System

Abstract

An object of the system according to the embodiment is to allow users having different cultural backgrounds to efficiently interact with each other and deepen mutual understanding.SOLUTION: A system according to an embodiment includes a user profile generation unit, a matching unit, and an online platform providing unit. A user profile creation part creates a profile on the basis of information on cultural background, interest, concern, and language skill of a user. The matching unit matches users having different cultural backgrounds based on the profiles created by the user profile creation unit. The online platform providing unit provides an online platform through which the users matched by the matching unit can interact with each other.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 technology has faced the challenge of lacking an appropriate platform for people with different cultural backgrounds to interact efficiently and deepen mutual understanding.

[0005] The system according to the embodiment aims to enable users with different cultural backgrounds to interact efficiently and deepen mutual understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a user profile creation unit, a matching unit, and an online platform providing unit. The user profile creation unit creates a profile based on information about the user's cultural background, interests, concerns, and language skills. The matching unit matches users with different cultural backgrounds based on the profiles created by the user profile creation unit. The online platform providing unit provides an online platform where users matched by the matching unit can interact with each other. [Effects of the Invention]

[0007] The system according to the embodiment allows users with different cultural backgrounds to interact efficiently and deepen mutual understanding. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) An online service according to an embodiment of the present invention is a system that promotes cultural understanding and exchange by connecting people with different cultural backgrounds. The system aims to deepen cultural exchange by using generative AI to perform appropriate matching based on users' interests. This allows the online service to enable people with different cultural backgrounds to understand each other and deepen their exchanges.

[0029] An online service according to an embodiment includes a user profile creation unit, a matching unit, and an online platform providing unit. The user profile creation unit creates a profile based on information about a user's cultural background, interests, and language skills. For example, if a user inputs "I'm interested in traditional Japanese culture," the generation AI creates a profile based on that information. The user profile creation unit analyzes the user's input information, and the generation AI generates a profile. For example, the generation AI generates a profile based on prompts containing instructions from the user about what the user wants the generation AI to do. The matching unit matches users with different cultural backgrounds based on the profiles created by the user profile creation unit. For example, a user interested in traditional Japanese culture is matched with a user knowledgeable about Japanese culture. The matching unit selects an optimal pair based on the user's interests, language skills, and other factors. The online platform providing unit provides an online platform where matched users can interact with each other. For example, users can freely communicate with each other using chat functions, video calling functions, forums, and the like. This allows the online service according to an embodiment to promote interaction between users with different cultural backgrounds and deepen cultural understanding.

[0030] The user profile creation unit can analyze a user's past travel history or cultural event participation history to generate a more detailed profile. For example, the user profile creation unit collects information on countries and regions the user has visited in the past and analyzes the user's cultural background in detail based on that travel history. For example, information about the culture and customs of countries the user has visited in the past can be reflected in the profile. The user profile creation unit also collects cultural event participation history and generates a profile based on that information. For example, the type, frequency, and content of events the user has participated in can be added to the profile. This allows for a more detailed understanding of the user's cultural background and enables more accurate matching.

[0031] The user profile creation unit can periodically collect surveys or feedback and dynamically update the profile to reflect changes in the user's daily activities and interests. For example, the user profile creation unit periodically surveys the user to collect changes in daily activities and interests. For example, the user profile creation unit checks recent interests and concerns through a monthly survey and updates the profile. The user profile creation unit also collects feedback from the user and dynamically updates the profile based on that information. For example, it adds new fields or activities that the user has become interested in to the profile. This makes it possible to maintain a profile that reflects the user's latest interests and concerns.

[0032] The user profile creation unit also collects entertainment information such as the user's favorite music or movies when creating the profile, thereby deepening understanding of the cultural background. The user profile creation unit, for example, collects information about the user's favorite music or movies when creating the profile, and analyzes the cultural background based on that information. For example, the user's favorite movie genres and artists are added to the profile. The user profile creation unit also analyzes the user's interests and concerns in detail based on the entertainment information. For example, the cultural background is reflected in the profile based on the user's favorite music genres and artists. This allows for a more detailed understanding of the user's cultural background, enabling more accurate matching.

[0033] The user profile creation unit provides cultural quizzes or games to users based on the profile information, allowing them to enrich their profiles while having fun. The user profile creation unit, for example, builds a system that provides cultural quizzes and games to users based on the profile information. For example, it presents quizzes related to cultures that interest the user. The user profile creation unit also analyzes the user's interests and concerns in detail through the game. For example, it adds new fields or activities that the user has become interested in through the game to the profile. This allows the user to enrich their profile while having fun.

