System

Abstract

An object of a system according to an embodiment is to provide a recovery program personalized based on a psychological state and a lifestyle of an individual user.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, a support unit, and a provision unit. The collection unit collects data on a user's psychological state, lifestyle, and past stress experience. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a personalized recovery program based on the data analyzed by the analysis unit. The support unit supports the user based on the recovery program created by the creation unit. The provision unit provides a workshop or a training session provided by the support unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not adequately provided personalized recovery programs based on the psychological state and lifestyle habits of individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized recovery program based on the psychological state and lifestyle habits of each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a support unit, and a provision unit. The collection unit collects data on a user's psychological state, lifestyle habits, and past stress experiences. The analysis unit analyzes the data collected by the collection unit. The generation unit creates a personalized recovery program based on the data analyzed by the analysis unit. The support unit supports the user based on the recovery program created by the generation unit. The provision unit provides workshops or training sessions provided by the support unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized recovery program based on the psychological state and lifestyle of each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A support system according to an embodiment of the present invention provides a personalized recovery program based on a user's psychological state, lifestyle habits, and past stress experiences, thereby supporting a smooth return to work. The support system collects data on the user's psychological state, lifestyle habits, past stress experiences, etc., and uses AI to analyze the data and create a personalized recovery program. For example, the support system collects data on the user's psychological state, lifestyle habits, past stress experiences, etc. Then, the support system uses AI to analyze the collected data and understand the user's condition. Next, the support system uses AI to create a personalized recovery program based on the analyzed data. Next, the support system supports the user in understanding themselves and acquiring stress management skills. For example, the support system provides support for the user to identify the causes of their stress and learn ways to deal with them. The support system can also provide training sessions for the user to acquire stress management skills. Next, the support system provides workshops and training sessions to reduce anxiety when returning to work. For example, the support system can provide workshops to improve workplace communication skills and training sessions to learn workplace stress management techniques. This allows the support system to help the user understand themselves, acquire stress management skills, and smoothly return to work. Furthermore, by utilizing AI technology, it is possible to provide personalized support tailored to each individual's condition. This allows the support system to provide a personalized recovery program based on the user's psychological state, lifestyle habits, and past stress experiences, supporting a smooth return to work. For example, the system can support the user's recovery by suggesting appropriate stress management techniques based on the type and frequency of stress the user has experienced in the past.

[0029] The support system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a support unit, and a provision unit. The collection unit collects data on a user's psychological state, lifestyle habits, and past stress experiences. For example, the collection unit collects data using a questionnaire or a sensor to evaluate the user's psychological state. The collection unit can also collect information on the user's eating habits, exercise habits, sleep patterns, and other information on the user's lifestyle habits to collect data on the user's past stress experiences. The collection unit can also collect information on the causes, frequency, and effects of stress the user has experienced in the past to collect data on the user's past stress experiences. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using AI to understand the user's psychological state, lifestyle habits, and past stress experiences. For example, the analysis unit can evaluate the user's stress level, emotional state, and psychological health state based on the collected data. The analysis unit can also analyze patterns and trends of the user's lifestyle habits based on the collected data. The generation unit creates a personalized recovery program based on the data analyzed by the analysis unit. The generation unit creates an optimal recovery program for the user based on data analyzed using AI, for example. The generation unit can suggest appropriate stress management techniques according to the user's stress level and emotional state, for example. The generation unit can also provide an appropriate recovery program according to the user's lifestyle habits and psychological state. The support unit supports the user based on the recovery program created by the generation unit. For example, the support unit provides support for the user to understand themselves and acquire stress management techniques. For example, the support unit provides support for the user to identify the causes of their stress and learn how to deal with them. The support unit can also provide training sessions for the user to acquire stress management techniques. The provision unit provides workshops and training sessions provided by the support unit. For example, the provision unit provides workshops and training sessions to reduce anxiety when returning to work.The providing unit can provide, for example, workshops to improve communication skills in the workplace and training sessions to learn stress management techniques in the workplace. As a result, the support system according to the embodiment can provide a personalized recovery program based on the user's psychological state, lifestyle habits, and past stress experiences, and support a smooth return to work.

[0030] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit can preferentially suggest collection methods that the user has used favorably in the past. For example, the collection unit can select the most efficient collection method from the user's past data collection history. For example, the collection unit can customize the collection method based on the user's past data collection history. This enables efficient data collection by selecting the optimal collection method based on the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0031] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting data related to areas in which the user is currently interested. For example, the collection unit can collect only necessary data depending on the user's living situation. For example, the collection unit can adjust the range of data to be collected, taking into account the user's current living situation. This makes it possible to collect only necessary data by filtering data based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's living situation data to a generation AI and have the generation AI perform data filtering.

