system

Abstract

The system of the embodiment aims to provide mental support and create study plans for cancer patients in the AYA generation. [Solution] A system according to an embodiment includes an analysis unit, a support unit, and a planning unit. The analysis unit analyzes the mental state of a user. The support unit provides mental support based on the mental state analyzed by the analysis unit. The planning unit creates a study plan based on the mental support provided by the support unit.

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 does not provide sufficient mental support or learning assistance to AYA cancer patients, and there is room for improvement.

[0005] The system of the embodiment aims to provide mental support and create study plans for cancer patients in the AYA generation. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a support unit, and a planning unit. The analysis unit analyzes the mental state of the user. The support unit provides mental support based on the mental state analyzed by the analysis unit. The planning unit creates a study plan based on the mental support provided by the support unit. [Effects of the Invention]

[0007] The system of the embodiment can provide mental support and create study plans for cancer patients in the AYA generation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online platform according to an embodiment of the present invention is a system that provides mental support and learning support for AYA cancer patients. This system analyzes the user's mental state, provides appropriate mental support, and, if necessary, utilizes a learning support platform to create a learning plan. This allows the online platform to reduce anxiety and alleviate feelings of loneliness for AYA cancer patients. For example, a user accesses the online platform and inputs their mental state. For example, the user may input, "I've been feeling very anxious lately." This information is then entered into a generating AI. The generating AI then analyzes the input information and understands the user's mental state. Based on the user's input, the generating AI provides appropriate mental support. For example, if the user is feeling anxious, the generating AI may suggest relaxation techniques or counseling. Furthermore, if necessary, the learning support platform is utilized to create a learning plan for the user. For example, if the user inputs, "I want to obtain a qualification for the future," the generating AI may suggest an appropriate learning plan. This allows the user to study in accordance with their own life plan. This allows the online platform to reduce anxiety and alleviate feelings of loneliness for AYA cancer patients. Users can receive mental support and learning assistance through the online platform, allowing them to focus on their treatment with peace of mind.

[0029] An online platform according to an embodiment includes an analysis unit, a support unit, and a planning unit. The analysis unit analyzes a user's mental state using a generation AI. Examples of mental states include, but are not limited to, stress levels, emotional states, and psychological health. For example, the analysis unit causes the generation AI to analyze the user's mental state based on information input by the user, such as "I've been feeling very anxious lately." The analysis unit can also extract information that enables the generation AI to provide appropriate mental support based on the user's input. The support unit provides appropriate mental support based on the mental state analyzed by the analysis unit. Examples of mental support include, but are not limited to, counseling, relaxation techniques, and the provision of mental health resources. For example, the support unit causes the generation AI to suggest relaxation methods and counseling. The support unit can also provide appropriate mental support based on the generation AI's mental state. The planning unit creates a study plan for the user based on the mental support provided by the support unit. The study plan can include, but is not limited to, study goals, schedules, and evaluation criteria. For example, the planning unit allows the generation AI to propose a study plan that matches the user's life plan. The planning unit also allows the generation AI to manage the user's learning progress and provide appropriate feedback. This allows the online platform according to the embodiment to analyze the user's mental state, provide appropriate mental support, and create a study plan.

[0030] The online platform further includes a community unit that provides a community function. The community unit provides a community function that allows users to interact with other users. Examples of the community function include, but are not limited to, a forum, a chat room, and a group discussion. For example, the community unit allows users to post questions in a forum and receive answers from other users. The community unit also allows users to interact with other users in real time through a chat room. Furthermore, the community unit also allows users to exchange opinions on a specific topic through a group discussion. In this way, users can interact with other users by using the community function.

[0031] The online platform further includes a peer support unit that provides peer support. The peer support unit provides a peer support function that allows users to receive support from other users. Peer support includes, but is not limited to, support from colleagues or peers, group sessions, and mentoring, for example. The peer support unit allows users to receive support from colleagues or peers, for example. The peer support unit also allows users to solve problems together with other users through group sessions. Furthermore, the peer support unit also allows users to receive advice from experienced mentors through mentoring, allowing users to receive peer support.

[0032] The online platform further includes a progress management unit that manages learning progress. The progress management unit provides a function for managing the user's learning progress. Learning progress includes, but is not limited to, for example, achievement level, progress status, and goal achievement rate. The progress management unit, for example, monitors the user's learning progress in real time and evaluates the achievement level. The progress management unit can also provide appropriate feedback based on the user's learning progress. Furthermore, the progress management unit can visualize the progress status based on the user's learning goals. This allows the user's learning progress to be managed.

[0033] The online platform further includes a feedback unit that provides feedback. The feedback unit provides a function that allows the user to receive feedback. The feedback includes, but is not limited to, evaluation comments, improvement suggestions, and progress reports. For example, the feedback unit provides evaluation comments based on the user's learning progress. The feedback unit can also make improvement suggestions based on the user's learning goals. The feedback unit can also provide a function that reports the user's learning progress, allowing the user to receive feedback.

[0034] The analysis unit can analyze the user's past mental state data and select an analysis algorithm. For example, the analysis unit allows the generation AI to select the most effective analysis algorithm based on the user's past mental state data. The analysis unit can also analyze the fluctuation pattern of the user's mental state and allow the generation AI to select an analysis algorithm appropriate for that pattern. Furthermore, the analysis unit can compare the user's past mental state data with their current state and allow the generation AI to select the optimal analysis algorithm. This allows the optimal analysis algorithm to be selected based on the user's past mental state data. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis methods, for example.

[0035] When analyzing the mental state, the analysis unit can improve the accuracy of the analysis based on the user's lifestyle and environmental factors. The analysis unit, for example, takes into account the user's lifestyle rhythm and daily activities, and the generation AI improves the accuracy of the mental state analysis. The analysis unit can also take into account the user's environmental factors (work environment, home environment, etc.) to improve the accuracy of the mental state analysis. Furthermore, the analysis unit can also take into account the user's lifestyle (work stress, family problems, etc.) to improve the accuracy of the mental state analysis. In this way, the analysis accuracy can be improved by taking into account the user's lifestyle and environmental factors. Lifestyle conditions include, for example, lifestyle habits, home environment, social situation, etc., but are not limited to these examples. Environmental factors include, for example, the workplace environment, weather conditions, and surrounding human relationships, but are not limited to these examples.

[0036] When analyzing the mental state, the analysis unit can select an analysis means according to the user's input method. For example, when the user uses voice input, the analysis unit has the generation AI analyze the voice data to understand the mental state. In addition, when the user uses text input, the analysis unit can also have the generation AI analyze the text data to understand the mental state. Furthermore, when the user uses image input, the analysis unit can have the generation AI analyze the image data to understand the mental state. This makes it possible to select the optimal analysis means according to the user's input method. Input methods include, but are not limited to, voice input, text input, and image input, for example.

[0037] When analyzing the mental state, the analysis unit can prioritize analysis of highly relevant data taking into account the user's geographical location information. For example, if the user lives in a specific area, the analysis unit causes the generation AI to analyze the mental state taking into account the environmental factors of that area. In addition, if the user is in a specific location, the analysis unit can also cause the generation AI to analyze the mental state taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the analysis unit can also cause the generation AI to analyze the mental state taking into account the environmental factors of that area. This allows the analysis of highly relevant data to be prioritized taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0038] When analyzing the mental state, the analysis unit can analyze the user's social media activity and analyze related data. The analysis unit, for example, analyzes the content of the user's social media posts, and the generation AI analyzes the mental state. The analysis unit can also have the generation AI analyze the mental state based on the activity of the user's friends on social media. Furthermore, the analysis unit can analyze the user's check-in information on social media, and the generation AI can analyze the mental state. This allows the user's social media activity to be analyzed and related data to be analyzed. Social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples.