[0034] The matching unit can reflect the user's past interaction history or feedback to achieve more accurate matching. The matching unit, for example, collects the user's past interaction history and improves the matching algorithm based on that information. For example, it analyzes patterns of successful matches in the past and reflects them in the algorithm. The matching unit also collects feedback from the user and improves the matching algorithm based on that information. For example, it analyzes elements of matches that satisfied the user and reflects them in the next match. This enables more accurate matching based on the user's past interaction history and feedback.

[0035] The matching unit can propose optimal interaction times by taking into account the user's lifestyle rhythm or time zone preferences. The matching unit, for example, collects the user's lifestyle rhythm and time zone preferences, and builds a system that proposes optimal interaction times based on that information. For example, matching is performed based on the user's active time zone. The matching unit also optimizes interaction times based on the user's lifestyle rhythm. For example, if the user is a night owl, priority is given to matching with users who can interact at night. This makes it possible to propose optimal interaction times that match the user's lifestyle rhythm and time zone preferences.

[0036] The matching unit can match users who share a common hobby with each other, taking into account the user's hobby or special skill. The matching unit, for example, collects users' hobby and special skill information and builds a system that matches users who share a common hobby with each other based on that information. For example, users who share the same hobby are matched with each other preferentially. The matching unit also matches users with each other based on hobby and special skill. For example, users who share a common hobby are matched with each other. This makes it possible to match users who share a common hobby with each other.

[0037] The matching unit can select the optimal pair by taking into consideration the learning style or communication style of the user. For example, the matching unit collects the learning style and communication style of the user and builds a system that selects the optimal pair based on that information. For example, it matches users with the same learning style. The matching unit also matches users based on their communication style. For example, it matches users with an interactive communication style. This makes it possible to select the optimal pair based on the learning style and communication style of the user.

[0038] The online platform providing unit can provide a virtual tour function that allows users to introduce their own culture. The online platform providing unit, for example, builds a system on the platform that provides a virtual tour function that allows users to introduce their own culture. For example, a user introduces famous places and cultural sites in their country through a virtual tour. The online platform providing unit also promotes interaction between users through the virtual tour function. For example, a user can communicate with other users in real time through a virtual tour. This allows a virtual tour function that allows users to introduce their own culture.

[0039] The online platform providing unit can provide a translation function in real time during interaction, facilitating communication between users who speak different languages. The online platform providing unit, for example, provides a translation function in real time during interaction, building a system that facilitates communication between users who speak different languages. For example, automatic translation is performed during chats and video calls. The online platform providing unit also supports interaction between users through the translation function. For example, users can communicate smoothly even if they speak different languages. This can facilitate communication between users who speak different languages.

[0040] The online platform providing unit can provide collaboration tools that allow users to work together on projects. The online platform providing unit, for example, builds a system on the platform that provides collaboration tools that allow users to work together on projects. For example, it provides document sharing and task management functions. The online platform providing unit also promotes cooperation between users through the collaboration tools. For example, users can communicate in real time when working together on a project. This makes it possible to provide collaboration tools that allow users to work together on a project.

[0041] The online platform providing unit can add a function that allows users to enjoy quizzes or games related to their own culture during interactions. The online platform providing unit, for example, builds a system that adds a function that allows users to enjoy quizzes or games related to their own culture during interactions. For example, a user poses a quiz related to their own culture. The online platform providing unit also promotes interactions between users through games. For example, a user can compete with other users through games. This makes it possible to provide a function that allows users to enjoy quizzes or games related to their own culture.

[0042] When planning an event, the online platform providing unit can analyze feedback from past event participants and propose more attractive events. For example, the online platform providing unit collects feedback from past event participants and builds a system to improve event planning based on that information. For example, it analyzes participant satisfaction and reflects it in the next event. The online platform providing unit also optimizes the content of the event based on the feedback. For example, it suggests activities that participants can enjoy. This makes it possible to propose more attractive events based on feedback from past event participants.

[0043] The online platform providing unit can optimize the event schedule to suit the user's lifestyle rhythm or time zone preferences. The online platform providing unit, for example, collects the user's lifestyle rhythm and time zone preferences and builds a system that optimizes the event schedule based on that information. For example, the event is held at a time that is convenient for the user to participate. The online platform providing unit also adjusts the content of the event based on the schedule. For example, the activity is held at a time that allows the user to relax. This makes it possible to optimize the event schedule to suit the user's lifestyle rhythm and time zone preferences.

