system

Abstract

The system according to this embodiment aims to provide individually optimized life balance improvement measures in real time. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes life balance improvement measures based on the analysis results obtained by the analysis unit. The provision unit provides the improvement measures proposed by the proposal unit in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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, providing individually optimized life balance improvement measures in real time has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individually optimized life balance improvement measures in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes life balance improvement measures based on the analysis results obtained by the analysis unit. The provision unit provides the improvement measures proposed by the proposal unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can provide individually optimized life balance improvement measures in real time. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 AI ​​service according to an embodiment of the present invention is a system that collects and analyzes anonymized employee health check results and work time data, and proposes individually optimized life balance improvement measures. This system collects anonymized employee health check results and work time data, and the AI ​​analyzes it to propose individually optimized life balance improvement measures. For example, the AI ​​service collects anonymized employee health check results and work time data. In this process, IoT devices and wearable devices are used to monitor the employee's health status and work status in real time. For example, data such as heart rate, sleep time, and work time are collected. This allows for a detailed understanding of the employee's health status and work status. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​analyzes the employee's health status and work status and proposes individually optimized life balance improvement measures. For example, it suggests that employees who have been working long hours take appropriate rest, and provides advice to improve the quality of sleep for employees who have been suffering from sleep deprivation. This allows for the provision of personalized improvement measures tailored to each employee. Furthermore, the proposed life balance improvement measures are provided in real time as rest and recovery plans that meet the user's individual needs. For example, based on data analyzed by AI, it suggests appropriate rest periods and exercise timings, providing them in a user-friendly format. This supports busy business professionals in adopting balanced health habits. This system allows for a detailed understanding of employees' health and work conditions, enabling the proposal of individually optimized life balance improvement measures. This is expected to improve employee health and work efficiency, contributing to increased productivity across the entire company. The AI ​​service collects and analyzes anonymized employee health check results and work time data, and proposes individually optimized life balance improvement measures.

[0029] The AI ​​service according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. For example, the data collection unit collects anonymized data such as employees' health checkup results and working hours. The data collection unit can monitor employees' health status and working conditions in real time using IoT devices or wearable devices. For example, the data collection unit collects data such as heart rate, sleep duration, and working hours. The data collection unit can also use technologies to anonymize the data. For example, when collecting data, the data collection unit uses technologies to anonymize the data so that individuals cannot be identified. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze employees' health status and working conditions based on the collected data. For example, the analysis unit suggests that employees who have been working long hours take appropriate rest, and provides advice to employees who have been suffering from sleep deprivation to improve the quality of their sleep. The proposal unit proposes life balance improvement measures based on the analysis results obtained by the analysis unit. For example, the proposal unit uses AI to propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department suggests appropriate rest periods and exercise timings, providing them in a way that is easy for users to implement. The delivery department provides the improvement measures proposed by the proposal department in real time. The delivery department, for example, uses AI to provide the proposed life balance improvement measures in real time. For example, the delivery department provides rest and recovery plans tailored to the user's individual needs in real time. As a result, the AI ​​service according to this embodiment can collect and analyze anonymized employee health check results and working time data, propose individually optimized life balance improvement measures, and provide them in real time.

[0030] The data collection unit collects data. For example, it collects anonymized employee health check results and work time data. Specifically, the data collection unit electronically obtains the results of health checks that employees regularly undergo and stores them in a database. In addition, it collects work time data such as time clock data when employees enter and leave the office, and login and logout times when employees work remotely. Furthermore, the data collection unit can monitor employees' health status and work status in real time using IoT devices and wearable devices. For example, it collects data such as heart rate, steps, calories burned, and sleep time from smartwatches and fitness trackers worn by employees. These devices transmit data to the data collection unit via Bluetooth® or Wi-Fi and store it in a central database. The data collection unit can use technologies to anonymize data. For example, it can remove personally identifiable information during data collection and assign a unique identifier instead to prevent individuals from being identified. It also uses data encryption technology to ensure data security. This allows the data collection unit to efficiently collect necessary data while protecting employee privacy. Furthermore, the data collection unit can flexibly adapt to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, the frequency of data collection can be increased to monitor changes in health status in detail over a specific period. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze employees' health and work performance based on the collected data. Specifically, the AI ​​uses machine learning algorithms to analyze data such as heart rate, sleep duration, and working hours to evaluate employees' health and stress levels. For example, it can suggest that employees who are working long hours take appropriate rest, and provide advice to employees who are experiencing sleep deprivation to improve their sleep quality. Based on past data and statistical information, the AI ​​can also analyze trends in employees' health and work performance and predict future risks. For example, based on past data, it can issue an early warning if a particular employee is at high risk of health problems due to overwork or stress. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early intervention. For example, if heart rate increases sharply or sleep duration becomes extremely short, it can immediately issue a warning and prompt appropriate action. This allows the analysis unit to quickly and accurately analyze collected data and understand employees' health and work performance in real time. Furthermore, the analysis department can provide information useful for improving employee health management and work conditions by conducting long-term risk assessments and trend analyses.

