System

Abstract

An object of the system according to the embodiment is to effectively match the needs of the elderly person and the student and promote smooth communication and exchange.SOLUTION: A system according to an embodiment includes a needs matching unit, a communication support unit, a volunteer opportunity providing unit, a regional revitalization unit, and a sustainable society realizing unit. The need matching unit matches needs of the elderly person and the student. The communication support unit supports communication between the elderly person and the student matched by the needs matching unit. The volunteer opportunity providing unit provides the student with an opportunity for volunteer activity or social contribution. The regional revitalization unit promotes regional revitalization through interaction between the elderly person and the student. The sustainable society realization part aims to realize a sustainable society through the interaction between the elderly and students.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 made it difficult to facilitate smooth communication and interaction between the elderly and students, and has had the problem of being unable to effectively match the needs of both parties.

[0005] The system according to the embodiment aims to effectively match the needs of the elderly and students and promote smooth communication and interaction. [Means for solving the problem]

[0006] The system according to the embodiment includes a needs matching unit, a communication support unit, a volunteer opportunity provision unit, a local revitalization unit, and a sustainable society realization unit. The needs matching unit matches the needs of elderly people and students. The communication support unit supports communication between elderly people and students matched by the needs matching unit. The volunteer opportunity provision unit provides students with opportunities for volunteer activities and social contribution. The local revitalization unit aims to revitalize the local community through interaction between elderly people and students. The sustainable society realization unit aims to realize a sustainable society through interaction between elderly people and students. [Effects of the Invention]

[0007] The system according to the embodiment can effectively match the needs of the elderly and students and promote smooth communication and interaction. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication promotion platform according to an embodiment of the present invention is a system that utilizes generative AI to promote communication and interaction between the elderly and students, thereby promoting communication and interaction between the elderly and students and providing them with a sense of social participation and purpose in life.

[0029] A communication promotion platform according to an embodiment includes a needs matching unit, a communication support unit, a volunteer opportunity provision unit, a community revitalization unit, and a sustainable society realization unit. The needs matching unit matches the needs of elderly people and students. For example, the generation AI analyzes the profile information and interests of elderly people and students to optimally match them. For example, the generation AI matches students who are interested in gardening or handicrafts, which are the hobbies of elderly people. The communication support unit supports communication between elderly people and students matched by the needs matching unit. For example, the generation AI suggests appropriate language and topics to bridge language and generational gaps. For example, the generation AI provides topics based on common hobbies and interests. The volunteer opportunity provision unit provides students with opportunities for volunteer activities and social contribution. For example, the generation AI suggests activities in which students can participate, such as supporting elderly people and organizing local events. The community revitalization unit aims to revitalize local communities through interactions between elderly people and students. For example, the generation AI deepens local ties by participating in local traditional events. The Sustainable Society Realization Department aims to realize a sustainable society through interactions between seniors and students. For example, Generative AI proposes projects related to environmental protection and energy efficiency, and seniors and students work together on them. This allows the communication promotion platform to promote communication and interaction between seniors and students, providing a sense of social participation and purpose in life. For example, Generative AI proposes projects to solve local issues, and seniors and students work together on these projects, promoting the revitalization of the entire community.

[0030] The needs matching unit can analyze the past interaction history between elderly people and students, learn successful matching patterns, and reflect them in the next match. For example, the generation AI in the needs matching unit stores the past interaction history between elderly people and students in a database, analyzes that data, and extracts successful matching patterns. For example, it finds commonalities between successful matches in the past and reflects them in the next match. This allows successful matching patterns to be learned based on past interaction history and reflected in the next match, improving the accuracy of matching.

[0031] The needs matching unit can analyze the psychological characteristics and communication styles of the elderly and students to generate pairs that are compatible. For example, the generation AI in the needs matching unit analyzes the psychological characteristics of the elderly and students to generate pairs that are compatible. For example, it uses personality diagnostic tests and psychological profiles to find pairs that are compatible. This improves the quality of interaction by analyzing psychological characteristics and communication styles and generating pairs that are compatible.