[0044] The online platform providing unit can provide workshops where users from different cultural backgrounds can jointly advance plans. The online platform providing unit, for example, builds a system that provides workshops where users from different cultural backgrounds can jointly advance plans. For example, it holds workshops where users introduce their own cultures to each other. The online platform providing unit also promotes interaction between users through workshops. For example, users can communicate in real time when working on a project together. This makes it possible to provide workshops where users from different cultural backgrounds can jointly advance plans.

[0045] The online platform providing unit can add an interactive function that allows event participants to exchange opinions or give feedback in real time. The online platform providing unit, for example, builds a system that adds an interactive function that allows event participants to exchange opinions or give feedback in real time. For example, feedback can be provided during chat or video calls. The online platform providing unit also promotes interaction between event participants through the interactive function. For example, participants can exchange opinions in real time. This makes it possible to provide an interactive function that allows event participants to exchange opinions or give feedback in real time.

[0046] When providing content, the online platform providing unit can analyze the user's past browsing history or feedback and suggest more interesting content. The online platform providing unit, for example, collects the user's past browsing history and builds a system that suggests interesting content based on that information. For example, it suggests new content based on content the user has previously viewed. The online platform providing unit also improves the quality of the content based on the feedback. For example, it suggests content similar to content that the user has given a high rating. This makes it possible to suggest more interesting content based on the user's past browsing history and feedback.

[0047] The online platform providing unit can incorporate expert opinions or reviews to improve the quality of the content. For example, the online platform providing unit collects expert opinions and reviews to improve the quality of the content and builds a system to improve the content based on that information. For example, the online platform providing unit updates the content based on the expert reviews. The online platform providing unit also evaluates the quality of the content based on the expert opinions. For example, content evaluated by experts is preferentially provided. This makes it possible to incorporate expert opinions and reviews to improve the quality of the content.

[0048] The online platform providing unit can add a function that allows users to post articles or videos related to their own culture when providing content. The online platform providing unit, for example, builds a system that adds a function that allows users to post articles or videos related to their own culture when providing content. For example, users post articles or videos introducing their own culture. The online platform providing unit also promotes interaction between users based on the posted content. For example, users can provide comments or feedback on articles or videos posted by users. This makes it possible to provide a function that allows users to post articles or videos related to their own culture.

[0049] The online platform providing unit can provide a function to customize content to suit a user's learning style or preferences. The online platform providing unit, for example, builds a system that provides a function to customize content to suit a user's learning style or preferences. For example, if a user has a visual learning style, visual content is preferentially provided. The online platform providing unit also optimizes content based on the user's preferences. For example, content in areas that interest the user is preferentially provided. This makes it possible to provide a function to customize content to suit a user's learning style or preferences.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The user profile creation unit can analyze a user's health condition and fitness level and reflect the results in the profile. For example, it can collect information on the user's daily exercise and health management and generate a profile based on that information. The user profile creation unit can also perform appropriate matching based on the user's health condition. For example, it can match users with the same fitness level. This enables highly accurate matching that takes the user's health condition into consideration.

[0052] The user profile creation unit can collect information about a user's food preferences and allergies and reflect this information in the profile. For example, a profile can be created by inputting information about a user's favorite dishes and ingredients they want to avoid. The user profile creation unit can also match users based on their food preferences. For example, it can match users who have the same food preferences. This makes it possible to perform matching that takes into account the user's food preferences.

[0053] The user profile creation unit can collect information about a user's hobbies and special skills and reflect it in the profile. For example, a profile is created by inputting information about the user's hobbies, such as sports, art, and music. The user profile creation unit can also match users based on their hobbies and special skills. For example, it can match users who have the same hobbies. This makes it possible to perform matching that takes into account the user's hobbies and special skills.

[0054] The user profile creation unit can collect information about a user's learning style and learning history and reflect it in the profile. For example, a profile can be created by inputting information about the user's preferred learning methods and what they have learned in the past. The user profile creation unit can also match users based on their learning style. For example, it can match users with the same learning style. This makes it possible to match users with the same learning style.

[0055] The matching unit can propose optimal interaction times by taking into account the user's lifestyle rhythm or time zone preferences. For example, a system can be constructed that collects the user's lifestyle rhythm and time zone preferences and proposes optimal interaction times based on that information. For example, matching is performed based on the user's active time zone. The matching unit also optimizes interaction times based on the user's lifestyle rhythm. For example, if the user is a night owl, priority is given to matching with users who can interact at night. This makes it possible to propose optimal interaction times that match the user's lifestyle rhythm and time zone preferences.