[0032] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. For example, if the user prefers text input, the collection unit can preferentially collect text data. For example, if the user prefers image input, the collection unit can preferentially collect image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data related to the user's current location. For example, the collection unit can select an optimal data collection point based on the user's geographical location information. For example, the collection unit can adjust the range of data to be collected by taking into account the user's geographical location information. This enables efficient data collection by preferentially collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0034] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit can collect related data, for example, based on information shared by the user on social media. The collection unit can analyze the user's social media activities and select an optimal data collection method. The collection unit can adjust the scope of data to be collected, for example, based on the content of the user's social media posts. This enables efficient data collection by analyzing the user's social media activities and collecting related data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0035] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can improve the collection method based on, for example, feedback provided by the user in the past. The collection unit can adjust the range of data to be collected by reflecting the user's past feedback. The collection unit can select the optimal collection method by referring to, for example, the user's past feedback. This enables efficient data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a psychological analysis algorithm to data related to psychological states. For example, the analysis unit can apply a behavioral analysis algorithm to data related to lifestyle habits. For example, the analysis unit can apply a stress analysis algorithm to data related to stress experiences. This enables efficient analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit, for example, can adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit, for example, can improve the accuracy of the analysis by using the user's past analysis results. In this way, by improving the accuracy of the analysis by referring to the user's past analysis results, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit can postpone analysis of older data. For example, the analysis unit can adjust the analysis priority according to the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can postpone analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, if the user does not have technical expertise, the analysis unit can avoid technical terms. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms.

[0042] The generation unit can adjust the level of detail of the program based on the importance of the data when generating the recovery program. The generation unit can provide a detailed recovery program, for example, based on data with high importance. The generation unit can provide a simplified recovery program, for example, based on data with low importance. The generation unit can adjust the level of detail of the recovery program, for example, according to the importance of the data. This makes it possible to provide an efficient recovery program by adjusting the level of detail of the program based on the importance of the data. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the program.

[0043] When generating a recovery program, the generation unit can apply different generation algorithms depending on the category of data. For example, the generation unit can apply a psychological generation algorithm to data related to psychological states. For example, the generation unit can apply a behavioral generation algorithm to data related to lifestyle habits. For example, the generation unit can apply a stress generation algorithm to data related to stress experiences. This makes it possible to provide an efficient recovery program by applying different generation algorithms depending on the category of data. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the category of data into the generation AI and cause the generation AI to apply different generation algorithms.

[0044] When generating a recovery program, the generation unit can improve the accuracy of generation by referring to the user's past program results. The generation unit, for example, corrects the current program based on the user's past program results. The generation unit, for example, can adjust the generation algorithm by referring to the user's past program results. The generation unit, for example, can improve the accuracy of generation by using the user's past program results. In this way, a more accurate recovery program can be provided by improving the accuracy of generation by referring to the user's past program results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the user's past program results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] When generating a recovery program, the generation unit can determine the priority of the programs based on the time when data was collected. The generation unit provides the most effective recovery program based on, for example, the latest data. The generation unit can, for example, prioritize the use of the latest data, leaving older data for later use. The generation unit can, for example, adjust the priority of the programs according to the time when data was collected. This makes it possible to provide an efficient recovery program by determining the priority of the programs based on the time when data was collected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when data was collected into the generation AI and have the generation AI determine the priority of the programs.

[0046] The generation unit can adjust the order of the programs based on the relevance of the data when generating the recovery program. The generation unit, for example, provides the most effective recovery program based on highly relevant data. The generation unit can, for example, postpone the use of less relevant data and prioritize the use of highly relevant data. The generation unit can, for example, adjust the order of the programs according to the relevance of the data. This makes it possible to provide an efficient recovery program by adjusting the order of the programs based on the relevance of the data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the data into the generation AI and cause the generation AI to adjust the order of the programs.

[0047] When generating a recovery program, the generation unit can adjust the use of technical terms in the program according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terms. For example, if the user does not have technical expertise, the generation unit can avoid technical terms. For example, the generation unit can adjust the way the program is expressed according to the user's level of expertise. This allows for adjusting the use of technical terms in the program according to the user's level of expertise, thereby providing a more appropriate recovery program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0048] When providing support, the support unit can analyze the user's past behavioral history and select an appropriate support method. The support unit can, for example, suggest an optimal support method based on the user's past behavioral history. The support unit can, for example, select an effective support method from the user's past behavioral history. The support unit can, for example, customize the support method by referring to the user's past behavioral history. This enables efficient support by selecting an optimal support method based on the user's past behavioral history. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's past behavioral history into a generation AI and have the generation AI select an optimal support method.

[0049] During support, the support unit can customize the support means based on the user's current living situation. The support unit, for example, provides the optimal support means taking into account the user's current living situation. The support unit can, for example, customize the support means according to the user's living situation. The support unit can, for example, adjust the support means based on the user's current living situation. This enables efficient support by customizing the support means based on the user's current living situation. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's living situation data into a generation AI and cause the generation AI to customize the support means.

