System

Abstract

An object of a system according to an embodiment is to provide appropriate support on the basis of an emotion of a user.SOLUTION: A system includes an emotion analysis part, a matching part, a community provision part, and an expert cooperation part. The emotion analysis unit analyzes an emotion of a user. The matching unit performs matching with another user or an expert having similar experience on the basis of the emotion analyzed by the emotion analysis unit. The community providing unit provides a community function in which users matched by the matching unit can share emotions. The expert cooperation unit supports cooperation with an appropriate expert on the basis of the emotion analyzed by the emotion analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately provided appropriate support based on the user's emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate assistance based on the user's emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion analysis unit, a matching unit, a community providing unit, and an expert collaboration unit. The emotion analysis unit analyzes the user's emotions. The matching unit matches the user with other users and experts who have similar experiences based on the emotions analyzed by the emotion analysis unit. The community providing unit provides a community function that allows users matched by the matching unit to share emotions. The expert collaboration unit supports collaboration with appropriate experts based on the emotions analyzed by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate assistance based on the user's emotions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Grief Sharing Compass, an embodiment of the present invention, is an AI-driven service to support people who have faced the tragedy of a family member. The platform connects people experiencing grief with other users and experts who have had similar experiences, and guides them on a journey to share their emotions and find emotional healing. This allows the Grief Sharing Compass to provide powerful support for people who have faced the tragedy of a family member to share their emotions and find emotional healing.

[0029] A sadness sharing compass according to an embodiment includes an emotion analysis unit, a matching unit, a community provision unit, and an expert collaboration unit. The emotion analysis unit analyzes a user's emotions. For example, the generation AI analyzes information about emotions and situations input by the user and classifies them into emotion categories. The generation AI can also evaluate the intensity of the user's emotions. The generation AI can also analyze the user's emotional fluctuation patterns. For example, if a user inputs "I recently lost a parent and I'm very sad," the generation AI analyzes the emotion and evaluates the intensity of the sadness. The matching unit matches the user with other users or experts who have had similar experiences based on the emotions analyzed by the emotion analysis unit. For example, the generation AI finds users or counselors who have had similar experiences. The generation AI can also perform matching that best suits the user's emotional state. The generation AI can also perform matching by referring to the user's past emotional history. For example, the generation AI can analyze emotional data previously input by the user and perform matching that best suits the user's current emotional state. The community provision unit provides a community function that allows users matched by the matching unit to share their feelings. For example, when a user posts something like, "Tell me how you overcame the grief of losing a parent," the generation AI analyzes the post and guides them to the appropriate community or thread. The generation AI can also analyze replies and comments from other users to provide useful information to users. The generation AI can also analyze the content of posts within the community and recommend posts that are most likely to resonate with users. For example, the generation AI prioritizes posts from users with similar experiences. The expert collaboration unit supports collaboration with appropriate experts based on the emotions analyzed by the emotion analysis unit. For example, if a user inputs, "I want expert advice," the generation AI analyzes the request and introduces appropriate experts. The generation AI can also schedule sessions with experts and manage feedback on the session content. The generation AI can also analyze the content of sessions with experts and suggest topics to cover in the next session.For example, the generation AI might suggest addressing unresolved issues from the previous session in the next session. In this way, the sadness sharing compass according to the embodiment analyzes the user's emotions, matches them with other users and experts who have similar experiences, provides a community function for sharing emotions, and supports collaboration with appropriate experts. For example, a user can reduce feelings of loneliness by interacting with users with similar experiences and take care of their mental health by receiving advice from experts. The generation AI can also provide content to promote mental healing (e.g., relaxation music, meditation guides, encouraging messages, etc.) based on the user's emotional state. For example, if a user inputs "I'm feeling very depressed," the generation AI can analyze the user's emotions and suggest relaxation music or meditation guides. Furthermore, the generation AI can track the user's emotional state and progress in mental healing and provide regular feedback. For example, if a user inputs "I'm feeling a little better," the generation AI records the user's progress and suggests effective next steps.