[0056] The online platform providing unit can provide a virtual tour function that allows users to introduce their own culture. For example, a system that provides a virtual tour function that allows users to introduce their own culture is constructed on the platform. For example, a user can introduce famous places and cultural sites in his or her country through a virtual tour. The online platform providing unit also promotes interaction between users through the virtual tour function. For example, a user can communicate with other users in real time through a virtual tour. This allows a virtual tour function that allows users to introduce their own culture.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The user profile creation unit creates a profile based on information about the user's cultural background, interests, concerns, and language skills. For example, if a user inputs "I'm interested in traditional Japanese culture," the generation AI creates a profile based on that information. The user profile creation unit also analyzes the user's input information, and the generation AI generates a profile. For example, the generation AI generates a profile based on prompts that include instructions on what the user wants the generation AI to do. Step 2: The matching unit matches users with different cultural backgrounds based on the profiles created by the user profile creation unit. For example, it matches a user who is interested in traditional Japanese culture with a user who is knowledgeable about Japanese culture. The matching unit selects the optimal pair taking into account the user's interests, concerns, language skills, etc. Step 3: The online platform provider provides an online platform where matched users can interact with each other. For example, users can freely communicate with each other using a chat function, a video call function, a forum, etc. As a result, the online service according to the embodiment can promote interaction between users with different cultural backgrounds and deepen cultural understanding.

[0059] (Example 2) An online service according to an embodiment of the present invention is a system that promotes cultural understanding and exchange by connecting people with different cultural backgrounds. The system aims to deepen cultural exchange by using generative AI to perform appropriate matching based on users' interests. This allows the online service to enable people with different cultural backgrounds to understand each other and deepen their exchanges.

[0060] An online service according to an embodiment includes a user profile creation unit, a matching unit, and an online platform providing unit. The user profile creation unit creates a profile based on information about a user's cultural background, interests, and language skills. For example, if a user inputs "I'm interested in traditional Japanese culture," the generation AI creates a profile based on that information. The user profile creation unit analyzes the user's input information, and the generation AI generates a profile. For example, the generation AI generates a profile based on prompts containing instructions from the user about what the user wants the generation AI to do. The matching unit matches users with different cultural backgrounds based on the profiles created by the user profile creation unit. For example, a user interested in traditional Japanese culture is matched with a user knowledgeable about Japanese culture. The matching unit selects an optimal pair based on the user's interests, language skills, and other factors. The online platform providing unit provides an online platform where matched users can interact with each other. For example, users can freely communicate with each other using chat functions, video calling functions, forums, and the like. This allows the online service according to an embodiment to promote interaction between users with different cultural backgrounds and deepen cultural understanding.

[0061] The user profile creation unit can analyze a user's past travel history or cultural event participation history to generate a more detailed profile. For example, the user profile creation unit collects information on countries and regions the user has visited in the past and analyzes the user's cultural background in detail based on that travel history. For example, information about the culture and customs of countries the user has visited in the past can be reflected in the profile. The user profile creation unit also collects cultural event participation history and generates a profile based on that information. For example, the type, frequency, and content of events the user has participated in can be added to the profile. This allows for a more detailed understanding of the user's cultural background and enables more accurate matching.

[0062] The user profile creation unit can periodically collect surveys or feedback and dynamically update the profile to reflect changes in the user's daily activities and interests. For example, the user profile creation unit periodically surveys the user to collect changes in daily activities and interests. For example, the user profile creation unit checks recent interests and concerns through a monthly survey and updates the profile. The user profile creation unit also collects feedback from the user and dynamically updates the profile based on that information. For example, it adds new fields or activities that the user has become interested in to the profile. This makes it possible to maintain a profile that reflects the user's latest interests and concerns.

[0063] The user profile creation unit can use the emotion estimation function to analyze the emotions felt by the user when creating a profile and generate prompts that elicit positive emotions. The user profile creation unit, for example, analyzes the emotions felt by the user when creating a profile and generates prompts that elicit positive emotions. For example, it displays encouraging messages so that the user can enjoy creating a profile. The user profile creation unit also uses the emotion estimation function to analyze the user's emotional state in real time and generate appropriate prompts. For example, if the user feels stressed, it makes suggestions for relaxation. This allows the user to enjoy creating a profile.

[0064] The user profile creation unit also collects entertainment information such as the user's favorite music or movies when creating the profile, thereby deepening understanding of the cultural background. The user profile creation unit, for example, collects information about the user's favorite music or movies when creating the profile, and analyzes the cultural background based on that information. For example, the user's favorite movie genres and artists are added to the profile. The user profile creation unit also analyzes the user's interests and concerns in detail based on the entertainment information. For example, the cultural background is reflected in the profile based on the user's favorite music genres and artists. This allows for a more detailed understanding of the user's cultural background, enabling more accurate matching.