[0050] The support unit can improve the support method by reflecting user feedback when providing support. The support unit can improve the support method based on, for example, user feedback. The support unit can adjust the support means by reflecting user feedback. The support unit can customize the support method by referring to, for example, user feedback. This enables efficient support by improving the support method by reflecting user feedback. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input user feedback data into a generation AI and have the generation AI improve the support method.

[0051] When providing support, the support unit can select an appropriate support method by taking into account the user's geographical location information. The support unit can provide an optimal support method based on, for example, the user's current location. The support unit can adjust the support means based on, for example, the user's geographical location information. The support unit can customize the support method by taking into account the user's geographical location information. This enables efficient support by selecting an optimal support method by taking into account the user's geographical location information. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input the user's geographical location information into the generation AI and cause the generation AI to select an optimal support method.

[0052] When providing support, the support unit can analyze the user's social media activity and suggest support methods. The support unit can, for example, suggest optimal support methods based on the user's social media activity. The support unit can, for example, analyze the user's social media activity and select a support method. The support unit can, for example, customize support methods by referring to the user's social media activity. This enables efficient support by analyzing the user's social media activity and suggesting support methods. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's social media data into a generation AI and have the generation AI execute the suggestion of support methods.

[0053] When providing support, the support unit can customize the support method by reflecting the user's past feedback. The support unit can, for example, improve the support method based on the user's past feedback. The support unit can, for example, adjust the support means by reflecting the user's past feedback. The support unit can, for example, customize the support method by referring to the user's past feedback. This enables efficient support by customizing the support method by reflecting the user's past feedback. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's past feedback data into the generation AI and have the generation AI customize the support method.

[0054] At the time of provision, the provision unit can select appropriate content by analyzing the user's past participation history. The provision unit, for example, suggests an optimal workshop or training session based on the user's past participation history. The provision unit, for example, can select effective content from the user's past participation history. The provision unit, for example, can customize the content by referring to the user's past participation history. This enables efficient provision by selecting optimal content based on the user's past participation history. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit can input the user's past participation history data into a generation AI and cause the generation AI to select optimal content.

[0055] The providing unit can customize the content based on the user's current living situation when providing the content. The providing unit, for example, provides optimal content taking into consideration the user's current living situation. The providing unit can, for example, customize the content according to the user's living situation. The providing unit can, for example, adjust the content based on the user's current living situation. This enables efficient provision by customizing the content based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into a generating AI and cause the generating AI to customize the content.

[0056] The providing unit can improve the content by reflecting user feedback when providing the content. For example, the providing unit improves the content of a workshop or training session based on user feedback. For example, the providing unit can adjust the content by reflecting user feedback. For example, the providing unit can customize the content by referring to user feedback. This enables efficient provision by improving the content by reflecting user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the content.

[0057] The providing unit can select appropriate content taking into consideration the user's geographical location information when providing the content. The providing unit can provide an optimal workshop or training session, for example, based on the user's current location. The providing unit can adjust the content, for example, based on the user's geographical location information. The providing unit can customize the content, for example, taking into consideration the user's geographical location information. This enables efficient provision by selecting optimal content taking into consideration the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the user's geographical location information to a generation AI and cause the generation AI to select optimal content.

[0058] At the time of provision, the provision unit can analyze the user's social media activity and suggest related content. The provision unit can, for example, suggest optimal workshops or training sessions based on the user's social media activity. The provision unit can, for example, select content by analyzing the user's social media activity. The provision unit can, for example, customize content by referring to the user's social media activity. This enables efficient provision by analyzing the user's social media activity and suggesting related content. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can input the user's social media data into a generation AI and cause the generation AI to suggest related content.

[0059] The providing unit can customize the content by reflecting the user's past feedback when providing the content. The providing unit can, for example, improve the content of a workshop or training session based on the user's past feedback. The providing unit can, for example, adjust the content by reflecting the user's past feedback. The providing unit can, for example, customize the content by referring to the user's past feedback. This enables efficient provision by customizing the content by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past feedback data into a generating AI and cause the generating AI to customize the content.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the user's behavioral patterns and determine the optimal timing for data collection. For example, if the user is most active in the morning, the collection unit can collect data during this time period. If the user relaxes at night, the collection unit can collect detailed data at night. Furthermore, if the user tends to engage in a particular activity on weekends, the collection unit can collect data related to that activity on weekends. This allows the collection unit to determine the optimal timing for data collection based on the user's behavioral patterns, enabling efficient data collection.

[0062] The support department can analyze the user's past behavior history and select the optimal support method. For example, it can prioritize support methods that have been effective for the user in the past. It can also select a support method that is effective in a specific situation from the user's past behavior history. Furthermore, it can customize the support method based on the user's past behavior history. This allows the support department to select the optimal support method based on the user's past behavior history, enabling efficient support.