[0030] The emotion analysis unit can refer to the user's past emotional history to perform more accurate matching. For example, the emotion analysis unit uses a generation AI to analyze emotional data previously entered by the user and understand emotional fluctuation patterns. For example, it performs matching that is most suitable for the current emotional state based on the user's past emotional history. This allows for more accurate matching by referring to the user's past emotional history.

[0031] The emotion analysis unit can track emotional changes in real time in response to the user's input content and perform matching at the optimal timing. The emotion analysis unit, for example, analyzes emotional data input by the user in real time and tracks emotional changes. For example, it calculates an emotion score according to the input content and performs matching at the optimal timing. This allows for tracking emotional changes in real time in response to the user's input content and performing matching at the optimal timing.

[0032] The emotion analysis unit uses the emotion estimation function to classify the user's emotions in detail and perform matching according to a specific emotional state. The emotion analysis unit, for example, uses the emotion estimation function to classify the user's emotions in detail. For example, emotions such as sadness, anger, and helplessness are finely classified and matching is performed accordingly. This makes it possible to classify the user's emotions in detail using the emotion estimation function and perform matching according to a specific emotional state.

[0033] The emotion analysis unit can suggest an appropriate self-care activity based on the result of the emotion analysis of the user. For example, the emotion analysis unit can suggest a self-care activity suitable for the user based on the result of the emotion analysis. For example, relaxation yoga can be suggested for a user who is feeling very sad. In this way, an appropriate self-care activity can be suggested based on the result of the emotion analysis of the user.

[0034] The emotion analysis unit can use the emotion analysis results to recommend online events and workshops that the user can participate in. For example, the emotion analysis unit can recommend online events and workshops that are suitable for the user based on the emotion analysis results. For example, the emotion analysis unit can suggest an online seminar to help users overcome sadness. In this way, the emotion analysis results can be used to recommend online events and workshops that the user can participate in.

[0035] The emotion analysis unit can use the emotion estimation function to provide personalized mental health resources according to the user's emotional state. For example, the emotion analysis unit can use the emotion estimation function to provide mental health resources according to the user's emotional state. For example, an encouraging video can be provided to a user who is feeling deeply sad. In this way, the emotion estimation function can be used to provide personalized mental health resources according to the user's emotional state.

[0036] The community providing unit can analyze the content of posts within a community and recommend posts that are most likely to resonate with users. For example, the community providing unit uses a generation AI to analyze the content of posts within a community and recommend posts that are most likely to resonate with users. For example, posts by users with the same experience are displayed preferentially. This allows the content of posts within a community to be analyzed and posts that are most likely to resonate with users to be recommended.

[0037] The community providing unit can analyze the flow of emotions within the community and encourage users to post at an appropriate time according to their emotional state. For example, the generation AI in the community providing unit analyzes the flow of emotions within the community and encourages users to post at an appropriate time. For example, it encourages users to post when their emotions are at their peak. This makes it possible to analyze the flow of emotions within the community and encourage users to post at an appropriate time according to their emotional state.

[0038] The community providing unit can use the emotion estimation function to analyze other users' emotional reactions to the user's posts and emphasize positive feedback. The community providing unit, for example, uses the emotion estimation function to analyze other users' emotional reactions to the user's posts. For example, posts with many positive reactions are highlighted. In this way, the emotion estimation function can be used to analyze other users' emotional reactions to the user's posts and emphasize positive feedback.

[0039] The community providing unit can suggest to the user that they keep an emotion diary in order to promote emotion sharing within the community. For example, the community providing unit can have the generation AI suggest to the user that they keep an emotion diary in order to promote emotion sharing within the community. For example, the generation AI can encourage the user to record their daily emotions. This can suggest to the user that they keep an emotion diary in order to promote emotion sharing within the community.