[0032] The Proposal Department proposes work-life balance improvement measures based on the analysis results obtained by the Analysis Department. For example, the Proposal Department uses AI to propose individually optimized work-life balance improvement measures based on the analyzed data. Specifically, the AI ​​considers the health status and work situation of employees and proposes appropriate rest periods and exercise timings. For example, it suggests that employees who have been working long hours take regular breaks, and recommends that employees who have been suffering from sleep deprivation adopt a habit of going to bed and waking up early. It also advises employees who are not getting enough exercise to incorporate moderate exercise into their routines. The Proposal Department provides these suggestions in a way that is easy for users to implement. For example, it provides specific action plans and reminders through smartphone apps and web portals. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of users implementing the suggested action plans and revise the suggestions based on that data. In this way, the Proposal Department can provide individually optimized work-life balance improvement measures to users and support improvements in employees' health status and work situation. Furthermore, the Proposal Department can combine multiple suggestions to provide comprehensive work-life balance improvement measures. For example, we propose a comprehensive plan that combines appropriate rest periods, exercise, and nutritional management to improve employee health and productivity.

[0033] The service provider will deliver the improvement measures proposed by the proposal provider in real time. For example, the service provider will use AI to deliver proposed work-life balance improvement measures in real time. Specifically, the service provider will provide rest and recovery plans tailored to the individual needs of each user in real time. For example, it will notify users of appropriate rest times and exercise timings and set reminders via a smartphone app. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, it will provide feedback on the results of users implementing the proposed plans and revise the services based on that data. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it will use not only smartphone notifications but also voice calls, SMS, and email to ensure that important information is delivered reliably. As a result, the service provider can quickly and reliably deliver work-life balance improvement measures to users and support improvements in employees' health and work conditions. Furthermore, the service provider can flexibly adjust the services provided according to the user's lifestyle and work situation. For example, employees who frequently work night shifts can receive advice on nighttime rest and nutrition management, while employees who work remotely can receive suggestions on exercise and stress management at home. This allows the service provider to offer optimal work-life balance solutions tailored to individual user needs, thereby improving employee health and productivity.

[0034] The data collection unit can collect data using IoT devices and wearable devices. For example, the data collection unit can use IoT devices and wearable devices to monitor employees' health status and work performance in real time. For example, the data collection unit can collect heart rate and sleep duration using smartwatches and fitness trackers. The data collection unit can also use heart rate monitors and activity trackers to gain a detailed understanding of employees' health status. This makes it possible to collect detailed data by using IoT devices and wearable devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from IoT devices and wearable devices into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze employees' health status and work performance based on the collected data. For example, the analysis unit can analyze data such as heart rate, sleep duration, and working hours to gain a detailed understanding of employees' health status and work performance. The analysis unit can also use AI to analyze employees' health status and work performance based on the collected data. For example, the analysis unit can use AI to suggest that employees who are working long hours take appropriate rest, and to provide advice to employees who are suffering from sleep deprivation on how to improve the quality of their sleep. In this way, by analyzing health status and work performance based on the collected data, it is possible to propose individually optimized improvement measures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0036] The proposal department can propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department can propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department can suggest appropriate rest times and exercise timings and provide them in a way that is easy for users to implement. The proposal department can also use AI to propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department can use AI to provide personalized improvement measures tailored to each employee. This allows for the improvement of users' health habits by proposing individually optimized life balance improvement measures. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analyzed data into a generating AI and have the generating AI execute the proposal of improvement measures.

[0037] The service provider can provide proposed life balance improvement measures in real time. For example, the service provider can provide proposed life balance improvement measures in real time. For example, the service provider can provide rest and recovery plans tailored to the user's individual needs in real time. The service provider can also use AI to provide proposed life balance improvement measures in real time. For example, the service provider can use AI to suggest appropriate rest times and exercise timings and provide them in a way that is easy for the user to implement. This allows the user to implement the improvement measures immediately by providing them in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input proposed improvement measures into a generating AI and have the generating AI perform the real-time provision.

[0038] The data collection unit can analyze the user's past health checkup results and work time data to select the optimal data collection method. For example, the data collection unit can analyze the user's past health checkup results and work time data to select the optimal data collection method. For example, the data collection unit can collect data focusing on specific health indicators from the user's past health checkup results. The data collection unit can also collect data outside of work hours based on the user's work time data. Furthermore, the data collection unit can analyze the user's past data collection history to select the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health checkup results and work time data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current living situation and work activities during data collection. For example, if the user is on a business trip, the data collection unit will prioritize data collection at the business trip location. The data collection unit can also minimize data collection if the user is busy with a project deadline. Furthermore, if the user is on vacation, the data collection unit can prioritize data collection that reflects their relaxed state at their vacation destination. This allows for the collection of highly relevant data by filtering data based on the user's living situation and work activities. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's current living situation and work activities into a generating AI and have the generating AI perform the data filtering.

