system

Abstract

The system according to the embodiment aims to assess an individual's stress level and provide personalized advice. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives questions. The analysis unit analyzes the answers received by the reception unit. The evaluation unit evaluates the stress level based on the answers analyzed by the analysis unit. The provision unit provides personalized advice based on the results of the evaluation by the evaluation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to accurately assess an individual's stress level and provide appropriate advice.

[0005] The system according to the embodiment aims to assess an individual's stress level and provide personalized advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives a question. The analysis unit analyzes the answer received by the reception unit. The evaluation unit evaluates the stress level based on the answer analyzed by the analysis unit. The provision unit provides personalized advice based on the result of the evaluation by the evaluation unit. [Effects of the Invention]

[0007] An embodiment of the system can assess an individual's stress level and provide personalized advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A stress check system according to an embodiment of the present invention communicates with a user in an interactive manner and uses AI to assess an individual's stress level through questions and answers. The system begins with the user accessing a service and answering questions interactively. Next, AI analyzes the answers and assesses the user's stress level. This assessment is conducted using a psychological approach, maintaining anonymity. Finally, customized stress management advice is provided based on the assessment results. This advice is designed to support the user's mental health and includes specific guidelines for action and relaxation techniques. For example, a user accesses the service and answers questions interactively. For example, questions such as "How often do you feel stressed recently?" and "How do you deal with stress when you feel stressed?" are asked. The user answers these questions according to their own situation. Next, AI analyzes the user's answers. The AI ​​assesses the user's stress level based on the user's answers. This assessment is conducted using a psychological approach, quantifying the stress level by analyzing the content and patterns of the user's answers. For example, the system analyzes the causes, frequency, and coping methods of stress from the answers to assess the overall stress level. Based on the evaluation results, customized stress management advice is provided. This advice includes specific guidelines for action and relaxation methods according to the user's stress level. For example, if stress is high, relaxation methods and specific guidelines for action to relieve stress are provided. On the other hand, if stress is low, advice to maintain the current situation is provided. In this way, the stress check system is an innovative system that supports the user's mental health by communicating with the user in an interactive format, using AI to evaluate the user's stress level and providing customized stress management advice. In this way, the stress check system can support mental health by evaluating the user's stress level and providing customized advice.

[0029] A stress check system according to an embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives responses from a user who accesses a service and answers questions in an interactive format. For example, the reception unit receives responses from the user to questions such as, "How often have you felt stressed recently?" and "How do you deal with stress when you feel stressed?" The analysis unit analyzes the responses received by the reception unit. For example, the analysis unit analyzes the content and pattern of the user's responses to quantify the stress level. The analysis unit employs a psychological approach to analyze the responses. For example, the analysis unit analyzes the causes, frequency, and coping methods of stress from the content of the responses to evaluate the overall stress level. The evaluation unit evaluates the stress level based on the responses analyzed by the analysis unit. For example, the evaluation unit evaluates the user's stress level based on the stress level quantified by the analysis unit. The evaluation unit analyzes the content and pattern of the responses to quantify the stress level. The provision unit provides customized advice based on the results of the evaluation by the evaluation unit. For example, the providing unit provides specific guidelines for action or relaxation methods according to the stress level. The providing unit provides specific guidelines for action or relaxation methods according to the stress level. For example, if stress is high, a relaxation method or specific guidelines for action for stress relief is provided. Also, if stress is low, advice for maintaining the current state is provided. In this way, the stress check system according to the embodiment can support mental health by evaluating the user's stress level and providing customized advice.

[0030] The stress check system includes a generation unit that generates questions. The generation unit generates questions appropriate for the user. For example, the generation unit generates questions such as "How often have you felt stressed recently?" and "How do you deal with stress when you feel stressed?" in order to evaluate the user's stress level. The generation unit can generate questions using an algorithm or a template. For example, the generation unit can generate optimal questions based on the user's past answer history and attribute information. This allows the generation unit to generate appropriate questions for the user.

[0031] The stress check system includes an anonymization unit for maintaining anonymity. The anonymization unit anonymizes data to protect the user's privacy. For example, the anonymization unit protects the user's privacy by masking the user's personal information or by pseudo-anonymizing the data. The anonymization unit can use an algorithm for masking or pseudo-anonymizing the data. For example, the anonymization unit can mask personal information such as the user's name and address to maintain anonymity. This allows the anonymization unit to evaluate the stress level while protecting the user's privacy.

[0032] The analysis unit can analyze the answers by employing psychological techniques. The analysis unit employs a psychological approach to analyze the answers. For example, the analysis unit analyzes the content of the user's answers using psychological techniques such as cognitive behavioral therapy and emotion analysis. The analysis unit analyzes the content and pattern of the user's answers to quantify the stress level. For example, the analysis unit analyzes the causes, frequency, and coping methods of stress from the content of the answers to evaluate the overall stress level. In this way, the analysis unit can use a psychological approach to more accurately evaluate the stress level.

[0033] The evaluation unit can analyze the content and format of the answers and quantify the stress level. The evaluation unit can analyze the content and format of the user's answers and quantify the stress level. For example, the evaluation unit can analyze the content of the user's answers using techniques such as text mining or sentiment analysis. The evaluation unit can adjust the analysis method depending on the format of the answers. For example, free-form answers are analyzed using text mining, and multiple-choice answers are analyzed by analyzing the selection patterns of the options. In this way, the evaluation unit can objectively evaluate the user's condition by quantifying the stress level.

[0034] The providing unit can provide guidelines for action and relaxation methods according to the stress level. The providing unit provides specific guidelines for action and relaxation methods according to the user's stress level. For example, when stress is high, the providing unit provides relaxation methods and specific guidelines for action for stress relief. When stress is low, the providing unit can provide advice for maintaining the current situation. The providing unit can use an algorithm for providing appropriate advice according to the user's stress level. This allows the providing unit to provide appropriate advice according to the user's stress level.