[0032] The needs matching section can match elderly people and students by taking into account their health conditions and lifestyle habits. For example, the generation AI analyzes the health conditions of the elderly people and students and matches them according to their health conditions. For example, it matches pairs with similar health conditions based on information on exercise habits and dietary habits. This allows for matching that takes health conditions and lifestyle habits into account, making it possible to generate more appropriate pairs.

[0033] The needs matching section can perform matching taking into account the characteristics and cultural background of the region. For example, the generation AI analyzes the regional characteristics of elderly people and students, promoting interactions rooted in the region. For example, it matches pairs that are suitable for the region, taking into account local traditional events and cultural background. This allows for matching that takes into account the characteristics and cultural background of the region, promoting interactions rooted in the region.

[0034] When analyzing the profile information of elderly people and students, the needs matching unit also takes into account past activity history and evaluations, making it possible to perform highly reliable matching. For example, when the generation AI analyzes the profile information of elderly people and students, the needs matching unit takes into account past activity history. For example, it performs highly reliable matching based on the history of events and activities participated in in the past. In this way, highly reliable matching can be achieved by performing matching taking into account activity history and evaluations.

[0035] The needs matching unit uses natural language processing technology to analyze profile information and can extract detailed interests. For example, the generation AI in the needs matching unit uses natural language processing technology to extract detailed interests from the profile information of seniors and students. For example, it analyzes self-introductions and descriptions of hobbies to understand detailed interests. In this way, by extracting detailed interests using natural language processing technology, more accurate matching can be achieved.

[0036] The needs matching unit uses image analysis technology to analyze profile information, allowing for a visual understanding of hobbies and interests. For example, the generation AI uses image analysis technology to visually understand the hobbies and interests of seniors and students from their profile information. For example, it analyzes profile photos and images of hobbies to understand their interests. This allows for a more accurate match by visually understanding hobbies and interests using image analysis technology.

[0037] The needs matching unit can integrate the results of the profile information analysis with other databases to achieve more accurate matching. For example, the generation AI can integrate the results of the profile information analysis with health data to achieve more accurate matching. For example, it can find the optimal pair taking into account health status and lifestyle habits. In this way, by integrating and analyzing databases, more accurate matching can be achieved.

[0038] The communication support unit can analyze past conversation histories between elderly people and students and learn effective communication patterns. For example, the generative AI stores past conversation histories between elderly people and students in a database, analyzes that data, and extracts effective communication patterns. For example, it finds commonalities between successful conversations and reflects them in the next communication. In this way, by analyzing past conversation histories and learning effective communication patterns, the quality of the next communication improves.

[0039] The communication support unit can use voice recognition technology to suggest appropriate language and topics in real time. For example, the generative AI uses voice recognition technology to analyze a conversation between an elderly person and a student in real time and suggest appropriate language and topics. For example, it provides appropriate phrases and questions according to the flow of the conversation. This improves the quality of communication by using voice recognition technology to suggest appropriate language and topics in real time.

[0040] The communication support unit can combine video calling functions and utilize visual information. For example, the generative AI can combine video calling functions to support communication between elderly people and students. For example, visual information can be shared through video calling to promote smooth communication. This improves the quality of communication by combining video calling functions and utilizing visual information.

[0041] The communication support unit can combine translation functions to promote communication between different languages. For example, the communication support unit combines generative AI with translation functions to support communication between different languages ​​between the elderly and students. For example, real-time translation can be performed to promote communication across language barriers. In this way, the quality of communication can be improved by combining translation functions and promoting communication between different languages.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The communication promotion platform can further include an entertainment provider, which provides entertainment content that can be enjoyed by both seniors and students. For example, the generative AI can suggest content such as movies, music, and games that seniors and students with common hobbies can enjoy together. The entertainment provider can also use virtual reality (VR) technology to provide shared experiences in virtual spaces. For example, it can enable sharing of experiences that would be difficult in real life, such as virtual travel or virtual museum visits. This makes interactions between seniors and students more enjoyable and fulfilling.

[0044] The communication promotion platform can also be equipped with a health management unit, which monitors the health status of seniors and students and provides health advice. For example, the generative AI analyzes daily health data and suggests appropriate exercise and dietary recommendations. The health management unit can also provide health support through online health consultations and collaboration with medical experts. This allows seniors and students to deepen their interactions while living a healthy lifestyle.

