System

Abstract

An object of a system according to an embodiment is to analyze a user's emotion or psychological state and propose personalized counseling or self-care activity.SOLUTION: A system includes a self-evaluation unit, an analysis unit, a proposal unit, and a tracking unit. The self-evaluation unit receives a self-evaluation of the user. The analysis unit analyzes the self-evaluation data received by the self-evaluation unit. The suggestion unit suggests a counseling session or a self-care activity based on the data analyzed by the analysis unit. The tracker tracks the user's progress and adjusts the plan as needed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately analyze a user's emotions and psychological state and suggest personalized counseling and self-care activities, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's emotions and psychological state and suggest personalized counseling and self-care activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a self-assessment unit, an analysis unit, a suggestion unit, and a tracking unit. The self-assessment unit receives a user's self-assessment. The analysis unit analyzes the self-assessment data received by the self-assessment unit. The suggestion unit suggests counseling sessions and self-care activities based on the data analyzed by the analysis unit. The tracking unit tracks the user's progress and adjusts the plan as needed. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's emotions and psychological state and suggest personalized counseling and self-care activities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-driven platform according to an embodiment of the present invention is a system that analyzes a user's emotions and psychological state and provides customized psychological counseling, stress relief methods, and guidance for the return-to-work process. As a result, the AI-driven platform analyzes a user's emotions and psychological state and provides customized support to help the user recover from a family tragedy or a serious stress experience and support the user's return to work.

[0029] An AI-driven platform according to an embodiment includes a self-assessment unit, an analysis unit, a suggestion unit, and a tracking unit. The self-assessment unit accepts a user's self-assessment. For example, the user conducts a self-assessment through an app and answers questions about their emotions, psychological state, stress level, lifestyle habits, etc. The self-assessment unit collects the user's responses and converts them into a format that is easy for the generation AI to analyze. The analysis unit analyzes the self-assessment data accepted by the self-assessment unit. For example, the generation AI analyzes the user's emotions and psychological state to understand the user's condition. The generation AI can also analyze the user's emotional fluctuation patterns by comparing them with past data. The suggestion unit suggests counseling sessions or self-care activities based on the data analyzed by the analysis unit. For example, the generation AI can suggest relaxation techniques or mindfulness sessions if the user's stress level is high. It can also suggest psychological counseling sessions if the user needs emotional support. The tracking unit tracks the user's progress and adjusts the plan as needed. For example, the generation AI can analyze the data after the user records their daily activities and emotional changes and evaluate their progress. In addition, if the user reports a decrease in stress level, the generating AI can evaluate the progress and adjust the support plan. As a result, the AI-driven platform according to the embodiment can provide effective support by suggesting customized counseling and self-care activities based on the user's self-assessment data, tracking the progress, and adjusting the plan.

[0030] The analysis unit can refer to the user's past emotional history and analyze long-term patterns of emotional fluctuation. For example, when a user performs self-evaluation through the app, the analysis unit refers to the past emotional history data, and the generation AI analyzes the long-term patterns of emotional fluctuation. For example, the analysis unit can analyze seasonal emotional fluctuations based on emotional data from the past year. The analysis unit can also analyze emotional fluctuations in response to specific events or situations based on the user's emotional history. This allows for more accurate support by analyzing long-term patterns of emotional fluctuation based on the user's past emotional history.

[0031] The analysis unit can analyze the user's self-assessment data in real time and provide instantaneous feedback. For example, when a user performs a self-assessment, the generation AI analyzes the data in real time and provides instantaneous feedback. For example, if the stress level is high, the analysis unit can suggest relaxation techniques. The analysis unit can also analyze the user's self-assessment data in real time and provide feedback according to changes in emotions. For example, if the user is feeling anxious, the analysis unit can provide advice on how to relax. This allows for real-time feedback to be provided, encouraging the user to take immediate action.

[0032] The analysis unit can integrate the user's self-assessment data with other health data to evaluate their overall health. For example, the analysis unit can integrate the user's self-assessment data with fitness tracker and sleep data, and the generative AI can evaluate their overall health. For example, it can analyze the correlation between the amount of exercise and sleep quality and their emotional state. The analysis unit can also integrate the user's self-assessment data with medical records to evaluate their health. For example, it can take into account the user's medical history and medication status to comprehensively evaluate their health. This allows the self-assessment data to be integrated with other health data to evaluate their overall health.