[0035] The reception unit can analyze the user's past answer history and select the optimal question reception method. The reception unit analyzes the user's past answer history and selects the optimal question reception method. For example, if the user has preferred detailed answers in the past, the reception unit can preferentially accept detailed questions. If the user has preferred concise answers in the past, the reception unit can preferentially accept concise questions. The reception unit can optimally collect information by accepting questions during specific time periods based on the user's past answer history. As a result, the reception unit can efficiently collect information by selecting the optimal question reception method based on the user's past answer history.

[0036] The reception unit can filter questions based on the user's current living situation and areas of interest when receiving the questions. The reception unit can filter questions based on the user's current living situation and areas of interest when receiving the questions. For example, if the user is at work, the reception unit can preferentially receive questions related to work. If the user is on vacation, the reception unit can preferentially receive questions related to relaxation. If the user is interested in a particular hobby, the reception unit can preferentially receive questions related to that hobby. This allows the reception unit to collect more relevant information by asking questions according to the user's living situation and areas of interest.

[0037] When accepting a question, the reception unit can prioritize receiving highly relevant questions by taking into consideration the user's geographical location information. When accepting a question, the reception unit prioritizes receiving highly relevant questions by taking into consideration the user's geographical location information. For example, when the user is in an urban area, the reception unit can prioritize receiving questions related to urban life. When the user is in a natural environment, the reception unit can prioritize receiving questions related to relaxation. When the user is in a specific area, the reception unit can prioritize receiving questions related to that area. This allows the reception unit to collect more appropriate information by asking highly relevant questions based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit analyzes the user's social media activity when receiving a question and receive related questions. For example, if the user has posted about stress on social media, the reception unit can preferentially receive questions about stress reduction. If the user has posted about relaxation on social media, the reception unit can preferentially receive questions related to relaxation. If the user has posted about a particular hobby on social media, the reception unit can preferentially receive questions related to that hobby. This allows the reception unit to collect more appropriate information by asking related questions based on the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the answer during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the answer during analysis. For example, the analysis unit can perform a detailed analysis on important answers to improve accuracy. The analysis unit can perform a simplified analysis on general answers to improve efficiency. The analysis unit can perform a theme-specific analysis on answers related to a specific topic. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the answer, enabling efficient analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the answer. During analysis, the analysis unit applies different analysis algorithms depending on the category of the answer. For example, the analysis unit can apply a stress analysis algorithm to answers about stress. The analysis unit can apply a relaxation analysis algorithm to answers about relaxation. The analysis unit can apply an analysis algorithm specialized for a particular hobby to answers about that hobby. In this way, the analysis unit can apply an analysis algorithm depending on the category of the answer, enabling more accurate analysis.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the answers. During analysis, the analysis unit determines the priority of analysis based on the time of submission of the answers. For example, the analysis unit prioritizes analysis of recently submitted answers to reflect the latest information. The analysis unit can prioritize analysis of answers submitted during a specific period to grasp trends during that period. The analysis unit can analyze past answers to grasp long-term trends. In this way, by determining the priority of analysis based on the time of submission of the answers, the analysis unit can perform analysis that reflects the latest information.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the answers during analysis. The analysis unit can adjust the order of analysis based on the relevance of the answers during analysis. For example, the analysis unit can prioritize analyzing answers related to stress and evaluate the stress level. The analysis unit can prioritize analyzing answers related to relaxation and suggest relaxation methods. The analysis unit can prioritize analyzing answers related to a specific hobby and provide advice related to that hobby. This allows the analysis unit to adjust the order of analysis based on the relevance of the answers, enabling more appropriate analysis.

[0043] The evaluation unit can analyze the response pattern during the evaluation to improve the accuracy of the stress level evaluation. The evaluation unit can analyze the response pattern during the evaluation to improve the accuracy of the stress level evaluation. For example, the evaluation unit analyzes the user's response pattern to grasp fluctuations in the stress level. The evaluation unit can improve the accuracy of the stress level evaluation based on a specific response pattern. The evaluation unit can analyze long-term response patterns to grasp trends in the stress level. As a result, the evaluation unit can analyze the response pattern to improve the accuracy of the stress level evaluation.

[0044] The evaluation unit can evaluate the stress level taking into account the user's attribute information when evaluating the stress level. The evaluation unit can evaluate the stress level taking into account the user's attribute information when evaluating the stress level. For example, the evaluation unit can evaluate the stress level taking into account the user's age and gender. The evaluation unit can evaluate the stress level taking into account the user's occupation and living environment. The evaluation unit can evaluate the stress level taking into account the user's health condition and past stress levels. This allows the evaluation unit to evaluate the stress level more accurately by taking into account the user's attribute information.

[0045] The evaluation unit can evaluate the stress level by taking into account the geographical distribution of the answers during the evaluation. The evaluation unit evaluates the stress level by taking into account the geographical distribution of the answers during the evaluation. For example, the evaluation unit can consider factors related to urban life to evaluate the stress level of a user living in an urban area. The evaluation unit can consider factors related to the natural environment to evaluate the stress level of a user living in a natural environment. The evaluation unit can consider factors related to a particular region to evaluate the stress level of a user living in that region. In this way, the evaluation unit can consider the geographical distribution of the answers to enable an evaluation that reflects stress factors specific to the region.

[0046] The evaluation unit can improve the accuracy of stress level evaluation by referring to related literature during evaluation. The evaluation unit can improve the accuracy of stress level evaluation by referring to related literature during evaluation. For example, the evaluation unit can improve the accuracy of stress level evaluation by referring to the latest psychological research. The evaluation unit can improve the accuracy of stress level evaluation by referring to past research data. The evaluation unit can improve the accuracy of stress level evaluation by referring to related literature. As a result, the evaluation unit improves the accuracy of stress level evaluation by referring to related literature.