[0040] 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, 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 the office, the data collection unit can prioritize the collection of work time data. If the user is at home, the data collection unit can also prioritize the collection of relaxation data. If the user is out, the data collection unit can also prioritize the collection of health data during travel. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media indicating they are stressed, the data collection unit can collect stress-related data. The data collection unit can also collect data on relaxation levels if a user posts on social media indicating relaxation. Furthermore, if a user posts on social media regarding health, the data collection unit can collect data on their health status. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0042] 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 analyze data related to important health indicators in detail. It can also simplify the analysis of general health data. Furthermore, the analysis unit can analyze user work time data in detail only for aspects related to work efficiency. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to evaluate health status to health data. It can also apply an algorithm to evaluate work efficiency to work time data. Furthermore, it can apply an algorithm to evaluate sleep quality to sleep data. By applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also analyze past data according to its importance. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data 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 AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal unit can adjust the level of detail of a proposal based on the importance of the improvement measures when making a proposal. For example, the proposal unit can propose important improvement measures in detail. It can also propose general improvement measures in a simplified manner. Furthermore, it can propose improvement measures that have a significant impact on the user's health in detail. In this way, by adjusting the level of detail of the proposal based on the importance of the improvement measures, important improvement measures can be proposed in detail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the improvement measures into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the improvement measure when making a proposal. For example, the proposal unit can apply an algorithm that evaluates health status to health improvement measures. It can also apply an algorithm that evaluates work efficiency to work time improvement measures. It can also apply an algorithm that evaluates sleep quality to sleep improvement measures. By applying different proposal algorithms depending on the category of the improvement measure, it can provide appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the improvement measure into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the timing of the submission of improvement measures. For example, the proposal department will prioritize proposals for improvement measures that are of high urgency. The proposal department can also postpone general improvement measures. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the timing of their submission. This allows for prioritizing proposals for improvement measures that are of high urgency by determining the priority of proposals based on the timing of their submission. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of the improvement measures' submission into a generating AI and have the generating AI determine the priority of the proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the improvement measures when making a proposal. For example, the proposal unit will prioritize proposing improvement measures that are highly relevant. The proposal unit can also postpone proposing improvement measures that are less relevant. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the improvement measures. This allows for prioritizing the proposal of highly relevant improvement measures by adjusting the order of proposals based on their relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the improvement measures into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0050] The service delivery unit can analyze the user's past behavior history at the time of delivery to select the optimal delivery method. For example, the service delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, the service delivery unit can prioritize selecting delivery methods that the user has preferred to use in the past. The service delivery unit can also select the most effective delivery method from the user's past behavior history. Furthermore, the service delivery unit can analyze the user's past behavior history and customize the delivery method. This allows the service delivery unit to select the optimal delivery method by analyzing past behavior history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's past behavior history into a generating AI and have the generating AI select the optimal delivery method.

[0051] The service provider can customize the means of service delivery based on the user's current living situation at the time of delivery. For example, if the user is on a business trip, the service provider can provide relaxation methods at the destination. If the user is at home, the service provider can also provide health improvement measures that can be implemented at home. If the user is at the office, the service provider can also provide stress reduction measures that can be implemented at the office. By customizing the means of service delivery based on the current living situation, appropriate improvement measures can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current living situation into a generating AI and have the generating AI perform the customization of the means of service delivery.

[0052] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in the office, the service provider can provide health improvement measures that can be practiced in the office. If the user is at home, the service provider can also provide relaxation methods that can be practiced at home. If the user is out, the service provider can also provide stress reduction measures that can be practiced while out. In this way, the optimal delivery method can be selected by taking geographical location information into account. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0053] The service provider can analyze the user's social media activity and propose a means of provision at the time of provision. For example, if the service provider is posting stressful content on social media, it can offer stress reduction measures. If the service provider is posting relaxing content on social media, it can also offer relaxation methods. If the service provider is posting health-related content on social media, it can also offer health improvement measures. In this way, by analyzing social media activity, an appropriate means of provision can be proposed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI propose a means of provision.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze the user's past health checkup results and work time data to select the optimal data collection method. For example, it can collect data focusing on specific health indicators from the user's past health checkup results. It can also collect data outside of work hours based on the user's work time data. Furthermore, it can analyze the user's past data collection history to select the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health checkup results and work time data into a generating AI and have the generating AI select the optimal data collection method.

