system

Abstract

The system according to this embodiment aims to understand the user's stress and emotional state and provide individualized countermeasures and support. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit monitors the user's behavior patterns and digital activity data. The analysis unit analyzes the data collected by the collection unit to understand the user's stress and emotional state. The provision unit provides individualized measures and support tailored to the user based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to manage daily stress and mental state.

[0005] The system according to the embodiment aims to grasp the stress and emotional state of a user and provide individual countermeasures and support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the user's behavior patterns and digital activity data. The analysis unit analyzes the data collected by the data collection unit to understand the user's stress and emotional state. The data provision unit provides individualized measures and support tailored to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the user's stress and emotional state and provide individualized countermeasures and support. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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). <​The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The mental well-being AI agent system according to an embodiment of the present invention is a system that analyzes a user's behavioral patterns and digital activity data and provides individualized psychological support. The mental well-being AI agent system monitors the user's behavioral patterns and digital activity data in real time and analyzes the collected data to understand the user's stress and emotional state. Subsequently, the mental well-being AI agent system provides individualized measures and support tailored to the user. For example, the mental well-being AI agent system uses voice control to guide the user with advice on relaxation methods and stress reduction. It also provides professional support in cooperation with industrial physicians. As a result, the user can maintain mental stability and manage stress. For example, the mental well-being AI agent system monitors the user's smartphone usage and SNS activity. This allows the system to understand the user's behavioral patterns. Next, the mental well-being AI agent system analyzes the collected data to understand the user's stress and emotional state. For example, it analyzes the user's SNS posts and smartphone usage frequency to identify the user's stress level and emotional changes. Subsequently, the mental well-being AI agent system provides individualized measures and support tailored to the user. For example, voice control can be used to guide users with advice on relaxation methods and stress reduction. If a user says "I want to relax," the mental well-being AI agent system will suggest ways to relax. Furthermore, professional support is provided through collaboration with industrial physicians. For instance, if a user's stress level is high, they can contact an industrial physician and receive expert advice. This allows users to maintain mental stability and manage stress. For example, if a user experiences stress at work, the mental well-being AI agent system can suggest relaxation methods and collaborate with an industrial physician to provide expert support, thereby reducing the user's stress.This allows the mental well-being AI agent system to maintain the user's mental stability and manage stress.

[0029] The mental well-being AI agent system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the user's behavior patterns and digital activity data. For example, the data collection unit monitors the user's smartphone usage and social media activity. The data collection unit can monitor smartphone usage and social media activity in real time in order to understand the user's behavior patterns. For example, the data collection unit collects the user's smartphone app usage time, call history, location information, etc. The data collection unit can also collect the user's social media posts, comments, and the number of likes. The analysis unit analyzes the data collected by the data collection unit to understand the user's stress and emotional state. For example, the analysis unit analyzes the user's social media posts and smartphone usage frequency. The analysis unit can analyze the collected data to identify the user's stress level and emotional changes. For example, the analysis unit uses text analysis technology to analyze social media posts and perform sentiment analysis. The analysis unit can also analyze smartphone usage frequency to identify the user's stress level. The service provider unit provides individualized measures and support tailored to the user based on the analysis results obtained by the analysis unit. For example, the service provider unit guides the user with advice on relaxation methods and stress reduction using voice control. The service provider unit can provide appropriate advice based on the analysis results in order to provide individualized measures and support tailored to the user. For example, if the user says "I want to relax" by voice, the service provider unit will suggest relaxation methods. The service provider unit also provides professional support in cooperation with industrial physicians. If the user's stress level is high, the service provider unit can contact an industrial physician and receive expert advice. As a result, the mental well-being AI agent system according to this embodiment can maintain the user's mental stability and manage stress.

[0030] The data collection unit monitors user behavior patterns and digital activity data. Specifically, it monitors users' smartphone usage and social media activity. The data collection unit can monitor smartphone usage and social media activity in real time to understand user behavior patterns. For example, the data collection unit collects data such as the user's smartphone app usage time, call history, and location information. This allows for a detailed understanding of user behavior patterns, such as which apps they use and how often, what times of day they make calls, and which places they frequently visit. The data collection unit can also collect data such as the content of users' social media posts, comments, and the number of likes. This allows for a detailed understanding of digital activity data, such as what kind of content users post, what kind of comments they receive, and which posts they like. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provisioning units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to understand users' stress levels and emotional states. Specifically, it analyzes users' social media posts and smartphone usage frequency. The analysis unit can analyze the collected data to identify users' stress levels and emotional changes. For example, the analysis unit uses text analysis technology to analyze social media posts and perform sentiment analysis. In sentiment analysis, natural language processing technology is used to classify the emotions in the posts as positive, negative, neutral, etc., to understand the user's emotional tendencies. The analysis unit can also analyze smartphone usage frequency to identify users' stress levels. For example, if smartphone usage time is prolonged or the frequency of use of a particular app suddenly increases, it may be determined that the user's stress level is rising. Furthermore, the analysis unit can integrate this data to perform a comprehensive stress assessment. For example, by combining the sentiment analysis results of social media posts with smartphone usage frequency data, it can more accurately assess the user's stress level. The analysis unit can also utilize past data and statistical information to analyze long-term stress trends and patterns. This allows the analysis unit to grasp the user's stress and emotional state in real time and provide information to take appropriate measures.