[0047] The providing unit can adjust the level of detail of the advice based on the stress level when providing the advice. The providing unit adjusts the level of detail of the advice based on the stress level when providing the advice. For example, the providing unit can provide detailed relaxation methods for a high stress level. The providing unit can provide simple relaxation methods for a medium stress level. The providing unit can provide simple advice for maintaining the current state for a low stress level. In this way, the providing unit can provide appropriate support to the user by providing detailed advice according to the stress level.

[0048] The providing unit can apply different advice depending on the stress level category when providing the advice. The providing unit applies different advice depending on the stress level category when providing the advice. For example, the providing unit provides relaxation methods and specific action guidelines for stress relief for a high stress level. The providing unit can provide general advice for stress management for a medium stress level. The providing unit can provide advice for maintaining the current state for a low stress level. In this way, the providing unit can provide optimal support to the user by providing advice according to the stress level category.

[0049] The providing unit can determine the priority of advice based on the time of submission of the stress level at the time of providing the advice. The providing unit can determine the priority of advice based on the time of submission of the stress level at the time of providing the advice. For example, the providing unit can provide advice preferentially for a recently submitted stress level. The providing unit can provide advice preferentially for a stress level submitted within a specific period. The providing unit can provide long-term advice for a past stress level. In this way, the providing unit can determine the priority of advice based on the time of submission of the stress level, thereby enabling support that reflects the latest information.

[0050] The providing unit can adjust the order of advice based on the relevance of stress levels when providing the advice. The providing unit adjusts the order of advice based on the relevance of stress levels when providing the advice. For example, the providing unit can preferentially provide relaxation methods for a high stress level. The providing unit can provide general advice for stress management for a medium stress level. The providing unit can provide advice for maintaining the status quo for a low stress level. In this way, the providing unit can adjust the order of advice based on the relevance of stress levels, thereby enabling more appropriate support.

[0051] The generation unit can generate an optimal question by referring to past question data when generating the question content. The generation unit generates an optimal question by referring to past question data when generating the question content. For example, the generation unit generates a related question based on questions previously answered by the user. The generation unit can generate a question related to the user's field of interest from the past question data. The generation unit can analyze the past question data and generate the most effective question. As a result, the generation unit can generate a more effective question by referring to the past question data.

[0052] The generation unit can generate optimal questions by taking into account the user's attribute information when generating question content. The generation unit can generate optimal questions by taking into account the user's attribute information when generating question content. For example, the generation unit can generate optimal questions by taking into account the user's age and gender. The generation unit can generate optimal questions by taking into account the user's occupation and living environment. The generation unit can generate optimal questions by taking into account the user's health condition and past stress level. In this way, the generation unit can generate more appropriate questions by taking into account the user's attribute information.

[0053] The anonymization unit can select the optimal anonymization method by referring to past anonymized data during anonymization. The anonymization unit selects the optimal anonymization method by referring to past anonymized data during anonymization. For example, the anonymization unit selects the optimal anonymization method based on past anonymized data. The anonymization unit can select an anonymization method according to user attributes from past anonymized data. The anonymization unit can analyze past anonymized data and select the most effective anonymization method. This allows the anonymization unit to perform more effective anonymization by referring to past anonymized data.

[0054] The anonymization unit can select the optimal anonymization method by taking into account the user's attribute information during anonymization. The anonymization unit selects the optimal anonymization method by taking into account the user's attribute information during anonymization. For example, the anonymization unit selects the optimal anonymization method by taking into account the user's age and gender. The anonymization unit can select the optimal anonymization method by taking into account the user's occupation and living environment. The anonymization unit can select the optimal anonymization method by taking into account the user's health condition and past stress level. This allows the anonymization unit to perform more appropriate anonymization by taking into account the user's attribute information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The analysis unit can identify the cause of stress based on the user's answers and suggest specific measures to address that cause. For example, if the user is feeling stressed at work, the analysis unit can suggest time management techniques to improve work efficiency. If the user is feeling stressed in their relationships, the analysis unit can suggest a workshop to improve communication skills. Furthermore, if the user is feeling stressed due to health issues, the analysis unit can provide specific advice for health management. This allows the analysis unit to suggest specific measures according to the cause of the user's stress.

[0057] When assessing a user's stress level, the evaluation unit can refer to the user's past stress level data to understand long-term stress trends. For example, the evaluation unit can analyze stress level data from the past few months to identify patterns of stress increase or decrease. If the user's stress tends to increase at a certain time, measures can be strengthened at that time. Furthermore, the evaluation unit can analyze what measures the user took to reduce stress based on past data and suggest effective measures. In this way, the evaluation unit can support long-term stress management.

[0058] The providing unit can suggest group sessions for stress management according to the user's stress level. For example, the providing unit can suggest a group session with other users experiencing similar stress to a user with a high stress level. An online workshop for stress management can be suggested to a user with a medium stress level. Furthermore, regular group activities for stress prevention can be suggested to a user with a low stress level. This allows the providing unit to provide appropriate support according to the user's stress level.

[0059] The analysis unit can suggest stress prevention measures based on the user's answers. For example, the analysis unit can identify signs that the user is feeling stressed and suggest measures to be taken when the signs appear. If the user is prone to feeling stressed in a particular situation, the analysis unit can provide advice on how to avoid that situation. The analysis unit can also suggest that the user practice relaxation techniques before feeling stressed. In this way, the analysis unit can provide the user with specific measures to prevent stress before it occurs.

[0060] The providing unit can create a personalized stress management plan according to the user's stress level. For example, the providing unit can create a detailed plan including daily relaxation exercises for a user with a high stress level. For a user with a medium stress level, the providing unit can suggest stress management activities to be done several times a week. For a user with a low stress level, the providing unit can suggest a monthly check-in and simple relaxation methods. In this way, the providing unit can provide an optimal stress management plan according to the user's stress level.

