system

Abstract

The system according to this embodiment aims to identify patterns in data and prevent mental health problems before they occur. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a identification unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies patterns based on the data analyzed by the analysis unit. The provision unit provides preventive learning based on the patterns identified by the identification 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, finding patterns from data and utilizing them to prevent mental health troubles have not been sufficiently carried out and there is room for improvement.

[0005] The system according to the embodiment aims to find patterns from data and prevent mental health troubles.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a identification unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The identification unit identifies patterns based on the data analyzed by the analysis unit. The provision unit provides preventive learning based on the patterns identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify patterns from data and prevent mental health problems before they occur. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving 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 health support system according to an embodiment of the present invention is a system that identifies patterns from data and utilizes them to prevent and address mental health problems. The mental health support system securely trains a large-scale language model (LLM) with records of occupational physician consultations and counseling sessions. Next, the LLM identifies patterns that may affect mental health from the seemingly dispersed and unpatterned data. This allows for the identification of employees at high risk of mental health problems, enabling early intervention and care. Furthermore, based on anonymized information accumulated by each company, it aims to realize a society that provides maximum support for the mental health of each individual. For example, records of occupational physician consultations and counseling sessions are securely trained on the LLM. In this process, mental health-related data such as records of occupational physician consultations, counseling sessions, and stress checks managed by each company are collected. This allows for the efficient analysis of a vast amount of complex records. Next, the LLM analyzes the collected data and identifies patterns that may affect mental health. For example, it can identify the impact of specific work environments or lifestyles on mental health. This allows for the identification of employees at high risk of mental health problems, enabling early intervention and care. Furthermore, based on the analysis results, follow-up and preventative learning will be provided to employees with attributes that require preventative measures. For example, training to enhance resilience and advice on stress management can be offered. This will help prevent mental health problems from occurring. In addition, based on anonymized information accumulated by each company, a society will be realized that provides maximum support for the mental health of each individual. For example, cases shared by industrial physicians and counselors can be uniformly analyzed to derive generalizable insights. This will enable early detection of mental health problems and the provision of appropriate care. This system will allow corporate mental health departments, human resources departments, industrial physicians, and counselors to efficiently provide mental healthcare. For example, by utilizing generative AI to respond to changes in the work environment and workplace stress due to the spread of remote work, the mental health of employees can be supported.This allows mental health support systems to identify patterns from data and utilize them to prevent and address mental health problems.

[0029] The mental health support system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects data. The collection unit collects mental health-related data such as industrial physician interview records, counseling records, and stress check records. The collection unit can collect mental health-related data such as industrial physician interview records, counseling records, and stress check records managed by each company. The collection unit can collect data stored in digital format, for example. The collection unit can also scan paper records to convert them into digital data and collect them, for example. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using LLM, for example. The analysis unit can analyze the collected data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the collected data using natural language processing technology, for example. The identification unit identifies patterns based on the data analyzed by the analysis unit. The identification unit identifies patterns from the data analyzed using LLM, for example. The Identification Unit can, for example, identify the impact of specific work environments or lifestyles on mental health. The Identification Unit can also, for example, identify patterns from analyzed data that may affect mental health. The Provision Unit provides preventive learning based on the patterns identified by the Identification Unit. The Provision Unit provides preventive learning based on patterns identified using LLM, for example. The Provision Unit can provide training to enhance resilience and advice on stress management, for example. The Provision Unit can also provide preventive learning that maximizes support for each individual's mental health based on anonymized information accumulated by each company, for example. As a result, the mental health support system according to the embodiment can find patterns from data and utilize them to prevent and address mental health problems.

[0030] The data collection unit collects data. For example, the data collection unit collects mental health-related data such as industrial physician consultation records, counseling records, and stress check records. Specifically, it can collect mental health-related data such as industrial physician consultation records, counseling records, and stress check records managed by each company. This data may be stored digitally or on paper. The data collection unit can directly collect data stored in digital format. It can also scan paper records, convert them into digital data, and collect them. For example, industrial physician consultation records contain information such as the content of the consultation, diagnosis results, and advice; collecting this information as digital data can be useful for later analysis. Counseling records include information such as the content of the counseling, the client's condition, and the counselor's observations; this data is also collected. Stress check records contain information such as employees' stress levels, stressors, and coping methods; this data is also collected by the data collection unit. The data collection unit centrally manages this diverse data and makes it accessible to the analysis unit and specific departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by collecting the results of regular stress checks, fluctuations in employees' mental health can be continuously monitored. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a Large-Scale Language Model (LLM) to analyze the collected data. Specifically, it can analyze the collected data using statistical analysis and machine learning algorithms. For instance, it can analyze text data from industrial physician interviews and counseling records using natural language processing technology to extract employees' mental health status and stressors. The LLM has learned from large amounts of text data, possessing the ability to understand context and extract important information. This allows the analysis unit to automatically extract and analyze important information about employees' mental health from interview and counseling records. Furthermore, it can statistically analyze numerical data from stress check records to identify employees' stress levels and stressors. Additionally, machine learning algorithms can be used to discover patterns and trends in the collected data and predict fluctuations in employees' mental health status. For example, based on past data, it can analyze the impact of specific work environments or lifestyles on mental health and predict future risks. This enables the analysis unit to quickly and accurately analyze collected data and understand employees' mental health status. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The identification unit identifies patterns based on data analyzed by the analysis unit. For example, the identification unit identifies patterns from data analyzed using LLM. Specifically, it can identify the impact of specific work environments or lifestyles on mental health. For example, it can analyze the impact of long working hours or excessive stress on mental health and identify how these factors affect employees' mental health. The identification unit can also identify patterns that may affect mental health from the analyzed data. For example, it can identify patterns based on the analyzed data to identify the impact of specific work environments or lifestyles on mental health. This allows the identification unit to identify factors that affect employees' mental health and provide information for taking preventive measures. Furthermore, the identification unit can predict fluctuations in employees' mental health status based on the analyzed data. For example, it can analyze the impact of specific work environments or lifestyles on mental health based on past data and predict future risks. This allows the identification unit to continuously monitor employees' mental health status and identify risks early. In addition, the identification unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The service provider offers preventative learning based on patterns identified by specific departments. For example, the service provider offers preventative learning based on patterns identified using LLM. Specifically, it can provide training to enhance resilience and advice on stress management. For example, based on identified patterns, it can provide training programs to teach employees stress management methods and relaxation techniques. This allows employees to acquire the skills to effectively manage stress and maintain mental health. The service provider can also provide preventative learning that maximizes support for each individual's mental health based on anonymized information accumulated within each company. For example, it can provide individually customized advice and training programs based on employees' mental health status and stressors. This allows employees to maintain mental health and effectively manage stress in a way that suits them. Furthermore, the service provider can continuously monitor the effectiveness of preventative learning and improve its content as needed. For example, based on feedback from employees, it can review the content of training programs and advice methods to provide more effective preventative learning. This allows the service provider to continuously support employees' mental health and prevent mental health problems from occurring.