[0039] When analyzing the mental state, the analysis unit can customize the analysis method by reflecting the user's past feedback. In the analysis unit, for example, the generation AI customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also have the generation AI select the optimal analysis method from the user's past feedback. Furthermore, the analysis unit can also have the generation AI adjust the analysis method by reflecting the user's past feedback. This allows the analysis method to be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0040] When providing mental support, the support unit can monitor changes in the user's mental state in real time and update the support content as appropriate. For example, if the user's mental state changes suddenly, the support unit causes the generation AI to update the support content in real time. In addition, if the user's mental state changes gradually, the support unit can also cause the generation AI to update the support content as appropriate. Furthermore, if the user's mental state is stable, the support unit can also provide support content to help the generation AI maintain that state. This makes it possible to monitor changes in the user's mental state in real time and update the support content as appropriate. Changes in the mental state include, but are not limited to, changes in stress levels, emotional fluctuations, and changes in psychological health.

[0041] When providing mental support, the support unit can select a support method by referring to the user's past support history. For example, the support unit allows the generation AI to select the optimal support method based on the user's past support history. The support unit can also allow the generation AI to select an effective support method from the user's past support history. Furthermore, the support unit can allow the generation AI to adjust the support method by referring to the user's past support history. In this way, the optimal support method can be selected by referring to the user's past support history. The support history includes, for example, past support content, frequency of support, and effectiveness of support, but is not limited to these examples.

[0042] When providing mental support, the support unit can adjust the timing of the support based on the user's lifestyle and daily activities. In the support unit, for example, the generation AI adjusts the timing of the support to match the user's lifestyle. In addition, the support unit can also adjust the timing of the support based on the user's daily activities. Furthermore, the support unit can also suggest the optimal timing of support by the generation AI taking into account the user's lifestyle and daily activities. This makes it possible to adjust the timing of support based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0043] When providing mental support, the support unit can select a support method taking into account the user's geographical location information. For example, if the user lives in a specific area, the support unit allows the generation AI to select the optimal support method taking into account the environmental factors of that area. In addition, if the user is in a specific location, the support unit can also select the optimal support method taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the support unit can also select the optimal support method taking into account the environmental factors of that area. In this way, the optimal support method can be selected taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0044] When providing mental support, the support unit can analyze the user's social media activity and provide relevant support. For example, the support unit analyzes the content of the user's social media posts, and the generation AI provides relevant support. The support unit can also refer to the activities of the user's friends on social media and the generation AI provides relevant support. Furthermore, the support unit can analyze the user's social media check-in information, and the generation AI can provide relevant support. In this way, the user's social media activity can be analyzed and relevant support can be provided. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to such examples.

[0045] When providing mental support, the support unit can customize the support content by reflecting the user's past feedback. In the support unit, for example, the generation AI customizes the support content based on feedback provided by the user in the past. The support unit can also select the optimal support content from the user's past feedback. Furthermore, the support unit can also adjust the support content by reflecting the user's past feedback. In this way, the support content can be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0046] When formulating a study plan, the planning unit can analyze the user's past study history and propose a plan. For example, the planning unit allows the generation AI to propose an optimal study plan based on the user's past study history. The planning unit can also allow the generation AI to propose an effective study method based on the user's past study history. Furthermore, the planning unit can allow the generation AI to adjust the study plan by referring to the user's past study history. This makes it possible to analyze the user's past study history and propose an optimal plan. The study history includes, for example, past study content, study progress, study results, etc., but is not limited to these examples.

[0047] When creating a study plan, the planning unit can adjust the timing of the plan based on the user's lifestyle and daily activities. For example, the planning unit allows the generation AI to adjust the timing of the study plan to match the user's lifestyle. The planning unit can also allow the generation AI to adjust the timing of the study plan based on the user's daily activities. Furthermore, the planning unit can also allow the generation AI to propose optimal timing for the study plan by taking into account the user's lifestyle and daily activities. This allows the timing of the plan to be adjusted based on the user's lifestyle and daily activities. Examples of lifestyle include, but are not limited to, sleep patterns, meal timings, and activity schedules.

[0048] The planning unit can customize the content of the study plan based on the user's goals and interests when creating the study plan. For example, the planning unit allows the generation AI to propose an optimal study plan based on the user's goals. The planning unit can also allow the generation AI to customize the study plan based on the user's interests. Furthermore, the planning unit can allow the generation AI to propose an optimal study plan taking the user's goals and interests into consideration. This allows the plan content to be customized based on the user's goals and interests. Examples of goals and interests include, but are not limited to, study goals, career goals, and personal interests.

[0049] When creating a study plan, the planning unit can propose a plan taking into account the user's geographical location information. For example, if the user lives in a specific area, the planning unit allows the generation AI to propose an optimal study plan taking into account the environmental factors of that area. In addition, if the user is in a specific location, the planning unit can also allow the generation AI to propose an optimal study plan taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the planning unit can also allow the generation AI to propose an optimal study plan taking into account the environmental factors of that area. In this way, the optimal plan can be proposed taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0050] When creating a study plan, the planning unit can analyze the user's social media activity and suggest a relevant plan. For example, the planning unit analyzes the content of the user's social media posts, and the generation AI suggests a relevant study plan. The planning unit can also refer to the activity of the user's friends on social media, and the generation AI can suggest a relevant study plan. Furthermore, the planning unit can analyze the user's social media check-in information, and the generation AI can suggest a relevant study plan. In this way, the user's social media activity can be analyzed to suggest a relevant plan. Social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples.

[0051] When creating a study plan, the planning unit can customize the plan contents by reflecting the user's past feedback. In the planning unit, for example, the generation AI customizes the study plan based on feedback provided by the user in the past. The planning unit can also have the generation AI select the optimal study plan from the user's past feedback. Furthermore, the planning unit can also have the generation AI adjust the study plan by reflecting the user's past feedback. In this way, the plan contents can be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0052] When joining a community, the community unit can analyze the user's past community activity history and suggest a participation method. In the community unit, for example, the generation AI can suggest an optimal participation method based on the user's past community activity history. In addition, the community unit can also have the generation AI suggest an effective participation method based on the user's past community activity history. Furthermore, the community unit can have the generation AI adjust the participation method based on the user's past community activity history. In this way, the user's past community activity history can be analyzed and the optimal participation method can be suggested. Community activity history includes, for example, past participation groups, activity content, activity frequency, etc., but is not limited to these examples.

[0053] When joining a community, the community unit can select a group to join based on the user's interests. For example, the community unit can have the generation AI suggest the most suitable community group based on the user's interests. The community unit can also have the generation AI select a community group based on the user's interests. Furthermore, the community unit can have the generation AI suggest the most suitable community group taking the user's interests and concerns into consideration. This allows the user to select a group to join based on their interests and concerns. Interests and concerns include, but are not limited to, hobbies, fields of expertise, and study themes.

[0054] When joining a community, the community unit can suggest a group taking into account the user's geographical location information. For example, if the user lives in a specific area, the community unit can suggest the optimal community group by the generation AI taking into account the environmental factors of that area. Also, if the user is in a specific location, the community unit can suggest the optimal community group by the generation AI taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the community unit can suggest the optimal community group by the generation AI taking into account the environmental factors of that area. In this way, the optimal group can be suggested taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location history, etc.

[0055] When joining a community, the community unit can analyze the user's social media activity and suggest related groups. For example, the community unit analyzes the content of the user's social media posts, and the generation AI suggests related community groups. The community unit can also refer to the activity of the user's friends on social media and suggest related community groups. Furthermore, the community unit can analyze the user's social media check-in information and suggest related community groups. In this way, related groups can be suggested by analyzing the user's social media activity. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to such examples.

[0056] When providing peer support, the peer support unit can select a support method by referring to the user's past support history. In the peer support unit, for example, the generation AI selects the optimal support method based on the user's past support history. The peer support unit can also select an effective support method by the generation AI from the user's past support history. Furthermore, the peer support unit can also adjust the support method by referring to the user's past support history. In this way, the optimal support method can be selected by referring to the user's past support history. The support history includes, for example, past support content, frequency of support, and effectiveness of support, but is not limited to these examples.