[0063] The analysis unit can determine the priority of analysis based on the time of data collection. For example, by prioritizing the analysis of the most recent data, it is possible to provide analysis results that are appropriate for the current situation. Also, by analyzing older data later, efficient analysis is possible. Furthermore, by adjusting the priority of analysis based on the time of data collection, it is possible to analyze important data without missing it. This allows the analysis unit to determine the priority of analysis based on the time of data collection, making efficient analysis possible.

[0064] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, by prioritizing collection of data related to the user's current location, data collection that reflects area-specific information is possible. In addition, the optimal data collection point can be selected based on the user's geographical location information. Furthermore, by adjusting the range of data to be collected by taking into account the user's geographical location information, efficient data collection is possible. As a result, the collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information, thereby enabling efficient data collection.

[0065] The support unit can improve the support method by reflecting the user's feedback. For example, by improving the support method based on the user's feedback, more effective support can be provided. The support means can also be adjusted by reflecting the user's feedback. Furthermore, by customizing the support method based on the user's feedback, the optimal support can be provided to the user. This allows the support unit to improve the support method by reflecting the user's feedback, enabling more efficient support.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data on the user's psychological state, lifestyle habits, and past stress experiences. The collection unit evaluates the user's psychological state using, for example, a questionnaire or sensors, and collects data on lifestyle habits such as eating habits, exercise habits, and sleep patterns. It also collects information on the causes, frequency, and effects of stress the user has experienced in the past. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and understand the user's psychological state, lifestyle habits, and past stress experiences. This allows the analysis unit to evaluate the user's stress level, emotional state, and psychological health state, and analyze lifestyle patterns and trends. Step 3: The generator creates a personalized recovery program based on the data analyzed by the analyzer. For example, the generator creates an optimal recovery program for the user based on the data analyzed using AI, and suggests stress management techniques and appropriate recovery programs. Step 4: The support section supports the user based on the recovery program created by the generation section. The support section helps the user understand themselves, acquire stress management techniques, identify the causes of stress, and learn how to deal with them. Step 5: The delivery department provides workshops and training sessions provided by the support department. For example, the delivery department provides workshops to reduce anxiety about returning to work and training sessions to improve communication skills in the workplace.

[0068] (Example 2) A support system according to an embodiment of the present invention provides a personalized recovery program based on a user's psychological state, lifestyle habits, and past stress experiences, thereby supporting a smooth return to work. The support system collects data on the user's psychological state, lifestyle habits, past stress experiences, etc., and uses AI to analyze the data and create a personalized recovery program. For example, the support system collects data on the user's psychological state, lifestyle habits, past stress experiences, etc. Then, the support system uses AI to analyze the collected data and understand the user's condition. Next, the support system uses AI to create a personalized recovery program based on the analyzed data. Next, the support system supports the user in understanding themselves and acquiring stress management skills. For example, the support system provides support for the user to identify the causes of their stress and learn ways to deal with them. The support system can also provide training sessions for the user to acquire stress management skills. Next, the support system provides workshops and training sessions to reduce anxiety when returning to work. For example, the support system can provide workshops to improve workplace communication skills and training sessions to learn workplace stress management techniques. This allows the support system to help the user understand themselves, acquire stress management skills, and smoothly return to work. Furthermore, by utilizing AI technology, it is possible to provide personalized support tailored to each individual's condition. This allows the support system to provide a personalized recovery program based on the user's psychological state, lifestyle habits, and past stress experiences, supporting a smooth return to work. For example, the system can support the user's recovery by suggesting appropriate stress management techniques based on the type and frequency of stress the user has experienced in the past.

[0069] The support system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a support unit, and a provision unit. The collection unit collects data on a user's psychological state, lifestyle habits, and past stress experiences. For example, the collection unit collects data using a questionnaire or a sensor to evaluate the user's psychological state. The collection unit can also collect information on the user's eating habits, exercise habits, sleep patterns, and other information on the user's lifestyle habits to collect data on the user's past stress experiences. The collection unit can also collect information on the causes, frequency, and effects of stress the user has experienced in the past to collect data on the user's past stress experiences. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using AI to understand the user's psychological state, lifestyle habits, and past stress experiences. For example, the analysis unit can evaluate the user's stress level, emotional state, and psychological health state based on the collected data. The analysis unit can also analyze patterns and trends of the user's lifestyle habits based on the collected data. The generation unit creates a personalized recovery program based on the data analyzed by the analysis unit. The generation unit creates an optimal recovery program for the user based on data analyzed using AI, for example. The generation unit can suggest appropriate stress management techniques according to the user's stress level and emotional state, for example. The generation unit can also provide an appropriate recovery program according to the user's lifestyle habits and psychological state. The support unit supports the user based on the recovery program created by the generation unit. For example, the support unit provides support for the user to understand themselves and acquire stress management techniques. For example, the support unit provides support for the user to identify the causes of their stress and learn how to deal with them. The support unit can also provide training sessions for the user to acquire stress management techniques. The provision unit provides workshops and training sessions provided by the support unit. For example, the provision unit provides workshops and training sessions to reduce anxiety when returning to work.The providing unit can provide, for example, workshops to improve communication skills in the workplace and training sessions to learn stress management techniques in the workplace. As a result, the support system according to the embodiment can provide a personalized recovery program based on the user's psychological state, lifestyle habits, and past stress experiences, and support a smooth return to work.