[0065] The user profile creation unit provides cultural quizzes or games to users based on the profile information, allowing them to enrich their profiles while having fun. The user profile creation unit, for example, builds a system that provides cultural quizzes and games to users based on the profile information. For example, it presents quizzes related to cultures that interest the user. The user profile creation unit also analyzes the user's interests and concerns in detail through the game. For example, it adds new fields or activities that the user has become interested in through the game to the profile. This allows the user to enrich their profile while having fun.

[0066] The user profile creation unit can use the emotion estimation function to find points of empathy with other users based on the emotions the user felt when creating their profile, and use this for matching. The user profile creation unit, for example, uses the emotion estimation function to analyze the emotions the user felt when creating their profile and find points of empathy. For example, it matches users who share the same emotions. The user profile creation unit also matches users based on points of empathy. For example, it preferentially matches users who have common interests or experiences. This makes it possible to match users based on points of empathy.

[0067] The matching unit can reflect the user's past interaction history or feedback to achieve more accurate matching. The matching unit, for example, collects the user's past interaction history and improves the matching algorithm based on that information. For example, it analyzes patterns of successful matches in the past and reflects them in the algorithm. The matching unit also collects feedback from the user and improves the matching algorithm based on that information. For example, it analyzes elements of matches that satisfied the user and reflects them in the next match. This enables more accurate matching based on the user's past interaction history and feedback.

[0068] The matching unit can propose optimal interaction times by taking into account the user's lifestyle rhythm or time zone preferences. The matching unit, for example, collects the user's lifestyle rhythm and time zone preferences, and builds a system that proposes optimal interaction times based on that information. For example, matching is performed based on the user's active time zone. The matching unit also optimizes interaction times based on the user's lifestyle rhythm. For example, if the user is a night owl, priority is given to matching with users who can interact at night. This makes it possible to propose optimal interaction times that match the user's lifestyle rhythm and time zone preferences.

[0069] The matching unit can use the emotion estimation function to analyze the emotional state of a user and match users who are emotionally compatible with each other. The matching unit, for example, uses the emotion estimation function to analyze the emotional state of a user and build a system that matches users who are emotionally compatible with each other. For example, users who have positive emotions are matched with each other preferentially. The matching unit also evaluates the compatibility between users based on the emotional state. For example, users who have common emotions are matched with each other. This makes it possible to match users who are emotionally compatible with each other.

[0070] The matching unit can match users who share a common hobby with each other, taking into account the user's hobby or special skill. The matching unit, for example, collects users' hobby and special skill information and builds a system that matches users who share a common hobby with each other based on that information. For example, users who share the same hobby are matched with each other preferentially. The matching unit also matches users with each other based on hobby and special skill. For example, users who share a common hobby are matched with each other. This makes it possible to match users who share a common hobby with each other.

[0071] The matching unit can select the optimal pair by taking into consideration the learning style or communication style of the user. For example, the matching unit collects the learning style and communication style of the user and builds a system that selects the optimal pair based on that information. For example, it matches users with the same learning style. The matching unit also matches users based on their communication style. For example, it matches users with an interactive communication style. This makes it possible to select the optimal pair based on the learning style and communication style of the user.

[0072] The matching unit can use the emotion estimation function to provide advice based on the user's emotional state so that post-matching interactions can proceed smoothly. The matching unit, for example, uses the emotion estimation function to analyze the user's emotional state and builds a system that provides advice so that post-matching interactions can proceed smoothly. For example, advice is provided that elicits positive emotions. The matching unit also supports interactions between users based on the emotional state. For example, suggestions are made that will help the user relax. This makes it possible to provide advice so that post-matching interactions can proceed smoothly.

[0073] The online platform providing unit can provide a virtual tour function that allows users to introduce their own culture. The online platform providing unit, for example, builds a system on the platform that provides a virtual tour function that allows users to introduce their own culture. For example, a user introduces famous places and cultural sites in their country through a virtual tour. The online platform providing unit also promotes interaction between users through the virtual tour function. For example, a user can communicate with other users in real time through a virtual tour. This allows a virtual tour function that allows users to introduce their own culture.

[0074] The online platform providing unit can provide a translation function in real time during interaction, facilitating communication between users who speak different languages. The online platform providing unit, for example, provides a translation function in real time during interaction, building a system that facilitates communication between users who speak different languages. For example, automatic translation is performed during chats and video calls. The online platform providing unit also supports interaction between users through the translation function. For example, users can communicate smoothly even if they speak different languages. This can facilitate communication between users who speak different languages.