[0033] The analysis unit can customize the content of the self-assessment questions according to the user's cultural background and language. For example, the analysis unit customizes the content of the self-assessment questions according to the user's cultural background and language, allowing the generation AI to provide a more personalized evaluation. For example, appropriate questions are set for users from different cultural backgrounds. The analysis unit can also translate the content of the questions according to the user's language and provide them in appropriate wording. For example, questions are provided that correspond to the dialect or technical terminology used by the user. This makes it possible to provide a more personalized evaluation through customization according to the cultural background and language.

[0034] The suggestion unit can analyze data from the user's past counseling sessions and identify the most effective session format and content. For example, the suggestion unit uses a generation AI to analyze data from the user's past counseling sessions and identify the most effective session format and content. For example, it discovers that a particular counseling method is effective. The suggestion unit can also compare the effectiveness of individual sessions and group sessions based on the user's past session data. For example, if the user received more support in a group session, it recommends that format. In this way, by analyzing data from past counseling sessions, the most effective session format and content can be identified and provided to the user.

[0035] The suggestion unit can dynamically adjust the timing and frequency of counseling sessions according to the user's psychological state. For example, the generation AI analyzes the user's psychological state and dynamically adjusts the timing and frequency of counseling sessions. For example, the frequency of sessions can be increased during periods of high stress. The suggestion unit can also adjust the timing of sessions according to the user's psychological state. For example, sessions can be scheduled for times when the user is relaxed. This makes it possible to provide more effective support by adjusting the timing and frequency of counseling sessions according to the user's psychological state.

[0036] The suggestion unit can customize the content of the counseling session based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and customizes the content of the counseling session. For example, if the user is interested in music, the suggestion unit can suggest music therapy. The suggestion unit can also adjust the counseling approach based on the user's hobbies and interests. For example, if the user is interested in sports, the suggestion unit can provide counseling that incorporates sports. In this way, by customizing the content of the counseling session based on the user's hobbies and interests, it is possible to provide a more friendly session.

[0037] The suggestion unit can take into account the user's living environment and daily routine when proposing self-care activities and suggest feasible activities. For example, the suggestion unit uses a generation AI to analyze the user's living environment and daily routine and suggest feasible self-care activities. For example, it can suggest relaxation methods that can be done in a short amount of time when the user is busy. The suggestion unit can also adjust the content of self-care activities based on the user's living environment. For example, it can suggest exercises that the user can do at home. This makes it possible to provide effective support by suggesting feasible self-care activities that take into account the user's living environment and daily routine.

[0038] The tracking unit analyzes the user's progress data, detects stagnation or reversal of progress early, and proposes appropriate countermeasures. For example, the tracking unit uses a generative AI to analyze the user's progress data and detects stagnation or reversal of progress early. For example, if the user's stress level rises again, the tracking unit may re-suggest relaxation techniques. The tracking unit can also identify the cause of stagnation or reversal based on the user's progress data and propose appropriate countermeasures. For example, if the user is struggling with a particular task, the tracking unit may propose ways to break down the task and progress in stages. This makes it possible to detect stagnation or reversal of progress early and propose appropriate countermeasures, thereby effectively supporting the user's return process.

[0039] The tracking unit can compare the user's progress data with the data of other users and set a benchmark to evaluate the progress. For example, the generation AI can compare the user's progress data with the data of other users and set a benchmark to evaluate the progress. For example, the progress can be evaluated in comparison with users with the same stress level. The tracking unit can also set a benchmark based on the user's progress data by comparing it with past data. For example, the progress can be evaluated in comparison with goals the user has achieved in the past. In this way, by comparing the user's progress with other users and setting a benchmark, the evaluation of progress can be made more objectively.

[0040] The tracking unit can display the user's progress data on a visual dashboard to enable intuitive understanding. The tracking unit, for example, allows the generation AI to display the user's progress data on a visual dashboard to enable intuitive understanding. For example, progress is visualized using graphs and charts. The tracking unit can also display the user's progress data in different colors. For example, green indicates steady progress, yellow indicates stagnant progress, and red indicates reversal of progress. In this way, by displaying the progress data on a visual dashboard, the user can intuitively understand their progress.

[0041] The tracking unit can incorporate feedback from the user's family and friends when tracking the user's progress to provide more comprehensive support. For example, the tracking unit can adjust the support plan based on opinions from family members. The tracking unit can also adjust the support content based on feedback from the user's friends. For example, if a friend reports a change in the user's stress level, the tracking unit can suggest relaxation techniques. This makes it possible to provide more comprehensive support by incorporating feedback from family and friends.

[0042] The tracking unit can analyze data on the user's return to work process and identify the most effective return steps. For example, the tracking unit uses a generation AI to analyze data on the user's return to work process and identify the most effective return steps. For example, it can suggest specific steps for the user to gradually return to work. The tracking unit can also evaluate the effectiveness of the return steps based on data on the user's return to work process. For example, it can evaluate the user's emotional state after completing a specific step and suggest the next step. In this way, by analyzing data on the return to work process and identifying the most effective return steps, it is possible to effectively support the user's return to work.