[0032] The service provider will provide personalized measures and support tailored to the user based on the analysis results obtained by the analysis unit. Specifically, it will guide the user with relaxation methods and stress reduction advice using voice control. The service provider can provide appropriate advice based on the analysis results in order to provide personalized measures and support tailored to the user. For example, if the user says "I want to relax" by voice, the service provider will suggest relaxation methods. Relaxation methods include deep breathing, meditation, and light exercise. In addition, if the user's stress level is high, the service provider can also propose a specific action plan for stress reduction. This may include suggestions for taking regular breaks or hobbies and activities to reduce stress. Furthermore, the service provider will also provide professional support in cooperation with an industrial physician. If the user's stress level is high, the service provider can contact an industrial physician and receive expert advice. The industrial physician will assess the user's health condition and provide support such as counseling or referral to medical institutions as needed. This allows the service provider to provide comprehensive support to maintain the user's mental stability and manage stress. Furthermore, the service provider can collect user feedback and continuously improve the quality of the support it provides. This allows the service provider to offer optimal support to users and contribute to improving their mental well-being.

[0033] The service provider can use voice control to guide users with advice on relaxation methods and stress reduction. For example, if a user gives a voice command such as "I want to relax," the service provider will suggest relaxation methods. The service provider can use voice recognition technology to analyze the user's voice commands and guide appropriate relaxation methods. For example, the service provider may suggest deep breathing or meditation to the user. The service provider can also use speech synthesis technology to guide users with relaxation methods verbally. For example, the service provider may instruct the user to play relaxing music. In this way, by using voice control, the service provider can provide users with advice on relaxation methods and stress reduction. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's voice commands into a generating AI and have the generating AI execute suggestions for relaxation methods.

[0034] The service provider can offer professional support through collaboration with industrial physicians. For example, if a user's stress level is high, the service provider can contact an industrial physician and receive expert advice. The service provider can share information about user stress management through regular meetings with industrial physicians. For example, the service provider can provide data on the user's stress level to the industrial physician and receive advice on how to receive appropriate support. The service provider can also establish a method for sharing data with industrial physicians and share information about user stress management in real time. For example, the service provider can share data with industrial physicians using a cloud-based data sharing platform. This allows the service provider to offer professional support to users through collaboration with industrial physicians. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input data on the user's stress level into a generating AI and have the generating AI generate advice to provide to the industrial physician.

[0035] The data collection unit can monitor users' smartphone usage and social media activity. For example, it collects data such as app usage time, call history, and location information. The unit can monitor smartphone usage in real time to understand user behavior patterns. For instance, it records app usage time and analyzes usage frequency. It can also collect call history and analyze call frequency and duration. Furthermore, it can collect user location information and analyze movement patterns. For example, it uses GPS data to record user movement history and identify behavior patterns. The data collection unit can also monitor users' social media activity. It collects social media posts, comments, and the number of likes, and analyzes this data to understand behavior patterns. For example, it uses text analysis technology to analyze social media posts and identify changes in user interests and emotions. This allows for understanding user behavior patterns by monitoring smartphone usage and social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user smartphone usage data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0036] The analysis unit can analyze a user's social media posts and smartphone usage frequency to identify the user's stress level and emotional changes. For example, the analysis unit can use text analysis technology to analyze social media posts and perform sentiment analysis. By analyzing a user's social media posts, the analysis unit can identify changes in the user's emotions. For example, the analysis unit can use natural language processing technology to analyze social media posts and identify positive and negative emotions. Furthermore, the analysis unit can analyze smartphone usage frequency to identify the user's stress level. By analyzing smartphone usage frequency, the analysis unit can understand the user's stress level. For example, the analysis unit can analyze app usage time and call history to identify changes in stress levels. This allows the analysis unit to identify stress levels and emotional changes by analyzing a user's social media posts and smartphone usage frequency. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user social media post data into a generating AI and have the generating AI perform sentiment analysis.