[0034] The data collection unit can collect mental health-related data such as industrial physician interview records, counseling records, and stress check records. For example, the data collection unit can collect industrial physician interview records in digital format. The data collection unit can also collect counseling records in digital format. The data collection unit can also collect stress check records in digital format. The data collection unit can also scan paper records and convert them into digital data for collection. This allows for the efficient collection of mental health-related data. Some or all of the above-described processes in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input mental health-related data such as industrial physician interview records, counseling records, and stress check records into an LLM and have the LLM perform the data collection.

[0035] The analysis unit can analyze the collected data and identify patterns that may affect mental health. For example, the analysis unit can analyze the collected data using statistical analysis. The analysis unit can also analyze the collected data using machine learning algorithms. The analysis unit can also analyze the collected data using natural language processing techniques. This allows for the identification of patterns that may affect mental health. Some or all of the above-described processes in the analysis unit may be performed using, for example, a Language Lifecycle Machine (LLM), or not using an LLM. For example, the analysis unit can input the collected data into an LLM and have the LLM perform the data analysis.

[0036] The identification unit can identify the impact of specific work environments or lifestyles on mental health. For example, the identification unit can identify the impact of a specific work environment on mental health. The identification unit can also identify the impact of a specific lifestyle on mental health. The identification unit can also identify specific elements of the work environment or lifestyle. This makes it possible to identify the impact of the work environment or lifestyle on mental health. Some or all of the above processing in the identification unit may be performed using, for example, an LLM, or not using an LLM. For example, the identification unit can input data on the work environment or lifestyle into an LLM and have the LLM perform the impact identification.

[0037] The service provider can provide training to enhance resilience and advice on stress management. The service provider can, for example, provide training to enhance resilience. The service provider can, for example, also provide advice on stress management. The service provider can, for example, provide specific training content to enhance resilience. The service provider can, for example, provide specific advice on stress management. This allows the service provider to provide training to enhance resilience and advice on stress management. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input training to enhance resilience and advice on stress management into an LLM and have the LLM perform the provision of preventive learning.

[0038] The service provider can provide preventative learning that maximizes support for each individual's mental health based on anonymized information accumulated by each company. For example, the service provider can provide preventative learning based on anonymized information accumulated by each company. The service provider can also provide preventative learning that supports each individual's mental health based on anonymized information. The service provider can also customize the content of preventative learning based on anonymized information. This allows the service provider to maximize support for each individual's mental health based on anonymized information accumulated by each company. Some or all of the above processing in the service provider may be performed using LLM, for example, or without using LLM. For example, the service provider can input anonymized information into LLM and have LLM perform the provision of preventative learning.

[0039] The data collection unit can analyze the user's past mental health history during data collection and select the optimal collection method. For example, if the user has experienced high stress levels in the past, the data collection unit can select a collection method to reduce stress. For example, if the user has had mental health problems in the past, the data collection unit can prioritize the collection of data related to those problems. For example, if the user has shown improvement in their mental health in the past, the data collection unit can collect data related to that improvement. In this way, the optimal collection method can be selected by analyzing the user's past mental health history. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input the user's past mental health history into an LLM and have the LLM select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current work situation and living environment during collection. For example, if a user is in a busy period, the data collection unit will prioritize collecting work-related data. For example, if a user is working remotely, the data collection unit can also collect data related to remote work. For example, if a user has experienced a change in their home environment, the data collection unit can also collect data related to that change. This allows the data to be filtered based on the user's current work situation and living environment. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input data about the user's work situation and living environment into an LLM and have the LLM perform the filtering.

[0041] 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 region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit can prioritize the collection of data related to the destination. For example, if the user is staying in a specific location for an extended period, the data collection unit can prioritize the collection of data related to that location. 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, for example, an LLM, or without an LLM. For example, the data collection unit can input the user's geographical location information into an LLM and have the LLM perform the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user is experiencing stress on social media, the data collection unit can collect data related to that stress. For example, if the user is relaxing on social media, the data collection unit can also collect data related to that relaxation. For example, if the user is in a hurry on social media, the data collection unit can also collect data related to that hurried situation. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input the user's social media activity into an LLM and have the LLM perform the collection of relevant data.

[0043] 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 can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the importance of the data into the LLM and have the LLM perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to mental health-related data. The analysis unit can also apply a different analysis algorithm to work-related data. The analysis unit can also apply yet another analysis algorithm to lifestyle-related data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the data category into the LLM and have the LLM perform the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also analyze current data while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the latest data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the data collection period into the LLM and have the LLM determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the relevance of the data into an LLM and have the LLM perform the adjustment of the order of analysis.