[0043] The tracking unit can evaluate the user's return process step by step and suggest the next step depending on the degree of achievement of each step. For example, the tracking unit uses a generation AI to evaluate the user's return process step by step and suggest the next step depending on the degree of achievement of each step. For example, the tracking unit suggests the next task after the user completes a specific task. The tracking unit can also adjust the progress of the return process based on the user's degree of achievement. For example, when the user achieves a goal, the next goal is set. In this way, the return process can be evaluated step by step and the next step suggested depending on the degree of achievement of each step, thereby effectively supporting the user's return.

[0044] The tracking unit can propose a specific action plan taking into account the user's work environment and work content. For example, the tracking unit uses a generation AI to analyze the user's work environment and work content and propose a specific action plan to guide the return process. For example, the tracking unit proposes specific measures to help the user reduce stress at work. The tracking unit can also adjust the content of the action plan based on the user's work content. For example, if the user does desk work, the tracking unit can propose an appropriate way to take a break. This makes it possible to effectively support the user's return by proposing a specific action plan taking into account the work environment and work content.

[0045] The tracking unit can customize the guide for the return process to match the user's pace and support a natural progression. For example, the generation AI can customize the guide for the return process to match the user's pace and support a natural progression. For example, the goal setting can be adjusted so that the user can proceed at their own pace. The tracking unit can also adjust the content of the guide based on the user's progress speed. For example, if the user wants to proceed quickly, more tasks can be suggested. This makes it possible to support a natural progression by providing guidance that matches the user's pace.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can integrate the user's self-assessment data with other health data to evaluate their overall health. For example, the analysis unit can integrate the user's self-assessment data with fitness tracker and sleep data to allow the generative AI to evaluate their overall health. For example, it can analyze the correlation between the amount of exercise and quality of sleep and their emotional state. The analysis unit can also integrate the user's self-assessment data with medical records to evaluate their health. For example, it can take into account the user's medical history and medication status to comprehensively evaluate their health. This allows the self-assessment data to be integrated with other health data to evaluate their overall health.

[0048] The suggestion unit can customize the content of the counseling session based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and customizes the content of the counseling session. For example, if the user is interested in music, it can suggest music therapy. The suggestion unit can also adjust the counseling approach based on the user's hobbies and interests. For example, if the user is interested in sports, it can provide counseling that incorporates sports. In this way, by customizing the content of the counseling session based on the user's hobbies and interests, it is possible to provide a more friendly session.

[0049] The tracking unit can display the user's progress data on a visual dashboard to enable intuitive understanding. For example, the generation AI can display the user's progress data on a visual dashboard to enable intuitive understanding. For example, progress can be visualized using graphs and charts. The tracking unit can also display the user's progress data in different colors. For example, green indicates steady progress, yellow indicates stagnant progress, and red indicates reversal of progress. In this way, displaying the progress data on a visual dashboard allows the user to intuitively understand their progress.

[0050] The suggestion unit can take into account the user's living environment and daily routine when proposing self-care activities and suggest feasible activities. For example, the generation AI can analyze the user's living environment and daily routine and suggest feasible self-care activities. For example, it can suggest relaxation methods that can be done in a short amount of time when the user is busy. The suggestion unit can also adjust the content of self-care activities based on the user's living environment. For example, it can suggest exercises that the user can do at home. This makes it possible to provide effective support by suggesting feasible self-care activities that take into account the user's living environment and daily routine.

[0051] The tracking unit can propose a specific action plan taking into account the user's work environment and work content. For example, the generation AI analyzes the user's work environment and work content and proposes a specific action plan to guide the return process. For example, it proposes specific measures for the user to reduce stress at work. The tracking unit can also adjust the content of the action plan based on the user's work content. For example, if the user does desk work, it can suggest appropriate ways to take a break. This makes it possible to effectively support the user's return by proposing a specific action plan taking into account the work environment and work content.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The self-evaluation unit accepts the user's self-evaluation. For example, the user evaluates themselves through the app and answers questions about their emotions, psychological state, stress level, lifestyle habits, etc. The self-evaluation unit also collects the data the user has answered and converts it into a format that is easy for the generation AI to analyze. Step 2: The analysis unit analyzes the self-evaluation data received by the self-evaluation unit. For example, the generation AI analyzes the user's emotions and psychological state to understand the user's condition. The generation AI can also compare the data with past data to analyze the user's emotional fluctuation patterns. Step 3: The suggestion unit suggests counseling sessions or self-care activities based on the data analyzed by the analysis unit. For example, the generative AI can suggest relaxation techniques or mindfulness sessions if the user's stress level is high. It can also suggest psychological counseling sessions if the user needs emotional support. Step 4: The tracking unit tracks the user's progress and adjusts the plan as needed. For example, when the user records their daily activities and emotional changes, the generating AI analyzes the data and evaluates their progress. If the user reports a decrease in their stress level, the generating AI can evaluate their progress and adjust the support plan.