[0070] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect data during a relaxed time period. For example, if the user is relaxed, the collection unit can collect detailed data. For example, if the user is in a hurry, the collection unit can collect simplified data. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit can preferentially suggest collection methods that the user has used favorably in the past. For example, the collection unit can select the most efficient collection method from the user's past data collection history. For example, the collection unit can customize the collection method based on the user's past data collection history. This enables efficient data collection by selecting the optimal collection method based on the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0072] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting data related to areas in which the user is currently interested. For example, the collection unit can collect only necessary data depending on the user's living situation. For example, the collection unit can adjust the range of data to be collected, taking into account the user's current living situation. This makes it possible to collect only necessary data by filtering data based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's living situation data to a generation AI and have the generation AI perform data filtering.

[0073] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. For example, if the user prefers text input, the collection unit can preferentially collect text data. For example, if the user prefers image input, the collection unit can preferentially collect image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0074] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress. For example, if the user is relaxed, the collection unit can prioritize collecting data related to relaxation. For example, if the user is excited, the collection unit can prioritize collecting data related to excitement. This enables more appropriate data collection by determining the priority of data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data related to the user's current location. For example, the collection unit can select an optimal data collection point based on the user's geographical location information. For example, the collection unit can adjust the range of data to be collected by taking into account the user's geographical location information. This enables efficient data collection by preferentially collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0076] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit can collect related data, for example, based on information shared by the user on social media. The collection unit can analyze the user's social media activities and select an optimal data collection method. The collection unit can adjust the scope of data to be collected, for example, based on the content of the user's social media posts. This enables efficient data collection by analyzing the user's social media activities and collecting related data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0077] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can improve the collection method based on, for example, feedback provided by the user in the past. The collection unit can adjust the range of data to be collected by reflecting the user's past feedback. The collection unit can select the optimal collection method by referring to, for example, the user's past feedback. This enables efficient data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the presentation method of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a psychological analysis algorithm to data related to psychological states. For example, the analysis unit can apply a behavioral analysis algorithm to data related to lifestyle habits. For example, the analysis unit can apply a stress analysis algorithm to data related to stress experiences. This enables efficient analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit, for example, can adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit, for example, can improve the accuracy of the analysis by using the user's past analysis results. In this way, by improving the accuracy of the analysis by referring to the user's past analysis results, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit can postpone analysis of older data. For example, the analysis unit can adjust the analysis priority according to the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can postpone analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, if the user does not have technical expertise, the analysis unit can avoid technical terms. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms.

[0086] The generation unit can estimate the user's emotions and adjust the presentation method of the recovery program based on the estimated user emotions. For example, if the user is relaxed, the generation unit can provide a recovery program that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can provide a short, effective recovery program. For example, if the user is excited, the generation unit can provide a visually stimulating recovery program. This allows for adjusting the presentation method of the recovery program according to the user's emotions, thereby providing a more appropriate recovery program. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the recovery program.

[0087] The generation unit can adjust the level of detail of the program based on the importance of the data when generating the recovery program. The generation unit can provide a detailed recovery program, for example, based on data with high importance. The generation unit can provide a simplified recovery program, for example, based on data with low importance. The generation unit can adjust the level of detail of the recovery program, for example, according to the importance of the data. This makes it possible to provide an efficient recovery program by adjusting the level of detail of the program based on the importance of the data. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the program.

[0088] When generating a recovery program, the generation unit can apply different generation algorithms depending on the category of data. For example, the generation unit can apply a psychological generation algorithm to data related to psychological states. For example, the generation unit can apply a behavioral generation algorithm to data related to lifestyle habits. For example, the generation unit can apply a stress generation algorithm to data related to stress experiences. This makes it possible to provide an efficient recovery program by applying different generation algorithms depending on the category of data. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the category of data into the generation AI and cause the generation AI to apply different generation algorithms.

[0089] When generating a recovery program, the generation unit can improve the accuracy of generation by referring to the user's past program results. The generation unit, for example, corrects the current program based on the user's past program results. The generation unit, for example, can adjust the generation algorithm by referring to the user's past program results. The generation unit, for example, can improve the accuracy of generation by using the user's past program results. In this way, a more accurate recovery program can be provided by improving the accuracy of generation by referring to the user's past program results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the user's past program results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0090] The generation unit can estimate the user's emotions and adjust the length of the recovery program based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can provide a short, concise recovery program. For example, if the user is relaxed, the generation unit can provide a longer recovery program with detailed explanations. For example, if the user is excited, the generation unit can provide a recovery program with visually stimulating effects. This allows for adjusting the length of the recovery program according to the user's emotions, thereby providing a more appropriate recovery program. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the recovery program.