[0037] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. For example, the data collection unit can optimize the timing of data collection based on actions the user has frequently performed in the past. The data collection unit can optimize the timing of data collection by analyzing the user's past behavior patterns. For example, the data collection unit can analyze the user's past travel history and digital device usage history and concentrate data collection during specific time periods. The data collection unit can also analyze the user's behavior patterns and select the most effective data collection method. For example, the data collection unit can optimize the timing and method of data collection based on the user's behavior patterns. This allows the optimal data collection method to be selected by analyzing the user's past behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current activities. The data collection unit can monitor the user's current activities in real time and prioritize collecting relevant data. For example, the data collection unit can filter relevant data based on the user's current location and activities. The data collection unit can also filter highly relevant data based on the user's areas of interest. The data collection unit can identify the user's areas of interest and prioritize collecting relevant data. For example, the data collection unit can identify areas of interest based on the user's past search history and social media following information. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity data into a generating AI and have the generating AI perform data filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. The data collection unit can monitor the user's geographical location information in real time and prioritize the collection of relevant data. For example, the data collection unit will filter highly relevant data based on the user's current location. The data collection unit can also select the optimal data collection method based on the user's current location. The data collection unit can optimize the timing and method of data collection based on the user's geographical location information. For example, the data collection unit will use GPS data to identify the user's current location and prioritize the collection of relevant data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant data. The data collection unit can monitor a user's social media activity in real time and collect relevant data. For example, the data collection unit can analyze a user's social media posts using text analysis technology and collect relevant data. The data collection unit can also collect data related to topics of interest from the user's social media activity. The data collection unit can select the optimal data collection method based on the user's social media activity. For example, the data collection unit can analyze a user's social media posts and prioritize the collection of data related to topics of interest. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can evaluate the importance of the data and dynamically adjust the level of detail of the analysis according to its importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a stress analysis algorithm to data related to stress. The analysis unit can identify the data category and apply the most suitable analysis algorithm according to the category. For example, the analysis unit can apply a relaxation analysis algorithm to data related to relaxation. The analysis unit can also select the most suitable analysis algorithm according to the data category. For example, the analysis unit can apply a text analysis algorithm to text data and an image analysis algorithm to image data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the most suitable analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can evaluate the data collection timing and dynamically adjust the analysis priority according to the collection timing. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis priority according to the data collection timing. For example, the analysis unit may prioritize the analysis of real-time data and postpone the analysis of historical data. This enables efficient analysis by determining the analysis priority according to the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can evaluate the relevance of the data and dynamically adjust the order of analysis according to the relevance. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can evaluate the correlation between the data and prioritize the analysis of highly relevant data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0045] The support unit can adjust the level of detail of support provided based on the user's stress level. For example, if the user's stress level is high, the support unit will provide detailed support. The support unit can monitor the user's stress level in real time and dynamically adjust the level of detail of support according to the stress level. For example, if the user's stress level is low, the support unit will provide simplified support. The support unit can also dynamically adjust the level of detail of support according to the user's stress level. For example, the support unit can evaluate the user's stress level and provide detailed support to users with high stress levels. This allows for the provision of more appropriate support by adjusting the level of detail of support according to the user's stress level. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user stress level data into a generating AI and have the generating AI perform the adjustment of the level of detail of support.

[0046] The service provider can apply different support algorithms depending on the user's category when providing support. For example, the service provider can apply a support algorithm related to stress management. The service provider can identify the user's category and apply the most suitable support algorithm according to that category. For example, the service provider can apply a support algorithm related to relaxation methods. The service provider can also select the most suitable support algorithm according to the user's category. For example, the service provider can apply the most suitable support algorithm according to the user's age and occupation. By applying the most suitable support algorithm according to the user's category, more effective support can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user category data into a generating AI and have the generating AI execute the application of the most suitable support algorithm.

[0047] The support unit can provide optimal support by considering the user's geographical location information when providing support. For example, if the user is in a specific location, the support unit can provide support relevant to that location. The support unit can monitor the user's geographical location information in real time and provide relevant support. For example, the support unit can select the optimal support method based on the user's current location. The support unit can also provide highly relevant support based on the user's current location. The support unit can optimize the content and method of support based on the user's geographical location information. For example, the support unit can use GPS data to identify the user's current location and provide relevant support. This allows for the provision of optimal support by considering the user's geographical location information. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of optimal support.

[0048] The service provider can analyze the user's social media activity and propose support methods when providing support. For example, the service provider can analyze the content of the user's social media posts and propose the most suitable support method. The service provider can monitor the user's social media activity in real time and propose relevant support methods. For example, the service provider can analyze the content of the user's social media posts using text analysis technology and propose the most suitable support method. The service provider can also propose support methods of interest based on the user's social media activity. The service provider can select the most suitable support method based on the user's social media activity. For example, the service provider can analyze the content of the user's social media posts and propose support methods of interest. In this way, the service provider can propose the most suitable support method by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI propose the most suitable support method.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The support department can analyze users' past behavior patterns and select the most appropriate support method. For example, it can optimize the content of support based on actions users have frequently performed in the past. By analyzing users' past behavior patterns, it can optimize the timing and method of support. For example, it can analyze users' past travel history and digital device usage history to concentrate support during specific time periods. It can also analyze users' behavior patterns to select the most effective support method. In this way, by analyzing users' past behavior patterns, it is possible to provide the most optimal support method.

[0051] The support team can provide optimal support by considering the user's geographical location. For example, if a user is in a specific location, they can provide support relevant to that location. The system can monitor the user's geographical location in real time and provide relevant support. For example, it can select the most appropriate support method based on the user's current location. It can also provide highly relevant support based on the user's current location. In this way, by considering the user's geographical location, the system can provide optimal support.