[0040] The community providing unit can make the community function for sharing emotions compatible with different languages ​​and cultural spheres, thereby realizing global emotional sharing. The community providing unit, for example, makes the community function for sharing emotions compatible with different languages, thereby realizing global emotional sharing. For example, it provides a multilingual interface. This makes it possible to make the community function for sharing emotions compatible with different languages ​​and cultural spheres, thereby realizing global emotional sharing.

[0041] The community providing unit can use the emotion estimation function to automatically generate support groups within the community according to the emotional state of the user. For example, the community providing unit uses the emotion estimation function to automatically generate support groups according to the emotional state of the user. For example, users who are feeling deep sadness are grouped together. In this way, the emotion estimation function can be used to automatically generate support groups within the community according to the emotional state of the user.

[0042] The expert collaboration unit can analyze the content of a session with an expert and suggest to the user topics to be covered in the next session. For example, the generative AI can analyze the content of a session with an expert and suggest topics to be covered in the next session. For example, it can suggest that an unresolved issue from the previous session be covered in the next session. This makes it possible to analyze the content of a session with an expert and suggest topics to be covered in the next session.

[0043] The expert collaboration unit can monitor the user's emotional state in real time and send alerts to experts as necessary. For example, the generation AI can monitor the user's emotional state in real time and send alerts to experts if there is a sudden change in emotion. For example, it can notify an expert when the user feels deep sadness. This makes it possible to monitor the user's emotional state in real time and send alerts to experts as necessary.

[0044] The expert collaboration unit can use the emotion estimation function to provide personalized expert advice according to the emotional state of the user. The expert collaboration unit, for example, uses the emotion estimation function to provide personalized expert advice according to the emotional state of the user. For example, a specific counseling method is suggested to a user who is feeling deep sadness. In this way, the emotion estimation function can be used to provide personalized expert advice according to the emotional state of the user.

[0045] The expert collaboration unit can recommend resources for self-care to the user in order to strengthen collaboration with experts. For example, the generation AI recommends resources for self-care to the user in order to strengthen collaboration with experts. For example, it suggests books and apps recommended by experts. In this way, it is possible to recommend resources for self-care to the user in order to strengthen collaboration with experts.

[0046] The expert collaboration unit can prompt the user to conduct a self-assessment of their emotional state before and after a session with an expert. For example, the expert collaboration unit prompts the user to conduct a self-assessment of their emotional state before and after a session with an expert by the generation AI. For example, the expert collaboration unit suggests that the user record an emotional score before the session. This makes it possible to prompt the user to conduct a self-assessment of their emotional state before and after a session with an expert.

[0047] The expert collaboration unit uses the emotion estimation function to select an expert according to the emotional state of the user and can recommend the most appropriate expert. The expert collaboration unit, for example, uses the emotion estimation function to select an expert according to the emotional state of the user. For example, a specific counselor is recommended for a user who is feeling deep sadness. In this way, the emotion estimation function can be used to select an expert according to the emotional state of the user and can recommend the most appropriate expert.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The sadness sharing compass can also provide communities based on the user's hobbies and interests, rather than based on the user's emotional state. For example, if the user is interested in music, the community providing unit can suggest a community of music lovers. If the user is interested in sports, the community providing unit can also suggest a community of sports fans. If the user is interested in cooking, the community providing unit can also suggest a community of cooking lovers. This allows users to enjoy interacting in communities based on their hobbies and interests, regardless of their emotional state.

[0050] The Sadness Sharing Compass can also suggest local events and groups based on the user's geographical location, rather than based on the user's emotional state. For example, the community providing unit can suggest events and groups in the area where the user lives. The community providing unit can also suggest local events that the user can participate in when traveling. Furthermore, the community providing unit can suggest local groups for the user to make new friends when they move. This allows the user to make new connections through local events and groups based on their geographical location.