[0047] The identification unit can improve the accuracy of a particular task by considering the interrelationships of the data at the time of identification. For example, the identification unit can analyze the interrelationships of the data and improve the accuracy of a particular task. The identification unit can also adjust the results of a particular task by considering the interrelationships of the data. For example, the identification unit can optimize a particular algorithm based on the interrelationships of the data. This allows for improvement of accuracy by considering the interrelationships of the data. Some or all of the above-described processes in the identification unit may be performed using an LLM, for example, or without an LLM. For example, the identification unit can input the interrelationships of the data into an LLM and have the LLM perform the improvement of accuracy.

[0048] The identification unit can perform identification by considering the attribute information of the data submitter. For example, the identification unit can perform identification by considering the occupation and position of the data submitter. For example, the identification unit can also perform identification by considering the age and gender of the data submitter. For example, the identification unit can also perform identification by considering the work history and job duties of the data submitter. In this way, the accuracy of identification can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the identification unit may be performed using an LLM, for example, or without using an LLM. For example, the identification unit can input the attribute information of the data submitter into an LLM and have the LLM perform the identification.

[0049] The identification unit can perform identification while considering the geographical distribution of the data. For example, the identification unit can analyze the geographical distribution of the data to improve the accuracy of identification. For example, the identification unit can also adjust the results of identification by considering the geographical distribution of the data. For example, the identification unit can optimize a specific algorithm based on the geographical distribution of the data. This improves the accuracy of identification by considering the geographical distribution of the data. Some or all of the above processing in the identification unit may be performed using an LLM, for example, or without an LLM. For example, the identification unit can input the geographical distribution of the data into an LLM and have the LLM perform the specific execution.

[0050] The identification unit can improve the accuracy of a particular task by referring to relevant data literature at the time of identification. For example, the identification unit can improve the accuracy of a particular task by referring to relevant data literature. The identification unit can also adjust a particular result by considering relevant data literature. For example, the identification unit can optimize a particular algorithm based on relevant data literature. This allows for improved accuracy of a particular task by referring to relevant data literature. Some or all of the above processing in the identification unit may be performed using an LLM, for example, or without an LLM. For example, the identification unit can input relevant data literature into an LLM and have the LLM perform a particular execution.

[0051] The service provider can analyze the user's past mental health history and select the most appropriate preventive learning at the time of delivery. For example, the service provider can analyze situations in which the user has experienced stress in the past and provide preventive learning that corresponds to those situations. For example, the service provider can also provide training that the user has previously shown to improve their mental health. For example, the service provider can provide individually customized preventive learning based on the user's past mental health history. This allows the service provider to provide the most appropriate preventive learning by analyzing the user's past mental health history. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input the user's past mental health history into an LLM and have the LLM select the most appropriate preventive learning.

[0052] The service provider can customize preventive learning methods based on the user's current work situation at the time of delivery. For example, if the user is in a busy period, the service provider can provide preventive learning that can be completed in a short time. For example, if the user is working remotely, the service provider can also provide preventive learning suitable for remote work. For example, if the user is working on a new project, the service provider can also provide preventive learning related to that project. By customizing the preventive learning methods based on the user's current work situation, more effective preventive learning can be provided. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input data on the user's work situation into an LLM and have the LLM perform the customization of the preventive learning methods.

[0053] The service provider can select the most appropriate preventative learning at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide preventative learning related to that region. For example, if the user is on the move, the service provider can also provide preventative learning related to the user's destination. For example, if the user is staying in a specific location for an extended period, the service provider can also provide preventative learning related to that location. In this way, the service provider can provide the most appropriate preventative learning by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or without using an LLM. For example, the service provider can input the user's geographical location information into an LLM and have the LLM select the most appropriate preventative learning.

[0054] The service provider can analyze the user's social media activity and propose preventive learning strategies at the time of delivery. For example, if the user is experiencing stress on social media, the service provider can provide preventive learning strategies to address that stress. For example, if the user is feeling relaxed on social media, the service provider can also provide preventive learning strategies to help maintain that relaxation. For example, if the user is feeling rushed on social media, the service provider can also provide preventive learning strategies to address that rushed situation. By analyzing the user's social media activity, the service provider can propose the most appropriate preventive learning strategies. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or without using an LLM. For example, the service provider can input the user's social media activity into an LLM and have the LLM execute the proposal of preventive learning strategies.

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

[0056] The mental health support system may further include a data integration unit. The data integration unit can integrate and centrally manage mental health-related data collected from different data sources. For example, it can integrate data such as industrial physician interview records, counseling records, and stress check records. The data integration unit can also eliminate data duplication and maintain data integrity. This allows for efficient management of data collected from different data sources. Some or all of the above-described processes in the data integration unit may be performed using, for example, a Data Lifecycle Management (LLM), or without an LLM. For example, the data integration unit can input the collected data into an LLM and have the LLM perform the data integration.

[0057] The mental health support system may further include a data visualization unit. The data visualization unit can visually display the collected mental health-related data, enabling users to intuitively understand the data. For example, fluctuations in stress levels can be displayed in a graph. The data visualization unit can also make it easier for users to grasp their own mental health status through data visualization. This allows users to intuitively understand the data by visually displaying the collected data. Some or all of the above processing in the data visualization unit may be performed using, for example, an LLM, or not using an LLM. For example, the data visualization unit can input the collected data into an LLM and have the LLM perform the data visualization.

[0058] The mental health support system may further include a data backup unit. The data backup unit can periodically back up the collected mental health-related data to prevent data loss. For example, data can be backed up regularly on a daily basis. The data backup unit can maintain the integrity of the backup data and restore it as needed. This prevents the loss of collected data and ensures data security. Some or all of the above-described processes in the data backup unit may be performed using, for example, an LLM, or without an LLM. For example, the data backup unit can input the collected data into an LLM and have the LLM perform the data backup.