[0061] The analysis unit can identify time periods when the user is more susceptible to stress based on the user's responses and strengthen measures during those time periods. For example, if the user is more likely to feel stressed during a particular time period, the analysis unit can suggest that the user practice relaxation methods during that time period. If the user is more likely to feel stressed at night, the analysis unit can provide nighttime relaxation methods. If the user is more likely to feel stressed in the morning, the analysis unit can suggest morning stress management methods. This allows the analysis unit to provide specific measures for the user to effectively manage stress.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit accepts that the user accesses the service and answers questions in an interactive format. For example, the reception unit accepts that the user answers questions such as "How often have you felt stressed recently?" and "When you feel stressed, how do you deal with it?" Step 2: The analysis unit analyzes the responses received by the reception unit. For example, the analysis unit analyzes the content and pattern of the user's responses and quantifies the stress level. The analysis unit employs a psychological approach to analyze the responses. For example, the analysis unit analyzes the causes, frequency, and coping methods of stress from the content of the responses to evaluate the overall stress level. Step 3: The evaluation unit evaluates the stress level based on the answers analyzed by the analysis unit. For example, the evaluation unit evaluates the user's stress level based on the stress level quantified by the analysis unit. The evaluation unit analyzes the content and pattern of the answers and quantifies the stress level. Step 4: The provision unit provides customized advice based on the results of the evaluation by the evaluation unit. For example, the provision unit provides specific guidelines for action or relaxation methods according to the stress level. The provision unit provides guidelines for action or relaxation methods according to the stress level. For example, if stress is high, relaxation methods or specific guidelines for action to relieve stress are provided. Also, if stress is low, advice to maintain the current state is provided.

[0064] (Example 2) A stress check system according to an embodiment of the present invention communicates with a user in an interactive manner and uses AI to assess an individual's stress level through questions and answers. The system begins with the user accessing a service and answering questions interactively. Next, AI analyzes the answers and assesses the user's stress level. This assessment is conducted using a psychological approach, maintaining anonymity. Finally, customized stress management advice is provided based on the assessment results. This advice is designed to support the user's mental health and includes specific guidelines for action and relaxation techniques. For example, a user accesses the service and answers questions interactively. For example, questions such as "How often do you feel stressed recently?" and "How do you deal with stress when you feel stressed?" are asked. The user answers these questions according to their own situation. Next, AI analyzes the user's answers. The AI ​​assesses the user's stress level based on the user's answers. This assessment is conducted using a psychological approach, quantifying the stress level by analyzing the content and patterns of the user's answers. For example, the system analyzes the causes, frequency, and coping methods of stress from the answers to assess the overall stress level. Based on the evaluation results, customized stress management advice is provided. This advice includes specific guidelines for action and relaxation methods according to the user's stress level. For example, if stress is high, relaxation methods and specific guidelines for action to relieve stress are provided. On the other hand, if stress is low, advice to maintain the current situation is provided. In this way, the stress check system is an innovative system that supports the user's mental health by communicating with the user in an interactive format, using AI to evaluate the user's stress level and providing customized stress management advice. In this way, the stress check system can support mental health by evaluating the user's stress level and providing customized advice.

[0065] A stress check system according to an embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives responses from a user who accesses a service and answers questions in an interactive format. For example, the reception unit receives responses from the user to questions such as, "How often have you felt stressed recently?" and "How do you deal with stress when you feel stressed?" The analysis unit analyzes the responses received by the reception unit. For example, the analysis unit analyzes the content and pattern of the user's responses to quantify the stress level. The analysis unit employs a psychological approach to analyze the responses. For example, the analysis unit analyzes the causes, frequency, and coping methods of stress from the content of the responses to evaluate the overall stress level. The evaluation unit evaluates the stress level based on the responses analyzed by the analysis unit. For example, the evaluation unit evaluates the user's stress level based on the stress level quantified by the analysis unit. The evaluation unit analyzes the content and pattern of the responses to quantify the stress level. The provision unit provides customized advice based on the results of the evaluation by the evaluation unit. For example, the providing unit provides specific guidelines for action or relaxation methods according to the stress level. The providing unit provides specific guidelines for action or relaxation methods according to the stress level. For example, if stress is high, a relaxation method or specific guidelines for action for stress relief is provided. Also, if stress is low, advice for maintaining the current state is provided. In this way, the stress check system according to the embodiment can support mental health by evaluating the user's stress level and providing customized advice.

[0066] The stress check system includes a generation unit that generates questions. The generation unit generates questions appropriate for the user. For example, the generation unit generates questions such as "How often have you felt stressed recently?" and "How do you deal with stress when you feel stressed?" in order to evaluate the user's stress level. The generation unit can generate questions using an algorithm or a template. For example, the generation unit can generate optimal questions based on the user's past answer history and attribute information. This allows the generation unit to generate appropriate questions for the user.

[0067] The stress check system includes an anonymization unit for maintaining anonymity. The anonymization unit anonymizes data to protect the user's privacy. For example, the anonymization unit protects the user's privacy by masking the user's personal information or by pseudo-anonymizing the data. The anonymization unit can use an algorithm for masking or pseudo-anonymizing the data. For example, the anonymization unit can mask personal information such as the user's name and address to maintain anonymity. This allows the anonymization unit to evaluate the stress level while protecting the user's privacy.

[0068] The analysis unit can analyze the answers by employing psychological techniques. The analysis unit employs a psychological approach to analyze the answers. For example, the analysis unit analyzes the content of the user's answers using psychological techniques such as cognitive behavioral therapy and emotion analysis. The analysis unit analyzes the content and pattern of the user's answers to quantify the stress level. For example, the analysis unit analyzes the causes, frequency, and coping methods of stress from the content of the answers to evaluate the overall stress level. In this way, the analysis unit can use a psychological approach to more accurately evaluate the stress level.