[0057] When providing peer support, the peer support unit can adjust the timing of support based on the user's lifestyle and daily activities. In the peer support unit, for example, the generation AI adjusts the timing of support to match the user's lifestyle. In addition, the peer support unit can also adjust the timing of support based on the user's daily activities. Furthermore, the peer support unit can also suggest the optimal timing of support by the generation AI taking into account the user's lifestyle and daily activities. This makes it possible to adjust the timing of support based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0058] When providing peer support, the peer support unit can select the optimal support method by referring to the user's past support history. In the peer support unit, for example, the generation AI selects the optimal support method based on the user's past support history. In addition, the peer support unit can also have the generation AI select an effective support method from the user's past support history. Furthermore, the peer support unit can also have the generation AI adjust the support method by referring to the user's past support history. In this way, the optimal support method can be selected by referring to the user's past support history. The support history includes, for example, past support content, frequency of support, and effectiveness of support, but is not limited to these examples.

[0059] When providing peer support, the peer support unit can select a support method taking into account the user's geographical location information. For example, if the user lives in a specific area, the peer support unit selects the optimal peer support method using the generation AI, taking into account the environmental factors of that area. Furthermore, if the user is in a specific location, the peer support unit can select the optimal peer support method using the generation AI, taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the peer support unit can select the optimal peer support method using the generation AI, taking into account the environmental factors of that area. This allows the optimal support method to be selected taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and location history.

[0060] When providing peer support, the peer support unit can analyze the user's social media activity and provide relevant support. For example, the peer support unit analyzes the content of the user's social media posts, and the generation AI provides relevant peer support. The peer support unit can also refer to the activities of the user's friends on social media to provide relevant peer support. Furthermore, the peer support unit can analyze the user's social media check-in information, and the generation AI can provide relevant peer support. In this way, the user's social media activity can be analyzed to provide relevant support. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to these examples.

[0061] When providing peer support, the peer support unit can customize the support content by reflecting the user's past feedback. In the peer support unit, for example, the generation AI customizes the peer support content based on feedback provided by the user in the past. In addition, the peer support unit can also select the optimal peer support content by the generation AI based on the user's past feedback. Furthermore, the peer support unit can also adjust the peer support content by reflecting the user's past feedback. In this way, the support content can be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0062] During progress management, the progress management unit can analyze the user's past learning history and propose a management method. In the progress management unit, for example, the generation AI proposes an optimal progress management method based on the user's past learning history. The progress management unit can also propose an effective progress management method based on the user's past learning history. Furthermore, the progress management unit can also allow the generation AI to adjust the progress management method by referring to the user's past learning history. This makes it possible to analyze the user's past learning history and propose an optimal management method. Learning history includes, for example, past learning content, learning progress, learning results, etc., but is not limited to these examples.

[0063] The progress management unit can adjust the timing of progress management based on the user's lifestyle and daily activities during progress management. In the progress management unit, for example, the generation AI adjusts the timing of progress management to match the user's lifestyle. In addition, the progress management unit can also adjust the timing of progress management based on the user's daily activities. Furthermore, the progress management unit can also suggest the optimal timing of progress management by the generation AI taking into account the user's lifestyle and daily activities. This allows the timing of management to be adjusted based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0064] The progress management unit can customize the management content based on the user's goals and interests when managing progress. In the progress management unit, for example, the generation AI proposes an optimal progress management method based on the user's goals. The progress management unit can also customize the progress management content based on the user's interests. Furthermore, the progress management unit can also propose an optimal progress management method by the generation AI taking the user's goals and interests into consideration. This allows the management content to be customized based on the user's goals and interests. Goals and interests include, but are not limited to, for example, learning goals, career goals, and personal interests.

[0065] The progress management unit can propose a management method taking into account the user's geographical location information when managing progress. For example, if the user lives in a specific area, the progress management unit proposes the optimal progress management method for the generation AI taking into account the environmental factors of that area. In addition, if the user is in a specific location, the progress management unit can also propose the optimal progress management method for the generation AI taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the progress management unit can also propose the optimal progress management method for the generation AI taking into account the environmental factors of that area. In this way, the optimal management method can be proposed taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0066] The progress management unit can analyze the user's social media activity during progress management and suggest a relevant management method. For example, the progress management unit analyzes the content of the user's social media posts, and the generation AI suggests a relevant progress management method. The progress management unit can also refer to the activity of the user's friends on social media and suggest a relevant progress management method. Furthermore, the progress management unit can analyze the user's social media check-in information, and the generation AI can suggest a relevant progress management method. In this way, the user's social media activity can be analyzed and a relevant management method can be suggested. Social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples.

[0067] When providing feedback, the feedback unit can analyze the user's past feedback history and suggest the optimal feedback method. In the feedback unit, for example, the generation AI can suggest the optimal feedback method based on the user's past feedback history. In addition, the feedback unit can also have the generation AI suggest an effective feedback method based on the user's past feedback history. Furthermore, the feedback unit can also have the generation AI adjust the feedback method by referring to the user's past feedback history. In this way, the user's past feedback history can be analyzed and the optimal feedback method can be suggested. The feedback history includes, for example, past feedback content, feedback frequency, feedback effect, etc., but is not limited to these examples.

[0068] When providing feedback, the feedback unit can adjust the timing of the feedback based on the user's lifestyle and daily activities. In the feedback unit, for example, the generation AI adjusts the timing of the feedback to match the user's lifestyle. In addition, the feedback unit can also adjust the timing of the feedback based on the user's daily activities. Furthermore, the feedback unit can also suggest the optimal timing of the feedback by the generation AI taking into account the user's lifestyle and daily activities. This makes it possible to adjust the timing of the feedback based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0069] When providing feedback, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. In the feedback unit, for example, the generation AI selects the optimal feedback method based on the user's past feedback history. In addition, the feedback unit can also have the generation AI select an effective feedback method from the user's past feedback history. Furthermore, the feedback unit can have the generation AI adjust the feedback method by referring to the user's past feedback history. In this way, the optimal feedback method can be selected by referring to the user's past feedback history. The feedback history includes, for example, past feedback content, feedback frequency, feedback effect, etc., but is not limited to these examples.

[0070] When providing feedback, the feedback unit can suggest a feedback method taking into account the user's geographical location information. For example, if the user lives in a specific area, the feedback unit can suggest the optimal feedback method by the generation AI taking into account the environmental factors of the area. Also, if the user is in a specific location, the feedback unit can suggest the optimal feedback method by the generation AI taking into account the environmental factors of the area. Furthermore, if the user frequently visits a specific area, the feedback unit can suggest the optimal feedback method by the generation AI taking into account the environmental factors of the area. In this way, the optimal feedback method can be suggested taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0071] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit analyzes the content of the user's social media posts, and the generation AI provides relevant feedback. The feedback unit can also refer to the activities of the user's friends on social media and the generation AI provides relevant feedback. Furthermore, the feedback unit can analyze the user's social media check-in information, and the generation AI can provide relevant feedback. In this way, the user's social media activity can be analyzed and relevant feedback can be provided. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to these examples.

[0072] When providing feedback, the feedback unit can customize the feedback content by reflecting the user's past feedback history. In the feedback unit, for example, the generation AI customizes the feedback content based on feedback provided by the user in the past. In addition, the feedback unit can also have the generation AI select optimal feedback content from the user's past feedback. Furthermore, the feedback unit can also have the generation AI adjust the feedback content by reflecting the user's past feedback. In this way, the feedback content can be customized by reflecting the user's past feedback history. The feedback history includes, for example, past feedback content, feedback frequency, feedback effect, etc., but is not limited to these examples.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The analysis unit can take the user's past medical history into consideration when analyzing the user's mental state. For example, the generation AI can improve the accuracy of its analysis of the user's mental state based on the treatments and diagnostic results the user has received in the past. The analysis unit can also enable the generation AI to select the optimal mental support method based on the user's past medical history. Furthermore, the analysis unit can also adjust the generation AI's analysis method of the user's mental state by reflecting the user's medical history. This allows the generation AI to improve the accuracy of its analysis of the user's mental state by taking the user's medical history into consideration.