[0075] The online platform providing unit can use the emotion estimation function to analyze the emotional state of users during interaction and make suggestions to promote positive interaction. The online platform providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of users during interaction and makes suggestions to promote positive interaction. For example, it makes suggestions to help users relax. The online platform providing unit also supports interaction between users based on the emotional state. For example, it suggests activities that users can enjoy. This makes it possible to analyze the emotional state of users during interaction and make suggestions to promote positive interaction.

[0076] The online platform providing unit can provide collaboration tools that allow users to work together on projects. The online platform providing unit, for example, builds a system on the platform that provides collaboration tools that allow users to work together on projects. For example, it provides document sharing and task management functions. The online platform providing unit also promotes cooperation between users through the collaboration tools. For example, users can communicate in real time when working together on a project. This makes it possible to provide collaboration tools that allow users to work together on a project.

[0077] The online platform providing unit can add a function that allows users to enjoy quizzes or games related to their own culture during interactions. The online platform providing unit, for example, builds a system that adds a function that allows users to enjoy quizzes or games related to their own culture during interactions. For example, a user poses a quiz related to their own culture. The online platform providing unit also promotes interactions between users through games. For example, a user can compete with other users through games. This makes it possible to provide a function that allows users to enjoy quizzes or games related to their own culture.

[0078] The online platform providing unit can use the emotion estimation function to suggest breaks and refreshment at appropriate times based on the emotional state of the user during interaction. The online platform providing unit, for example, uses the emotion estimation function to analyze the emotional state of the user during interaction and build a system that suggests breaks and refreshment at appropriate times. For example, it suggests a break when the user feels tired. The online platform providing unit also supports interaction between users based on the emotional state. For example, it makes suggestions that allow the user to refresh. This makes it possible to suggest breaks and refreshment at appropriate times based on the emotional state of the user during interaction.

[0079] When planning an event, the online platform providing unit can analyze feedback from past event participants and propose more attractive events. For example, the online platform providing unit collects feedback from past event participants and builds a system to improve event planning based on that information. For example, it analyzes participant satisfaction and reflects it in the next event. The online platform providing unit also optimizes the content of the event based on the feedback. For example, it suggests activities that participants can enjoy. This makes it possible to propose more attractive events based on feedback from past event participants.

[0080] The online platform providing unit can optimize the event schedule to suit the user's lifestyle rhythm or time zone preferences. The online platform providing unit, for example, collects the user's lifestyle rhythm and time zone preferences and builds a system that optimizes the event schedule based on that information. For example, the event is held at a time that is convenient for the user to participate. The online platform providing unit also adjusts the content of the event based on the schedule. For example, the activity is held at a time that allows the user to relax. This makes it possible to optimize the event schedule to suit the user's lifestyle rhythm and time zone preferences.

[0081] The online platform providing unit can use the emotion estimation function to analyze the emotional state of event participants and suggest event content that will elicit positive emotions. For example, the online platform providing unit uses the emotion estimation function to build a system that analyzes the emotional state of event participants and suggests event content that will elicit positive emotions. For example, it suggests activities that participants can enjoy. The online platform providing unit also optimizes the event content based on the emotional state. For example, it provides activities that will help participants relax. This makes it possible to analyze the emotional state of event participants and suggest event content that will elicit positive emotions.

[0082] The online platform providing unit can provide workshops where users from different cultural backgrounds can jointly advance plans. The online platform providing unit, for example, builds a system that provides workshops where users from different cultural backgrounds can jointly advance plans. For example, it holds workshops where users introduce their own cultures to each other. The online platform providing unit also promotes interaction between users through workshops. For example, users can communicate in real time when working on a project together. This makes it possible to provide workshops where users from different cultural backgrounds can jointly advance plans.

[0083] The online platform providing unit can add an interactive function that allows event participants to exchange opinions or give feedback in real time. The online platform providing unit, for example, builds a system that adds an interactive function that allows event participants to exchange opinions or give feedback in real time. For example, feedback can be provided during chat or video calls. The online platform providing unit also promotes interaction between event participants through the interactive function. For example, participants can exchange opinions in real time. This makes it possible to provide an interactive function that allows event participants to exchange opinions or give feedback in real time.

[0084] The online platform providing unit can use the emotion estimation function to dynamically adjust activities and content during an event based on the emotional state of event participants. For example, the online platform providing unit uses the emotion estimation function to analyze the emotional state of event participants and build a system that dynamically adjusts activities and content during an event. For example, it suggests activities that participants can enjoy. The online platform providing unit also optimizes the content of the event based on the emotional state. For example, it provides activities that allow participants to relax. This makes it possible to dynamically adjust activities and content during an event based on the emotional state of event participants.