[0054] (Example 2) An AI-driven platform according to an embodiment of the present invention is a system that analyzes a user's emotions and psychological state and provides customized psychological counseling, stress relief methods, and guidance for the return-to-work process. As a result, the AI-driven platform analyzes a user's emotions and psychological state and provides customized support to help the user recover from a family tragedy or a serious stress experience and support the user's return to work.

[0055] An AI-driven platform according to an embodiment includes a self-assessment unit, an analysis unit, a suggestion unit, and a tracking unit. The self-assessment unit accepts a user's self-assessment. For example, the user conducts a self-assessment through an app and answers questions about their emotions, psychological state, stress level, lifestyle habits, etc. The self-assessment unit collects the user's responses and converts them into a format that is easy for the generation AI to analyze. The analysis unit analyzes the self-assessment data accepted by the self-assessment unit. For example, the generation AI analyzes the user's emotions and psychological state to understand the user's condition. The generation AI can also analyze the user's emotional fluctuation patterns by comparing them with past data. The suggestion unit suggests counseling sessions or self-care activities based on the data analyzed by the analysis unit. For example, the generation AI can suggest relaxation techniques or mindfulness sessions if the user's stress level is high. It can also suggest psychological counseling sessions if the user needs emotional support. The tracking unit tracks the user's progress and adjusts the plan as needed. For example, the generation AI can analyze the data after the user records their daily activities and emotional changes and evaluate their progress. In addition, if the user reports a decrease in stress level, the generating AI can evaluate the progress and adjust the support plan. As a result, the AI-driven platform according to the embodiment can provide effective support by suggesting customized counseling and self-care activities based on the user's self-assessment data, tracking the progress, and adjusting the plan.

[0056] The analysis unit can refer to the user's past emotional history and analyze long-term patterns of emotional fluctuation. For example, when a user performs self-evaluation through the app, the analysis unit refers to the past emotional history data, and the generation AI analyzes the long-term patterns of emotional fluctuation. For example, the analysis unit can analyze seasonal emotional fluctuations based on emotional data from the past year. The analysis unit can also analyze emotional fluctuations in response to specific events or situations based on the user's emotional history. This allows for more accurate support by analyzing long-term patterns of emotional fluctuation based on the user's past emotional history.

[0057] The analysis unit can analyze the user's self-assessment data in real time and provide instantaneous feedback. For example, when a user performs a self-assessment, the generation AI analyzes the data in real time and provides instantaneous feedback. For example, if the stress level is high, the analysis unit can suggest relaxation techniques. The analysis unit can also analyze the user's self-assessment data in real time and provide feedback according to changes in emotions. For example, if the user is feeling anxious, the analysis unit can provide advice on how to relax. This allows for real-time feedback to be provided, encouraging the user to take immediate action.

[0058] The analysis unit can use the emotion estimation function to estimate the user's emotions when assessing themselves, and dynamically adjust the content of the assessment questions based on those emotions. For example, when a user assesses themselves, the analysis unit uses the emotion estimation function to estimate the user's emotions using the generation AI, and dynamically adjusts the content of the assessment questions based on those emotions. For example, if the user is feeling stressed, the analysis unit can add questions about relaxation. The analysis unit can also adjust the difficulty and content of the assessment questions based on the user's emotions. For example, if the user is relaxed, the analysis unit can add more detailed questions. This makes it possible to provide a more personalized assessment by dynamically adjusting the assessment questions based on the user's emotions.

[0059] The analysis unit can integrate the user's self-assessment data with other health data to evaluate their overall health. For example, the analysis unit can integrate the user's self-assessment data with fitness tracker and sleep data, and the generative AI can evaluate their overall health. For example, it can analyze the correlation between the amount of exercise and sleep quality and their emotional state. The analysis unit can also integrate the user's self-assessment data with medical records to evaluate their health. For example, it can take into account the user's medical history and medication status to comprehensively evaluate their health. This allows the self-assessment data to be integrated with other health data to evaluate their overall health.