[0059] The mental health support system may further include a data security unit. The data security unit can ensure the security of collected mental health-related data and prevent unauthorized access and data breaches. For example, it can encrypt data and implement access control. The data security unit can also set and enforce data security policies. This ensures the security of collected data and prevents unauthorized access and data breaches. Some or all of the above-described processes in the data security unit may be performed using, for example, a Data Locksmith (LLM), or not. For example, the data security unit can input collected data into an LLM and have the LLM perform data security.

[0060] The mental health support system may further include a data archiving unit. The data archiving unit can store collected mental health-related data for extended periods and make it accessible as needed. For example, it can archive data from the past several years. The data archiving unit can also maintain the integrity of the archived data and make it easier to search for data as needed. This allows for the long-term storage and accessibility of collected data. Some or all of the above-described processes in the data archiving unit may be performed, for example, using an LLM (Language-Limited Management) or not. For example, the data archiving unit can input collected data into an LLM and have the LLM perform the data archiving.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects data. The collection unit collects mental health-related data, such as industrial physician consultation records, counseling records, and stress check records. The collection unit can collect mental health-related data, such as industrial physician consultation records, counseling records, and stress check records, managed by each company. The collection unit can collect data stored in digital format, for example. The collection unit can also scan paper records, convert them into digital data, and collect them, for example. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, LLM. The analysis unit can analyze the collected data using, for example, statistical analysis or machine learning algorithms. The analysis unit can also analyze the collected data using, for example, natural language processing techniques. Step 3: The Identification Unit identifies patterns based on the data analyzed by the Analysis Unit. For example, the Identification Unit identifies patterns from data analyzed using LLM. For example, the Identification Unit can identify the impact of specific work environments or lifestyles on mental health. For example, the Identification Unit can also identify patterns from the analyzed data that may have an impact on mental health. Step 4: The service provider provides preventive learning based on patterns identified by the specific service provider. The service provider provides preventive learning based on patterns identified using, for example, LLM. The service provider can provide, for example, training to enhance resilience or advice on stress management. The service provider can also provide preventive learning that maximizes support for each individual's mental health based on anonymized information accumulated by each company.

[0063] (Example of form 2) The mental health support system according to an embodiment of the present invention is a system that identifies patterns from data and utilizes them to prevent and address mental health problems. The mental health support system securely trains a large-scale language model (LLM) with records of occupational physician consultations and counseling sessions. Next, the LLM identifies patterns that may affect mental health from the seemingly dispersed and unpatterned data. This allows for the identification of employees at high risk of mental health problems, enabling early intervention and care. Furthermore, based on anonymized information accumulated by each company, it aims to realize a society that provides maximum support for the mental health of each individual. For example, records of occupational physician consultations and counseling sessions are securely trained on the LLM. In this process, mental health-related data such as records of occupational physician consultations, counseling sessions, and stress checks managed by each company are collected. This allows for the efficient analysis of a vast amount of complex records. Next, the LLM analyzes the collected data and identifies patterns that may affect mental health. For example, it can identify the impact of specific work environments or lifestyles on mental health. This allows for the identification of employees at high risk of mental health problems, enabling early intervention and care. Furthermore, based on the analysis results, follow-up and preventative learning will be provided to employees with attributes that require preventative measures. For example, training to enhance resilience and advice on stress management can be offered. This will help prevent mental health problems from occurring. In addition, based on anonymized information accumulated by each company, a society will be realized that provides maximum support for the mental health of each individual. For example, cases shared by industrial physicians and counselors can be uniformly analyzed to derive generalizable insights. This will enable early detection of mental health problems and the provision of appropriate care. This system will allow corporate mental health departments, human resources departments, industrial physicians, and counselors to efficiently provide mental healthcare. For example, by utilizing generative AI to respond to changes in the work environment and workplace stress due to the spread of remote work, the mental health of employees can be supported.This allows mental health support systems to identify patterns from data and utilize them to prevent and address mental health problems.

[0064] The mental health support system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects data. The collection unit collects mental health-related data such as industrial physician interview records, counseling records, and stress check records. The collection unit can collect mental health-related data such as industrial physician interview records, counseling records, and stress check records managed by each company. The collection unit can collect data stored in digital format, for example. The collection unit can also scan paper records to convert them into digital data and collect them, for example. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using LLM, for example. The analysis unit can analyze the collected data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the collected data using natural language processing technology, for example. The identification unit identifies patterns based on the data analyzed by the analysis unit. The identification unit identifies patterns from the data analyzed using LLM, for example. The Identification Unit can, for example, identify the impact of specific work environments or lifestyles on mental health. The Identification Unit can also, for example, identify patterns from analyzed data that may affect mental health. The Provision Unit provides preventive learning based on the patterns identified by the Identification Unit. The Provision Unit provides preventive learning based on patterns identified using LLM, for example. The Provision Unit can provide training to enhance resilience and advice on stress management, for example. The Provision Unit can also provide preventive learning that maximizes support for each individual's mental health based on anonymized information accumulated by each company, for example. As a result, the mental health support system according to the embodiment can find patterns from data and utilize them to prevent and address mental health problems.