[0069] The evaluation unit can analyze the content and format of the answers and quantify the stress level. The evaluation unit can analyze the content and format of the user's answers and quantify the stress level. For example, the evaluation unit can analyze the content of the user's answers using techniques such as text mining or sentiment analysis. The evaluation unit can adjust the analysis method depending on the format of the answers. For example, free-form answers are analyzed using text mining, and multiple-choice answers are analyzed by analyzing the selection patterns of the options. In this way, the evaluation unit can objectively evaluate the user's condition by quantifying the stress level.

[0070] The providing unit can provide guidelines for action and relaxation methods according to the stress level. The providing unit provides specific guidelines for action and relaxation methods according to the user's stress level. For example, when stress is high, the providing unit provides relaxation methods and specific guidelines for action for stress relief. When stress is low, the providing unit can provide advice for maintaining the current situation. The providing unit can use an algorithm for providing appropriate advice according to the user's stress level. This allows the providing unit to provide appropriate advice according to the user's stress level.

[0071] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. The reception unit estimates the user's emotions and adjusts the timing of receiving questions based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit increases the interval between questions to allow the user to receive questions in a relaxed state. When the user is relaxed, the reception unit shortens the interval between questions, allowing the user to quickly collect information. When the user is in a hurry, the reception unit prioritizes receiving important questions and can collect necessary information in a short time. As a result, the reception unit can adjust the timing of receiving questions according to the user's emotions, allowing the user to ask more appropriate questions.

[0072] The reception unit can analyze the user's past answer history and select the optimal question reception method. The reception unit analyzes the user's past answer history and selects the optimal question reception method. For example, if the user has preferred detailed answers in the past, the reception unit can preferentially accept detailed questions. If the user has preferred concise answers in the past, the reception unit can preferentially accept concise questions. The reception unit can optimally collect information by accepting questions during specific time periods based on the user's past answer history. As a result, the reception unit can efficiently collect information by selecting the optimal question reception method based on the user's past answer history.

[0073] The reception unit can filter questions based on the user's current living situation and areas of interest when receiving the questions. The reception unit can filter questions based on the user's current living situation and areas of interest when receiving the questions. For example, if the user is at work, the reception unit can preferentially receive questions related to work. If the user is on vacation, the reception unit can preferentially receive questions related to relaxation. If the user is interested in a particular hobby, the reception unit can preferentially receive questions related to that hobby. This allows the reception unit to collect more relevant information by asking questions according to the user's living situation and areas of interest.

[0074] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user's emotions. The reception unit estimates the user's emotions and determines the priority of questions to be received based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit can preferentially receive questions related to stress reduction. When the user is relaxed, the reception unit can preferentially receive questions aimed at collecting detailed information. When the user is in a hurry, the reception unit can preferentially receive important questions and collect necessary information in a short time. In this way, the reception unit can determine the priority of questions according to the user's emotions, thereby enabling more effective information collection.

[0075] When accepting a question, the reception unit can prioritize receiving highly relevant questions by taking into consideration the user's geographical location information. When accepting a question, the reception unit prioritizes receiving highly relevant questions by taking into consideration the user's geographical location information. For example, when the user is in an urban area, the reception unit can prioritize receiving questions related to urban life. When the user is in a natural environment, the reception unit can prioritize receiving questions related to relaxation. When the user is in a specific area, the reception unit can prioritize receiving questions related to that area. This allows the reception unit to collect more appropriate information by asking highly relevant questions based on the user's geographical location information.

[0076] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit analyzes the user's social media activity when receiving a question and receive related questions. For example, if the user has posted about stress on social media, the reception unit can preferentially receive questions about stress reduction. If the user has posted about relaxation on social media, the reception unit can preferentially receive questions related to relaxation. If the user has posted about a particular hobby on social media, the reception unit can preferentially receive questions related to that hobby. This allows the reception unit to collect more appropriate information by asking related questions based on the user's social media activity.

[0077] The analysis unit can estimate the user's emotions and adjust the method of analyzing the answers based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the method of analyzing the answers based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing answers related to stress reduction. If the user is relaxed, the analysis unit can prioritize analyzing answers aimed at collecting detailed information. If the user is in a hurry, the analysis unit can prioritize analyzing important answers and collect necessary information in a short amount of time. This allows the analysis unit to adjust the method of analyzing the answers according to the user's emotions, enabling more accurate analysis.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the answer during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the answer during analysis. For example, the analysis unit can perform a detailed analysis on important answers to improve accuracy. The analysis unit can perform a simplified analysis on general answers to improve efficiency. The analysis unit can perform a theme-specific analysis on answers related to a specific topic. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the answer, enabling efficient analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the answer. During analysis, the analysis unit applies different analysis algorithms depending on the category of the answer. For example, the analysis unit can apply a stress analysis algorithm to answers about stress. The analysis unit can apply a relaxation analysis algorithm to answers about relaxation. The analysis unit can apply an analysis algorithm specialized for a particular hobby to answers about that hobby. In this way, the analysis unit can apply an analysis algorithm depending on the category of the answer, enabling more accurate analysis.

[0080] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user's emotions. The analysis unit estimates the user's emotions and determines the analysis priorities based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing answers related to stress reduction. If the user is relaxed, the analysis unit can prioritize analyzing answers aimed at collecting detailed information. If the user is in a hurry, the analysis unit can prioritize analyzing important answers and collect necessary information in a short amount of time. This allows the analysis unit to determine the analysis priorities according to the user's emotions, enabling more effective analysis.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the answers. During analysis, the analysis unit determines the priority of analysis based on the time of submission of the answers. For example, the analysis unit prioritizes analysis of recently submitted answers to reflect the latest information. The analysis unit can prioritize analysis of answers submitted during a specific period to grasp trends during that period. The analysis unit can analyze past answers to grasp long-term trends. In this way, by determining the priority of analysis based on the time of submission of the answers, the analysis unit can perform analysis that reflects the latest information.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the answers during analysis. The analysis unit can adjust the order of analysis based on the relevance of the answers during analysis. For example, the analysis unit can prioritize analyzing answers related to stress and evaluate the stress level. The analysis unit can prioritize analyzing answers related to relaxation and suggest relaxation methods. The analysis unit can prioritize analyzing answers related to a specific hobby and provide advice related to that hobby. This allows the analysis unit to adjust the order of analysis based on the relevance of the answers, enabling more appropriate analysis.