[0060] The analysis unit can customize the content of the self-assessment questions according to the user's cultural background and language. For example, the analysis unit customizes the content of the self-assessment questions according to the user's cultural background and language, allowing the generation AI to provide a more personalized evaluation. For example, appropriate questions are set for users from different cultural backgrounds. The analysis unit can also translate the content of the questions according to the user's language and provide them in appropriate wording. For example, questions are provided that correspond to the dialect or technical terminology used by the user. This makes it possible to provide a more personalized evaluation through customization according to the cultural background and language.

[0061] The analysis unit can use the emotion estimation function to estimate the user's emotions in real time when they evaluate themselves, and suggest questions that elicit positive emotions. For example, when a user evaluates themselves, the analysis unit uses the emotion estimation function to estimate the user's emotions in real time and suggest questions that elicit positive emotions. For example, it adds questions that help the user relax. The analysis unit can also suggest questions that include words of encouragement or reflections on successful experiences based on the user's emotions. For example, it adds questions that make the user feel a sense of accomplishment. This makes it possible to improve the user's psychological state by suggesting questions that elicit positive emotions.

[0062] The suggestion unit can analyze data from the user's past counseling sessions and identify the most effective session format and content. For example, the suggestion unit uses a generation AI to analyze data from the user's past counseling sessions and identify the most effective session format and content. For example, it discovers that a particular counseling method is effective. The suggestion unit can also compare the effectiveness of individual sessions and group sessions based on the user's past session data. For example, if the user received more support in a group session, it recommends that format. In this way, by analyzing data from past counseling sessions, the most effective session format and content can be identified and provided to the user.

[0063] The suggestion unit can dynamically adjust the timing and frequency of counseling sessions according to the user's psychological state. For example, the generation AI analyzes the user's psychological state and dynamically adjusts the timing and frequency of counseling sessions. For example, the frequency of sessions can be increased during periods of high stress. The suggestion unit can also adjust the timing of sessions according to the user's psychological state. For example, sessions can be scheduled for times when the user is relaxed. This makes it possible to provide more effective support by adjusting the timing and frequency of counseling sessions according to the user's psychological state.

[0064] The suggestion unit can use the emotion estimation function to monitor the user's emotional state in real time and suggest counseling content according to the emotion. For example, the generation AI can use the emotion estimation function to monitor the user's emotional state in real time and suggest counseling content according to the emotion. For example, if the user is feeling anxious, the suggestion unit can suggest relaxation techniques. The suggestion unit can also adjust the counseling content based on the user's emotional state. For example, if the user is relaxed, the suggestion unit can provide deeper counseling. In this way, the user's psychological state can be improved by monitoring the emotional state in real time using the emotion estimation function and suggesting appropriate counseling content.

[0065] The suggestion unit can customize the content of the counseling session based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and customizes the content of the counseling session. For example, if the user is interested in music, the suggestion unit can suggest music therapy. The suggestion unit can also adjust the counseling approach based on the user's hobbies and interests. For example, if the user is interested in sports, the suggestion unit can provide counseling that incorporates sports. In this way, by customizing the content of the counseling session based on the user's hobbies and interests, it is possible to provide a more friendly session.

[0066] The suggestion unit can take into account the user's living environment and daily routine when proposing self-care activities and suggest feasible activities. For example, the suggestion unit uses a generation AI to analyze the user's living environment and daily routine and suggest feasible self-care activities. For example, it can suggest relaxation methods that can be done in a short amount of time when the user is busy. The suggestion unit can also adjust the content of self-care activities based on the user's living environment. For example, it can suggest exercises that the user can do at home. This makes it possible to provide effective support by suggesting feasible self-care activities that take into account the user's living environment and daily routine.

[0067] The suggestion unit can use the emotion estimation function to estimate the emotions the user feels during a counseling session in real time and adjust the progress of the session. For example, the suggestion unit uses the emotion estimation function to estimate the emotions the user feels during a counseling session in real time and adjusts the progress of the session. For example, if the user is feeling anxious, the suggestion unit can switch to a topic to help them relax. The suggestion unit can also adjust the speed and content of the session based on the user's emotions. For example, if the user is relaxed, the suggestion unit can provide deeper counseling. In this way, more effective counseling can be provided by estimating the emotions during a counseling session in real time and adjusting the progress of the session.

[0068] The tracking unit analyzes the user's progress data, detects stagnation or reversal of progress early, and proposes appropriate countermeasures. For example, the tracking unit uses a generative AI to analyze the user's progress data and detects stagnation or reversal of progress early. For example, if the user's stress level rises again, the tracking unit may re-suggest relaxation techniques. The tracking unit can also identify the cause of stagnation or reversal based on the user's progress data and propose appropriate countermeasures. For example, if the user is struggling with a particular task, the tracking unit may propose ways to break down the task and progress in stages. This makes it possible to detect stagnation or reversal of progress early and propose appropriate countermeasures, thereby effectively supporting the user's return process.