[0065] The data collection unit collects data. For example, the data collection unit collects mental health-related data such as industrial physician consultation records, counseling records, and stress check records. Specifically, it can collect mental health-related data such as industrial physician consultation records, counseling records, and stress check records managed by each company. This data may be stored digitally or on paper. The data collection unit can directly collect data stored in digital format. It can also scan paper records, convert them into digital data, and collect them. For example, industrial physician consultation records contain information such as the content of the consultation, diagnosis results, and advice; collecting this information as digital data can be useful for later analysis. Counseling records include information such as the content of the counseling, the client's condition, and the counselor's observations; this data is also collected. Stress check records contain information such as employees' stress levels, stressors, and coping methods; this data is also collected by the data collection unit. The data collection unit centrally manages this diverse data and makes it accessible to the analysis unit and specific departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by collecting the results of regular stress checks, fluctuations in employees' mental health can be continuously monitored. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a Large-Scale Language Model (LLM) to analyze the collected data. Specifically, it can analyze the collected data using statistical analysis and machine learning algorithms. For instance, it can analyze text data from industrial physician interviews and counseling records using natural language processing technology to extract employees' mental health status and stressors. The LLM has learned from large amounts of text data, possessing the ability to understand context and extract important information. This allows the analysis unit to automatically extract and analyze important information about employees' mental health from interview and counseling records. Furthermore, it can statistically analyze numerical data from stress check records to identify employees' stress levels and stressors. Additionally, machine learning algorithms can be used to discover patterns and trends in the collected data and predict fluctuations in employees' mental health status. For example, based on past data, it can analyze the impact of specific work environments or lifestyles on mental health and predict future risks. This enables the analysis unit to quickly and accurately analyze collected data and understand employees' mental health status. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The identification unit identifies patterns based on data analyzed by the analysis unit. For example, the identification unit identifies patterns from data analyzed using LLM. Specifically, it can identify the impact of specific work environments or lifestyles on mental health. For example, it can analyze the impact of long working hours or excessive stress on mental health and identify how these factors affect employees' mental health. The identification unit can also identify patterns that may affect mental health from the analyzed data. For example, it can identify patterns based on the analyzed data to identify the impact of specific work environments or lifestyles on mental health. This allows the identification unit to identify factors that affect employees' mental health and provide information for taking preventive measures. Furthermore, the identification unit can predict fluctuations in employees' mental health status based on the analyzed data. For example, it can analyze the impact of specific work environments or lifestyles on mental health based on past data and predict future risks. This allows the identification unit to continuously monitor employees' mental health status and identify risks early. In addition, the identification unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The service provider offers preventative learning based on patterns identified by specific departments. For example, the service provider offers preventative learning based on patterns identified using LLM. Specifically, it can provide training to enhance resilience and advice on stress management. For example, based on identified patterns, it can provide training programs to teach employees stress management methods and relaxation techniques. This allows employees to acquire the skills to effectively manage stress and maintain mental health. The service provider can also provide preventative learning that maximizes support for each individual's mental health based on anonymized information accumulated within each company. For example, it can provide individually customized advice and training programs based on employees' mental health status and stressors. This allows employees to maintain mental health and effectively manage stress in a way that suits them. Furthermore, the service provider can continuously monitor the effectiveness of preventative learning and improve its content as needed. For example, based on feedback from employees, it can review the content of training programs and advice methods to provide more effective preventative learning. This allows the service provider to continuously support employees' mental health and prevent mental health problems from occurring.

[0069] The data collection unit can collect mental health-related data such as industrial physician interview records, counseling records, and stress check records. For example, the data collection unit can collect industrial physician interview records in digital format. The data collection unit can also collect counseling records in digital format. The data collection unit can also collect stress check records in digital format. The data collection unit can also scan paper records and convert them into digital data for collection. This allows for the efficient collection of mental health-related data. Some or all of the above-described processes in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input mental health-related data such as industrial physician interview records, counseling records, and stress check records into an LLM and have the LLM perform the data collection.

[0070] The analysis unit can analyze the collected data and identify patterns that may affect mental health. For example, the analysis unit can analyze the collected data using statistical analysis. The analysis unit can also analyze the collected data using machine learning algorithms. The analysis unit can also analyze the collected data using natural language processing techniques. This allows for the identification of patterns that may affect mental health. Some or all of the above-described processes in the analysis unit may be performed using, for example, a Language Lifecycle Machine (LLM), or not using an LLM. For example, the analysis unit can input the collected data into an LLM and have the LLM perform the data analysis.

[0071] The identification unit can identify the impact of specific work environments or lifestyles on mental health. For example, the identification unit can identify the impact of a specific work environment on mental health. The identification unit can also identify the impact of a specific lifestyle on mental health. The identification unit can also identify specific elements of the work environment or lifestyle. This makes it possible to identify the impact of the work environment or lifestyle on mental health. Some or all of the above processing in the identification unit may be performed using, for example, an LLM, or not using an LLM. For example, the identification unit can input data on the work environment or lifestyle into an LLM and have the LLM perform the impact identification.

[0072] The service provider can provide training to enhance resilience and advice on stress management. The service provider can, for example, provide training to enhance resilience. The service provider can, for example, also provide advice on stress management. The service provider can, for example, provide specific training content to enhance resilience. The service provider can, for example, provide specific advice on stress management. This allows the service provider to provide training to enhance resilience and advice on stress management. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input training to enhance resilience and advice on stress management into an LLM and have the LLM perform the provision of preventive learning.

[0073] The service provider can provide preventative learning that maximizes support for each individual's mental health based on anonymized information accumulated by each company. For example, the service provider can provide preventative learning based on anonymized information accumulated by each company. The service provider can also provide preventative learning that supports each individual's mental health based on anonymized information. The service provider can also customize the content of preventative learning based on anonymized information. This allows the service provider to maximize support for each individual's mental health based on anonymized information accumulated by each company. Some or all of the above processing in the service provider may be performed using LLM, for example, or without using LLM. For example, the service provider can input anonymized information into LLM and have LLM perform the provision of preventative learning.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to match the user's schedule. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or not using an LLM. For example, the data collection unit can input user emotion data into an LLM and have the LLM adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past mental health history during data collection and select the optimal collection method. For example, if the user has experienced high stress levels in the past, the data collection unit can select a collection method to reduce stress. For example, if the user has had mental health problems in the past, the data collection unit can prioritize the collection of data related to those problems. For example, if the user has shown improvement in their mental health in the past, the data collection unit can collect data related to that improvement. In this way, the optimal collection method can be selected by analyzing the user's past mental health history. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input the user's past mental health history into an LLM and have the LLM select the optimal collection method.