[0051] The Sadness Sharing Compass can also provide specialized communities according to the user's occupation or career, rather than based on the user's emotional state. For example, if the user is a medical professional, the community providing unit can suggest a community of medical professionals. If the user is an educator, the community providing unit can also suggest a community of educators. If the user is an engineer, the community providing unit can also suggest a community of engineers. This allows the user to exchange information and receive support in specialized communities according to their occupation or career.

[0052] The Sadness Sharing Compass can also provide communities according to the user's life stage, rather than based on the user's emotional state. For example, if the user is newly married, the community providing unit can suggest a community for newlywed couples. If the user is raising children, the community providing unit can also suggest a community for childcare support. Furthermore, if the user is enjoying life after retirement, the community providing unit can also suggest a community for seniors. This allows the user to interact with people who share common experiences in communities according to their life stage.

[0053] The Sadness Sharing Compass can also provide support groups based on the user's health condition, rather than based on the user's emotional state. For example, if the user has a chronic illness, the community providing unit can suggest a support group for people with the same illness. Also, if the user is undergoing rehabilitation, the community providing unit can suggest a group to support rehabilitation. Furthermore, if the user has mental health issues, the community providing unit can suggest a mental health support group. This allows the user to receive the necessary support in a support group based on their health condition.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The emotion analysis unit analyzes the user's emotions. For example, the generation AI analyzes the information about the emotions and situation entered by the user and classifies the emotion category. The generation AI also evaluates the intensity of the user's emotions and analyzes the pattern of emotional fluctuations. For example, if a user enters "I recently lost a parent and I'm very sad," the generation AI analyzes the emotion and evaluates the intensity of sadness. Step 2: The matching unit matches the user with other users or experts who have had similar experiences based on the emotions analyzed by the emotion analysis unit. For example, the generation AI finds users or counselors with similar experiences and matches them with the most appropriate match for the user's emotional state. The generation AI can also refer to the user's past emotional history when making a match. Step 3: The community provider provides a community function that allows users matched by the matching unit to share their feelings with each other. For example, when a user posts something like, "Tell me how you overcame the grief of losing a parent," the generation AI analyzes the post and guides the user to the appropriate community or thread. The generation AI can also analyze replies and comments from other users to provide useful information to the user. Step 4: The expert collaboration unit supports collaboration with an appropriate expert based on the emotions analyzed by the emotion analysis unit. For example, if a user inputs "I want expert advice," the generation AI will analyze that request and introduce an appropriate expert. The generation AI can also schedule sessions with experts and manage feedback on the session content.

[0056] (Example 2) The Grief Sharing Compass, an embodiment of the present invention, is an AI-driven service to support people who have faced the tragedy of a family member. The platform connects people experiencing grief with other users and experts who have had similar experiences, and guides them on a journey to share their emotions and find emotional healing. This allows the Grief Sharing Compass to provide powerful support for people who have faced the tragedy of a family member to share their emotions and find emotional healing.