[0075] When providing mental support to a user, the support unit can customize the support content based on the user's hobbies and interests. For example, if the user is interested in music, the generation AI can suggest music therapy. Also, if the user is interested in art, the generation AI can suggest art therapy. Furthermore, if the user is interested in sports, the generation AI can suggest relaxation methods through sports. This allows the content of mental support to be customized based on the user's hobbies and interests.

[0076] When creating a user's study plan, the planning unit can adjust the contents of the plan taking into account the user's career goals. For example, if the user is aiming for a specific occupation, the generation AI can propose a study plan that includes the skills and knowledge required for that occupation. Also, if the user is considering a career change, the generation AI can propose a study plan required for the new career. Furthermore, if the user is aiming for promotion, the generation AI can propose a study plan for improving the skills required for promotion. This allows the contents of the study plan to be adjusted based on the user's career goals.

[0077] When a user joins a community, the community unit can analyze the user's past community activity history and suggest the optimal way to participate. For example, the generation AI can suggest the optimal community activity based on the content and frequency of community activities in which the user has participated in the past. The generation AI can also suggest an effective way to participate based on the user's past community activity history. Furthermore, the generation AI can adjust the content of the community activity by referring to the user's past community activity history. In this way, the generation AI can suggest the optimal way to participate by analyzing the user's past community activity history.

[0078] When providing peer support to a user, the peer support unit can adjust the timing of support based on the user's lifestyle and daily activities. For example, the generation AI adjusts the timing of support to match the user's lifestyle. The generation AI can also adjust the timing of support based on the user's daily activities. Furthermore, the generation AI can propose the optimal timing of support by taking the user's lifestyle and daily activities into consideration. This makes it possible to adjust the timing of support based on the user's lifestyle and daily activities.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The analysis unit uses the generation AI to analyze the user's mental state. Mental state includes stress level, emotional state, and psychological health. For example, if the user inputs information such as "I've been feeling very anxious lately," the generation AI analyzes the user's mental state. The analysis unit can also extract information that allows the generation AI to provide appropriate mental support based on the user's input. Step 2: The support unit provides appropriate mental support based on the mental state analyzed by the analysis unit. Mental support can include counseling, relaxation techniques, and the provision of mental health resources. For example, the generation AI can suggest relaxation methods and counseling. The support unit can also provide appropriate mental support based on the user's mental state. Step 3: The planning unit creates a study plan for the user based on the mental support provided by the support unit. The study plan includes learning goals, schedules, evaluation criteria, etc. For example, the generation AI proposes a study plan that matches the user's life plan. The planning unit also allows the generation AI to manage the user's study progress and provide appropriate feedback.

[0081] (Example 2) An online platform according to an embodiment of the present invention is a system that provides mental support and learning support for AYA cancer patients. This system analyzes the user's mental state, provides appropriate mental support, and, if necessary, utilizes a learning support platform to create a learning plan. This allows the online platform to reduce anxiety and alleviate feelings of loneliness for AYA cancer patients. For example, a user accesses the online platform and inputs their mental state. For example, the user may input, "I've been feeling very anxious lately." This information is then entered into a generating AI. The generating AI then analyzes the input information and understands the user's mental state. Based on the user's input, the generating AI provides appropriate mental support. For example, if the user is feeling anxious, the generating AI may suggest relaxation techniques or counseling. Furthermore, if necessary, the learning support platform is utilized to create a learning plan for the user. For example, if the user inputs, "I want to obtain a qualification for the future," the generating AI may suggest an appropriate learning plan. This allows the user to study in accordance with their own life plan. This allows the online platform to reduce anxiety and alleviate feelings of loneliness for AYA cancer patients. Users can receive mental support and learning assistance through the online platform, allowing them to focus on their treatment with peace of mind.

[0082] An online platform according to an embodiment includes an analysis unit, a support unit, and a planning unit. The analysis unit analyzes a user's mental state using a generation AI. Examples of mental states include, but are not limited to, stress levels, emotional states, and psychological health. For example, the analysis unit causes the generation AI to analyze the user's mental state based on information input by the user, such as "I've been feeling very anxious lately." The analysis unit can also extract information that enables the generation AI to provide appropriate mental support based on the user's input. The support unit provides appropriate mental support based on the mental state analyzed by the analysis unit. Examples of mental support include, but are not limited to, counseling, relaxation techniques, and the provision of mental health resources. For example, the support unit causes the generation AI to suggest relaxation methods and counseling. The support unit can also provide appropriate mental support based on the generation AI's mental state. The planning unit creates a study plan for the user based on the mental support provided by the support unit. The study plan can include, but is not limited to, study goals, schedules, and evaluation criteria. For example, the planning unit allows the generation AI to propose a study plan that matches the user's life plan. The planning unit also allows the generation AI to manage the user's learning progress and provide appropriate feedback. This allows the online platform according to the embodiment to analyze the user's mental state, provide appropriate mental support, and create a study plan.

[0083] The online platform further includes a community unit that provides a community function. The community unit provides a community function that allows users to interact with other users. Examples of the community function include, but are not limited to, a forum, a chat room, and a group discussion. For example, the community unit allows users to post questions in a forum and receive answers from other users. The community unit also allows users to interact with other users in real time through a chat room. Furthermore, the community unit also allows users to exchange opinions on a specific topic through a group discussion. In this way, users can interact with other users by using the community function.

[0084] The online platform further includes a peer support unit that provides peer support. The peer support unit provides a peer support function that allows users to receive support from other users. Peer support includes, but is not limited to, support from colleagues or peers, group sessions, and mentoring, for example. The peer support unit allows users to receive support from colleagues or peers, for example. The peer support unit also allows users to solve problems together with other users through group sessions. Furthermore, the peer support unit also allows users to receive advice from experienced mentors through mentoring, allowing users to receive peer support.

[0085] The online platform further includes a progress management unit that manages learning progress. The progress management unit provides a function for managing the user's learning progress. Learning progress includes, but is not limited to, for example, achievement level, progress status, and goal achievement rate. The progress management unit, for example, monitors the user's learning progress in real time and evaluates the achievement level. The progress management unit can also provide appropriate feedback based on the user's learning progress. Furthermore, the progress management unit can visualize the progress status based on the user's learning goals. This allows the user's learning progress to be managed.

[0086] The online platform further includes a feedback unit that provides feedback. The feedback unit provides a function that allows the user to receive feedback. The feedback includes, but is not limited to, evaluation comments, improvement suggestions, and progress reports. For example, the feedback unit provides evaluation comments based on the user's learning progress. The feedback unit can also make improvement suggestions based on the user's learning goals. The feedback unit can also provide a function that reports the user's learning progress, allowing the user to receive feedback.

[0087] The analysis unit can estimate the user's emotions and adjust the mental state analysis method based on the estimated user emotions. For example, if the user is feeling strong anxiety, the analysis unit can cause the generation AI to prioritize suggestions for relaxation methods or counseling. Furthermore, if the user is feeling mild stress, the analysis unit can also cause the generation AI to suggest simple exercises or relaxation techniques to reduce stress. Furthermore, if the user is feeling positive emotions, the analysis unit can provide positive feedback and motivation for the generation AI to maintain those emotions. This allows the mental state analysis method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The analysis unit can analyze the user's past mental state data and select an analysis algorithm. For example, the analysis unit allows the generation AI to select the most effective analysis algorithm based on the user's past mental state data. The analysis unit can also analyze the fluctuation pattern of the user's mental state and allow the generation AI to select an analysis algorithm appropriate for that pattern. Furthermore, the analysis unit can compare the user's past mental state data with their current state and allow the generation AI to select the optimal analysis algorithm. This allows the optimal analysis algorithm to be selected based on the user's past mental state data. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis methods, for example.