[0045] The communication promotion platform can also be equipped with a learning support unit, which provides opportunities for elderly people and students to learn together. For example, the generative AI can suggest learning topics of common interest and hold online courses and workshops. The learning support unit can also provide opportunities for students to teach elderly people digital technologies and new knowledge. This allows elderly people and students to learn from each other and share knowledge, further deepening their interactions.

[0046] The communication promotion platform can further include a hobby sharing section, which provides opportunities for seniors and students to interact through common hobbies. For example, the generative AI can match seniors and students who share common hobbies and suggest online hobby activities. The hobby sharing section can also provide information and resources related to hobbies, allowing seniors and students to deepen their interactions through their common hobbies. This allows seniors and students to interact and deepen their bonds through their common hobbies.

[0047] The communication promotion platform can further include a community contribution section, which provides opportunities for seniors and students to contribute to the local community. For example, the generative AI can suggest local volunteer activities and events that seniors and students can participate in together. The community contribution section can also suggest projects to solve local issues, which seniors and students can work on together. This allows seniors and students to deepen their interactions while contributing to the local community.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The needs matching unit matches the needs of seniors and students. For example, the generation AI analyzes the profile information and interests of seniors and students to make the optimal match. For example, the generation AI matches students who are interested in gardening or handicrafts, which are the hobbies of seniors. Step 2: The communication support unit supports communication between the elderly and students matched by the needs matching unit. For example, the generative AI suggests appropriate language and topics to bridge language and generational gaps. For example, the generative AI provides topics based on common hobbies and interests. Step 3: The Volunteer Opportunity Providing Department will provide students with opportunities to volunteer and contribute to society. For example, the Generative AI will suggest activities in which students can participate, such as supporting the elderly or organizing local events. Step 4: The Regional Revitalization Department aims to revitalize the community through interactions between the elderly and students. For example, the AI ​​will deepen community ties by participating in local traditional events and festivals. Step 5: The Sustainable Society Realization Department aims to realize a sustainable society through interactions between seniors and students. For example, the Generative AI will propose projects related to environmental protection and energy efficiency, and seniors and students will work together on these projects.

[0050] (Example 2) A communication promotion platform according to an embodiment of the present invention is a system that utilizes generative AI to promote communication and interaction between the elderly and students, thereby promoting communication and interaction between the elderly and students and providing them with a sense of social participation and purpose in life.

[0051] A communication promotion platform according to an embodiment includes a needs matching unit, a communication support unit, a volunteer opportunity provision unit, a community revitalization unit, and a sustainable society realization unit. The needs matching unit matches the needs of elderly people and students. For example, the generation AI analyzes the profile information and interests of elderly people and students to optimally match them. For example, the generation AI matches students who are interested in gardening or handicrafts, which are the hobbies of elderly people. The communication support unit supports communication between elderly people and students matched by the needs matching unit. For example, the generation AI suggests appropriate language and topics to bridge language and generational gaps. For example, the generation AI provides topics based on common hobbies and interests. The volunteer opportunity provision unit provides students with opportunities for volunteer activities and social contribution. For example, the generation AI suggests activities in which students can participate, such as supporting elderly people and organizing local events. The community revitalization unit aims to revitalize local communities through interactions between elderly people and students. For example, the generation AI deepens local ties by participating in traditional local events. The Sustainable Society Realization Department aims to realize a sustainable society through interactions between seniors and students. For example, Generative AI proposes projects related to environmental protection and energy efficiency, and seniors and students work together on them. This allows the communication promotion platform to promote communication and interaction between seniors and students, providing a sense of social participation and purpose in life. For example, Generative AI proposes projects to solve local issues, and seniors and students work together on these projects, promoting the revitalization of the entire community.

[0052] The needs matching unit can analyze the past interaction history between elderly people and students, learn successful matching patterns, and reflect them in the next match. For example, the generation AI in the needs matching unit stores the past interaction history between elderly people and students in a database, analyzes that data, and extracts successful matching patterns. For example, it finds commonalities between successful matches in the past and reflects them in the next match. This allows successful matching patterns to be learned based on past interaction history and reflected in the next match, improving the accuracy of matching.