[0085] When providing content, the online platform providing unit can analyze the user's past browsing history or feedback and suggest more interesting content. The online platform providing unit, for example, collects the user's past browsing history and builds a system that suggests interesting content based on that information. For example, it suggests new content based on content the user has previously viewed. The online platform providing unit also improves the quality of the content based on the feedback. For example, it suggests content similar to content that the user has given a high rating. This makes it possible to suggest more interesting content based on the user's past browsing history and feedback.

[0086] The online platform providing unit can incorporate expert opinions or reviews to improve the quality of the content. For example, the online platform providing unit collects expert opinions and reviews to improve the quality of the content and builds a system to improve the content based on that information. For example, the online platform providing unit updates the content based on the expert reviews. The online platform providing unit also evaluates the quality of the content based on the expert opinions. For example, content evaluated by experts is preferentially provided. This makes it possible to incorporate expert opinions and reviews to improve the quality of the content.

[0087] The online platform providing unit can use the emotion estimation function to analyze the emotional state of the user and provide content that elicits positive emotions. The online platform providing unit, for example, uses the emotion estimation function to analyze the emotional state of the user and build a system that provides content that elicits positive emotions. For example, it suggests content that the user can enjoy. The online platform providing unit also optimizes the content based on the emotional state. For example, it provides content that allows the user to relax. This makes it possible to analyze the emotional state of the user and provide content that elicits positive emotions.

[0088] The online platform providing unit can add a function that allows users to post articles or videos related to their own culture when providing content. The online platform providing unit, for example, builds a system that adds a function that allows users to post articles or videos related to their own culture when providing content. For example, users post articles or videos introducing their own culture. The online platform providing unit also promotes interaction between users based on the posted content. For example, users can provide comments or feedback on articles or videos posted by users. This makes it possible to provide a function that allows users to post articles or videos related to their own culture.

[0089] The online platform providing unit can provide a function to customize content to suit a user's learning style or preferences. The online platform providing unit, for example, builds a system that provides a function to customize content to suit a user's learning style or preferences. For example, if a user has a visual learning style, visual content is preferentially provided. The online platform providing unit also optimizes content based on the user's preferences. For example, content in areas that interest the user is preferentially provided. This makes it possible to provide a function to customize content to suit a user's learning style or preferences.

[0090] The online platform providing unit can use the emotion estimation function to suggest breaks and refreshment at appropriate times while the user is viewing content, based on the user's emotional state. The online platform providing unit, for example, uses the emotion estimation function to analyze the user's emotional state and build a system that suggests breaks and refreshment at appropriate times while the user is viewing content. For example, the system suggests taking a break when the user feels tired. The online platform providing unit also optimizes the user's viewing experience based on the user's emotional state. For example, the system makes suggestions that allow the user to refresh themselves. This makes it possible to suggest breaks and refreshment at appropriate times while the user is viewing content, based on the user's emotional state.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The user profile creation unit can analyze a user's health condition and fitness level and reflect the results in the profile. For example, it can collect information on the user's daily exercise and health management and generate a profile based on that information. The user profile creation unit can also perform appropriate matching based on the user's health condition. For example, it can match users with the same fitness level. This enables highly accurate matching that takes the user's health condition into consideration.

[0093] The user profile creation unit can collect information about a user's food preferences and allergies and reflect this information in the profile. For example, a profile can be created by inputting information about a user's favorite dishes and ingredients they want to avoid. The user profile creation unit can also match users based on their food preferences. For example, it can match users who have the same food preferences. This makes it possible to perform matching that takes into account the user's food preferences.

[0094] The user profile creation unit can collect information about a user's hobbies and special skills and reflect it in the profile. For example, a profile is created by inputting information about the user's hobbies, such as sports, art, and music. The user profile creation unit can also match users based on their hobbies and special skills. For example, it can match users who have the same hobbies. This makes it possible to perform matching that takes into account the user's hobbies and special skills.

[0095] The user profile creation unit can use the emotion estimation function to analyze the emotions felt by the user when creating a profile and generate prompts that elicit positive emotions. For example, the emotion estimation function can analyze the emotions felt by the user when creating a profile and generate prompts that elicit positive emotions. For example, the emotion estimation function can display an encouraging message so that the user can enjoy creating a profile. The user profile creation unit can also use the emotion estimation function to analyze the user's emotional state in real time and generate appropriate prompts. For example, if the user feels stressed, the emotion estimation function can make suggestions for relaxation. This allows the user to enjoy creating a profile.

[0096] The user profile creation unit can collect information about a user's learning style and learning history and reflect it in the profile. For example, a profile can be created by inputting information about the user's preferred learning methods and what they have learned in the past. The user profile creation unit can also match users based on their learning style. For example, it can match users with the same learning style. This makes it possible to match users with the same learning style.