[0057] A sadness sharing compass according to an embodiment includes an emotion analysis unit, a matching unit, a community provision unit, and an expert collaboration unit. The emotion analysis unit analyzes a user's emotions. For example, the generation AI analyzes information about emotions and situations input by the user and classifies them into emotion categories. The generation AI can also evaluate the intensity of the user's emotions. The generation AI can also analyze the user's emotional fluctuation patterns. For example, if a user inputs "I recently lost a parent and I'm very sad," the generation AI analyzes the emotion and evaluates the intensity of the sadness. The matching unit matches the user with other users or experts who have had similar experiences based on the emotions analyzed by the emotion analysis unit. For example, the generation AI finds users or counselors who have had similar experiences. The generation AI can also perform matching that best suits the user's emotional state. The generation AI can also perform matching by referring to the user's past emotional history. For example, the generation AI can analyze emotional data previously input by the user and perform matching that best suits the user's current emotional state. The community provision unit provides a community function that allows users matched by the matching unit to share their feelings. For example, when a user posts something like, "Tell me how you overcame the grief of losing a parent," the generation AI analyzes the post and guides them to the appropriate community or thread. The generation AI can also analyze replies and comments from other users to provide useful information to users. The generation AI can also analyze the content of posts within the community and recommend posts that are most likely to resonate with users. For example, the generation AI prioritizes posts from users with similar experiences. The expert collaboration unit supports collaboration with appropriate experts based on the emotions analyzed by the emotion analysis unit. For example, if a user inputs, "I want expert advice," the generation AI analyzes the request and introduces appropriate experts. The generation AI can also schedule sessions with experts and manage feedback on the session content. The generation AI can also analyze the content of sessions with experts and suggest topics to cover in the next session.For example, the generation AI might suggest addressing unresolved issues from the previous session in the next session. In this way, the sadness sharing compass according to the embodiment analyzes the user's emotions, matches them with other users and experts who have similar experiences, provides a community function for sharing emotions, and supports collaboration with appropriate experts. For example, a user can reduce feelings of loneliness by interacting with users with similar experiences and take care of their mental health by receiving advice from experts. The generation AI can also provide content to promote mental healing (e.g., relaxation music, meditation guides, encouraging messages, etc.) based on the user's emotional state. For example, if a user inputs "I'm feeling very depressed," the generation AI can analyze the user's emotions and suggest relaxation music or meditation guides. Furthermore, the generation AI can track the user's emotional state and progress in mental healing and provide regular feedback. For example, if a user inputs "I'm feeling a little better," the generation AI records the user's progress and suggests effective next steps.

[0058] The emotion analysis unit can refer to the user's past emotional history to perform more accurate matching. For example, the emotion analysis unit uses a generation AI to analyze emotional data previously entered by the user and understand emotional fluctuation patterns. For example, it performs matching that is most suitable for the current emotional state based on the user's past emotional history. This allows for more accurate matching by referring to the user's past emotional history.

[0059] The emotion analysis unit can track emotional changes in real time in response to the user's input content and perform matching at the optimal timing. The emotion analysis unit, for example, analyzes emotional data input by the user in real time and tracks emotional changes. For example, it calculates an emotion score according to the input content and performs matching at the optimal timing. This allows for tracking emotional changes in real time in response to the user's input content and performing matching at the optimal timing.

[0060] The emotion analysis unit uses the emotion estimation function to classify the user's emotions in detail and perform matching according to a specific emotional state. The emotion analysis unit, for example, uses the emotion estimation function to classify the user's emotions in detail. For example, emotions such as sadness, anger, and helplessness are finely classified and matching is performed accordingly. This makes it possible to classify the user's emotions in detail using the emotion estimation function and perform matching according to a specific emotional state.

[0061] The emotion analysis unit can suggest an appropriate self-care activity based on the result of the emotion analysis of the user. For example, the emotion analysis unit can suggest a self-care activity suitable for the user based on the result of the emotion analysis. For example, relaxation yoga can be suggested for a user who is feeling very sad. In this way, an appropriate self-care activity can be suggested based on the result of the emotion analysis of the user.

[0062] The emotion analysis unit can use the emotion analysis results to recommend online events and workshops that the user can participate in. For example, the emotion analysis unit can recommend online events and workshops that are suitable for the user based on the emotion analysis results. For example, the emotion analysis unit can suggest an online seminar to help users overcome sadness. In this way, the emotion analysis results can be used to recommend online events and workshops that the user can participate in.

[0063] The emotion analysis unit can use the emotion estimation function to provide personalized mental health resources according to the user's emotional state. For example, the emotion analysis unit can use the emotion estimation function to provide mental health resources according to the user's emotional state. For example, an encouraging video can be provided to a user who is feeling deeply sad. In this way, the emotion estimation function can be used to provide personalized mental health resources according to the user's emotional state.