[0053] The needs matching unit can analyze the psychological characteristics and communication styles of the elderly and students to generate pairs that are compatible. For example, the generation AI in the needs matching unit analyzes the psychological characteristics of the elderly and students to generate pairs that are compatible. For example, it uses personality diagnostic tests and psychological profiles to find pairs that are compatible. This improves the quality of interaction by analyzing psychological characteristics and communication styles and generating pairs that are compatible.

[0054] The needs matching unit uses an emotion estimation function to monitor the emotional states of the elderly and students in real time and match them at the optimal timing. For example, the generation AI uses the emotion estimation function to monitor the emotional states of the elderly and students in real time. For example, it analyzes facial expressions and voice tone to understand their emotional state. This allows for real-time monitoring of emotional states and matching at the optimal timing, improving the success rate of interactions.

[0055] The needs matching section can match elderly people and students by taking into account their health conditions and lifestyle habits. For example, the generation AI analyzes the health conditions of the elderly people and students and matches them according to their health conditions. For example, it matches pairs with similar health conditions based on information on exercise habits and dietary habits. This allows for matching that takes health conditions and lifestyle habits into account, making it possible to generate more appropriate pairs.

[0056] The needs matching section can perform matching taking into account the characteristics and cultural background of the region. For example, the generation AI analyzes the regional characteristics of elderly people and students, promoting interactions rooted in the region. For example, it matches pairs that are suitable for the region, taking into account local traditional events and cultural background. This allows for matching that takes into account the characteristics and cultural background of the region, promoting interactions rooted in the region.

[0057] The needs matching unit can use the emotion estimation function to evaluate the quality of the interaction after matching and improve the matching algorithm based on the feedback. In the needs matching unit, for example, the generation AI uses the emotion estimation function to evaluate the quality of the interaction after matching in real time. For example, it monitors the emotional state during the interaction and evaluates the quality of the interaction. In this way, the quality of the interaction is evaluated and the matching algorithm is improved based on the feedback, thereby improving the accuracy of the next match.

[0058] When analyzing the profile information of elderly people and students, the needs matching unit also takes into account past activity history and evaluations, making it possible to perform highly reliable matching. For example, when the generation AI analyzes the profile information of elderly people and students, the needs matching unit takes into account past activity history. For example, it performs highly reliable matching based on the history of events and activities participated in in the past. In this way, highly reliable matching can be achieved by performing matching taking into account activity history and evaluations.

[0059] The needs matching unit uses natural language processing technology to analyze profile information and can extract detailed interests. For example, the generation AI in the needs matching unit uses natural language processing technology to extract detailed interests from the profile information of seniors and students. For example, it analyzes self-introductions and descriptions of hobbies to understand detailed interests. In this way, by extracting detailed interests using natural language processing technology, more accurate matching can be achieved.

[0060] The needs matching unit can use the emotion estimation function to analyze emotional tendencies from profile information and generate pairs with good emotional compatibility. For example, the generation AI in the needs matching unit uses the emotion estimation function to analyze emotional tendencies from the profile information of elderly people and students. For example, it can grasp emotional tendencies from self-introductions and descriptions of hobbies. This improves the quality of interactions by analyzing emotional tendencies and generating pairs with good emotional compatibility.

[0061] The needs matching unit uses image analysis technology to analyze profile information, allowing for a visual understanding of hobbies and interests. For example, the generation AI uses image analysis technology to visually understand the hobbies and interests of seniors and students from their profile information. For example, it analyzes profile photos and images of hobbies to understand their interests. This allows for a more accurate match by visually understanding hobbies and interests using image analysis technology.

[0062] The needs matching unit can integrate the results of the profile information analysis with other databases to achieve more accurate matching. For example, the generation AI can integrate the results of the profile information analysis with health data to achieve more accurate matching. For example, it can find the optimal pair taking into account health status and lifestyle habits. In this way, by integrating and analyzing databases, more accurate matching can be achieved.

[0063] The needs matching unit can use the emotion estimation function to suggest points to be careful about when interacting and recommended topics based on the analysis results of the profile information. For example, the generation AI in the needs matching unit uses the emotion estimation function to suggest points to be careful about when interacting based on the analysis results of the profile information. For example, it may advise avoiding emotionally sensitive topics. In this way, the quality of interactions is improved by using the emotion estimation function to suggest points to be careful about when interacting and recommended topics.