[0056] The data collection unit can filter data based on the user's current living situation and work activities during data collection. For example, if the user is on a business trip, data collection at the business trip location will be prioritized. Also, if the user is busy before a project deadline, data collection can be minimized. Furthermore, if the user is on vacation, data collection can be prioritized based on their relaxed state at their vacation location. By filtering data based on the user's living situation and work activities, highly relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current living situation and work activities into a generating AI and have the generating AI perform data filtering.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, data related to important health indicators can be analyzed in detail. General health data can be analyzed in a simplified manner. Furthermore, user work time data can be analyzed in detail for parts related to work efficiency. In this way, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0058] The proposal unit can adjust the level of detail in a proposal based on the importance of the improvement measures. For example, important improvement measures can be proposed in detail. General improvement measures can be proposed in a simplified manner. Furthermore, improvement measures that have a significant impact on the user's health can also be proposed in detail. In this way, by adjusting the level of detail in a proposal based on the importance of the improvement measures, important improvement measures can be proposed in detail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the improvement measures into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0059] The delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, it can prioritize selecting delivery methods that the user has preferred in the past. It can also select the most effective delivery method based on the user's past behavior history. Furthermore, it can customize the delivery method by analyzing the user's past behavior history. This allows for the selection of the optimal delivery method by analyzing past behavior history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past behavior history into a generating AI and have the generating AI select the optimal delivery method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data. For example, it collects anonymized employee health check results and work time data. The data collection unit can monitor employees' health status and work status in real time using IoT devices and wearable devices. For example, it collects data such as heart rate, sleep time, and work time. The data collection unit can also use technologies to anonymize the data. For example, it uses technologies to anonymize data so that individuals cannot be identified when collecting it. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it uses AI to analyze employees' health status and work conditions based on the collected data. For employees who are working long hours, it suggests taking appropriate rest, and for employees who are suffering from chronic sleep deprivation, it provides advice on how to improve the quality of their sleep. Step 3: The proposal unit proposes lifestyle improvement measures based on the analysis results obtained by the analysis unit. For example, using AI, it proposes individually optimized lifestyle improvement measures based on the analyzed data. It suggests appropriate rest times and exercise timings and provides them in a way that is easy for the user to implement. Step 4: The provision department provides the improvement measures proposed by the proposal department in real time. For example, AI is used to provide proposed life balance improvement measures in real time. Rest and recovery plans tailored to the user's individual needs are provided in real time.

[0062] (Example of form 2) The AI ​​service according to an embodiment of the present invention is a system that collects and analyzes anonymized employee health check results and work time data, and proposes individually optimized life balance improvement measures. This system collects anonymized employee health check results and work time data, and the AI ​​analyzes it to propose individually optimized life balance improvement measures. For example, the AI ​​service collects anonymized employee health check results and work time data. In this process, IoT devices and wearable devices are used to monitor the employee's health status and work status in real time. For example, data such as heart rate, sleep time, and work time are collected. This allows for a detailed understanding of the employee's health status and work status. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​analyzes the employee's health status and work status and proposes individually optimized life balance improvement measures. For example, it suggests that employees who have been working long hours take appropriate rest, and provides advice to improve the quality of sleep for employees who have been suffering from sleep deprivation. This allows for the provision of personalized improvement measures tailored to each employee. Furthermore, the proposed life balance improvement measures are provided in real time as rest and recovery plans that meet the user's individual needs. For example, based on data analyzed by AI, it suggests appropriate rest periods and exercise timings, providing them in a user-friendly format. This supports busy business professionals in adopting balanced health habits. This system allows for a detailed understanding of employees' health and work conditions, enabling the proposal of individually optimized life balance improvement measures. This is expected to improve employee health and work efficiency, contributing to increased productivity across the entire company. The AI ​​service collects and analyzes anonymized employee health check results and work time data, and proposes individually optimized life balance improvement measures.

[0063] The AI ​​service according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. For example, the data collection unit collects anonymized data such as employees' health checkup results and working hours. The data collection unit can monitor employees' health status and working conditions in real time using IoT devices or wearable devices. For example, the data collection unit collects data such as heart rate, sleep duration, and working hours. The data collection unit can also use technologies to anonymize the data. For example, when collecting data, the data collection unit uses technologies to anonymize the data so that individuals cannot be identified. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze employees' health status and working conditions based on the collected data. For example, the analysis unit suggests that employees who have been working long hours take appropriate rest, and provides advice to employees who have been suffering from sleep deprivation to improve the quality of their sleep. The proposal unit proposes life balance improvement measures based on the analysis results obtained by the analysis unit. For example, the proposal unit uses AI to propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department suggests appropriate rest periods and exercise timings, providing them in a way that is easy for users to implement. The delivery department provides the improvement measures proposed by the proposal department in real time. The delivery department, for example, uses AI to provide the proposed life balance improvement measures in real time. For example, the delivery department provides rest and recovery plans tailored to the user's individual needs in real time. As a result, the AI ​​service according to this embodiment can collect and analyze anonymized employee health check results and working time data, propose individually optimized life balance improvement measures, and provide them in real time.