[0097] The user profile creation unit can use the emotion estimation function to find points of empathy with other users based on the emotions the user felt when creating their profile, and use this for matching. For example, the emotion estimation function can be used to analyze the emotions the user felt when creating their profile and find points of empathy. For example, users who share the same emotions can be matched. The user profile creation unit also matches users based on points of empathy. For example, users who share common interests or experiences can be matched preferentially. This makes it possible to match users based on points of empathy.

[0098] The matching unit can propose optimal interaction times by taking into account the user's lifestyle rhythm or time zone preferences. For example, a system can be constructed that collects the user's lifestyle rhythm and time zone preferences and proposes optimal interaction times based on that information. For example, matching is performed based on the user's active time zone. The matching unit also optimizes interaction times based on the user's lifestyle rhythm. For example, if the user is a night owl, priority is given to matching with users who can interact at night. This makes it possible to propose optimal interaction times that match the user's lifestyle rhythm and time zone preferences.

[0099] The matching unit can use the emotion estimation function to analyze the emotional state of a user and match users who are emotionally compatible with each other. For example, a system can be constructed that uses the emotion estimation function to analyze the emotional state of a user and match users who are emotionally compatible with each other. For example, users who have positive emotions are matched with each other preferentially. The matching unit also evaluates the compatibility between users based on the emotional state. For example, users who have common emotions are matched with each other. This makes it possible to match users who are emotionally compatible with each other.

[0100] The online platform providing unit can provide a virtual tour function that allows users to introduce their own culture. For example, a system that provides a virtual tour function that allows users to introduce their own culture is constructed on the platform. For example, a user can introduce famous places and cultural sites in his or her country through a virtual tour. The online platform providing unit also promotes interaction between users through the virtual tour function. For example, a user can communicate with other users in real time through a virtual tour. This allows a virtual tour function that allows users to introduce their own culture.

[0101] The online platform providing unit can use the emotion estimation function to analyze the emotional state of users during interaction and make suggestions to promote positive interaction. For example, a system can be constructed that uses the emotion estimation function to analyze the emotional state of users during interaction and make suggestions to promote positive interaction. For example, suggestions are made to help users relax. The online platform providing unit also supports interaction between users based on the emotional state. For example, activities that users can enjoy are suggested. This makes it possible to analyze the emotional state of users during interaction and make suggestions to promote positive interaction.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The user profile creation unit creates a profile based on information about the user's cultural background, interests, concerns, and language skills. For example, if a user inputs "I'm interested in traditional Japanese culture," the generation AI creates a profile based on that information. The user profile creation unit also analyzes the user's input information, and the generation AI generates a profile. For example, the generation AI generates a profile based on prompts that include instructions on what the user wants the generation AI to do. Step 2: The matching unit matches users with different cultural backgrounds based on the profiles created by the user profile creation unit. For example, it matches a user who is interested in traditional Japanese culture with a user who is knowledgeable about Japanese culture. The matching unit selects the optimal pair taking into account the user's interests, concerns, language skills, etc. Step 3: The online platform provider provides an online platform where matched users can interact with each other. For example, users can freely communicate with each other using a chat function, a video call function, a forum, etc. As a result, the online service according to the embodiment can promote interaction between users with different cultural backgrounds and deepen cultural understanding.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 user profile creation unit that creates a profile based on information about the user's cultural background, interests, concerns, and language skills; a matching unit that matches users having different cultural backgrounds based on the profile created by the user profile creation unit; an online platform providing unit that provides an online platform where users matched by the matching unit can interact with each other; A system characterized by:

2. The user profile creation unit Collecting periodic surveys or feedback and dynamically updating the profile to reflect changes in the user's daily activities and interests 2. The system of claim 1.

3. The matching unit Reflecting the user's past interaction history or feedback to achieve more accurate matching 2. The system of claim 1.

4. The online platform providing unit: Provides a virtual tour function for users to introduce their culture.

2. The system of claim 1.

5. The online platform providing unit: Using emotion estimation, we analyze the emotional state of event participants and propose event content that will elicit positive emotions.

2. The system of claim 1.

6. The user profile creation unit Using emotion estimation, the emotion the user felt when creating their profile is analyzed and prompts are generated to elicit positive emotions.

2. The system of claim 1.

7. The matching unit Using an emotion estimation function, the emotional state of the user is analyzed, and users who are emotionally compatible with each other are matched.

2. The system of claim 1.

8. The online platform providing unit: Using emotion estimation capabilities to analyze the user's emotional state during interactions and make suggestions to promote positive interactions.

2. The system of claim 1.

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