[0052] The service provider can analyze users' social media activity and propose support measures. For example, it can analyze the content of users' social media posts and propose the most suitable support measures. It can also monitor users' social media activity in real time and propose relevant support measures. For example, it can analyze the content of users' social media posts using text analysis technology and propose the most suitable support measures. Furthermore, it can propose support measures of interest based on users' social media activity. In this way, by analyzing users' social media activity, the service provider can provide the most suitable support measures.

[0053] The analysis unit can determine the priority of analysis based on the data collection timing. For example, it can prioritize the analysis of the most recent data. It can also evaluate the data collection timing and dynamically adjust the analysis priority according to the collection timing. For example, it can postpone the analysis of older data. Furthermore, it can dynamically adjust the analysis priority according to the data collection timing. This allows for efficient analysis by determining the analysis priority according to the data collection timing.

[0054] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, it can prioritize the analysis of highly relevant data. It can also evaluate the relevance of the data and dynamically adjust the order of analysis according to that relevance. For example, it can postpone the analysis of less relevant data. Furthermore, it can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit monitors user behavior patterns and digital activity data. For example, the data collection unit monitors users' smartphone usage and social media activity in real time, collecting data such as app usage time, call history, location information, social media posts, comments, and the number of likes. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the user's stress and emotional state. For example, the analysis unit uses text analysis technology to analyze the content of social media posts and perform sentiment analysis. It also analyzes the frequency of smartphone use to identify the user's stress level. Step 3: Based on the analysis results obtained by the analysis unit, the service provider will provide individualized measures and support tailored to the user. For example, the service provider will use voice control to guide users through relaxation methods and stress reduction advice. In addition, in cooperation with industrial physicians, specialized support will be provided if the user's stress level is high.

[0057] (Example of form 2) The mental well-being AI agent system according to an embodiment of the present invention is a system that analyzes a user's behavioral patterns and digital activity data and provides individualized psychological support. The mental well-being AI agent system monitors the user's behavioral patterns and digital activity data in real time and analyzes the collected data to understand the user's stress and emotional state. Subsequently, the mental well-being AI agent system provides individualized measures and support tailored to the user. For example, the mental well-being AI agent system uses voice control to guide the user with advice on relaxation methods and stress reduction. It also provides professional support in cooperation with industrial physicians. As a result, the user can maintain mental stability and manage stress. For example, the mental well-being AI agent system monitors the user's smartphone usage and SNS activity. This allows the system to understand the user's behavioral patterns. Next, the mental well-being AI agent system analyzes the collected data to understand the user's stress and emotional state. For example, it analyzes the user's SNS posts and smartphone usage frequency to identify the user's stress level and emotional changes. Subsequently, the mental well-being AI agent system provides individualized measures and support tailored to the user. For example, voice control can be used to guide users with advice on relaxation methods and stress reduction. If a user says "I want to relax," the mental well-being AI agent system will suggest ways to relax. Furthermore, professional support is provided through collaboration with industrial physicians. For instance, if a user's stress level is high, they can contact an industrial physician and receive expert advice. This allows users to maintain mental stability and manage stress. For example, if a user experiences stress at work, the mental well-being AI agent system can suggest relaxation methods and collaborate with an industrial physician to provide expert support, thereby reducing the user's stress.This allows the mental well-being AI agent system to maintain the user's mental stability and manage stress.

[0058] The mental well-being AI agent system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the user's behavior patterns and digital activity data. For example, the data collection unit monitors the user's smartphone usage and social media activity. The data collection unit can monitor smartphone usage and social media activity in real time in order to understand the user's behavior patterns. For example, the data collection unit collects the user's smartphone app usage time, call history, location information, etc. The data collection unit can also collect the user's social media posts, comments, and the number of likes. The analysis unit analyzes the data collected by the data collection unit to understand the user's stress and emotional state. For example, the analysis unit analyzes the user's social media posts and smartphone usage frequency. The analysis unit can analyze the collected data to identify the user's stress level and emotional changes. For example, the analysis unit uses text analysis technology to analyze social media posts and perform sentiment analysis. The analysis unit can also analyze smartphone usage frequency to identify the user's stress level. The service provider unit provides individualized measures and support tailored to the user based on the analysis results obtained by the analysis unit. For example, the service provider unit guides the user with advice on relaxation methods and stress reduction using voice control. The service provider unit can provide appropriate advice based on the analysis results in order to provide individualized measures and support tailored to the user. For example, if the user says "I want to relax" by voice, the service provider unit will suggest relaxation methods. The service provider unit also provides professional support in cooperation with industrial physicians. If the user's stress level is high, the service provider unit can contact an industrial physician and receive expert advice. As a result, the mental well-being AI agent system according to this embodiment can maintain the user's mental stability and manage stress.