[0089] When analyzing the mental state, the analysis unit can improve the accuracy of the analysis based on the user's lifestyle and environmental factors. The analysis unit, for example, takes into account the user's lifestyle rhythm and daily activities, and the generation AI improves the accuracy of the mental state analysis. The analysis unit can also take into account the user's environmental factors (work environment, home environment, etc.) to improve the accuracy of the mental state analysis. Furthermore, the analysis unit can also take into account the user's lifestyle (work stress, family problems, etc.) to improve the accuracy of the mental state analysis. In this way, the analysis accuracy can be improved by taking into account the user's lifestyle and environmental factors. Lifestyle conditions include, for example, lifestyle habits, home environment, social situation, etc., but are not limited to these examples. Environmental factors include, for example, the workplace environment, weather conditions, and surrounding human relationships, but are not limited to these examples.

[0090] When analyzing the mental state, the analysis unit can select an analysis means according to the user's input method. For example, when the user uses voice input, the analysis unit has the generation AI analyze the voice data to understand the mental state. In addition, when the user uses text input, the analysis unit can also have the generation AI analyze the text data to understand the mental state. Furthermore, when the user uses image input, the analysis unit can have the generation AI analyze the image data to understand the mental state. This makes it possible to select the optimal analysis means according to the user's input method. Input methods include, but are not limited to, voice input, text input, and image input, for example.

[0091] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling strong anxiety, the analysis unit can cause the generation AI to prioritize analysis results for anxiety reduction. Furthermore, if the user is feeling mild stress, the analysis unit can also cause the generation AI to prioritize analysis results for stress reduction. Furthermore, if the user is feeling positive emotions, the analysis unit can also prioritize analysis results for maintaining those emotions. This allows the priority of the analysis results to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] When analyzing the mental state, the analysis unit can prioritize analysis of highly relevant data taking into account the user's geographical location information. For example, if the user lives in a specific area, the analysis unit causes the generation AI to analyze the mental state taking into account the environmental factors of that area. In addition, if the user is in a specific location, the analysis unit can also cause the generation AI to analyze the mental state taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the analysis unit can also cause the generation AI to analyze the mental state taking into account the environmental factors of that area. This allows the analysis of highly relevant data to be prioritized taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0093] When analyzing the mental state, the analysis unit can analyze the user's social media activity and analyze related data. The analysis unit, for example, analyzes the content of the user's social media posts, and the generation AI analyzes the mental state. The analysis unit can also have the generation AI analyze the mental state based on the activity of the user's friends on social media. Furthermore, the analysis unit can analyze the user's check-in information on social media, and the generation AI can analyze the mental state. This allows the user's social media activity to be analyzed and related data to be analyzed. Social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples.

[0094] When analyzing the mental state, the analysis unit can customize the analysis method by reflecting the user's past feedback. In the analysis unit, for example, the generation AI customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also have the generation AI select the optimal analysis method from the user's past feedback. Furthermore, the analysis unit can also have the generation AI adjust the analysis method by reflecting the user's past feedback. This allows the analysis method to be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0095] The support unit can estimate the user's emotions and adjust the content of the mental support based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI of the support unit can suggest relaxation methods or counseling. Furthermore, if the user is feeling mild stress, the generation AI of the support unit can also suggest simple exercises or relaxation techniques to reduce stress. Furthermore, if the user is feeling positive emotions, the support unit can provide positive feedback and motivation for the generation AI to maintain those emotions. This allows the content of the mental support to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] When providing mental support, the support unit can monitor changes in the user's mental state in real time and update the support content as appropriate. For example, if the user's mental state changes suddenly, the support unit causes the generation AI to update the support content in real time. In addition, if the user's mental state changes gradually, the support unit can also cause the generation AI to update the support content as appropriate. Furthermore, if the user's mental state is stable, the support unit can also provide support content to help the generation AI maintain that state. This makes it possible to monitor changes in the user's mental state in real time and update the support content as appropriate. Changes in the mental state include, but are not limited to, changes in stress levels, emotional fluctuations, and changes in psychological health.

[0097] When providing mental support, the support unit can select a support method by referring to the user's past support history. For example, the support unit allows the generation AI to select the optimal support method based on the user's past support history. The support unit can also allow the generation AI to select an effective support method from the user's past support history. Furthermore, the support unit can allow the generation AI to adjust the support method by referring to the user's past support history. In this way, the optimal support method can be selected by referring to the user's past support history. The support history includes, for example, past support content, frequency of support, and effectiveness of support, but is not limited to these examples.

[0098] When providing mental support, the support unit can adjust the timing of the support based on the user's lifestyle and daily activities. In the support unit, for example, the generation AI adjusts the timing of the support to match the user's lifestyle. In addition, the support unit can also adjust the timing of the support based on the user's daily activities. Furthermore, the support unit can also suggest the optimal timing of support by the generation AI taking into account the user's lifestyle and daily activities. This makes it possible to adjust the timing of support based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0099] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user is feeling strong anxiety, the support unit can cause the generation AI to prioritize support for reducing anxiety. Also, if the user is feeling mild stress, the support unit can cause the generation AI to prioritize support for reducing stress. Furthermore, if the user is feeling positive emotions, the support unit can prioritize support for the generation AI to maintain those emotions. This makes it possible to determine the priority of support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When providing mental support, the support unit can select a support method taking into account the user's geographical location information. For example, if the user lives in a specific area, the support unit allows the generation AI to select the optimal support method taking into account the environmental factors of that area. In addition, if the user is in a specific location, the support unit can also select the optimal support method taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the support unit can also select the optimal support method taking into account the environmental factors of that area. In this way, the optimal support method can be selected taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0101] When providing mental support, the support unit can analyze the user's social media activity and provide relevant support. For example, the support unit analyzes the content of the user's social media posts, and the generation AI provides relevant support. The support unit can also refer to the activities of the user's friends on social media and the generation AI provides relevant support. Furthermore, the support unit can analyze the user's social media check-in information, and the generation AI can provide relevant support. In this way, the user's social media activity can be analyzed and relevant support can be provided. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to such examples.

[0102] When providing mental support, the support unit can customize the support content by reflecting the user's past feedback. In the support unit, for example, the generation AI customizes the support content based on feedback provided by the user in the past. The support unit can also select the optimal support content from the user's past feedback. Furthermore, the support unit can also adjust the support content by reflecting the user's past feedback. In this way, the support content can be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0103] The planning unit can estimate the user's emotions and adjust the content of the study plan based on the estimated user emotions. For example, if the user is feeling strong anxiety, the planning unit can cause the generation AI to create a study plan that includes relaxation methods and counseling suggestions. Furthermore, if the user is feeling mild stress, the planning unit can cause the generation AI to create a study plan that includes simple exercises and relaxation techniques for stress reduction. Furthermore, if the user is feeling positive emotions, the planning unit can cause the generation AI to create a study plan that includes positive feedback and motivation to maintain those emotions. This allows the content of the study plan to be adjusted based on the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When formulating a study plan, the planning unit can analyze the user's past study history and propose a plan. For example, the planning unit allows the generation AI to propose an optimal study plan based on the user's past study history. The planning unit can also allow the generation AI to propose an effective study method based on the user's past study history. Furthermore, the planning unit can allow the generation AI to adjust the study plan by referring to the user's past study history. This makes it possible to analyze the user's past study history and propose an optimal plan. The study history includes, for example, past study content, study progress, study results, etc., but is not limited to these examples.