[0076] The data collection unit can filter data based on the user's current work situation and living environment during collection. For example, if a user is in a busy period, the data collection unit will prioritize collecting work-related data. For example, if a user is working remotely, the data collection unit can also collect data related to remote work. For example, if a user has experienced a change in their home environment, the data collection unit can also collect data related to that change. This allows the data to be filtered based on the user's current work situation and living environment. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input data about the user's work situation and living environment into an LLM and have the LLM perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress. For example, if the user is relaxed, the data collection unit may also prioritize collecting data related to relaxation. For example, if the user is in a hurry, the data collection unit may also prioritize collecting data related to the hurried situation. In this way, by prioritizing the data to collect based on the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or not using an LLM. For example, the data collection unit can input user emotion data into an LLM and have the LLM perform the data prioritization.

[0078] 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 region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit can prioritize the collection of data related to the destination. For example, if the user is staying in a specific location for an extended period, the data collection unit can prioritize the collection of data related to that location. 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, for example, an LLM, or without an LLM. For example, the data collection unit can input the user's geographical location information into an LLM and have the LLM perform the collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user is experiencing stress on social media, the data collection unit can collect data related to that stress. For example, if the user is relaxing on social media, the data collection unit can also collect data related to that relaxation. For example, if the user is in a hurry on social media, the data collection unit can also collect data related to that hurried situation. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, an LLM, or without an LLM. For example, the data collection unit can input the user's social media activity into an LLM and have the LLM perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visual presentation. For example, if the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a presentation that gets straight to the point. By adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, LLM, or not using LLM. For example, the analysis unit can input user emotion data into LLM and have LLM perform the adjustment of the presentation of the analysis.

[0081] 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 can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the importance of the data into the LLM and have the LLM perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to mental health-related data. The analysis unit can also apply a different analysis algorithm to work-related data. The analysis unit can also apply yet another analysis algorithm to lifestyle-related data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the data category into the LLM and have the LLM perform the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, LLM, or not using LLM. For example, the analysis unit can input user emotion data into LLM and have LLM perform the adjustment of the analysis length.

[0084] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also analyze current data while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the latest data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the data collection period into the LLM and have the LLM determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, an LLM, or without an LLM. For example, the analysis unit can input the relevance of the data into an LLM and have the LLM perform the adjustment of the order of analysis.

[0086] The identification unit can estimate the user's emotions and adjust specific criteria based on the estimated user emotions. For example, if the user is stressed, the identification unit may prioritize adjusting criteria related to stress. For example, if the user is relaxed, the identification unit may also prioritize adjusting criteria related to relaxation. For example, if the user is in a hurry, the identification unit may also prioritize adjusting criteria related to the hurried situation. This allows for more accurate identification by adjusting specific criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using, for example, an LLM, or not using an LLM. For example, the identification unit can input user emotion data into an LLM and have the LLM perform the adjustment of specific criteria.

[0087] The identification unit can improve the accuracy of a particular task by considering the interrelationships of the data at the time of identification. For example, the identification unit can analyze the interrelationships of the data and improve the accuracy of a particular task. The identification unit can also adjust the results of a particular task by considering the interrelationships of the data. For example, the identification unit can optimize a particular algorithm based on the interrelationships of the data. This allows for improvement of accuracy by considering the interrelationships of the data. Some or all of the above-described processes in the identification unit may be performed using an LLM, for example, or without an LLM. For example, the identification unit can input the interrelationships of the data into an LLM and have the LLM perform the improvement of accuracy.

[0088] The identification unit can perform identification by considering the attribute information of the data submitter. For example, the identification unit can perform identification by considering the occupation and position of the data submitter. For example, the identification unit can also perform identification by considering the age and gender of the data submitter. For example, the identification unit can also perform identification by considering the work history and job duties of the data submitter. In this way, the accuracy of identification can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the identification unit may be performed using an LLM, for example, or without using an LLM. For example, the identification unit can input the attribute information of the data submitter into an LLM and have the LLM perform the identification.

[0089] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user emotions. For example, if the user is stressed, the identification unit will prioritize displaying results related to stress. For example, if the user is relaxed, the identification unit may also prioritize displaying results related to relaxation. For example, if the user is in a hurry, the identification unit may also prioritize displaying results related to the hurried situation. By adjusting the order in which specific results are displayed based on the user's emotions, the system can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, an LLM, or not using an LLM. For example, the identification unit can input user emotion data into an LLM and have the LLM adjust the order in which results are displayed.

[0090] The identification unit can perform identification while considering the geographical distribution of the data. For example, the identification unit can analyze the geographical distribution of the data to improve the accuracy of identification. For example, the identification unit can also adjust the results of identification by considering the geographical distribution of the data. For example, the identification unit can optimize a specific algorithm based on the geographical distribution of the data. This improves the accuracy of identification by considering the geographical distribution of the data. Some or all of the above processing in the identification unit may be performed using an LLM, for example, or without an LLM. For example, the identification unit can input the geographical distribution of the data into an LLM and have the LLM perform the specific execution.

[0091] The identification unit can improve the accuracy of a particular task by referring to relevant data literature at the time of identification. For example, the identification unit can improve the accuracy of a particular task by referring to relevant data literature. The identification unit can also adjust a particular result by considering relevant data literature. For example, the identification unit can optimize a particular algorithm based on relevant data literature. This allows for improved accuracy of a particular task by referring to relevant data literature. Some or all of the above processing in the identification unit may be performed using an LLM, for example, or without an LLM. For example, the identification unit can input relevant data literature into an LLM and have the LLM perform a particular execution.