[0091] When generating a recovery program, the generation unit can determine the priority of the programs based on the time when data was collected. The generation unit provides the most effective recovery program based on, for example, the latest data. The generation unit can, for example, prioritize the use of the latest data, leaving older data for later use. The generation unit can, for example, adjust the priority of the programs according to the time when data was collected. This makes it possible to provide an efficient recovery program by determining the priority of the programs based on the time when data was collected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when data was collected into the generation AI and have the generation AI determine the priority of the programs.

[0092] The generation unit can adjust the order of the programs based on the relevance of the data when generating the recovery program. The generation unit, for example, provides the most effective recovery program based on highly relevant data. The generation unit can, for example, postpone the use of less relevant data and prioritize the use of highly relevant data. The generation unit can, for example, adjust the order of the programs according to the relevance of the data. This makes it possible to provide an efficient recovery program by adjusting the order of the programs based on the relevance of the data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the data into the generation AI and cause the generation AI to adjust the order of the programs.

[0093] When generating a recovery program, the generation unit can adjust the use of technical terms in the program according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terms. For example, if the user does not have technical expertise, the generation unit can avoid technical terms. For example, the generation unit can adjust the way the program is expressed according to the user's level of expertise. This allows for adjusting the use of technical terms in the program according to the user's level of expertise, thereby providing a more appropriate recovery program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0094] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, if the user is feeling stressed, the support unit can provide a support method that helps the user relax. For example, if the user is relaxed, the support unit can provide a detailed support method. For example, if the user is excited, the support unit can provide a visually stimulating support method. This allows the support method to be adjusted according to the user's emotions, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI adjust the support method.

[0095] When providing support, the support unit can analyze the user's past behavioral history and select an appropriate support method. The support unit can, for example, suggest an optimal support method based on the user's past behavioral history. The support unit can, for example, select an effective support method from the user's past behavioral history. The support unit can, for example, customize the support method by referring to the user's past behavioral history. This enables efficient support by selecting an optimal support method based on the user's past behavioral history. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's past behavioral history into a generation AI and have the generation AI select an optimal support method.

[0096] During support, the support unit can customize the support means based on the user's current living situation. The support unit, for example, provides the optimal support means taking into account the user's current living situation. The support unit can, for example, customize the support means according to the user's living situation. The support unit can, for example, adjust the support means based on the user's current living situation. This enables efficient support by customizing the support means based on the user's current living situation. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's living situation data into a generation AI and cause the generation AI to customize the support means.

[0097] The support unit can improve the support method by reflecting user feedback when providing support. The support unit can improve the support method based on, for example, user feedback. The support unit can adjust the support means by reflecting user feedback. The support unit can customize the support method by referring to, for example, user feedback. This enables efficient support by improving the support method by reflecting user feedback. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input user feedback data into a generation AI and have the generation AI improve the support method.

[0098] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user is feeling stressed, the support unit can prioritize stress-reducing support. For example, if the user is relaxed, the support unit can prioritize detailed support. For example, if the user is excited, the support unit can prioritize visually stimulating support. This allows for more appropriate support to be provided by determining the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of support.

[0099] When providing support, the support unit can select an appropriate support method by taking into account the user's geographical location information. The support unit can provide an optimal support method based on, for example, the user's current location. The support unit can adjust the support means based on, for example, the user's geographical location information. The support unit can customize the support method by taking into account the user's geographical location information. This enables efficient support by selecting an optimal support method by taking into account the user's geographical location information. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input the user's geographical location information into the generation AI and cause the generation AI to select an optimal support method.

[0100] When providing support, the support unit can analyze the user's social media activity and suggest support methods. The support unit can, for example, suggest optimal support methods based on the user's social media activity. The support unit can, for example, analyze the user's social media activity and select a support method. The support unit can, for example, customize support methods by referring to the user's social media activity. This enables efficient support by analyzing the user's social media activity and suggesting support methods. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's social media data into a generation AI and have the generation AI execute the suggestion of support methods.

[0101] When providing support, the support unit can customize the support method by reflecting the user's past feedback. The support unit can, for example, improve the support method based on the user's past feedback. The support unit can, for example, adjust the support means by reflecting the user's past feedback. The support unit can, for example, customize the support method by referring to the user's past feedback. This enables efficient support by customizing the support method by reflecting the user's past feedback. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's past feedback data into the generation AI and have the generation AI customize the support method.

[0102] The providing unit can estimate the user's emotions and adjust the content of a workshop or training session based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a workshop with relaxing content. For example, if the user is relaxed, the providing unit can provide a training session with detailed content. For example, if the user is excited, the providing unit can provide a workshop with visually stimulating content. This allows the content of the workshop or training session to be adjusted according to the user's emotions, thereby providing more appropriate content. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the workshop or training session.