[0064] The data collection unit collects data. For example, it collects anonymized employee health check results and work time data. Specifically, the data collection unit electronically obtains the results of health checks that employees regularly undergo and stores them in a database. In addition, it collects work time data such as time clock data when employees enter and leave the office, and login and logout times when employees work remotely. Furthermore, the data collection unit can monitor employees' health status and work status in real time using IoT devices and wearable devices. For example, it collects data such as heart rate, steps, calories burned, and sleep time from smartwatches and fitness trackers worn by employees. These devices transmit data to the data collection unit via Bluetooth or Wi-Fi and store it in a central database. The data collection unit can use technologies to anonymize data. For example, it can remove personally identifiable information during data collection and assign a unique identifier instead to prevent individuals from being identified. It also uses data encryption technology to ensure data security. This allows the data collection unit to efficiently collect necessary data while protecting employee privacy. Furthermore, the data collection unit can flexibly adapt to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, the frequency of data collection can be increased to monitor changes in health status in detail over a specific period. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0065] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze employees' health and work performance based on the collected data. Specifically, the AI ​​uses machine learning algorithms to analyze data such as heart rate, sleep duration, and working hours to evaluate employees' health and stress levels. For example, it can suggest that employees who are working long hours take appropriate rest, and provide advice to employees who are experiencing sleep deprivation to improve their sleep quality. Based on past data and statistical information, the AI ​​can also analyze trends in employees' health and work performance and predict future risks. For example, based on past data, it can issue an early warning if a particular employee is at high risk of health problems due to overwork or stress. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early intervention. For example, if heart rate increases sharply or sleep duration becomes extremely short, it can immediately issue a warning and prompt appropriate action. This allows the analysis unit to quickly and accurately analyze collected data and understand employees' health and work performance in real time. Furthermore, the analysis department can provide information useful for improving employee health management and work conditions by conducting long-term risk assessments and trend analyses.

[0066] The Proposal Department proposes work-life balance improvement measures based on the analysis results obtained by the Analysis Department. For example, the Proposal Department uses AI to propose individually optimized work-life balance improvement measures based on the analyzed data. Specifically, the AI ​​considers the health status and work situation of employees and proposes appropriate rest periods and exercise timings. For example, it suggests that employees who have been working long hours take regular breaks, and recommends that employees who have been suffering from sleep deprivation adopt a habit of going to bed and waking up early. It also advises employees who are not getting enough exercise to incorporate moderate exercise into their routines. The Proposal Department provides these suggestions in a way that is easy for users to implement. For example, it provides specific action plans and reminders through smartphone apps and web portals. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of users implementing the suggested action plans and revise the suggestions based on that data. In this way, the Proposal Department can provide individually optimized work-life balance improvement measures to users and support improvements in employees' health status and work situation. Furthermore, the Proposal Department can combine multiple suggestions to provide comprehensive work-life balance improvement measures. For example, we propose a comprehensive plan that combines appropriate rest periods, exercise, and nutritional management to improve employee health and productivity.

[0067] The service provider will deliver the improvement measures proposed by the proposal provider in real time. For example, the service provider will use AI to deliver proposed work-life balance improvement measures in real time. Specifically, the service provider will provide rest and recovery plans tailored to the individual needs of each user in real time. For example, it will notify users of appropriate rest times and exercise timings and set reminders via a smartphone app. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, it will provide feedback on the results of users implementing the proposed plans and revise the services based on that data. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it will use not only smartphone notifications but also voice calls, SMS, and email to ensure that important information is delivered reliably. As a result, the service provider can quickly and reliably deliver work-life balance improvement measures to users and support improvements in employees' health and work conditions. Furthermore, the service provider can flexibly adjust the services provided according to the user's lifestyle and work situation. For example, employees who frequently work night shifts can receive advice on nighttime rest and nutrition management, while employees who work remotely can receive suggestions on exercise and stress management at home. This allows the service provider to offer optimal work-life balance solutions tailored to individual user needs, thereby improving employee health and productivity.

[0068] The data collection unit can collect data using IoT devices and wearable devices. For example, the data collection unit can use IoT devices and wearable devices to monitor employees' health status and work performance in real time. For example, the data collection unit can collect heart rate and sleep duration using smartwatches and fitness trackers. The data collection unit can also use heart rate monitors and activity trackers to gain a detailed understanding of employees' health status. This makes it possible to collect detailed data by using IoT devices and wearable devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from IoT devices and wearable devices into a generating AI and have the generating AI perform data analysis.