[0064] The community providing unit can analyze the content of posts within a community and recommend posts that are most likely to resonate with users. For example, the community providing unit uses a generation AI to analyze the content of posts within a community and recommend posts that are most likely to resonate with users. For example, posts by users with the same experience are displayed preferentially. This allows the content of posts within a community to be analyzed and posts that are most likely to resonate with users to be recommended.

[0065] The community providing unit can analyze the flow of emotions within the community and encourage users to post at an appropriate time according to their emotional state. For example, the generation AI in the community providing unit analyzes the flow of emotions within the community and encourages users to post at an appropriate time. For example, it encourages users to post when their emotions are at their peak. This makes it possible to analyze the flow of emotions within the community and encourage users to post at an appropriate time according to their emotional state.

[0066] The community providing unit can use the emotion estimation function to analyze other users' emotional reactions to the user's posts and emphasize positive feedback. The community providing unit, for example, uses the emotion estimation function to analyze other users' emotional reactions to the user's posts. For example, posts with many positive reactions are highlighted. In this way, the emotion estimation function can be used to analyze other users' emotional reactions to the user's posts and emphasize positive feedback.

[0067] The community providing unit can suggest to the user that they keep an emotion diary in order to promote emotion sharing within the community. For example, the community providing unit can have the generation AI suggest to the user that they keep an emotion diary in order to promote emotion sharing within the community. For example, the generation AI can encourage the user to record their daily emotions. This can suggest to the user that they keep an emotion diary in order to promote emotion sharing within the community.

[0068] The community providing unit can make the community function for sharing emotions compatible with different languages ​​and cultural spheres, thereby realizing global emotional sharing. The community providing unit, for example, makes the community function for sharing emotions compatible with different languages, thereby realizing global emotional sharing. For example, it provides a multilingual interface. This makes it possible to make the community function for sharing emotions compatible with different languages ​​and cultural spheres, thereby realizing global emotional sharing.

[0069] The community providing unit can use the emotion estimation function to automatically generate support groups within the community according to the emotional state of the user. For example, the community providing unit uses the emotion estimation function to automatically generate support groups according to the emotional state of the user. For example, users who are feeling deep sadness are grouped together. In this way, the emotion estimation function can be used to automatically generate support groups within the community according to the emotional state of the user.

[0070] The expert collaboration unit can analyze the content of a session with an expert and suggest to the user topics to be covered in the next session. For example, the generative AI can analyze the content of a session with an expert and suggest topics to be covered in the next session. For example, it can suggest that an unresolved issue from the previous session be covered in the next session. This makes it possible to analyze the content of a session with an expert and suggest topics to be covered in the next session.

[0071] The expert collaboration unit can monitor the user's emotional state in real time and send alerts to experts as necessary. For example, the generation AI can monitor the user's emotional state in real time and send alerts to experts if there is a sudden change in emotion. For example, it can notify an expert when the user feels deep sadness. This makes it possible to monitor the user's emotional state in real time and send alerts to experts as necessary.

[0072] The expert collaboration unit can use the emotion estimation function to provide personalized expert advice according to the emotional state of the user. The expert collaboration unit, for example, uses the emotion estimation function to provide personalized expert advice according to the emotional state of the user. For example, a specific counseling method is suggested to a user who is feeling deep sadness. In this way, the emotion estimation function can be used to provide personalized expert advice according to the emotional state of the user.

[0073] The expert collaboration unit can recommend resources for self-care to the user in order to strengthen collaboration with experts. For example, the generation AI recommends resources for self-care to the user in order to strengthen collaboration with experts. For example, it suggests books and apps recommended by experts. In this way, it is possible to recommend resources for self-care to the user in order to strengthen collaboration with experts.

[0074] The expert collaboration unit can prompt the user to conduct a self-assessment of their emotional state before and after a session with an expert. For example, the expert collaboration unit prompts the user to conduct a self-assessment of their emotional state before and after a session with an expert by the generation AI. For example, the expert collaboration unit suggests that the user record an emotional score before the session. This makes it possible to prompt the user to conduct a self-assessment of their emotional state before and after a session with an expert.