[0092] The service provider can estimate the user's emotions and adjust the content of preventative learning based on the estimated emotions. For example, if the user is feeling stressed, the service provider can provide stress management advice. For example, if the user is relaxed, the service provider can also provide training to maintain that relaxation. For example, if the user is in a hurry, the service provider can provide preventative learning that can be completed in a short time. By adjusting the content of preventative learning based on the user's emotions, more effective preventative learning can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input user emotion data into an LLM and have the LLM adjust the content of preventative learning.

[0093] The service provider can analyze the user's past mental health history and select the most appropriate preventive learning at the time of delivery. For example, the service provider can analyze situations in which the user has experienced stress in the past and provide preventive learning that corresponds to those situations. For example, the service provider can also provide training that the user has previously shown to improve their mental health. For example, the service provider can provide individually customized preventive learning based on the user's past mental health history. This allows the service provider to provide the most appropriate preventive learning by analyzing the user's past mental health history. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input the user's past mental health history into an LLM and have the LLM select the most appropriate preventive learning.

[0094] The service provider can customize preventive learning methods based on the user's current work situation at the time of delivery. For example, if the user is in a busy period, the service provider can provide preventive learning that can be completed in a short time. For example, if the user is working remotely, the service provider can also provide preventive learning suitable for remote work. For example, if the user is working on a new project, the service provider can also provide preventive learning related to that project. By customizing the preventive learning methods based on the user's current work situation, more effective preventive learning can be provided. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input data on the user's work situation into an LLM and have the LLM perform the customization of the preventive learning methods.

[0095] The service provider can estimate the user's emotions and determine the priority of preventative learning to be provided based on the estimated emotions. For example, if the user is feeling stressed, the service provider will prioritize stress management preventative learning. For example, if the user is relaxed, the service provider may also prioritize preventative learning to maintain relaxation. For example, if the user is in a hurry, the service provider may also prioritize preventative learning that can be completed in a short time. This allows for more effective preventative learning by prioritizing preventative learning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or not using an LLM. For example, the service provider can input user emotion data into an LLM and have the LLM determine the priority of preventative learning.

[0096] The service provider can select the most appropriate preventative learning at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide preventative learning related to that region. For example, if the user is on the move, the service provider can also provide preventative learning related to the user's destination. For example, if the user is staying in a specific location for an extended period, the service provider can also provide preventative learning related to that location. In this way, the service provider can provide the most appropriate preventative learning by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or without using an LLM. For example, the service provider can input the user's geographical location information into an LLM and have the LLM select the most appropriate preventative learning.

[0097] The service provider can analyze the user's social media activity and propose preventive learning strategies at the time of delivery. For example, if the user is experiencing stress on social media, the service provider can provide preventive learning strategies to address that stress. For example, if the user is feeling relaxed on social media, the service provider can also provide preventive learning strategies to help maintain that relaxation. For example, if the user is feeling rushed on social media, the service provider can also provide preventive learning strategies to address that rushed situation. By analyzing the user's social media activity, the service provider can propose the most appropriate preventive learning strategies. Some or all of the above processing in the service provider may be performed using, for example, an LLM, or without using an LLM. For example, the service provider can input the user's social media activity into an LLM and have the LLM execute the proposal of preventive learning strategies.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The mental health support system may further include a feedback unit. The feedback unit can collect feedback from users and use it to improve the system. For example, it can provide feedback on how users felt about the preventative learning provided and how effective it was. The feedback unit can also estimate the user's emotions and analyze the content of the feedback based on the estimated emotions. This allows for the collection of emotion-based feedback and its use in improving the system. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using, for example, an LLM, or not using an LLM. For example, the feedback unit can input user emotion data into an LLM and have the LLM perform the analysis of the feedback content.

[0100] The mental health support system may also include a notification unit. The notification unit can notify the user of important information and alerts regarding mental health. For example, if the user is in a high-stress state, it can notify them to seek appropriate care. The notification unit can also estimate the user's emotions and adjust the content and timing of notifications based on the estimated emotions. This allows for notifications that are sensitive to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, an LLM, or not using an LLM. For example, the notification unit can input the user's emotion data into an LLM and have the LLM adjust the content and timing of notifications.

[0101] The mental health support system may further include a monitoring unit. The monitoring unit can continuously monitor the user's mental health status and issue alerts if an abnormality is detected. For example, if the user's stress level rises sharply, it can issue an alert prompting appropriate action. The monitoring unit can also estimate the user's emotions and adjust the accuracy of the monitoring based on the estimated emotions. This enables highly accurate monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an LLM, or not using an LLM. For example, the monitoring unit can input user emotion data into an LLM and have the LLM adjust the accuracy of the monitoring.

[0102] The mental health support system may further include an advice unit. The advice unit can provide advice tailored to the user's mental health status. For example, if the user is experiencing stress, it can provide specific advice to reduce stress. The advice unit can also estimate the user's emotions and adjust the advice based on the estimated emotions. This allows for the provision of advice that takes the user's emotions into consideration. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using, for example, an LLM, or without an LLM. For example, the advice unit can input the user's emotion data into an LLM and have the LLM adjust the content of the advice.

[0103] The mental health support system may further include a reminder unit. The reminder unit can periodically send mental health care reminders to the user. For example, it can remind the user to perform stress checks regularly. The reminder unit can also estimate the user's emotions and adjust the content and timing of the reminders based on the estimated emotions. This allows the system to send reminders that are sensitive to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using, for example, an LLM, or not using an LLM. For example, the reminder unit can input the user's emotion data into an LLM and have the LLM adjust the content and timing of the reminders.