[0105] When creating a study plan, the planning unit can adjust the timing of the plan based on the user's lifestyle and daily activities. For example, the planning unit allows the generation AI to adjust the timing of the study plan to match the user's lifestyle. The planning unit can also allow the generation AI to adjust the timing of the study plan based on the user's daily activities. Furthermore, the planning unit can also allow the generation AI to propose optimal timing for the study plan by taking into account the user's lifestyle and daily activities. This allows the timing of the plan to be adjusted based on the user's lifestyle and daily activities. Examples of lifestyle include, but are not limited to, sleep patterns, meal timings, and activity schedules.

[0106] The planning unit can customize the content of the study plan based on the user's goals and interests when creating the study plan. For example, the planning unit allows the generation AI to propose an optimal study plan based on the user's goals. The planning unit can also allow the generation AI to customize the study plan based on the user's interests. Furthermore, the planning unit can allow the generation AI to propose an optimal study plan taking the user's goals and interests into consideration. This allows the plan content to be customized based on the user's goals and interests. Examples of goals and interests include, but are not limited to, study goals, career goals, and personal interests.

[0107] The planning unit can estimate the user's emotions and prioritize study plans based on the estimated user emotions. For example, if the user is feeling strong anxiety, the planning unit can cause the generation AI to prioritize study plans to reduce anxiety. Furthermore, if the user is feeling mild stress, the planning unit can cause the generation AI to prioritize study plans to reduce stress. Furthermore, if the user is feeling positive emotions, the planning unit can cause the generation AI to prioritize study plans to maintain those emotions. This allows the priority of study plans to be determined based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] When creating a study plan, the planning unit can propose a plan taking into account the user's geographical location information. For example, if the user lives in a specific area, the planning unit allows the generation AI to propose an optimal study plan taking into account the environmental factors of that area. In addition, if the user is in a specific location, the planning unit can also allow the generation AI to propose an optimal study plan taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the planning unit can also allow the generation AI to propose an optimal study plan taking into account the environmental factors of that area. In this way, the optimal plan can be proposed taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0109] When creating a study plan, the planning unit can analyze the user's social media activity and suggest a relevant plan. For example, the planning unit analyzes the content of the user's social media posts, and the generation AI suggests a relevant study plan. The planning unit can also refer to the activity of the user's friends on social media, and the generation AI can suggest a relevant study plan. Furthermore, the planning unit can analyze the user's social media check-in information, and the generation AI can suggest a relevant study plan. In this way, the user's social media activity can be analyzed to suggest a relevant plan. Social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples.

[0110] When creating a study plan, the planning unit can customize the plan contents by reflecting the user's past feedback. In the planning unit, for example, the generation AI customizes the study plan based on feedback provided by the user in the past. The planning unit can also have the generation AI select the optimal study plan from the user's past feedback. Furthermore, the planning unit can also have the generation AI adjust the study plan by reflecting the user's past feedback. In this way, the plan contents can be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0111] The community unit can estimate the user's emotions and adjust the community participation method based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI can suggest a relaxing community activity. Also, if the user is feeling mild stress, the community unit can suggest a stress-reducing community activity. Furthermore, if the user is feeling positive emotions, the generation AI can suggest a community activity to maintain those emotions. This allows the community participation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0112] When joining a community, the community unit can analyze the user's past community activity history and suggest a participation method. In the community unit, for example, the generation AI can suggest an optimal participation method based on the user's past community activity history. In addition, the community unit can also have the generation AI suggest an effective participation method based on the user's past community activity history. Furthermore, the community unit can have the generation AI adjust the participation method based on the user's past community activity history. In this way, the user's past community activity history can be analyzed and the optimal participation method can be suggested. Community activity history includes, for example, past participation groups, activity content, activity frequency, etc., but is not limited to these examples.

[0113] When joining a community, the community unit can select a group to join based on the user's interests. For example, the community unit can have the generation AI suggest the most suitable community group based on the user's interests. The community unit can also have the generation AI select a community group based on the user's interests. Furthermore, the community unit can have the generation AI suggest the most suitable community group taking the user's interests and concerns into consideration. This allows the user to select a group to join based on their interests and concerns. Interests and concerns include, but are not limited to, hobbies, fields of expertise, and study themes.

[0114] The community unit can estimate the user's emotions and determine the priority of communities based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI can prioritize community activities to reduce anxiety. Also, if the user is feeling mild stress, the community unit can prioritize community activities to reduce stress. Furthermore, if the user is feeling positive emotions, the generation AI can prioritize community activities to maintain those emotions. This makes it possible to determine the priority of communities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] When joining a community, the community unit can suggest a group taking into account the user's geographical location information. For example, if the user lives in a specific area, the community unit can suggest the optimal community group by the generation AI taking into account the environmental factors of that area. Also, if the user is in a specific location, the community unit can suggest the optimal community group by the generation AI taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the community unit can suggest the optimal community group by the generation AI taking into account the environmental factors of that area. In this way, the optimal group can be suggested taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location history, etc.

[0116] When joining a community, the community unit can analyze the user's social media activity and suggest related groups. For example, the community unit analyzes the content of the user's social media posts, and the generation AI suggests related community groups. The community unit can also refer to the activity of the user's friends on social media and suggest related community groups. Furthermore, the community unit can analyze the user's social media check-in information and suggest related community groups. In this way, related groups can be suggested by analyzing the user's social media activity. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to such examples.

[0117] The peer support unit can estimate the user's emotions and adjust the content of the peer support based on the estimated user's emotions. For example, if the user is feeling strong anxiety, the generation AI in the peer support unit can suggest relaxation methods or counseling. Furthermore, if the user is feeling mild stress, the generation AI in the peer support unit can suggest simple exercises or relaxation techniques to reduce stress. Furthermore, if the user is feeling positive emotions, the peer support unit can provide positive feedback and motivation to help the generation AI maintain those emotions. This allows the content of the peer support to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] When providing peer support, the peer support unit can select a support method by referring to the user's past support history. In the peer support unit, for example, the generation AI selects the optimal support method based on the user's past support history. The peer support unit can also select an effective support method by the generation AI from the user's past support history. Furthermore, the peer support unit can also adjust the support method by referring to the user's past support history. In this way, the optimal support method can be selected by referring to the user's past support history. The support history includes, for example, past support content, frequency of support, and effectiveness of support, but is not limited to these examples.

[0119] When providing peer support, the peer support unit can adjust the timing of support based on the user's lifestyle and daily activities. In the peer support unit, for example, the generation AI adjusts the timing of support to match the user's lifestyle. In addition, the peer support unit can also adjust the timing of support based on the user's daily activities. Furthermore, the peer support unit can also suggest the optimal timing of support by the generation AI taking into account the user's lifestyle and daily activities. This makes it possible to adjust the timing of support based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0120] When providing peer support, the peer support unit can select the optimal support method by referring to the user's past support history. In the peer support unit, for example, the generation AI selects the optimal support method based on the user's past support history. In addition, the peer support unit can also have the generation AI select an effective support method from the user's past support history. Furthermore, the peer support unit can also have the generation AI adjust the support method by referring to the user's past support history. In this way, the optimal support method can be selected by referring to the user's past support history. The support history includes, for example, past support content, frequency of support, and effectiveness of support, but is not limited to these examples.

[0121] The peer support unit can estimate the user's emotions and determine the priority of peer support based on the estimated user's emotions. For example, if the user is feeling strong anxiety, the generation AI can prioritize peer support to reduce anxiety. Also, if the user is feeling mild stress, the peer support unit can prioritize peer support to reduce stress. Furthermore, if the user is feeling positive emotions, the generation AI can prioritize peer support to maintain those emotions. This allows the priority of peer support to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0122] When providing peer support, the peer support unit can select a support method taking into account the user's geographical location information. For example, if the user lives in a specific area, the peer support unit selects the optimal peer support method using the generation AI, taking into account the environmental factors of that area. Furthermore, if the user is in a specific location, the peer support unit can select the optimal peer support method using the generation AI, taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the peer support unit can select the optimal peer support method using the generation AI, taking into account the environmental factors of that area. This allows the optimal support method to be selected taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and location history.