[0075] The expert collaboration unit uses the emotion estimation function to select an expert according to the emotional state of the user and can recommend the most appropriate expert. The expert collaboration unit, for example, uses the emotion estimation function to select an expert according to the emotional state of the user. For example, a specific counselor is recommended for a user who is feeling deep sadness. In this way, the emotion estimation function can be used to select an expert according to the emotional state of the user and can recommend the most appropriate expert.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The Sadness Sharing Compass can also suggest appropriate physical activities based on the user's emotional state. For example, if the user is feeling deeply sad, the emotion analysis unit can suggest relaxation exercises such as a light walk or yoga. If the user is feeling stressed, the emotion analysis unit can also suggest stretching or deep breathing exercises. If the user is feeling anxious, the emotion analysis unit can also suggest meditation or mindfulness practice. This allows the user to maintain physical and mental balance through physical activities that correspond to their emotional state.

[0078] The Sadness Sharing Compass can also provide appropriate dietary and nutritional advice based on the user's emotional state. For example, if the user is feeling sad, the emotion analysis unit can suggest calming herbal tea or a nutritionally balanced meal. If the user is feeling stressed, the emotion analysis unit can also suggest foods and supplements that are effective in reducing stress. Furthermore, if the user is feeling anxious, the emotion analysis unit can also suggest foods and drinks that have a relaxing effect. This allows users to maintain their physical and mental health through dietary and nutritional advice tailored to their emotional state.

[0079] The Sadness Sharing Compass can also suggest appropriate art therapy activities based on the user's emotional state. For example, if the user is feeling deep sadness, the emotion analysis unit can suggest drawing or listening to music. If the user is feeling stressed, the emotion analysis unit can also suggest arts and crafts activities. If the user is feeling anxious, the emotion analysis unit can also suggest expressive activities such as dancing or theater. This allows the user to find emotional healing through art therapy that suits their emotional state.

[0080] The Sadness Sharing Compass can also suggest appropriate reading lists based on the user's emotional state. For example, if the user is feeling deep sadness, the emotion analysis unit can suggest books or poetry collections that have a soothing effect. If the user is feeling stressed, the emotion analysis unit can also suggest books on relaxation and stress management. If the user is feeling anxious, the emotion analysis unit can also suggest books on self-improvement and mindfulness. This allows the user to regain peace of mind through reading that suits their emotional state.

[0081] The Sadness Sharing Compass can also suggest appropriate travel destinations or retreats based on the user's emotional state. For example, if the user is feeling deep sadness, the emotion analysis unit can suggest a relaxing place in nature or a quiet retreat. If the user is feeling stressed, the emotion analysis unit can also suggest hot spring resorts or relaxation facilities. Furthermore, if the user is feeling anxious, the emotion analysis unit can also suggest meditation or yoga retreats. This allows users to refresh their mind and body through trips or retreats that suit their emotional state.

[0082] The sadness sharing compass can also provide communities based on the user's hobbies and interests, rather than based on the user's emotional state. For example, if the user is interested in music, the community providing unit can suggest a community of music lovers. If the user is interested in sports, the community providing unit can also suggest a community of sports fans. If the user is interested in cooking, the community providing unit can also suggest a community of cooking lovers. This allows users to enjoy interacting in communities based on their hobbies and interests, regardless of their emotional state.

[0083] The Sadness Sharing Compass can also suggest local events and groups based on the user's geographical location, rather than based on the user's emotional state. For example, the community providing unit can suggest events and groups in the area where the user lives. The community providing unit can also suggest local events that the user can participate in when traveling. Furthermore, the community providing unit can suggest local groups for the user to make new friends when they move. This allows the user to make new connections through local events and groups based on their geographical location.