[0059] The data collection unit monitors user behavior patterns and digital activity data. Specifically, it monitors users' smartphone usage and social media activity. The data collection unit can monitor smartphone usage and social media activity in real time to understand user behavior patterns. For example, the data collection unit collects data such as the user's smartphone app usage time, call history, and location information. This allows for a detailed understanding of user behavior patterns, such as which apps they use and how often, what times of day they make calls, and which places they frequently visit. The data collection unit can also collect data such as the content of users' social media posts, comments, and the number of likes. This allows for a detailed understanding of digital activity data, such as what kind of content users post, what kind of comments they receive, and which posts they like. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provisioning units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0060] The analysis unit analyzes data collected by the data collection unit to understand users' stress levels and emotional states. Specifically, it analyzes users' social media posts and smartphone usage frequency. The analysis unit can analyze the collected data to identify users' stress levels and emotional changes. For example, the analysis unit uses text analysis technology to analyze social media posts and perform sentiment analysis. In sentiment analysis, natural language processing technology is used to classify the emotions in the posts as positive, negative, neutral, etc., to understand the user's emotional tendencies. The analysis unit can also analyze smartphone usage frequency to identify users' stress levels. For example, if smartphone usage time is prolonged or the frequency of use of a particular app suddenly increases, it may be determined that the user's stress level is rising. Furthermore, the analysis unit can integrate this data to perform a comprehensive stress assessment. For example, by combining the sentiment analysis results of social media posts with smartphone usage frequency data, it can more accurately assess the user's stress level. The analysis unit can also utilize past data and statistical information to analyze long-term stress trends and patterns. This allows the analysis unit to grasp the user's stress and emotional state in real time and provide information to take appropriate measures.

[0061] The service provider will provide personalized measures and support tailored to the user based on the analysis results obtained by the analysis unit. Specifically, it will guide the user with relaxation methods and stress reduction advice using voice control. The service provider can provide appropriate advice based on the analysis results in order to provide personalized measures and support tailored to the user. For example, if the user says "I want to relax" by voice, the service provider will suggest relaxation methods. Relaxation methods include deep breathing, meditation, and light exercise. In addition, if the user's stress level is high, the service provider can also propose a specific action plan for stress reduction. This may include suggestions for taking regular breaks or hobbies and activities to reduce stress. Furthermore, the service provider will also provide professional support in cooperation with an industrial physician. If the user's stress level is high, the service provider can contact an industrial physician and receive expert advice. The industrial physician will assess the user's health condition and provide support such as counseling or referral to medical institutions as needed. This allows the service provider to provide comprehensive support to maintain the user's mental stability and manage stress. Furthermore, the service provider can collect user feedback and continuously improve the quality of the support it provides. This allows the service provider to offer optimal support to users and contribute to improving their mental well-being.

[0062] The service provider can use voice control to guide users with advice on relaxation methods and stress reduction. For example, if a user gives a voice command such as "I want to relax," the service provider will suggest relaxation methods. The service provider can use voice recognition technology to analyze the user's voice commands and guide appropriate relaxation methods. For example, the service provider may suggest deep breathing or meditation to the user. The service provider can also use speech synthesis technology to guide users with relaxation methods verbally. For example, the service provider may instruct the user to play relaxing music. In this way, by using voice control, the service provider can provide users with advice on relaxation methods and stress reduction. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's voice commands into a generating AI and have the generating AI execute suggestions for relaxation methods.

[0063] The service provider can offer professional support through collaboration with industrial physicians. For example, if a user's stress level is high, the service provider can contact an industrial physician and receive expert advice. The service provider can share information about user stress management through regular meetings with industrial physicians. For example, the service provider can provide data on the user's stress level to the industrial physician and receive advice on how to receive appropriate support. The service provider can also establish a method for sharing data with industrial physicians and share information about user stress management in real time. For example, the service provider can share data with industrial physicians using a cloud-based data sharing platform. This allows the service provider to offer professional support to users through collaboration with industrial physicians. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input data on the user's stress level into a generating AI and have the generating AI generate advice to provide to the industrial physician.

[0064] The data collection unit can monitor users' smartphone usage and social media activity. For example, it collects data such as app usage time, call history, and location information. The unit can monitor smartphone usage in real time to understand user behavior patterns. For instance, it records app usage time and analyzes usage frequency. It can also collect call history and analyze call frequency and duration. Furthermore, it can collect user location information and analyze movement patterns. For example, it uses GPS data to record user movement history and identify behavior patterns. The data collection unit can also monitor users' social media activity. It collects social media posts, comments, and the number of likes, and analyzes this data to understand behavior patterns. For example, it uses text analysis technology to analyze social media posts and identify changes in user interests and emotions. This allows for understanding user behavior patterns by monitoring smartphone usage and social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user smartphone usage data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0065] The analysis unit can analyze a user's social media posts and smartphone usage frequency to identify the user's stress level and emotional changes. For example, the analysis unit can use text analysis technology to analyze social media posts and perform sentiment analysis. By analyzing a user's social media posts, the analysis unit can identify changes in the user's emotions. For example, the analysis unit can use natural language processing technology to analyze social media posts and identify positive and negative emotions. Furthermore, the analysis unit can analyze smartphone usage frequency to identify the user's stress level. By analyzing smartphone usage frequency, the analysis unit can understand the user's stress level. For example, the analysis unit can analyze app usage time and call history to identify changes in stress levels. This allows the analysis unit to identify stress levels and emotional changes by analyzing a user's social media posts and smartphone usage frequency. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user social media post data into a generating AI and have the generating AI perform sentiment analysis.