[0069] The analysis unit can analyze employees' health status and work performance based on the collected data. For example, the analysis unit can analyze data such as heart rate, sleep duration, and working hours to gain a detailed understanding of employees' health status and work performance. The analysis unit can also use AI to analyze employees' health status and work performance based on the collected data. For example, the analysis unit can use AI to suggest that employees who are working long hours take appropriate rest, and to provide advice to employees who are suffering from sleep deprivation on how to improve the quality of their sleep. In this way, by analyzing health status and work performance based on the collected data, it is possible to propose individually optimized improvement measures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0070] The proposal department can propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department can propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department can suggest appropriate rest times and exercise timings and provide them in a way that is easy for users to implement. The proposal department can also use AI to propose individually optimized life balance improvement measures based on the analyzed data. For example, the proposal department can use AI to provide personalized improvement measures tailored to each employee. This allows for the improvement of users' health habits by proposing individually optimized life balance improvement measures. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analyzed data into a generating AI and have the generating AI execute the proposal of improvement measures.

[0071] The service provider can provide proposed life balance improvement measures in real time. For example, the service provider can provide proposed life balance improvement measures in real time. For example, the service provider can provide rest and recovery plans tailored to the user's individual needs in real time. The service provider can also use AI to provide proposed life balance improvement measures in real time. For example, the service provider can use AI to suggest appropriate rest times and exercise timings and provide them in a way that is easy for the user to implement. This allows the user to implement the improvement measures immediately by providing them in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input proposed improvement measures into a generating AI and have the generating AI perform the real-time provision.

[0072] 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 burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of collection to collect more detailed data. Furthermore, if the user is busy, the data collection unit can perform data collection in a shorter time to avoid disrupting their work. This reduces the burden by adjusting the timing of data collection according 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 processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past health checkup results and work time data to select the optimal data collection method. For example, the data collection unit can analyze the user's past health checkup results and work time data to select the optimal data collection method. For example, the data collection unit can collect data focusing on specific health indicators from the user's past health checkup results. The data collection unit can also collect data outside of work hours based on the user's work time data. Furthermore, the data collection unit can analyze the user's past data collection history to select the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health checkup results and work time data into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current living situation and work activities during data collection. For example, if the user is on a business trip, the data collection unit will prioritize data collection at the business trip location. The data collection unit can also minimize data collection if the user is busy with a project deadline. Furthermore, if the user is on vacation, the data collection unit can prioritize data collection that reflects their relaxed state at their vacation destination. This allows for the collection of highly relevant data by filtering data based on the user's living situation and work activities. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's current living situation and work activities into a generating AI and have the generating AI perform the data filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. If the user is relaxed, the data collection unit may also prioritize collecting data related to their health status. If the user is tired, the data collection unit may also prioritize collecting sleep data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0076] 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, 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 the office, the data collection unit can prioritize the collection of work time data. If the user is at home, the data collection unit can also prioritize the collection of relaxation data. If the user is out, the data collection unit can also prioritize the collection of health data during travel. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media indicating they are stressed, the data collection unit can collect stress-related data. The data collection unit can also collect data on relaxation levels if a user posts on social media indicating relaxation. Furthermore, if a user posts on social media regarding health, the data collection unit can collect data on their health status. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0078] 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 can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide an easy-to-understand analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0079] 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 analyze data related to important health indicators in detail. It can also simplify the analysis of general health data. Furthermore, the analysis unit can analyze user work time data in detail only for aspects related to work efficiency. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to evaluate health status to health data. It can also apply an algorithm to evaluate work efficiency to work time data. Furthermore, it can apply an algorithm to evaluate sleep quality to sleep data. By applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] 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. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, an analysis of appropriate length can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also analyze past data according to its importance. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data 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 AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, it is possible to provide highly visual suggestions. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0085] The proposal unit can adjust the level of detail of a proposal based on the importance of the improvement measures when making a proposal. For example, the proposal unit can propose important improvement measures in detail. It can also propose general improvement measures in a simplified manner. Furthermore, it can propose improvement measures that have a significant impact on the user's health in detail. In this way, by adjusting the level of detail of the proposal based on the importance of the improvement measures, important improvement measures can be proposed in detail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the improvement measures into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms depending on the category of the improvement measure when making a proposal. For example, the proposal unit can apply an algorithm that evaluates health status to health improvement measures. It can also apply an algorithm that evaluates work efficiency to work time improvement measures. It can also apply an algorithm that evaluates sleep quality to sleep improvement measures. By applying different proposal algorithms depending on the category of the improvement measure, it can provide appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the improvement measure into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, the suggestion unit can provide suggestions of appropriate length. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0088] The proposal department can determine the priority of proposals based on the timing of the submission of improvement measures. For example, the proposal department will prioritize proposals for improvement measures that are of high urgency. The proposal department can also postpone general improvement measures. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the timing of their submission. This allows for prioritizing proposals for improvement measures that are of high urgency by determining the priority of proposals based on the timing of their submission. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of the improvement measures' submission into a generating AI and have the generating AI determine the priority of the proposals.