[0083] The evaluation unit can estimate the user's emotions and adjust the stress level evaluation method based on the estimated user emotions. The evaluation unit estimates the user's emotions and adjusts the stress level evaluation method based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can preferentially apply an evaluation method related to stress reduction. If the user is relaxed, the evaluation unit can apply an evaluation method aimed at collecting detailed information. If the user is in a hurry, the evaluation unit can preferentially apply an important evaluation method and collect necessary information in a short time. This allows the evaluation unit to adjust the stress level evaluation method according to the user's emotions, enabling a more accurate evaluation.

[0084] The evaluation unit can analyze the response pattern during the evaluation to improve the accuracy of the stress level evaluation. The evaluation unit can analyze the response pattern during the evaluation to improve the accuracy of the stress level evaluation. For example, the evaluation unit analyzes the user's response pattern to grasp fluctuations in the stress level. The evaluation unit can improve the accuracy of the stress level evaluation based on a specific response pattern. The evaluation unit can analyze long-term response patterns to grasp trends in the stress level. As a result, the evaluation unit can analyze the response pattern to improve the accuracy of the stress level evaluation.

[0085] The evaluation unit can evaluate the stress level taking into account the user's attribute information when evaluating the stress level. The evaluation unit can evaluate the stress level taking into account the user's attribute information when evaluating the stress level. For example, the evaluation unit can evaluate the stress level taking into account the user's age and gender. The evaluation unit can evaluate the stress level taking into account the user's occupation and living environment. The evaluation unit can evaluate the stress level taking into account the user's health condition and past stress levels. This allows the evaluation unit to evaluate the stress level more accurately by taking into account the user's attribute information.

[0086] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user's emotions. The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user's emotions. For example, if the user is feeling stressed, the evaluation unit can provide a simple, highly visible display method. If the user is relaxed, the evaluation unit can provide a display method including detailed information. If the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. This allows the evaluation unit to provide more appropriate information by adjusting the display method of the evaluation results according to the user's emotions.

[0087] The evaluation unit can evaluate the stress level by taking into account the geographical distribution of the answers during the evaluation. The evaluation unit evaluates the stress level by taking into account the geographical distribution of the answers during the evaluation. For example, the evaluation unit can consider factors related to urban life to evaluate the stress level of a user living in an urban area. The evaluation unit can consider factors related to the natural environment to evaluate the stress level of a user living in a natural environment. The evaluation unit can consider factors related to a particular region to evaluate the stress level of a user living in that region. In this way, the evaluation unit can consider the geographical distribution of the answers to enable an evaluation that reflects stress factors specific to the region.

[0088] The evaluation unit can improve the accuracy of stress level evaluation by referring to related literature during evaluation. The evaluation unit can improve the accuracy of stress level evaluation by referring to related literature during evaluation. For example, the evaluation unit can improve the accuracy of stress level evaluation by referring to the latest psychological research. The evaluation unit can improve the accuracy of stress level evaluation by referring to past research data. The evaluation unit can improve the accuracy of stress level evaluation by referring to related literature. As a result, the evaluation unit improves the accuracy of stress level evaluation by referring to related literature.

[0089] The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user's emotions. The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide relaxation techniques preferentially. If the user is relaxed, the providing unit can provide advice including detailed information. If the user is in a hurry, the providing unit can provide advice that focuses on the main points. This allows the providing unit to adjust the method of providing advice according to the user's emotions, enabling more effective advice.

[0090] The providing unit can adjust the level of detail of the advice based on the stress level when providing the advice. The providing unit adjusts the level of detail of the advice based on the stress level when providing the advice. For example, the providing unit can provide detailed relaxation methods for a high stress level. The providing unit can provide simple relaxation methods for a medium stress level. The providing unit can provide simple advice for maintaining the current state for a low stress level. In this way, the providing unit can provide appropriate support to the user by providing detailed advice according to the stress level.

[0091] The providing unit can apply different advice depending on the stress level category when providing the advice. The providing unit applies different advice depending on the stress level category when providing the advice. For example, the providing unit provides relaxation methods and specific action guidelines for stress relief for a high stress level. The providing unit can provide general advice for stress management for a medium stress level. The providing unit can provide advice for maintaining the current state for a low stress level. In this way, the providing unit can provide optimal support to the user by providing advice according to the stress level category.

[0092] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide relaxation methods preferentially. If the user is relaxed, the providing unit can provide advice including detailed information. If the user is in a hurry, the providing unit can provide advice that focuses on the main points. This allows the providing unit to determine the priority of advice according to the user's emotions, thereby enabling more effective support.

[0093] The providing unit can determine the priority of advice based on the time of submission of the stress level at the time of providing the advice. The providing unit can determine the priority of advice based on the time of submission of the stress level at the time of providing the advice. For example, the providing unit can provide advice preferentially for a recently submitted stress level. The providing unit can provide advice preferentially for a stress level submitted within a specific period. The providing unit can provide long-term advice for a past stress level. In this way, the providing unit can determine the priority of advice based on the time of submission of the stress level, thereby enabling support that reflects the latest information.

