be provided by analyzing the user's past dating history.

[0058] The feedback collection unit can analyze user feedback and identify areas for improvement in the service. For example, the feedback collection unit uses a generation AI to analyze user feedback and suggest effective improvements for a specific partner. For example, it can suggest new improvements based on failures in past dates. The feedback collection unit can also analyze user feedback and suggest improvements when a specific activity fails. For example, it can suggest improvements for outdoor activities that failed in the past. The feedback collection unit can also analyze feedback and suggest improvements for date plans that failed during specific times or seasons. For example, it can suggest improvements for seasonal events that failed in the past. In this way, it is possible to identify areas for improvement in the service by analyzing user feedback.

[0059] The feedback collection unit can make individually customized improvement suggestions based on the user's feedback. For example, the generation AI analyzes the user's feedback and makes individually customized improvement suggestions. For example, the next date plan is adjusted based on feedback on a specific date plan. The feedback collection unit also makes improvement suggestions for a specific partner based on the user's feedback. For example, advice on the next conversation can be provided based on feedback on communication on past dates. The feedback collection unit also analyzes the feedback and makes improvement suggestions according to the user's individual needs. For example, improvement suggestions can be made for date plans based on specific hobbies or interests. This makes it possible to make individually customized improvement suggestions based on the user's feedback.

[0060] The feedback collection unit uses the emotion estimation function to collect feedback based on the user's emotions and make improvements that will draw out positive emotions. The feedback collection unit, for example, uses the emotion estimation function to collect feedback based on the user's emotions. For example, the emotion estimation function may be used to analyze the user's emotions after a date and make improvement suggestions that will draw out positive emotions. The feedback collection unit also analyzes the user's emotions and collects feedback that will draw out positive emotions. For example, the factors that contributed to a successful date may be identified and reflected in the next date. The feedback collection unit also uses the emotion estimation function to collect feedback based on the user's emotions in real time and make improvement suggestions that will draw out positive emotions. For example, the emotion estimation function may be used to analyze the user's emotions during a date and make improvement suggestions in real time. This makes it possible to use the emotion estimation function to collect feedback based on the user's emotions and make improvements that will draw out positive emotions.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The profile analysis unit can analyze a user's past travel history and recommend people who love to travel. For example, based on information about past travel destinations, it can match users who have visited the same places. The profile analysis unit can also analyze a user's travel style (e.g., backpacking, resort enthusiast, etc.) and match users with the same style. Furthermore, the profile analysis unit can analyze a user's travel interests (e.g., historical places, natural scenery, etc.) and match users with common interests. This makes it possible to match users who love to travel.

[0063] The matching unit can analyze a user's musical preferences and recommend people who share a common musical genre. For example, based on information about the users' favorite artists and bands, it can match users who like the same artists. The matching unit can also analyze a user's music listening history and match users who share the same playlist. Furthermore, the matching unit can analyze a user's music event participation history and match users who have participated in the same event. This makes it possible to match users who share musical preferences.

[0064] The communication support unit can estimate the user's emotions and suggest example messages according to the emotions. For example, if the user is nervous, it can suggest example messages that will relax the user. If the user is happy, it can also suggest example messages that share that joy. Furthermore, if the user is sad, it can suggest example messages that will comfort the user. In this way, it is possible to suggest appropriate example messages according to the user's emotions.

[0065] The date plan suggestion unit can estimate the user's emotions and suggest date plans that correspond to the emotions. For example, if the user is feeling stressed, a relaxing date plan can be suggested. If the user is excited, an active date plan can be suggested. Furthermore, if the user is feeling down, a date plan that will lift the user's spirits can be suggested. In this way, date plans that correspond to the user's emotions can be suggested.

[0066] The advice providing unit can estimate the user's emotions and provide advice according to the emotions. For example, if the user is feeling anxious, it can provide reassuring advice. If the user is confident, it can also provide advice that will further increase that confidence. Furthermore, if the user is unsure, it can provide advice that clearly shows the direction. In this way, it is possible to provide appropriate advice according to the user's emotions.