[0123] When providing peer support, the peer support unit can analyze the user's social media activity and provide relevant support. For example, the peer support unit analyzes the content of the user's social media posts, and the generation AI provides relevant peer support. The peer support unit can also refer to the activities of the user's friends on social media to provide relevant peer support. Furthermore, the peer support unit can analyze the user's social media check-in information, and the generation AI can provide relevant peer support. In this way, the user's social media activity can be analyzed to provide relevant support. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to these examples.

[0124] When providing peer support, the peer support unit can customize the support content by reflecting the user's past feedback. In the peer support unit, for example, the generation AI customizes the peer support content based on feedback provided by the user in the past. In addition, the peer support unit can also select the optimal peer support content by the generation AI based on the user's past feedback. Furthermore, the peer support unit can also adjust the peer support content by reflecting the user's past feedback. In this way, the support content can be customized by reflecting the user's past feedback. Feedback includes, for example, evaluation comments, improvement suggestions, progress reports, etc., but is not limited to these examples.

[0125] The progress management unit can estimate the user's emotions and adjust the progress management method based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI can provide a progress management method that includes relaxation techniques and counseling suggestions. Furthermore, if the user is feeling mild stress, the progress management unit can also provide a progress management method that includes simple exercises and relaxation techniques for stress reduction. Furthermore, if the user is feeling positive emotions, the progress management unit can also provide a progress management method that includes positive feedback and motivation to maintain those emotions. This allows the progress management method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0126] During progress management, the progress management unit can analyze the user's past learning history and propose a management method. In the progress management unit, for example, the generation AI proposes an optimal progress management method based on the user's past learning history. The progress management unit can also propose an effective progress management method based on the user's past learning history. Furthermore, the progress management unit can also allow the generation AI to adjust the progress management method by referring to the user's past learning history. This makes it possible to analyze the user's past learning history and propose an optimal management method. Learning history includes, for example, past learning content, learning progress, learning results, etc., but is not limited to these examples.

[0127] The progress management unit can adjust the timing of progress management based on the user's lifestyle and daily activities during progress management. In the progress management unit, for example, the generation AI adjusts the timing of progress management to match the user's lifestyle. In addition, the progress management unit can also adjust the timing of progress management based on the user's daily activities. Furthermore, the progress management unit can also suggest the optimal timing of progress management by the generation AI taking into account the user's lifestyle and daily activities. This allows the timing of management to be adjusted based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0128] The progress management unit can customize the management content based on the user's goals and interests when managing progress. In the progress management unit, for example, the generation AI proposes an optimal progress management method based on the user's goals. The progress management unit can also customize the progress management content based on the user's interests. Furthermore, the progress management unit can also propose an optimal progress management method by the generation AI taking the user's goals and interests into consideration. This allows the management content to be customized based on the user's goals and interests. Goals and interests include, but are not limited to, for example, learning goals, career goals, and personal interests.

[0129] The progress management unit can estimate the user's emotions and determine the priority of progress management based on the estimated user emotions. For example, if the user is feeling strong anxiety, the progress management unit can cause the generation AI to prioritize progress management for anxiety reduction. Also, if the user is feeling mild stress, the progress management unit can cause the generation AI to prioritize progress management for stress reduction. Furthermore, if the user is feeling positive emotions, the progress management unit can cause the generation AI to prioritize progress management for maintaining those emotions. This allows the priority of progress management to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0130] The progress management unit can propose a management method taking into account the user's geographical location information when managing progress. For example, if the user lives in a specific area, the progress management unit proposes the optimal progress management method for the generation AI taking into account the environmental factors of that area. In addition, if the user is in a specific location, the progress management unit can also propose the optimal progress management method for the generation AI taking into account the environmental factors of that location. Furthermore, if the user frequently visits a specific area, the progress management unit can also propose the optimal progress management method for the generation AI taking into account the environmental factors of that area. In this way, the optimal management method can be proposed taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0131] The progress management unit can analyze the user's social media activity during progress management and suggest a relevant management method. For example, the progress management unit analyzes the content of the user's social media posts, and the generation AI suggests a relevant progress management method. The progress management unit can also refer to the activity of the user's friends on social media and suggest a relevant progress management method. Furthermore, the progress management unit can analyze the user's social media check-in information, and the generation AI can suggest a relevant progress management method. In this way, the user's social media activity can be analyzed and a relevant management method can be suggested. Social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples.

[0132] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user emotions. For example, if the user is feeling strong anxiety, the feedback unit can cause the generation AI to provide feedback including relaxation methods or counseling suggestions. Furthermore, if the user is feeling mild stress, the feedback unit can also cause the generation AI to provide feedback including simple exercises or relaxation techniques for stress reduction. Furthermore, if the user is feeling positive emotions, the feedback unit can also provide positive feedback or motivation for the generation AI to maintain those emotions. This allows the content of the feedback to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0133] When providing feedback, the feedback unit can analyze the user's past feedback history and suggest the optimal feedback method. In the feedback unit, for example, the generation AI can suggest the optimal feedback method based on the user's past feedback history. In addition, the feedback unit can also have the generation AI suggest an effective feedback method based on the user's past feedback history. Furthermore, the feedback unit can also have the generation AI adjust the feedback method by referring to the user's past feedback history. In this way, the user's past feedback history can be analyzed and the optimal feedback method can be suggested. The feedback history includes, for example, past feedback content, feedback frequency, feedback effect, etc., but is not limited to these examples.

[0134] When providing feedback, the feedback unit can adjust the timing of the feedback based on the user's lifestyle and daily activities. In the feedback unit, for example, the generation AI adjusts the timing of the feedback to match the user's lifestyle. In addition, the feedback unit can also adjust the timing of the feedback based on the user's daily activities. Furthermore, the feedback unit can also suggest the optimal timing of the feedback by the generation AI taking into account the user's lifestyle and daily activities. This makes it possible to adjust the timing of the feedback based on the user's lifestyle and daily activities. Lifestyle rhythms include, but are not limited to, for example, sleep patterns, meal timings, and activity schedules.

[0135] When providing feedback, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. In the feedback unit, for example, the generation AI selects the optimal feedback method based on the user's past feedback history. In addition, the feedback unit can also have the generation AI select an effective feedback method from the user's past feedback history. Furthermore, the feedback unit can have the generation AI adjust the feedback method by referring to the user's past feedback history. In this way, the optimal feedback method can be selected by referring to the user's past feedback history. The feedback history includes, for example, past feedback content, feedback frequency, feedback effect, etc., but is not limited to these examples.

[0136] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling strong anxiety, the feedback unit can cause the generation AI to prioritize feedback to reduce anxiety. Also, if the user is feeling mild stress, the feedback unit can cause the generation AI to prioritize feedback to reduce stress. Furthermore, if the user is feeling positive emotions, the feedback unit can also prioritize feedback to maintain those emotions. This makes it possible to determine the priority of feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0137] When providing feedback, the feedback unit can suggest a feedback method taking into account the user's geographical location information. For example, if the user lives in a specific area, the feedback unit can suggest the optimal feedback method by the generation AI taking into account the environmental factors of the area. Also, if the user is in a specific location, the feedback unit can suggest the optimal feedback method by the generation AI taking into account the environmental factors of the area. Furthermore, if the user frequently visits a specific area, the feedback unit can suggest the optimal feedback method by the generation AI taking into account the environmental factors of the area. In this way, the optimal feedback method can be suggested taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history, for example.