[0103] At the time of provision, the provision unit can select appropriate content by analyzing the user's past participation history. The provision unit, for example, suggests an optimal workshop or training session based on the user's past participation history. The provision unit, for example, can select effective content from the user's past participation history. The provision unit, for example, can customize the content by referring to the user's past participation history. This enables efficient provision by selecting optimal content based on the user's past participation history. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit can input the user's past participation history data into a generation AI and cause the generation AI to select optimal content.

[0104] The providing unit can customize the content based on the user's current living situation when providing the content. The providing unit, for example, provides optimal content taking into consideration the user's current living situation. The providing unit can, for example, customize the content according to the user's living situation. The providing unit can, for example, adjust the content based on the user's current living situation. This enables efficient provision by customizing the content based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into a generating AI and cause the generating AI to customize the content.

[0105] The providing unit can improve the content by reflecting user feedback when providing the content. For example, the providing unit improves the content of a workshop or training session based on user feedback. For example, the providing unit can adjust the content by reflecting user feedback. For example, the providing unit can customize the content by referring to user feedback. This enables efficient provision by improving the content by reflecting user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the content.

[0106] The providing unit can estimate the user's emotions and prioritize workshops or training sessions based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize a stress reduction workshop. For example, if the user is feeling relaxed, the providing unit can prioritize a detailed training session. For example, if the user is feeling excited, the providing unit can prioritize a visually stimulating workshop. This allows for more appropriate content to be provided by prioritizing workshops and training sessions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priorities of workshops and training sessions.

[0107] The providing unit can select appropriate content taking into consideration the user's geographical location information when providing the content. The providing unit can provide an optimal workshop or training session, for example, based on the user's current location. The providing unit can adjust the content, for example, based on the user's geographical location information. The providing unit can customize the content, for example, taking into consideration the user's geographical location information. This enables efficient provision by selecting optimal content taking into consideration the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the user's geographical location information to a generation AI and cause the generation AI to select optimal content.

[0108] At the time of provision, the provision unit can analyze the user's social media activity and suggest related content. The provision unit can, for example, suggest optimal workshops or training sessions based on the user's social media activity. The provision unit can, for example, select content by analyzing the user's social media activity. The provision unit can, for example, customize content by referring to the user's social media activity. This enables efficient provision by analyzing the user's social media activity and suggesting related content. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can input the user's social media data into a generation AI and cause the generation AI to suggest related content.

[0109] The providing unit can customize the content by reflecting the user's past feedback when providing the content. The providing unit can, for example, improve the content of a workshop or training session based on the user's past feedback. The providing unit can, for example, adjust the content by reflecting the user's past feedback. The providing unit can, for example, customize the content by referring to the user's past feedback. This enables efficient provision by customizing the content by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past feedback data into a generating AI and cause the generating AI to customize the content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, support unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data on the user's psychological state, lifestyle habits, and past stress experiences using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's condition. The generation unit creates a personalized recovery program based on the data analyzed by the specific processing unit 290 of the data processing device 12. The support unit supports the user in understanding themselves and acquiring stress management techniques using the control unit 46A of the smart device 14. The provision unit offers workshops and training sessions using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, support unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data on the user's psychological state, lifestyle habits, and past stress experiences using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's condition. The generation unit creates a personalized recovery program based on the data analyzed by the specific processing unit 290 of the data processing device 12. The support unit supports the user in understanding themselves and acquiring stress management techniques using the control unit 46A of the smart glasses 214. The provision unit provides workshops and training sessions using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, support unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data on the user's psychological state, lifestyle habits, and past stress experiences using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's condition. The generation unit creates a personalized recovery program based on the data analyzed by the specific processing unit 290 of the data processing device 12. The support unit supports the user in understanding themselves and acquiring stress management techniques using the control unit 46A of the headset-type terminal 314. The provision unit offers workshops and training sessions using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, support unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data on the user's psychological state, lifestyle habits, and past stress experiences using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's condition. The generation unit creates a personalized recovery program based on the data analyzed by the specific processing unit 290 of the data processing device 12. The support unit supports the user in understanding themselves and acquiring stress management techniques using the control unit 46A of the robot 414. The provision unit offers workshops and training sessions using the speaker 240 of the robot 414.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the user's psychological state and adjust the accuracy of the analysis based on the estimated psychological state. For example, if the user is in a high stress state, the analysis unit can perform a detailed analysis and prioritize analyzing data to identify the cause of stress. Furthermore, if the user is relaxed, the analysis unit can perform a simplified analysis to avoid imposing a burden on the user. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results to attract the user's interest. In this way, the analysis unit can adjust the accuracy of the analysis according to the user's psychological state and provide more appropriate analysis results.

[0112] The collection unit can analyze the user's behavioral patterns and determine the optimal timing for data collection. For example, if the user is most active in the morning, the collection unit can collect data during this time period. If the user relaxes at night, the collection unit can collect detailed data at night. Furthermore, if the user tends to engage in a particular activity on weekends, the collection unit can collect data related to that activity on weekends. This allows the collection unit to determine the optimal timing for data collection based on the user's behavioral patterns, enabling efficient data collection.