[0067] The feedback collection unit can estimate the user's emotions and collect feedback based on the emotions. For example, it can analyze the user's emotions after a date and make improvement suggestions to elicit positive emotions. It can also analyze the user's emotions and collect feedback to elicit positive emotions. Furthermore, it can use the emotion estimation function to collect feedback based on the user's emotions in real time and make improvement suggestions to elicit positive emotions. This makes it possible to use the emotion estimation function to collect feedback based on the user's emotions and make improvements to elicit positive emotions.

[0068] The profile analysis unit can analyze the user's health data and recommend health-conscious partners. For example, it can match users with the same health consciousness based on the user's exercise habits and dietary habits. The profile analysis unit can also analyze the user's sleep patterns and match users with the same sleep patterns. Furthermore, the profile analysis unit can analyze the user's health goals (e.g., diet, strength training, etc.) and match users with the same goals. This makes it possible to match health-conscious users with each other.

[0069] The matching unit can analyze a user's reading history and recommend people who share the same reading hobby. For example, based on information about the books the user has read, it can match users who have read the same books. The matching unit can also analyze a user's reading genre preferences and match users who like the same genre. Furthermore, the matching unit can analyze a user's reviews of books and match users who have the same ratings. This makes it possible to match users who share a reading hobby.

[0070] The communication support unit can analyze the user's hobbies and interests and support conversations with people who share the same hobbies. For example, if the user likes movies, it can provide topics related to movies. If the user likes sports, it can also provide topics related to sports. Furthermore, if the user likes cooking, it can provide topics related to cooking. This allows conversations with people who share the same hobbies to proceed smoothly.

[0071] The date plan suggestion unit can analyze the user's past dating history and suggest date plans with a high success rate. For example, it can re-suggest date spots that were popular in the past. The date plan suggestion unit can also analyze the user's dating history and suggest similar activities if a specific activity was successful. Furthermore, the date plan suggestion unit can analyze the dating history and suggest date plans that were successful during specific times of the day or season. In this way, by analyzing the user's past dating history, it is possible to suggest date plans with a high success rate.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The profile analysis unit analyzes the user's profile information and desired conditions. For example, it analyzes information entered by the user, such as age, gender, occupation, and hobbies, and also analyzes the desired conditions of the other person, such as age, hobbies, and values. Step 2: The matching unit performs optimal matching based on the profile information and desired conditions analyzed by the profile analysis unit. For example, it calculates a compatibility score and matches users with common hobbies and values. Step 3: The communication support unit supports communication between users matched by the matching unit. For example, it suggests examples of appropriate messages when sending a first message, and provides new topics of conversation if the conversation dies down. Step 4: The date plan suggestion unit suggests date plans based on the user's preferences and interests. For example, if the user inputs "I like nature," the unit suggests date spots and activities where you can enjoy nature, and suggests the best date and time to suit the user's schedule. Step 5: The advice provider provides the user with advice on romance and marriage. For example, if a user says, "I don't know what to talk about on a date," the advice provider will provide conversation tips and example topics, as well as provide individually customized advice based on the user's profile and feedback from past dates. Step 6: The feedback collection unit collects user feedback and uses it to improve the service. For example, if a user enters their impressions after a date, the feedback is analyzed and reflected in the next match and date plan suggestions.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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. Using generative AI, a profile analysis unit that analyzes the user's profile information and desired conditions; a matching unit that performs optimal matching based on the profile information analyzed by the profile analysis unit and the desired conditions; a communication support unit that supports communication between users matched by the matching unit; a date plan suggestion unit that suggests a date plan based on the user's wishes and interests; an advice providing unit that provides the user with advice on romance and marriage hunting; a feedback collection unit that collects feedback from the users and uses the feedback to improve the service. A system characterized by:

2. The profile analysis unit Analyzing the user's past behavior history and message content to perform more accurate matching 2. The system of claim 1.

3. The profile analysis unit Analyzing the user's mental state and emotions and recommending compatible partners 2. The system of claim 1.

4. The profile analysis unit In addition to the user's hobbies and interests, the system analyzes their lifestyle and values ​​to perform more comprehensive matching.

2. The system of claim 1.

5. The communication support unit Analyze the user's past message history and suggest personalized message examples 2. The system of claim 1.

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