[0066] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will increase the frequency of data collection and collect more detailed information. The data collection unit can monitor the user's emotions in real time and dynamically adjust the frequency of data collection in response to changes in emotions. For example, if the user is relaxed, the data collection unit will decrease the frequency of data collection and collect only the minimum necessary information. The data collection unit can also dynamically adjust the frequency of data collection in response to changes in the user's emotions. For example, the data collection unit can estimate changes in the user's emotions using an emotion engine or generative AI and adjust the frequency of data collection. This allows for more appropriate data collection by adjusting the frequency of data collection according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of data collection.

[0067] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. For example, the data collection unit can optimize the timing of data collection based on actions the user has frequently performed in the past. The data collection unit can optimize the timing of data collection by analyzing the user's past behavior patterns. For example, the data collection unit can analyze the user's past travel history and digital device usage history and concentrate data collection during specific time periods. The data collection unit can also analyze the user's behavior patterns and select the most effective data collection method. For example, the data collection unit can optimize the timing and method of data collection based on the user's behavior patterns. This allows the optimal data collection method to be selected by analyzing the user's past behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current activities. The data collection unit can monitor the user's current activities in real time and prioritize collecting relevant data. For example, the data collection unit can filter relevant data based on the user's current location and activities. The data collection unit can also filter highly relevant data based on the user's areas of interest. The data collection unit can identify the user's areas of interest and prioritize collecting relevant data. For example, the data collection unit can identify areas of interest based on the user's past search history and social media following information. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity data into a generating AI and have the generating AI perform data filtering.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. The data collection unit can monitor the user's emotions in real time and dynamically adjust the priority of data to collect in response to changes in emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting relaxation-related data. The data collection unit can also dynamically adjust the priority of data to collect in response to changes in the user's emotions. For example, the data collection unit can estimate changes in the user's emotions using an emotion engine or generative AI and determine the priority of data to collect. This allows for the priority collection of important data based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. The data collection unit can monitor the user's geographical location information in real time and prioritize the collection of relevant data. For example, the data collection unit will filter highly relevant data based on the user's current location. The data collection unit can also select the optimal data collection method based on the user's current location. The data collection unit can optimize the timing and method of data collection based on the user's geographical location information. For example, the data collection unit will use GPS data to identify the user's current location and prioritize the collection of relevant data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0071] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant data. The data collection unit can monitor a user's social media activity in real time and collect relevant data. For example, the data collection unit can analyze a user's social media posts using text analysis technology and collect relevant data. The data collection unit can also collect data related to topics of interest from the user's social media activity. The data collection unit can select the optimal data collection method based on the user's social media activity. For example, the data collection unit can analyze a user's social media posts and prioritize the collection of data related to topics of interest. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant data.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit applies an algorithm that emphasizes stress-related data. The analysis unit can monitor the user's emotions in real time and dynamically adjust the analysis algorithm in response to changes in emotions. For example, if the user is relaxed, the analysis unit applies an algorithm that emphasizes relaxation-related data. The analysis unit can also dynamically adjust the analysis algorithm in response to changes in the user's emotions. For example, the analysis unit can estimate changes in the user's emotions using an emotion engine or generative AI and adjust the analysis algorithm. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can evaluate the importance of the data and dynamically adjust the level of detail of the analysis according to its importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a stress analysis algorithm to data related to stress. The analysis unit can identify the data category and apply the most suitable analysis algorithm according to the category. For example, the analysis unit can apply a relaxation analysis algorithm to data related to relaxation. The analysis unit can also select the most suitable analysis algorithm according to the data category. For example, the analysis unit can apply a text analysis algorithm to text data and an image analysis algorithm to image data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the most suitable analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. The analysis unit can monitor the user's emotions in real time and dynamically adjust the display method of the analysis results in response to changes in emotions. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can also dynamically adjust the display method of the analysis results in response to changes in the user's emotions. For example, the analysis unit estimates changes in the user's emotions using an emotion engine or a generative AI and adjusts the display method of the analysis results. This allows for a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0076] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can evaluate the data collection timing and dynamically adjust the analysis priority according to the collection timing. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis priority according to the data collection timing. For example, the analysis unit may prioritize the analysis of real-time data and postpone the analysis of historical data. This enables efficient analysis by determining the analysis priority according to the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can evaluate the relevance of the data and dynamically adjust the order of analysis according to the relevance. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can evaluate the correlation between the data and prioritize the analysis of highly relevant data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0078] The support unit can estimate the user's emotions and adjust the way it expresses support based on those emotions. For example, if the user is stressed, the support unit will provide support in a calm manner. The support unit can monitor the user's emotions in real time and dynamically adjust the way it expresses support according to changes in those emotions. For example, if the user is relaxed, the support unit will provide support in a cheerful manner. The support unit can also dynamically adjust the way it expresses support according to changes in the user's emotions. For example, the support unit can estimate changes in the user's emotions using an emotion engine or generative AI and adjust the way it expresses support. This allows for the provision of more appropriate support by adjusting the way it expresses support according to the user's emotions. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the way it expresses support.