[0113] The generation unit can estimate the user's emotions and adjust the content of the recovery program based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can provide a recovery program with relaxing content. If the user is relaxed, the generation unit can provide a recovery program with detailed explanations. If the user is excited, the generation unit can provide a recovery program with visually stimulating effects. In this way, the generation unit can adjust the content of the recovery program according to the user's emotions, thereby providing a more appropriate recovery program.

[0114] The support department can analyze the user's past behavior history and select the optimal support method. For example, it can prioritize support methods that have been effective for the user in the past. It can also select a support method that is effective in a specific situation from the user's past behavior history. Furthermore, it can customize the support method based on the user's past behavior history. This allows the support department to select the optimal support method based on the user's past behavior history, enabling efficient support.

[0115] The providing unit can estimate the user's emotions and adjust the content of a workshop or training session based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide a workshop with relaxing content. If the user is relaxed, the providing unit can provide a training session with detailed content. Furthermore, if the user is excited, the providing unit can provide a workshop with visually stimulating content. In this way, the providing unit can provide more appropriate content by adjusting the content of the workshop or training session according to the user's emotions.

[0116] The analysis unit can determine the priority of analysis based on the time of data collection. For example, by prioritizing the analysis of the most recent data, it is possible to provide analysis results that are appropriate for the current situation. Also, by analyzing older data later, efficient analysis is possible. Furthermore, by adjusting the priority of analysis based on the time of data collection, it is possible to analyze important data without missing it. This allows the analysis unit to determine the priority of analysis based on the time of data collection, making efficient analysis possible.

[0117] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, by prioritizing collection of data related to the user's current location, data collection that reflects area-specific information is possible. In addition, the optimal data collection point can be selected based on the user's geographical location information. Furthermore, by adjusting the range of data to be collected by taking into account the user's geographical location information, efficient data collection is possible. As a result, the collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information, thereby enabling efficient data collection.

[0118] The generation unit can estimate the user's emotions and adjust the length of the recovery program based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide a short, to-the-point recovery program. If the user is relaxed, the generation unit can provide a longer recovery program with detailed explanations. If the user is excited, the generation unit can provide a recovery program with visually stimulating effects. In this way, the generation unit can provide a more appropriate recovery program by adjusting the length of the recovery program according to the user's emotions.

[0119] The support unit can improve the support method by reflecting the user's feedback. For example, by improving the support method based on the user's feedback, more effective support can be provided. The support means can also be adjusted by reflecting the user's feedback. Furthermore, by customizing the support method based on the user's feedback, the optimal support can be provided to the user. This allows the support unit to improve the support method by reflecting the user's feedback, enabling more efficient support.

[0120] The providing unit can estimate the user's emotions and prioritize workshops and training sessions based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can prioritize a stress reduction workshop. If the user is feeling relaxed, the providing unit can prioritize a detailed training session. Furthermore, if the user is excited, the providing unit can prioritize a visually stimulating workshop. In this way, the providing unit can provide more appropriate content by prioritizing workshops and training sessions according to the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects data on the user's psychological state, lifestyle habits, and past stress experiences. The collection unit evaluates the user's psychological state using, for example, a questionnaire or sensors, and collects data on lifestyle habits such as eating habits, exercise habits, and sleep patterns. It also collects information on the causes, frequency, and effects of stress the user has experienced in the past. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and understand the user's psychological state, lifestyle habits, and past stress experiences. This allows the analysis unit to evaluate the user's stress level, emotional state, and psychological health state, and analyze lifestyle patterns and trends. Step 3: The generator creates a personalized recovery program based on the data analyzed by the analyzer. For example, the generator creates an optimal recovery program for the user based on the data analyzed using AI, and suggests stress management techniques and appropriate recovery programs. Step 4: The support section supports the user based on the recovery program created by the generation section. The support section helps the user understand themselves, acquire stress management techniques, identify the causes of stress, and learn how to deal with them. Step 5: The delivery department provides workshops and training sessions provided by the support department. For example, the delivery department provides workshops to reduce anxiety about returning to work and training sessions to improve communication skills in the workplace.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0137] 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.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 collection unit that collects data on the user's psychological state, lifestyle habits, and past stress experiences; an analysis unit that analyzes the data collected by the collection unit; a generator that generates a personalized recovery program based on the data analyzed by the analyzer; a support unit that supports a user based on the recovery program created by the generation unit; a providing unit that provides the workshops or training sessions provided by the support unit; A system characterized by:

2. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze the user's past data collection history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit Filtering data collection based on the user's current life situation or interests 2. The system of claim 1.

5. The collecting unit When collecting data, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting data, consider the user's geographic location to prioritize collecting the most relevant data.

2. The system of claim 1.

8. The collecting unit When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1.

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