[0094] The providing unit can adjust the order of advice based on the relevance of stress levels when providing the advice. The providing unit adjusts the order of advice based on the relevance of stress levels when providing the advice. For example, the providing unit can preferentially provide relaxation methods for a high stress level. The providing unit can provide general advice for stress management for a medium stress level. The providing unit can provide advice for maintaining the status quo for a low stress level. In this way, the providing unit can adjust the order of advice based on the relevance of stress levels, thereby enabling more appropriate support.

[0095] The generation unit can estimate the user's emotions and adjust the method for generating question content based on the estimated user's emotions. The generation unit can estimate the user's emotions and adjust the method for generating question content based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit generates questions that are easy to answer in a relaxed state. When the user is relaxed, the generation unit can generate questions aimed at collecting detailed information. When the user is in a hurry, the generation unit can generate important questions with priority and collect necessary information in a short time. In this way, the generation unit can generate more appropriate questions by adjusting the method for generating question content according to the user's emotions.

[0096] The generation unit can generate an optimal question by referring to past question data when generating the question content. The generation unit generates an optimal question by referring to past question data when generating the question content. For example, the generation unit generates a related question based on questions previously answered by the user. The generation unit can generate a question related to the user's field of interest from the past question data. The generation unit can analyze the past question data and generate the most effective question. As a result, the generation unit can generate a more effective question by referring to the past question data.

[0097] The generation unit can estimate the user's emotions and determine the priority of the question contents based on the estimated user's emotions. The generation unit can estimate the user's emotions and determine the priority of the question contents based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit can prioritize generating questions related to stress reduction. When the user is relaxed, the generation unit can prioritize generating questions aimed at collecting detailed information. When the user is in a hurry, the generation unit can prioritize generating important questions and collect necessary information in a short time. In this way, the generation unit can prioritize the question contents according to the user's emotions, thereby enabling more effective information collection.

[0098] The generation unit can generate optimal questions by taking into account the user's attribute information when generating question content. The generation unit can generate optimal questions by taking into account the user's attribute information when generating question content. For example, the generation unit can generate optimal questions by taking into account the user's age and gender. The generation unit can generate optimal questions by taking into account the user's occupation and living environment. The generation unit can generate optimal questions by taking into account the user's health condition and past stress level. In this way, the generation unit can generate more appropriate questions by taking into account the user's attribute information.

[0099] The anonymization unit can estimate the user's emotions and adjust the anonymization method based on the estimated user's emotions. The anonymization unit can estimate the user's emotions and adjust the anonymization method based on the estimated user's emotions. For example, if the user is feeling stressed, the anonymization unit deletes detailed personal information to enhance anonymity. If the user is relaxed, the anonymization unit can retain necessary information while maintaining anonymity. If the user is in a hurry, the anonymization unit can quickly perform anonymization and provide necessary information. In this way, the anonymization unit can adjust the anonymization method according to the user's emotions, thereby enabling more appropriate anonymization.

[0100] The anonymization unit can select the optimal anonymization method by referring to past anonymized data during anonymization. The anonymization unit selects the optimal anonymization method by referring to past anonymized data during anonymization. For example, the anonymization unit selects the optimal anonymization method based on past anonymized data. The anonymization unit can select an anonymization method according to user attributes from past anonymized data. The anonymization unit can analyze past anonymized data and select the most effective anonymization method. This allows the anonymization unit to perform more effective anonymization by referring to past anonymized data.

[0101] The anonymization unit can estimate the user's emotions and determine the priority of anonymization based on the estimated user's emotions. The anonymization unit can estimate the user's emotions and determine the priority of anonymization based on the estimated user's emotions. For example, when the user is feeling stressed, the anonymization unit preferentially deletes detailed personal information to enhance anonymity. When the user is relaxed, the anonymization unit can retain necessary information while maintaining anonymity. When the user is in a hurry, the anonymization unit can quickly perform anonymization and provide necessary information. As a result, the anonymization unit can determine the priority of anonymization according to the user's emotions, thereby enabling more appropriate anonymization.

[0102] The anonymization unit can select the optimal anonymization method by taking into account the user's attribute information during anonymization. The anonymization unit selects the optimal anonymization method by taking into account the user's attribute information during anonymization. For example, the anonymization unit selects the optimal anonymization method by taking into account the user's age and gender. The anonymization unit can select the optimal anonymization method by taking into account the user's occupation and living environment. The anonymization unit can select the optimal anonymization method by taking into account the user's health condition and past stress level. This allows the anonymization unit to perform more appropriate anonymization by taking into account the user's attribute information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, evaluation unit, provision unit, generation unit, and anonymization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a user's answer to a question in an interactive format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the stress level based on the analyzed answer. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides customized advice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate question content. The anonymization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes data to protect the user's privacy. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, evaluation unit, provision unit, generation unit, and anonymization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a user's answer to a question in an interactive format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the stress level based on the analyzed answer. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides customized advice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate question content. The anonymization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes data to protect the user's privacy. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, evaluation unit, provision unit, generation unit, and anonymization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives a user's answers to questions in an interactive format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the stress level based on the analyzed answers. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides customized advice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate question content. The anonymization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes data to protect the user's privacy. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, evaluation unit, provision unit, generation unit, and anonymization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a user's answer to a question in an interactive format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the stress level based on the analyzed answer. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides customized advice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate question content. The anonymization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes data to protect the user's privacy.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The reception unit monitors the user's stress level in real time, and if stress suddenly rises, it can start a chat session with an expert as an emergency response. For example, if it is determined that the user is experiencing very high stress, the reception unit immediately starts a chat with a psychological counselor. If the user is experiencing moderate stress, the reception unit can provide a self-care guide for stress management. Furthermore, if the user maintains a low stress level, the reception unit can conduct regular check-ins and monitor fluctuations in the stress level. This allows the reception unit to provide appropriate responses according to the user's stress level.