[0079] The support unit can adjust the level of detail of support provided based on the user's stress level. For example, if the user's stress level is high, the support unit will provide detailed support. The support unit can monitor the user's stress level in real time and dynamically adjust the level of detail of support according to the stress level. For example, if the user's stress level is low, the support unit will provide simplified support. The support unit can also dynamically adjust the level of detail of support according to the user's stress level. For example, the support unit can evaluate the user's stress level and provide detailed support to users with high stress levels. This allows for the provision of more appropriate support by adjusting the level of detail of support according to the user's stress level. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user stress level data into a generating AI and have the generating AI perform the adjustment of the level of detail of support.

[0080] The service provider can apply different support algorithms depending on the user's category when providing support. For example, the service provider can apply a support algorithm related to stress management. The service provider can identify the user's category and apply the most suitable support algorithm according to that category. For example, the service provider can apply a support algorithm related to relaxation methods. The service provider can also select the most suitable support algorithm according to the user's category. For example, the service provider can apply the most suitable support algorithm according to the user's age and occupation. By applying the most suitable support algorithm according to the user's category, more effective support can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user category data into a generating AI and have the generating AI execute the application of the most suitable support algorithm.

[0081] The service provider can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is feeling stressed, the service provider will prioritize providing stress reduction support. The service provider can monitor the user's emotions in real time and dynamically adjust the support priority according to changes in emotions. For example, if the user is relaxed, the service provider will prioritize providing support on relaxation methods. The service provider can also dynamically adjust the support priority according to changes in the user's emotions. For example, the service provider can estimate changes in the user's emotions using an emotion engine or generative AI and determine the support priority. This allows for the provision of more appropriate support by determining the support priority according to the user's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the support priority.

[0082] The support unit can provide optimal support by considering the user's geographical location information when providing support. For example, if the user is in a specific location, the support unit can provide support relevant to that location. The support unit can monitor the user's geographical location information in real time and provide relevant support. For example, the support unit can select the optimal support method based on the user's current location. The support unit can also provide highly relevant support based on the user's current location. The support unit can optimize the content and method of support based on the user's geographical location information. For example, the support unit can use GPS data to identify the user's current location and provide relevant support. This allows for the provision of optimal support by considering the user's geographical location information. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of optimal support.

[0083] The service provider can analyze the user's social media activity and propose support methods when providing support. For example, the service provider can analyze the content of the user's social media posts and propose the most suitable support method. The service provider can monitor the user's social media activity in real time and propose relevant support methods. For example, the service provider can analyze the content of the user's social media posts using text analysis technology and propose the most suitable support method. The service provider can also propose support methods of interest based on the user's social media activity. The service provider can select the most suitable support method based on the user's social media activity. For example, the service provider can analyze the content of the user's social media posts and propose support methods of interest. In this way, the service provider can propose the most suitable support method by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI propose the most suitable support method.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The support unit can estimate the user's emotions and adjust the timing of support based on those estimates. For example, if a user is feeling stressed, it can immediately provide stress-reducing support. If a user is relaxed, it can refrain from providing support and only provide it when necessary. It can also dynamically adjust the timing of support in response to changes in the user's emotions. For example, if a user suddenly starts feeling stressed, it can immediately suggest ways to relax. This allows for support to be provided at the optimal time according to the user's emotions.

[0086] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, it provides a simple and highly visible notification method. If the user is relaxed, it provides a notification method that includes detailed information. It can also dynamically adjust the notification method in response to changes in the user's emotions. For example, if the user suddenly starts feeling stressed, it provides a concise notification with only the necessary information. This allows for the provision of the most appropriate notification method according to the user's emotions.

[0087] The support unit can estimate the user's emotions and adjust the support content based on those estimates. For example, if the user is feeling stressed, it can provide specific advice to reduce stress. If the user is relaxed, it can provide light advice to help maintain that relaxation. It can also dynamically adjust the support content in response to changes in the user's emotions. For example, if the user suddenly starts feeling stressed, it can immediately suggest ways to relax. This allows the system to provide the most appropriate support content according to the user's emotions.

[0088] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, it collects detailed data. If the user is relaxed, it collects only the minimum necessary data. It can also dynamically adjust the data collection method in response to changes in the user's emotions. For example, if the user suddenly starts to feel stressed, it collects detailed data and provides it to the analysis unit. This allows for the provision of the optimal data collection method according to the user's emotions.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is feeling stressed, it will prioritize analyzing stress-related data. If the user is relaxed, it will prioritize analyzing relaxation-related data. It can also dynamically adjust the analysis priority in response to changes in the user's emotions. For example, if the user suddenly starts feeling stressed, it will immediately analyze stress-related data. This allows the system to provide the optimal analysis priority according to the user's emotions.