[0138] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit analyzes the content of the user's social media posts, and the generation AI provides relevant feedback. The feedback unit can also refer to the activities of the user's friends on social media and the generation AI provides relevant feedback. Furthermore, the feedback unit can analyze the user's social media check-in information, and the generation AI can provide relevant feedback. In this way, the user's social media activity can be analyzed and relevant feedback can be provided. Social media activity includes, for example, post content, comments, number of likes, etc., but is not limited to these examples.

[0139] When providing feedback, the feedback unit can customize the feedback content by reflecting the user's past feedback history. In the feedback unit, for example, the generation AI customizes the feedback content based on feedback provided by the user in the past. In addition, the feedback unit can also have the generation AI select optimal feedback content from the user's past feedback. Furthermore, the feedback unit can also have the generation AI adjust the feedback content by reflecting the user's past feedback. In this way, the feedback content can be customized by reflecting the user's past feedback history. The feedback history includes, for example, past feedback content, feedback frequency, feedback effect, etc., but is not limited to these examples. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, support unit, planning unit, community unit, peer support unit, progress management unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The planning unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The community unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The peer support unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The progress management unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The feedback unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, support unit, planning unit, community unit, peer support unit, progress management unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The planning unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The community unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The peer support unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The progress management unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The feedback unit is realized by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, support unit, planning unit, community unit, peer support unit, progress management unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The planning unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The community unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The peer support unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The progress management unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, support unit, planning unit, community unit, peer support unit, progress management unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The planning unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The community unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The peer support unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The progress management unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The feedback unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0140] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0141] The analysis unit can take the user's past medical history into consideration when analyzing the user's mental state. For example, the generation AI can improve the accuracy of its analysis of the user's mental state based on the treatments and diagnostic results the user has received in the past. The analysis unit can also enable the generation AI to select the optimal mental support method based on the user's past medical history. Furthermore, the analysis unit can also adjust the generation AI's analysis method of the user's mental state by reflecting the user's medical history. This allows the generation AI to improve the accuracy of its analysis of the user's mental state by taking the user's medical history into consideration.

[0142] When providing mental support to a user, the support unit can customize the support content based on the user's hobbies and interests. For example, if the user is interested in music, the generation AI can suggest music therapy. Also, if the user is interested in art, the generation AI can suggest art therapy. Furthermore, if the user is interested in sports, the generation AI can suggest relaxation methods through sports. This allows the content of mental support to be customized based on the user's hobbies and interests.

[0143] When creating a user's study plan, the planning unit can adjust the contents of the plan taking into account the user's career goals. For example, if the user is aiming for a specific occupation, the generation AI can propose a study plan that includes the skills and knowledge required for that occupation. Also, if the user is considering a career change, the generation AI can propose a study plan required for the new career. Furthermore, if the user is aiming for promotion, the generation AI can propose a study plan for improving the skills required for promotion. This allows the contents of the study plan to be adjusted based on the user's career goals.

[0144] When a user joins a community, the community unit can analyze the user's past community activity history and suggest the optimal way to participate. For example, the generation AI can suggest the optimal community activity based on the content and frequency of community activities in which the user has participated in the past. The generation AI can also suggest an effective way to participate based on the user's past community activity history. Furthermore, the generation AI can adjust the content of the community activity by referring to the user's past community activity history. In this way, the generation AI can suggest the optimal way to participate by analyzing the user's past community activity history.

[0145] When providing peer support to a user, the peer support unit can adjust the timing of support based on the user's lifestyle and daily activities. For example, the generation AI adjusts the timing of support to match the user's lifestyle. The generation AI can also adjust the timing of support based on the user's daily activities. Furthermore, the generation AI can propose the optimal timing of support by taking the user's lifestyle and daily activities into consideration. This makes it possible to adjust the timing of support based on the user's lifestyle and daily activities.

[0146] The analysis unit can estimate the user's emotions and adjust the method of analyzing the mental state based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI will prioritize suggestions for relaxation methods or counseling. In addition, if the user is feeling mild stress, the analysis unit can also suggest simple exercises or relaxation techniques to reduce stress. Furthermore, if the user is feeling positive emotions, the analysis unit can provide positive feedback and motivation for the generation AI to maintain those emotions. This allows the method of analyzing the mental state to be adjusted based on the user's emotions.

[0147] The support unit can estimate the user's emotions and adjust the content of mental support based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI can suggest relaxation methods or counseling. In addition, if the user is feeling mild stress, the support unit can suggest simple exercises or relaxation techniques to reduce stress. Furthermore, if the user is feeling positive emotions, the support unit can provide positive feedback and motivation to help the generation AI maintain those emotions. This allows the content of mental support to be adjusted based on the user's emotions.

[0148] The planning unit can estimate the user's emotions and adjust the content of the study plan based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI can create a study plan that includes relaxation methods and counseling suggestions. In addition, if the user is feeling mild stress, the planning unit can create a study plan that includes simple exercises and relaxation techniques for stress reduction. Furthermore, if the user is feeling positive emotions, the planning unit can create a study plan that includes positive feedback and motivation to maintain those emotions. This allows the content of the study plan to be adjusted based on the user's emotions.

[0149] The community unit can estimate the user's emotions and adjust the way the user participates in the community based on the estimated user's emotions. For example, if the user is feeling strong anxiety, the generation AI can suggest community activities that will help them relax. In addition, if the user is feeling mild stress, the community unit can also suggest community activities to reduce stress. Furthermore, if the user is feeling positive emotions, the generation AI can also suggest community activities to maintain those emotions. This makes it possible to adjust the way the user participates in the community based on the user's emotions.

[0150] The peer support unit can estimate the user's emotions and adjust the content of peer support based on the estimated user emotions. For example, if the user is feeling strong anxiety, the generation AI can suggest relaxation methods or counseling. In addition, if the user is feeling mild stress, the peer support unit can suggest simple exercises or relaxation techniques to reduce stress. Furthermore, if the user is feeling positive emotions, the generation AI can provide positive feedback and motivation to maintain those emotions. This allows the content of peer support to be adjusted based on the user's emotions.

[0151] The processing flow of the second embodiment will be briefly explained below.

[0152] Step 1: The analysis unit uses the generation AI to analyze the user's mental state. Mental state includes stress level, emotional state, and psychological health. For example, if the user inputs information such as "I've been feeling very anxious lately," the generation AI analyzes the user's mental state. The analysis unit can also extract information that allows the generation AI to provide appropriate mental support based on the user's input. Step 2: The support unit provides appropriate mental support based on the mental state analyzed by the analysis unit. Mental support can include counseling, relaxation techniques, and the provision of mental health resources. For example, the generation AI can suggest relaxation methods and counseling. The support unit can also provide appropriate mental support based on the user's mental state. Step 3: The planning unit creates a study plan for the user based on the mental support provided by the support unit. The study plan includes learning goals, schedules, evaluation criteria, etc. For example, the generation AI proposes a study plan that matches the user's life plan. The planning unit also allows the generation AI to manage the user's study progress and provide appropriate feedback.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0158] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0174] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0200] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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."

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Explanation of symbols]

[0225] 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 analysis unit that analyzes the mental state of the user; a support unit that provides mental support based on the mental state analyzed by the analysis unit; a planning unit that creates a study plan based on the mental support provided by the support unit; Equipped with A system characterized by:

2. Equipped with a community department that provides community functions 2. The system of claim 1.

3. Have a peer support department that provides peer support 2. The system of claim 1.

4. Equipped with a progress management department that manages learning progress 2. The system of claim 1.

5. A feedback unit is provided to provide feedback.

2. The system of claim 1.

6. The analysis unit Estimate the user's emotions and adjust the mental state analysis method based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit Analyze the user's past mental state data and select an analysis algorithm 2. The system of claim 1.

8. The analysis unit Improve the accuracy of mental state analysis based on the user's lifestyle and environmental factors 2. The system of claim 1.

9. The analysis unit When analyzing mental states, select the analysis method according to the user's input method.

2. The system of claim 1.

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