[0069] The tracking unit can compare the user's progress data with the data of other users and set a benchmark to evaluate the progress. For example, the generation AI can compare the user's progress data with the data of other users and set a benchmark to evaluate the progress. For example, the progress can be evaluated in comparison with users with the same stress level. The tracking unit can also set a benchmark based on the user's progress data by comparing it with past data. For example, the progress can be evaluated in comparison with goals the user has achieved in the past. In this way, by comparing the user's progress with other users and setting a benchmark, the evaluation of progress can be made more objectively.

[0070] The tracking unit can use the emotion estimation function to analyze the user's emotional response to the progress and adjust the plan based on the emotion. For example, the generation AI in the tracking unit can use the emotion estimation function to analyze the user's emotional response to the progress and adjust the plan based on the emotion. For example, if the user shows positive emotion, the goal can be raised. The tracking unit can also adjust the content of the support plan based on the user's emotional response. For example, if the user shows negative emotion, relaxation techniques can be suggested. In this way, more effective support can be provided by analyzing the user's emotional response to the progress and adjusting the plan based on the emotion.

[0071] The tracking unit can display the user's progress data on a visual dashboard to enable intuitive understanding. The tracking unit, for example, allows the generation AI to display the user's progress data on a visual dashboard to enable intuitive understanding. For example, progress is visualized using graphs and charts. The tracking unit can also display the user's progress data in different colors. For example, green indicates steady progress, yellow indicates stagnant progress, and red indicates reversal of progress. In this way, by displaying the progress data on a visual dashboard, the user can intuitively understand their progress.

[0072] The tracking unit can incorporate feedback from the user's family and friends when tracking the user's progress to provide more comprehensive support. For example, the tracking unit can adjust the support plan based on opinions from family members. The tracking unit can also adjust the support content based on feedback from the user's friends. For example, if a friend reports a change in the user's stress level, the tracking unit can suggest relaxation techniques. This makes it possible to provide more comprehensive support by incorporating feedback from family and friends.

[0073] The tracking unit can use the emotion estimation function to monitor the user's emotions regarding progress in real time and propose plans to elicit positive emotions. For example, the tracking unit uses the generation AI emotion estimation function to monitor the user's emotions regarding progress in real time and propose plans to elicit positive emotions. For example, it can propose tasks that will give the user a sense of accomplishment. The tracking unit can also propose plans to improve motivation based on the user's emotions. For example, it can introduce a reward system when the user shows positive emotions. In this way, it is possible to improve the user's motivation by monitoring emotions regarding progress in real time and proposing plans to elicit positive emotions.

[0074] The tracking unit can analyze data on the user's return to work process and identify the most effective return steps. For example, the tracking unit uses a generation AI to analyze data on the user's return to work process and identify the most effective return steps. For example, it can suggest specific steps for the user to gradually return to work. The tracking unit can also evaluate the effectiveness of the return steps based on data on the user's return to work process. For example, it can evaluate the user's emotional state after completing a specific step and suggest the next step. In this way, by analyzing data on the return to work process and identifying the most effective return steps, it is possible to effectively support the user's return to work.

[0075] The tracking unit can evaluate the user's return process step by step and suggest the next step depending on the degree of achievement of each step. For example, the tracking unit uses a generation AI to evaluate the user's return process step by step and suggest the next step depending on the degree of achievement of each step. For example, the tracking unit suggests the next task after the user completes a specific task. The tracking unit can also adjust the progress of the return process based on the user's degree of achievement. For example, when the user achieves a goal, the next goal is set. In this way, the return process can be evaluated step by step and the next step suggested depending on the degree of achievement of each step, thereby effectively supporting the user's return.

[0076] The tracking unit can use the emotion estimation function to monitor the user's emotions during the recovery process in real time and adjust the guide content based on the emotions. For example, the generation AI in the tracking unit can use the emotion estimation function to monitor the user's emotions during the recovery process in real time and adjust the guide content based on the emotions. For example, if the user is feeling anxious, the tracking unit can provide guidance that gives a sense of security. The tracking unit can also adjust the progress speed and content of the guide content based on the user's emotions. For example, if the user is relaxed, the tracking unit can provide more detailed guidance. In this way, by monitoring the user's emotions during the recovery process in real time and adjusting the guide content based on the emotions, it is possible to provide support that suits the user's psychological state.