[0064] The communication support unit can analyze past conversation histories between elderly people and students and learn effective communication patterns. For example, the generative AI stores past conversation histories between elderly people and students in a database, analyzes that data, and extracts effective communication patterns. For example, it finds commonalities between successful conversations and reflects them in the next communication. In this way, by analyzing past conversation histories and learning effective communication patterns, the quality of the next communication improves.

[0065] The communication support unit can use voice recognition technology to suggest appropriate language and topics in real time. For example, the generative AI uses voice recognition technology to analyze a conversation between an elderly person and a student in real time and suggest appropriate language and topics. For example, it provides appropriate phrases and questions according to the flow of the conversation. This improves the quality of communication by using voice recognition technology to suggest appropriate language and topics in real time.

[0066] The communication support unit uses the emotion estimation function to monitor changes in emotions during a conversation and can suggest changing the topic at the appropriate time. For example, the generative AI in the communication support unit uses the emotion estimation function to monitor changes in emotions in real time during a conversation between an elderly person and a student. For example, it analyzes facial expressions and voice tone to understand the emotional state. This allows the unit to monitor changes in emotions and suggest changing the topic at the appropriate time, improving the quality of communication.

[0067] The communication support unit can combine video calling functions and utilize visual information. For example, the generative AI can combine video calling functions to support communication between elderly people and students. For example, visual information can be shared through video calling to promote smooth communication. This improves the quality of communication by combining video calling functions and utilizing visual information.

[0068] The communication support unit can combine translation functions to promote communication between different languages. For example, the communication support unit combines generative AI with translation functions to support communication between different languages ​​between the elderly and students. For example, real-time translation can be performed to promote communication across language barriers. In this way, the quality of communication can be improved by combining translation functions and promoting communication between different languages.

[0069] The communication support unit can use the emotion estimation function to suggest relaxing topics and activities based on changes in emotions during a conversation. For example, the generative AI can use the emotion estimation function to monitor changes in emotions in real time during a conversation between an elderly person and a student, and suggest relaxing topics. For example, it can provide relaxing topics when emotions are high. This improves the quality of communication by suggesting relaxing topics and activities based on changes in emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The communication promotion platform can further include an entertainment provider, which provides entertainment content that can be enjoyed by both seniors and students. For example, the generative AI can suggest content such as movies, music, and games that seniors and students with common hobbies can enjoy together. The entertainment provider can also use virtual reality (VR) technology to provide shared experiences in virtual spaces. For example, it can enable sharing of experiences that would be difficult in real life, such as virtual travel or virtual museum visits. This makes interactions between seniors and students more enjoyable and fulfilling.

[0072] The communication promotion platform can also be equipped with a health management unit, which monitors the health status of seniors and students and provides health advice. For example, the generative AI analyzes daily health data and suggests appropriate exercise and dietary recommendations. The health management unit can also provide health support through online health consultations and collaboration with medical experts. This allows seniors and students to deepen their interactions while living a healthy lifestyle.

[0073] The communication promotion platform can also be equipped with a learning support unit, which provides opportunities for elderly people and students to learn together. For example, the generative AI can suggest learning topics of common interest and hold online courses and workshops. The learning support unit can also provide opportunities for students to teach elderly people digital technologies and new knowledge. This allows elderly people and students to learn from each other and share knowledge, further deepening their interactions.

[0074] The communication promotion platform can also use emotion estimation to suggest points to be careful of when interacting and recommended topics. For example, the generative AI can use emotion estimation to monitor the emotional state of elderly people and students in real time and advise them to avoid emotionally sensitive topics. It can also use emotion estimation to suggest relaxing topics and activities. This can improve the quality of interactions based on changes in emotions.

[0075] The communication promotion platform can further use emotion estimation to provide emotional support during interactions. For example, the generative AI can use emotion estimation to monitor the emotional state of elderly people and students in real time and provide encouraging messages if they are feeling depressed. It can also use emotion estimation to suggest relaxing activities if they are feeling emotionally excited. This improves the quality of interactions by providing emotional support based on changes in emotions.