[0084] The Sadness Sharing Compass can also provide specialized communities according to the user's occupation or career, rather than based on the user's emotional state. For example, if the user is a medical professional, the community providing unit can suggest a community of medical professionals. If the user is an educator, the community providing unit can also suggest a community of educators. If the user is an engineer, the community providing unit can also suggest a community of engineers. This allows the user to exchange information and receive support in specialized communities according to their occupation or career.

[0085] The Sadness Sharing Compass can also provide communities according to the user's life stage, rather than based on the user's emotional state. For example, if the user is newly married, the community providing unit can suggest a community for newlywed couples. If the user is raising children, the community providing unit can also suggest a community for childcare support. Furthermore, if the user is enjoying life after retirement, the community providing unit can also suggest a community for seniors. This allows the user to interact with people who share common experiences in communities according to their life stage.

[0086] The Sadness Sharing Compass can also provide support groups based on the user's health condition, rather than based on the user's emotional state. For example, if the user has a chronic illness, the community providing unit can suggest a support group for people with the same illness. Also, if the user is undergoing rehabilitation, the community providing unit can suggest a group to support rehabilitation. Furthermore, if the user has mental health issues, the community providing unit can suggest a mental health support group. This allows the user to receive the necessary support in a support group based on their health condition.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The emotion analysis unit analyzes the user's emotions. For example, the generation AI analyzes the information about the emotions and situation entered by the user and classifies the emotion category. The generation AI also evaluates the intensity of the user's emotions and analyzes the pattern of emotional fluctuations. For example, if a user enters "I recently lost a parent and I'm very sad," the generation AI analyzes the emotion and evaluates the intensity of sadness. Step 2: The matching unit matches the user with other users or experts who have had similar experiences based on the emotions analyzed by the emotion analysis unit. For example, the generation AI finds users or counselors with similar experiences and matches them with the most appropriate match for the user's emotional state. The generation AI can also refer to the user's past emotional history when making a match. Step 3: The community provider provides a community function that allows users matched by the matching unit to share their feelings with each other. For example, when a user posts something like, "Tell me how you overcame the grief of losing a parent," the generation AI analyzes the post and guides the user to the appropriate community or thread. The generation AI can also analyze replies and comments from other users to provide useful information to the user. Step 4: The expert collaboration unit supports collaboration with an appropriate expert based on the emotions analyzed by the emotion analysis unit. For example, if a user inputs "I want expert advice," the generation AI will analyze that request and introduce an appropriate expert. The generation AI can also schedule sessions with experts and manage feedback on the session content.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The 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.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 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.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[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 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.

[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 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).

[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] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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. an emotion analysis unit that analyzes the emotion of a user; a matching unit that matches users with other users or experts who have similar experiences based on the emotions analyzed by the emotion analysis unit; a community providing unit that provides a community function that allows users matched by the matching unit to share feelings with each other; an expert collaboration unit that supports collaboration with an appropriate expert based on the emotion analyzed by the emotion analysis unit. A system characterized by:

2. The emotion analysis unit Refer to the user's past emotion history to perform more accurate matching 2. The system of claim 1.

3. The emotion analysis unit Suggesting appropriate self-care activities based on the results of user sentiment analysis 2. The system of claim 1.

4. The community providing unit Analyze the content of posts within the community and recommend posts that are most likely to resonate with users.

2. The system of claim 1.

5. The Expert Collaboration Department shall: Analyzes the content of sessions with experts and suggests topics for the user to cover in the next session 2. The system of claim 1.

6. The emotion analysis unit Tracks changes in emotions in real time based on user input and matches at the optimal time 2. The system of claim 1.

7. The community providing unit Using emotion estimation, the app analyzes other users' emotional reactions to your posts and highlights positive feedback.

2. The system of claim 1.

8. The Expert Collaboration Department shall: Monitor users' emotional state in real time and alert experts as needed 2. The system of claim 1.

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