[0105] The analysis unit can identify the cause of stress based on the user's answers and suggest specific measures to address that cause. For example, if the user is feeling stressed at work, the analysis unit can suggest time management techniques to improve work efficiency. If the user is feeling stressed in their relationships, the analysis unit can suggest a workshop to improve communication skills. Furthermore, if the user is feeling stressed due to health issues, the analysis unit can provide specific advice for health management. This allows the analysis unit to suggest specific measures according to the cause of the user's stress.

[0106] When assessing a user's stress level, the evaluation unit can refer to the user's past stress level data to understand long-term stress trends. For example, the evaluation unit can analyze stress level data from the past few months to identify patterns of stress increase or decrease. If the user's stress tends to increase at a certain time, measures can be strengthened at that time. Furthermore, the evaluation unit can analyze what measures the user took to reduce stress based on past data and suggest effective measures. In this way, the evaluation unit can support long-term stress management.

[0107] The providing unit can suggest group sessions for stress management according to the user's stress level. For example, the providing unit can suggest a group session with other users experiencing similar stress to a user with a high stress level. An online workshop for stress management can be suggested to a user with a medium stress level. Furthermore, regular group activities for stress prevention can be suggested to a user with a low stress level. This allows the providing unit to provide appropriate support according to the user's stress level.

[0108] The reception unit can estimate the user's emotions and, based on the estimated emotions, receive questions in an environment where the user can be most relaxed. For example, if the user is relaxing at home, the reception unit can recommend receiving questions at home. If the user is feeling stressed while out and about, the reception unit can recommend receiving questions in a quiet place. Furthermore, if the user is feeling stressed at work, the reception unit can suggest avoiding receiving questions at work. In this way, the reception unit can provide an optimal question reception environment according to the user's emotions.

[0109] The analysis unit can suggest stress prevention measures based on the user's answers. For example, the analysis unit can identify signs that the user is feeling stressed and suggest measures to be taken when the signs appear. If the user is prone to feeling stressed in a particular situation, the analysis unit can provide advice on how to avoid that situation. The analysis unit can also suggest that the user practice relaxation techniques before feeling stressed. In this way, the analysis unit can provide the user with specific measures to prevent stress before it occurs.

[0110] The evaluation unit can estimate the user's emotions and adjust the method of conveying the stress level evaluation result to the user based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can provide a simple and easy-to-understand evaluation result. If the user is relaxed, the evaluation unit can provide a detailed evaluation result. If the user is in a hurry, the evaluation unit can provide an evaluation result that focuses on the main points. This allows the evaluation unit to select the optimal method of presenting the evaluation result according to the user's emotions.

[0111] The providing unit can create a personalized stress management plan according to the user's stress level. For example, the providing unit can create a detailed plan including daily relaxation exercises for a user with a high stress level. For a user with a medium stress level, the providing unit can suggest stress management activities to be done several times a week. For a user with a low stress level, the providing unit can suggest a monthly check-in and simple relaxation methods. In this way, the providing unit can provide an optimal stress management plan according to the user's stress level.

[0112] The reception unit can estimate the user's emotions and adjust the content of the questions based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can prioritize questions that will help the user relax. If the user is relaxed, the reception unit can prioritize questions aimed at gathering detailed information. If the user is in a hurry, the reception unit can prioritize important questions and collect necessary information in a short amount of time. This allows the reception unit to provide optimal question content according to the user's emotions.

[0113] The analysis unit can identify time periods when the user is more susceptible to stress based on the user's responses and strengthen measures during those time periods. For example, if the user is more likely to feel stressed during a particular time period, the analysis unit can suggest that the user practice relaxation methods during that time period. If the user is more likely to feel stressed at night, the analysis unit can provide nighttime relaxation methods. If the user is more likely to feel stressed in the morning, the analysis unit can suggest morning stress management methods. This allows the analysis unit to provide specific measures for the user to effectively manage stress.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit accepts that the user accesses the service and answers questions in an interactive format. For example, the reception unit accepts that the user answers questions such as "How often have you felt stressed recently?" and "When you feel stressed, how do you deal with it?" Step 2: The analysis unit analyzes the responses received by the reception unit. For example, the analysis unit analyzes the content and pattern of the user's responses and quantifies the stress level. The analysis unit employs a psychological approach to analyze the responses. For example, the analysis unit analyzes the causes, frequency, and coping methods of stress from the content of the responses to evaluate the overall stress level. Step 3: The evaluation unit evaluates the stress level based on the answers analyzed by the analysis unit. For example, the evaluation unit evaluates the user's stress level based on the stress level quantified by the analysis unit. The evaluation unit analyzes the content and pattern of the answers and quantifies the stress level. Step 4: The provision unit provides customized advice based on the results of the evaluation by the evaluation unit. For example, the provision unit provides specific guidelines for action or relaxation methods according to the stress level. The provision unit provides guidelines for action or relaxation methods according to the stress level. For example, if stress is high, relaxation methods or specific guidelines for action to relieve stress are provided. Also, if stress is low, advice to maintain the current state is provided.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 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 reception section for accepting questions; an analysis unit that analyzes the response received by the reception unit; an evaluation unit that evaluates a stress level based on the answers analyzed by the analysis unit; a providing unit that provides personalized advice based on the results of the evaluation by the evaluating unit. A system characterized by:

2. A generator for generating questions 2. The system of claim 1.

3. Equipped with an anonymization unit to maintain anonymity 2. The system of claim 1.

4. The analysis unit Applying psychological methods to analyze responses 2. The system of claim 1.

5. The evaluation unit Analyze the content and format of responses to quantify stress levels 2. The system of claim 1.

6. The providing unit Providing guidelines and relaxation methods according to stress levels 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past response history and select the optimal method for accepting questions 2. The system of claim 1.

9. The reception unit When questions are submitted, they are filtered based on the user's current life situation and areas of interest.

2. The system of claim 1.

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