[0077] The tracking unit can propose a specific action plan taking into account the user's work environment and work content. For example, the tracking unit uses a generation AI to analyze the user's work environment and work content and propose a specific action plan to guide the return process. For example, the tracking unit proposes specific measures to help the user reduce stress at work. The tracking unit can also adjust the content of the action plan based on the user's work content. For example, if the user does desk work, the tracking unit can propose an appropriate way to take a break. This makes it possible to effectively support the user's return by proposing a specific action plan taking into account the work environment and work content.

[0078] The tracking unit can customize the guide for the return process to match the user's pace and support a natural progression. For example, the generation AI can customize the guide for the return process to match the user's pace and support a natural progression. For example, the goal setting can be adjusted so that the user can proceed at their own pace. The tracking unit can also adjust the content of the guide based on the user's progress speed. For example, if the user wants to proceed quickly, more tasks can be suggested. This makes it possible to support a natural progression by providing guidance that matches the user's pace.

[0079] The tracking unit can use the emotion estimation function to monitor the user's emotions during the return process in real time and suggest guidance that will elicit positive emotions. For example, the tracking unit uses the generative AI emotion estimation function to monitor the user's emotions during the return process in real time and suggest guidance that will elicit positive emotions. For example, it can suggest tasks that will give the user a sense of accomplishment. The tracking unit can also adjust the content of the guidance based on the user's emotions. For example, if the user expresses positive emotions, it can suggest the next step. In this way, by monitoring the user's emotions in real time during the return process and suggesting guidance that will elicit positive emotions, it is possible to improve the user's motivation.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can integrate the user's self-assessment data with other health data to evaluate their overall health. For example, the analysis unit can integrate the user's self-assessment data with fitness tracker and sleep data to allow the generative AI to evaluate their overall health. For example, it can analyze the correlation between the amount of exercise and quality of sleep and their emotional state. The analysis unit can also integrate the user's self-assessment data with medical records to evaluate their health. For example, it can take into account the user's medical history and medication status to comprehensively evaluate their health. This allows the self-assessment data to be integrated with other health data to evaluate their overall health.

[0082] The suggestion unit can customize the content of the counseling session based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and customizes the content of the counseling session. For example, if the user is interested in music, it can suggest music therapy. The suggestion unit can also adjust the counseling approach based on the user's hobbies and interests. For example, if the user is interested in sports, it can provide counseling that incorporates sports. In this way, by customizing the content of the counseling session based on the user's hobbies and interests, it is possible to provide a more friendly session.

[0083] The tracking unit can display the user's progress data on a visual dashboard to enable intuitive understanding. For example, the generation AI can display the user's progress data on a visual dashboard to enable intuitive understanding. For example, progress can be visualized using graphs and charts. The tracking unit can also display the user's progress data in different colors. For example, green indicates steady progress, yellow indicates stagnant progress, and red indicates reversal of progress. In this way, displaying the progress data on a visual dashboard allows the user to intuitively understand their progress.

[0084] The suggestion unit can take into account the user's living environment and daily routine when proposing self-care activities and suggest feasible activities. For example, the generation AI can analyze the user's living environment and daily routine and suggest feasible self-care activities. For example, it can suggest relaxation methods that can be done in a short amount of time when the user is busy. The suggestion unit can also adjust the content of self-care activities based on the user's living environment. For example, it can suggest exercises that the user can do at home. This makes it possible to provide effective support by suggesting feasible self-care activities that take into account the user's living environment and daily routine.

[0085] The tracking unit can propose a specific action plan taking into account the user's work environment and work content. For example, the generation AI analyzes the user's work environment and work content and proposes a specific action plan to guide the return process. For example, it proposes specific measures for the user to reduce stress at work. The tracking unit can also adjust the content of the action plan based on the user's work content. For example, if the user does desk work, it can suggest appropriate ways to take a break. This makes it possible to effectively support the user's return by proposing a specific action plan taking into account the work environment and work content.

[0086] The analysis unit can use the emotion estimation function to estimate the user's emotions when assessing themselves, and dynamically adjust the content of the assessment questions based on those emotions. For example, when a user assesses themselves, the generation AI uses the emotion estimation function to estimate the user's emotions and dynamically adjusts the content of the assessment questions based on those emotions. For example, if the user is feeling stressed, it can add questions about relaxation. The analysis unit can also adjust the difficulty and content of the assessment questions based on the user's emotions. For example, if the user is relaxed, it can add more detailed questions. This makes it possible to provide a more personalized assessment by dynamically adjusting the assessment questions based on the user's emotions.