[0089] The proposal unit can adjust the order of proposals based on the relevance of the improvement measures when making a proposal. For example, the proposal unit will prioritize proposing improvement measures that are highly relevant. The proposal unit can also postpone proposing improvement measures that are less relevant. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the improvement measures. This allows for prioritizing the proposal of highly relevant improvement measures by adjusting the order of proposals based on their relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the improvement measures into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0090] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is stressed, the delivery unit will deliver in a simple and visually clear manner. If the user is relaxed, the delivery unit may also deliver in a way that includes detailed information. If the user is in a hurry, the delivery unit may also deliver in a concise manner. By adjusting the delivery method according to the user's emotions, the delivery can be made in a visually clear manner. 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the delivery method.

[0091] The service delivery unit can analyze the user's past behavior history at the time of delivery to select the optimal delivery method. For example, the service delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, the service delivery unit can prioritize selecting delivery methods that the user has preferred to use in the past. The service delivery unit can also select the most effective delivery method from the user's past behavior history. Furthermore, the service delivery unit can analyze the user's past behavior history and customize the delivery method. This allows the service delivery unit to select the optimal delivery method by analyzing past behavior history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's past behavior history into a generating AI and have the generating AI select the optimal delivery method.

[0092] The service provider can customize the means of service delivery based on the user's current living situation at the time of delivery. For example, if the user is on a business trip, the service provider can provide relaxation methods at the destination. If the user is at home, the service provider can also provide health improvement measures that can be implemented at home. If the user is at the office, the service provider can also provide stress reduction measures that can be implemented at the office. By customizing the means of service delivery based on the current living situation, appropriate improvement measures can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current living situation into a generating AI and have the generating AI perform the customization of the means of service delivery.

[0093] The service provider can estimate the user's emotions and determine the priority of services based on the estimated emotions. For example, if the user is feeling stressed, the service provider may prioritize providing stress reduction measures. If the user is relaxed, the service provider may also prioritize providing health maintenance measures. If the user is tired, the service provider may also prioritize providing rest methods. By determining the priority of services according to the user's emotions, important improvement measures can be prioritized. 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 AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of services.

[0094] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in the office, the service provider can provide health improvement measures that can be practiced in the office. If the user is at home, the service provider can also provide relaxation methods that can be practiced at home. If the user is out, the service provider can also provide stress reduction measures that can be practiced while out. In this way, the optimal delivery method can be selected by taking geographical location information into account. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0095] The service provider can analyze the user's social media activity and propose a means of provision at the time of provision. For example, if the service provider is posting stressful content on social media, it can offer stress reduction measures. If the service provider is posting relaxing content on social media, it can also offer relaxation methods. If the service provider is posting health-related content on social media, it can also offer health improvement measures. In this way, by analyzing social media activity, an appropriate means of provision can be proposed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI propose a means of provision.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] 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 frequency of data collection can be reduced to alleviate the burden. Conversely, if the user is relaxed, the collection frequency can be increased to collect more detailed data. Furthermore, if the user is busy, data collection can be completed in a shorter time to avoid disrupting their work. In this way, the burden can be reduced by adjusting the timing of data collection according 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 processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0098] 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, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide an easy-to-understand analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0099] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide simple and highly visible suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, highly visible suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0100] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is stressed, the delivery method can be simple and easy to understand. If the user is relaxed, the delivery method can include detailed information. Furthermore, if the user is in a hurry, the delivery method can be concise and to the point. By adjusting the delivery method according to the user's emotions, the delivery method can be made easy 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI adjust the delivery method.

[0101] The service provider can estimate the user's emotions and determine the priority of services based on the estimated emotions. For example, if the user is stressed, stress reduction measures can be prioritized. If the user is relaxed, health maintenance measures can be prioritized. Furthermore, if the user is tired, rest methods can be prioritized. By prioritizing services according to the user's emotions, important improvement measures can be provided preferentially. 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of services.

[0102] The data collection unit can analyze the user's past health checkup results and work time data to select the optimal data collection method. For example, it can collect data focusing on specific health indicators from the user's past health checkup results. It can also collect data outside of work hours based on the user's work time data. Furthermore, it can analyze the user's past data collection history to select the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health checkup results and work time data into a generating AI and have the generating AI select the optimal data collection method.