[0076] The communication promotion platform can also use the emotion estimation function to provide emotional feedback during interactions. For example, the generative AI can use the emotion estimation function to monitor the emotional states of elderly people and students in real time and provide emotional feedback after the interaction. The emotion estimation function can also be used to record emotional changes during interactions and use this information for the next interaction. This improves the quality of interactions by providing emotional feedback based on emotional changes.

[0077] The communication promotion platform can further use the emotion estimation function to provide emotional training during interactions. For example, the generative AI can use the emotion estimation function to monitor the emotional states of elderly people and students in real time and provide emotional training programs. It can also use the emotion estimation function to provide advice on improving emotional skills. This improves the quality of interactions by providing emotional training based on changes in emotions.

[0078] The communication promotion platform can further use the emotion estimation function to provide emotional reflection during interactions. For example, the generative AI can use the emotion estimation function to monitor the emotional states of elderly people and students in real time and provide emotional reflection after the interaction. The emotion estimation function can also be used to reflect on emotional changes and use them for the next interaction. This improves the quality of interactions by providing emotional reflection based on emotional changes.

[0079] The communication promotion platform can further include a hobby sharing section, which provides opportunities for seniors and students to interact through common hobbies. For example, the generative AI can match seniors and students who share common hobbies and suggest online hobby activities. The hobby sharing section can also provide information and resources related to hobbies, allowing seniors and students to deepen their interactions through their common hobbies. This allows seniors and students to interact and deepen their bonds through their common hobbies.

[0080] The communication promotion platform can further include a community contribution section, which provides opportunities for seniors and students to contribute to the local community. For example, the generative AI can suggest local volunteer activities and events that seniors and students can participate in together. The community contribution section can also suggest projects to solve local issues, which seniors and students can work on together. This allows seniors and students to deepen their interactions while contributing to the local community.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The needs matching unit matches the needs of seniors and students. For example, the generation AI analyzes the profile information and interests of seniors and students to make the optimal match. For example, the generation AI matches students who are interested in gardening or handicrafts, which are the hobbies of seniors. Step 2: The communication support unit supports communication between the elderly and students matched by the needs matching unit. For example, the generative AI suggests appropriate language and topics to bridge language and generational gaps. For example, the generative AI provides topics based on common hobbies and interests. Step 3: The Volunteer Opportunity Providing Department will provide students with opportunities to volunteer and contribute to society. For example, the Generative AI will suggest activities in which students can participate, such as supporting the elderly or organizing local events. Step 4: The Regional Revitalization Department aims to revitalize the community through interactions between the elderly and students. For example, the AI ​​will deepen community ties by participating in local traditional events and festivals. Step 5: The Sustainable Society Realization Department aims to realize a sustainable society through interactions between seniors and students. For example, the Generative AI will propose projects related to environmental protection and energy efficiency, and seniors and students will work together on these projects.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 needs matching department that matches the needs of the elderly and students; a communication support unit that supports communication between the elderly and students matched by the needs matching unit; A volunteer opportunity provision department that provides the students with opportunities to participate in volunteer activities and contribute to society; A regional revitalization department that aims to revitalize the region through exchanges between the elderly and the students; and a Sustainable Society Realization Department that aims to realize a sustainable society through exchanges between the elderly and the students. A system characterized by:

2. The needs matching unit The emotional states of the elderly and the students are monitored in real time, and the matching is performed at the optimal timing.

2. The system of claim 1.

3. The needs matching unit The matching is carried out taking into consideration the health conditions and lifestyle habits of the elderly and the students.

2. The system of claim 1.

4. The needs matching unit When analyzing the profile information of the elderly person and the student, activity history and evaluations are also taken into consideration to perform the highly reliable matching.

2. The system of claim 1.

5. The communication support unit Analyze the conversation history between the elderly person and the student and learn effective communication patterns.

2. The system of claim 1.

6. The communication support unit Monitors emotional changes during conversations and suggests changing the topic at the right time 2. The system of claim 1.

7. The communication support unit Combine video calling functionality to utilize visual information 2. The system of claim 1.

8. The communication support unit Suggest relaxing topics and activities based on emotional changes during the conversation 2. The system of claim 1.

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