[0087] The suggestion unit can use the emotion estimation function to monitor the user's emotional state in real time and suggest counseling content according to the emotion. For example, the generation AI can use the emotion estimation function to monitor the user's emotional state in real time and suggest counseling content according to the emotion. For example, if the user is feeling anxious, it can suggest relaxation techniques. The suggestion unit can also adjust the counseling content based on the user's emotional state. For example, if the user is relaxed, it can provide deeper counseling. In this way, the user's psychological state can be improved by using the emotion estimation function to monitor the emotional state in real time and suggesting appropriate counseling content.

[0088] The tracking unit can use the emotion estimation function to analyze the user's emotional response to their progress and adjust the plan based on their emotions. For example, the generation AI can use the emotion estimation function to analyze the user's emotional response to their progress and adjust the plan based on their emotions. For example, if the user shows positive emotions, it can raise the goal. The tracking unit can also adjust the content of the support plan based on the user's emotional response. For example, if the user shows negative emotions, it can suggest relaxation techniques. This makes it possible to provide more effective support by analyzing the user's emotional response to their progress and adjusting the plan based on their emotions.

[0089] The tracking unit can use the emotion estimation function to monitor the user's emotions during the recovery process in real time and adjust the guide content based on the emotions. For example, the generation AI can use the emotion estimation function to monitor the user's emotions during the recovery process in real time and adjust the guide content based on the emotions. For example, if the user is feeling anxious, it can provide guidance that gives a sense of security. The tracking unit can also adjust the progress speed and content of the guide content based on the user's emotions. For example, if the user is relaxed, it can provide more detailed guidance. In this way, by monitoring the user's emotions during the recovery process in real time and adjusting the guide content based on the emotions, it is possible to provide support that suits the user's psychological state.

[0090] The suggestion unit can use the emotion estimation function to estimate the emotions the user feels during a counseling session in real time and adjust the progress of the session. For example, the generation AI can use the emotion estimation function to estimate the emotions the user feels during a counseling session in real time and adjust the progress of the session. For example, if the user feels anxious, the suggestion unit can switch to a topic to help the user relax. The suggestion unit can also adjust the speed and content of the session based on the user's emotions. For example, if the user feels relaxed, the suggestion unit can provide deeper counseling. In this way, more effective counseling can be provided by estimating the emotions during a counseling session in real time and adjusting the progress of the session.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The self-evaluation unit accepts the user's self-evaluation. For example, the user evaluates themselves through the app and answers questions about their emotions, psychological state, stress level, lifestyle habits, etc. The self-evaluation unit also collects the data the user has answered and converts it into a format that is easy for the generation AI to analyze. Step 2: The analysis unit analyzes the self-evaluation data received by the self-evaluation unit. For example, the generation AI analyzes the user's emotions and psychological state to understand the user's condition. The generation AI can also compare the data with past data to analyze the user's emotional fluctuation patterns. Step 3: The suggestion unit suggests counseling sessions or self-care activities based on the data analyzed by the analysis unit. For example, the generative AI can suggest relaxation techniques or mindfulness sessions if the user's stress level is high. It can also suggest psychological counseling sessions if the user needs emotional support. Step 4: The tracking unit tracks the user's progress and adjusts the plan as needed. For example, when the user records their daily activities and emotional changes, the generating AI analyzes the data and evaluates their progress. If the user reports a decrease in their stress level, the generating AI can evaluate their progress and adjust the support plan.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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, in order to avoid confusion and to 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a self-evaluation unit that accepts a self-evaluation from a user; an analysis unit that analyzes the self-assessment data received by the self-assessment unit; a suggestion unit that suggests counseling sessions and self-care activities based on the data analyzed by the analysis unit; a tracking unit that tracks the user's progress and adjusts the plan as needed. A system characterized by:

2. The analysis unit The self-evaluation data of the user is analyzed in real time, and instant feedback is provided.

2. The system of claim 1.

3. The analysis unit Integrating the user's self-assessment data with other health data to assess overall health status 2. The system of claim 1.

4. The proposal unit Analyzing data from the user's past counseling sessions to identify the most effective session formats and content 2. The system of claim 1.

5. The tracking unit Analyze the user's progress data, detect stagnation or reversal of progress early, and propose appropriate measures 2. The system of claim 1.

6. The analysis unit An emotion estimation function is used to estimate the emotion of the user when he / she evaluates himself / herself, and the content of the evaluation questions is dynamically adjusted based on the emotion.

2. The system of claim 1.

7. The proposal unit Using an emotion estimation function, the emotional state of the user is monitored in real time, and counseling content is proposed according to the emotion.

2. The system of claim 1.

8. The tracking unit Using emotion estimation to analyze the user's emotional response to their progress and adjust the plan based on their emotions.

2. The system of claim 1.

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