[0103] The data collection unit can filter data based on the user's current living situation and work activities during data collection. For example, if the user is on a business trip, data collection at the business trip location will be prioritized. Also, if the user is busy before a project deadline, data collection can be minimized. Furthermore, if the user is on vacation, data collection can be prioritized based on their relaxed state at their vacation location. By filtering data based on the user's living situation and work activities, highly relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current living situation and work activities into a generating AI and have the generating AI perform data filtering.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, data related to important health indicators can be analyzed in detail. General health data can be analyzed in a simplified manner. Furthermore, user work time data can be analyzed in detail for parts related to work efficiency. In this way, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0105] The proposal unit can adjust the level of detail in a proposal based on the importance of the improvement measures. For example, important improvement measures can be proposed in detail. General improvement measures can be proposed in a simplified manner. Furthermore, improvement measures that have a significant impact on the user's health can also be proposed in detail. In this way, by adjusting the level of detail in a proposal based on the importance of the improvement measures, important improvement measures can be proposed in detail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the improvement measures into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0106] The delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, it can prioritize selecting delivery methods that the user has preferred in the past. It can also select the most effective delivery method based on the user's past behavior history. Furthermore, it can customize the delivery method by analyzing the user's past behavior history. This allows for the selection of the optimal delivery method by analyzing past behavior history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past behavior history into a generating AI and have the generating AI select the optimal delivery method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data. For example, it collects anonymized employee health check results and work time data. The data collection unit can monitor employees' health status and work status in real time using IoT devices and wearable devices. For example, it collects data such as heart rate, sleep time, and work time. The data collection unit can also use technologies to anonymize the data. For example, it uses technologies to anonymize data so that individuals cannot be identified when collecting it. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it uses AI to analyze employees' health status and work conditions based on the collected data. For employees who are working long hours, it suggests taking appropriate rest, and for employees who are suffering from chronic sleep deprivation, it provides advice on how to improve the quality of their sleep. Step 3: The proposal unit proposes lifestyle improvement measures based on the analysis results obtained by the analysis unit. For example, using AI, it proposes individually optimized lifestyle improvement measures based on the analyzed data. It suggests appropriate rest times and exercise timings and provides them in a way that is easy for the user to implement. Step 4: The provision department provides the improvement measures proposed by the proposal department in real time. For example, AI is used to provide proposed life balance improvement measures in real time. Rest and recovery plans tailored to the user's individual needs are provided in real time.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit monitors the health status and work status of employees in real time using IoT devices or wearable devices of the smart device 14, and collects the data anonymized by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and evaluates the health status and work status of employees. The proposal unit proposes work-life balance improvement measures based on the analysis results by the specific processing unit 290 of the data processing unit 12. The provision unit provides the proposed improvement measures in real time by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit monitors the health status and work status of employees in real time using the IoT device or wearable device of the smart glasses 214, and collects the data anonymized by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and evaluates the health status and work status of employees. The proposal unit proposes work-life balance improvement measures based on the analysis results by the specific processing unit 290 of the data processing unit 12. The provision unit provides the proposed improvement measures in real time by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit monitors the health status and work status of employees in real time using IoT devices or wearable devices of the headset terminal 314, and collects the data anonymized by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and evaluates the health status and work status of employees. The proposal unit proposes work-life balance improvement measures based on the analysis results by the specific processing unit 290 of the data processing unit 12. The provision unit provides the proposed improvement measures in real time by the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices or control units is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit monitors the health status and work status of employees in real time using IoT devices and wearable devices of the robot 414, and collects the data anonymized by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and evaluates the health status and work status of employees. The proposal unit proposes work-life balance improvement measures based on the analysis results by the specific processing unit 290 of the data processing unit 12. The provision unit provides the proposed improvement measures in real time by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes measures to improve life balance, The system comprises a provisioning unit that provides the proposed improvement measures in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data using IoT devices and wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we analyze the health status and work performance of our employees. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analyzed data, we propose individually optimized life balance improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Proposed work-life balance improvement measures are provided in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is Analyze users' past health checkup results and work time data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current living situation and work content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, 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 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the improvement measure. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of the submission of improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of the suggestions based on the relevance of the improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, At the time of delivery, the system analyzes the user's past behavior history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes measures to improve life balance, The system comprises a provisioning unit that provides the proposed improvement measures in real time. A system characterized by the following features.

2. The aforementioned collection unit is Collect data using IoT devices and wearable devices. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we analyze the health status and work performance of our employees. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analyzed data, we propose individually optimized life balance improvement measures. The system according to feature 1.

5. The aforementioned supply unit is, Proposed work-life balance improvement measures are provided in real time. The system according to feature 1.

6. 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.

7. The aforementioned collection unit is Analyze users' past health checkup results and work time data to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current living situation and work content. The system according to feature 1.

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

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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