[0090] The support department can analyze users' past behavior patterns and select the most appropriate support method. For example, it can optimize the content of support based on actions users have frequently performed in the past. By analyzing users' past behavior patterns, it can optimize the timing and method of support. For example, it can analyze users' past travel history and digital device usage history to concentrate support during specific time periods. It can also analyze users' behavior patterns to select the most effective support method. In this way, by analyzing users' past behavior patterns, it is possible to provide the most optimal support method.

[0091] The support team can provide optimal support by considering the user's geographical location. For example, if a user is in a specific location, they can provide support relevant to that location. The system can monitor the user's geographical location in real time and provide relevant support. For example, it can select the most appropriate support method based on the user's current location. It can also provide highly relevant support based on the user's current location. In this way, by considering the user's geographical location, the system can provide optimal support.

[0092] The service provider can analyze users' social media activity and propose support measures. For example, it can analyze the content of users' social media posts and propose the most suitable support measures. It can also monitor users' social media activity in real time and propose relevant support measures. For example, it can analyze the content of users' social media posts using text analysis technology and propose the most suitable support measures. Furthermore, it can propose support measures of interest based on users' social media activity. In this way, by analyzing users' social media activity, the service provider can provide the most suitable support measures.

[0093] The analysis unit can determine the priority of analysis based on the data collection timing. For example, it can prioritize the analysis of the most recent data. It can also evaluate the data collection timing and dynamically adjust the analysis priority according to the collection timing. For example, it can postpone the analysis of older data. Furthermore, it can dynamically adjust the analysis priority according to the data collection timing. This allows for efficient analysis by determining the analysis priority according to the data collection timing.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, it can prioritize the analysis of highly relevant data. It can also evaluate the relevance of the data and dynamically adjust the order of analysis according to that relevance. For example, it can postpone the analysis of less relevant data. Furthermore, it can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit monitors user behavior patterns and digital activity data. For example, the data collection unit monitors users' smartphone usage and social media activity in real time, collecting data such as app usage time, call history, location information, social media posts, comments, and the number of likes. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the user's stress and emotional state. For example, the analysis unit uses text analysis technology to analyze the content of social media posts and perform sentiment analysis. It also analyzes the frequency of smartphone use to identify the user's stress level. Step 3: Based on the analysis results obtained by the analysis unit, the service provider will provide individualized measures and support tailored to the user. For example, the service provider will use voice control to guide users through relaxation methods and stress reduction advice. In addition, in cooperation with industrial physicians, specialized support will be provided if the user's stress level is high.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit monitors the user's behavior patterns and digital activity data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand the user's stress and emotional state. The data provision unit is implemented in the control unit 46A of the smart device 14, and uses voice control to guide the user with advice on relaxation methods and stress reduction. The data provision unit also provides professional support in cooperation with an industrial physician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 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.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit monitors the user's behavior patterns and digital activity data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to understand the user's stress and emotional state. The data provision unit is implemented in the control unit 46A of the smart glasses 214, for example, and uses voice control to guide the user with advice on relaxation methods and stress reduction. The data provision unit also provides professional support in cooperation with an industrial physician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit monitors the user's behavior patterns and digital activity data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to understand the user's stress and emotional state. The data provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and uses voice control to guide the user with advice on relaxation methods and stress reduction. The data provision unit also provides professional support in cooperation with an industrial physician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit monitors the user's behavior patterns and digital activity data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to understand the user's stress and emotional state. The data provision unit is implemented, for example, by the control unit 46A of the robot 414, and uses voice control to guide the user with advice on relaxation methods and stress reduction. The data provision unit also provides professional support in cooperation with an industrial physician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A collection unit that monitors user behavior patterns and digital activity data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's stress and emotional state, The system includes a provisioning unit that provides individualized countermeasures and support tailored to the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, The system uses voice control to guide users with advice on relaxation techniques and stress reduction. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide professional support through collaboration with industrial physicians. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Monitor users' smartphone usage and social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, By analyzing users' social media posts and smartphone usage frequency, we identify their stress levels and emotional changes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze users' past behavioral patterns and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing support, we adjust the level of detail based on the user's stress level. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing support, different support algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing support, we take the user's geographical location into consideration to provide the most suitable support. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that monitors user behavior patterns and digital activity data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's stress and emotional state, The system includes a provisioning unit that provides individualized countermeasures and support tailored to the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, The system uses voice control to guide users with advice on relaxation techniques and stress reduction. The system according to feature 1.

3. The aforementioned supply unit is, We provide professional support through collaboration with industrial physicians. The system according to feature 1.

4. The aforementioned collection unit is Monitor users' smartphone usage and social media activity. The system according to feature 1.

5. The aforementioned analysis unit, By analyzing users' social media posts and smartphone usage frequency, we identify their stress levels and emotional changes. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze users' past behavioral patterns and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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