[0104] The mental health support system may further include a data integration unit. The data integration unit can integrate and centrally manage mental health-related data collected from different data sources. For example, it can integrate data such as industrial physician interview records, counseling records, and stress check records. The data integration unit can also eliminate data duplication and maintain data integrity. This allows for efficient management of data collected from different data sources. Some or all of the above-described processes in the data integration unit may be performed using, for example, a Data Lifecycle Management (LLM), or without an LLM. For example, the data integration unit can input the collected data into an LLM and have the LLM perform the data integration.

[0105] The mental health support system may further include a data visualization unit. The data visualization unit can visually display the collected mental health-related data, enabling users to intuitively understand the data. For example, fluctuations in stress levels can be displayed in a graph. The data visualization unit can also make it easier for users to grasp their own mental health status through data visualization. This allows users to intuitively understand the data by visually displaying the collected data. Some or all of the above processing in the data visualization unit may be performed using, for example, an LLM, or not using an LLM. For example, the data visualization unit can input the collected data into an LLM and have the LLM perform the data visualization.

[0106] The mental health support system may further include a data backup unit. The data backup unit can periodically back up the collected mental health-related data to prevent data loss. For example, data can be backed up regularly on a daily basis. The data backup unit can maintain the integrity of the backup data and restore it as needed. This prevents the loss of collected data and ensures data security. Some or all of the above-described processes in the data backup unit may be performed using, for example, an LLM, or without an LLM. For example, the data backup unit can input the collected data into an LLM and have the LLM perform the data backup.

[0107] The mental health support system may further include a data security unit. The data security unit can ensure the security of collected mental health-related data and prevent unauthorized access and data breaches. For example, it can encrypt data and implement access control. The data security unit can also set and enforce data security policies. This ensures the security of collected data and prevents unauthorized access and data breaches. Some or all of the above-described processes in the data security unit may be performed using, for example, a Data Locksmith (LLM), or not. For example, the data security unit can input collected data into an LLM and have the LLM perform data security.

[0108] The mental health support system may further include a data archiving unit. The data archiving unit can store collected mental health-related data for extended periods and make it accessible as needed. For example, it can archive data from the past several years. The data archiving unit can also maintain the integrity of the archived data and make it easier to search for data as needed. This allows for the long-term storage and accessibility of collected data. Some or all of the above-described processes in the data archiving unit may be performed, for example, using an LLM (Language-Limited Management) or not. For example, the data archiving unit can input collected data into an LLM and have the LLM perform the data archiving.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects data. The collection unit collects mental health-related data, such as industrial physician consultation records, counseling records, and stress check records. The collection unit can collect mental health-related data, such as industrial physician consultation records, counseling records, and stress check records, managed by each company. The collection unit can collect data stored in digital format, for example. The collection unit can also scan paper records, convert them into digital data, and collect them, for example. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, LLM. The analysis unit can analyze the collected data using, for example, statistical analysis or machine learning algorithms. The analysis unit can also analyze the collected data using, for example, natural language processing techniques. Step 3: The Identification Unit identifies patterns based on the data analyzed by the Analysis Unit. For example, the Identification Unit identifies patterns from data analyzed using LLM. For example, the Identification Unit can identify the impact of specific work environments or lifestyles on mental health. For example, the Identification Unit can also identify patterns from the analyzed data that may have an impact on mental health. Step 4: The service provider provides preventive learning based on patterns identified by the specific service provider. The service provider provides preventive learning based on patterns identified using, for example, LLM. The service provider can provide, for example, training to enhance resilience or advice on stress management. The service provider can also provide preventive learning that maximizes support for each individual's mental health based on anonymized information accumulated by each company.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects mental health-related data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12 and analyzes the collected data using LLM. The identification unit is implemented in the identification unit 290 of the data processing unit 12 and identifies patterns that affect mental health from the analyzed data. The provision unit is implemented in the control unit 46A of the smart device 14 and provides preventive learning based on the identified patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects mental health-related data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using LLM. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies patterns that affect mental health from the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides preventive learning based on the identified patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects mental health-related data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using LLM. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies patterns that affect mental health from the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides preventive learning based on the identified patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects mental health-related data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using LLM. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies patterns that affect mental health from the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides preventive learning based on the identified patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A specific unit identifies patterns based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides preventive learning based on the pattern identified by the specified unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect mental health-related data such as occupational physician consultation records, counseling records, and stress check records. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify patterns that may be influencing mental health. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, Identify the impact of specific work environments and lifestyles on mental health. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide training to enhance resilience and advice on stress management. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Based on anonymized data accumulated by each company, we provide preventative learning that maximizes support for each individual's mental health. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes the user's past mental health history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current work situation and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) 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 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) 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 19) The specified part is, It estimates the user's emotions and adjusts certain criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, At specific times, improve specific accuracy by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, When identifying data, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, It estimates the user's emotions and adjusts the order in which specific results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, When identifying data, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, At specific times, we refer to relevant literature for data to improve specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the content of preventative learning provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, At the time of delivery, the system analyzes the user's past mental health history to select the most suitable preventative learning program. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, At the time of delivery, the preventative learning methods will be customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of preventative learning to be provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the system selects the most appropriate preventative learning program, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest preventative learning methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A specific unit identifies patterns based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides preventive learning based on the pattern identified by the specified unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect mental health-related data such as occupational physician consultation records, counseling records, and stress check records. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify patterns that may be influencing mental health. The system according to feature 1.

4. The specified part is, Identify the impact of specific work environments and lifestyles on mental health. The system according to feature 1.

5. The aforementioned supply unit is, We provide training to enhance resilience and advice on stress management. The system according to feature 1.

6. The aforementioned supply unit is, Based on anonymized data accumulated by each company, we provide preventative learning that maximizes support for each individual's mental health. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes the user's past mental health history to select the most suitable collection method. The system according to feature 1.

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