System

Abstract

To provide a system according to an embodiment which makes it more efficient to allocate staff or steps by using biological information of employees.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and an optimization unit. The collection unit tracks biometric information of an employee. The analysis unit evaluates the biometric information collected by the collection unit. The evaluation unit evaluates the correlation between employee performance and sales on the basis of the result of the analysis obtained by the analysis unit. The optimization unit makes it more efficient to allocate staff or steps on the basis of the result of the analysis obtained by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was an issue that it was not possible to fully utilize employees' biometric information to analyze the correlation between performance and sales, and to efficiently allocate personnel and labor hours within the company.

[0005] The system according to the embodiment aims to improve the efficiency of personnel placement and labor allocation within a company by utilizing biometric information of employees. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit tracks biometric information of employees. The analysis unit analyzes the biometric information collected by the collection unit. The analysis unit analyzes the correlation between employee performance and sales based on the analysis results obtained by the analysis unit. The optimization unit improves the efficiency of in-house personnel placement and labor allocation based on the analysis results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can utilize employee biometric information to improve the efficiency of personnel placement and labor allocation within a company. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that uses a device that tracks biometric information to track employees' sleep and pulse states, and analyzes the correlation between employees' performance and sales based on these data. The system includes a collection unit that tracks employees' biometric information, an analysis unit that analyzes the collected biometric information, an analysis unit that analyzes the correlation between employees' performance and sales based on the analysis results, and an optimization unit that optimizes in-house personnel placement and labor allocation based on the analysis results. For example, the device worn by the employee records sleep quality and pulse rate fluctuations in real time. Next, the generation AI analyzes the collected data and analyzes the correlation between employees' performance and sales. For example, it can be determined whether employees with high sleep quality tend to improve their performance and increase their sales. Furthermore, the generation AI optimizes in-house personnel placement and labor allocation based on the analysis results. For example, by assigning less burdensome tasks to employees with low sleep quality and assigning important tasks to employees with high sleep quality, overall business efficiency can be improved. This makes it possible to grasp the health status of employees and improve business efficiency. For example, by taking measures such as having employees with poor quality sleep take more breaks to refresh themselves, it is possible to maximize work efficiency while maintaining the health of employees. In this way, the system can grasp the health status of employees and improve work efficiency.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, an analysis unit, and an optimization unit. The collection unit tracks the employee's biometric information. The employee's biometric information includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The collection unit records the quality of sleep and pulse fluctuations in real time, for example, using a device worn by the employee. For example, the collection unit collects data such as the time when the employee is in deep sleep and the time when the pulse is stable. The analysis unit analyzes the collected biometric information using a generation AI. For example, the analysis unit analyzes the correlation between the employee's performance and sales based on the collected data. For example, the analysis unit can determine whether an employee with high quality sleep tends to improve performance and increase sales. The analysis unit analyzes the correlation between the employee's performance and sales based on the analysis results using the generation AI. For example, the analysis unit can determine whether an employee with high quality sleep tends to improve performance and increase sales. The optimization unit optimizes the internal personnel placement and labor allocation based on the analysis results using the generation AI. For example, the optimization unit can improve overall work efficiency by allocating less burdensome tasks to employees with poor sleep quality and assigning important tasks to employees with good sleep quality. This allows the system according to the embodiment to grasp the health status of employees and improve work efficiency. For example, the optimization unit can maximize work efficiency while maintaining the health of employees by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves.

[0030] The collection unit tracks the employee's biological information. The employee's biological information includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. For example, the collection unit uses a device worn by the employee to record sleep quality and pulse fluctuations in real time. Specifically, the wearable device worn by the employee is equipped with a heart rate sensor, a blood pressure sensor, a body temperature sensor, and the like, and these sensors continuously collect data. The heart rate sensor records heart rate fluctuations in milliseconds, and the blood pressure sensor periodically measures systolic blood pressure and diastolic blood pressure. The body temperature sensor monitors the temperature of the skin surface in real time and detects body temperature fluctuations. These data are transmitted to a central database via Bluetooth (registered trademark) or Wi-Fi, and are managed centrally by the collection unit. Furthermore, in order to record the employee's sleep pattern in detail, the collection unit measures the frequency of turning over and movement using an acceleration sensor and a gyro sensor. This makes it possible to accurately grasp the time the employee is in deep sleep and the time the employee is in light sleep. The data collection department can collect this data in real time and continuously monitor the health status of employees, which allows the department to track employees' biometric information in detail and detect changes in their health status at an early stage.

[0031] The analysis unit uses the generation AI to analyze the collected biometric information. For example, the analysis unit analyzes the correlation between employee performance and sales based on the collected data. Specifically, the generation AI multidimensionally analyzes the collected heart rate, blood pressure, body temperature, sleep data, etc., to clarify the relationship between the employee's health condition and work performance. The generation AI first preprocesses the data and complements outliers and missing values. Next, it extracts features of the data and creates a profile of the health condition of each employee. For example, it evaluates the employee's stress level and fatigue level using heart rate fluctuation patterns and sleep quality indicators. Furthermore, the generation AI integrates these health indicators with work performance data (for example, sales, project progress, task completion rate, etc.) and performs correlation analysis. Specifically, it quantitatively evaluates the relationship between health condition and work performance using regression analysis and clustering methods. For example, it can determine whether employees with high sleep quality tend to improve their performance and increase their sales. Based on these analysis results, the analysis unit can clarify the impact of employees' health conditions on work performance and provide specific guidelines for improvement.

[0032] The analysis department uses the generation AI to further analyze the correlation between employee performance and sales based on the analysis results. Specifically, based on the correlation results obtained by the analysis department, the generation AI creates a detailed performance profile for each employee. For example, the generation AI performs multivariate analysis combining multiple variables to identify under what conditions an employee's health condition most affects performance. For example, it combines heart rate stability, sleep depth, daytime activity level, etc. to identify the factors that most affect employee performance. In addition, the generation AI analyzes data along the time axis to clarify fluctuation patterns of health condition and performance. For example, it can analyze fluctuations in performance during specific times of the day or days of the week and propose optimal work schedules. Based on these detailed analysis results, the analysis department can formulate specific action plans to improve the performance of each employee. For example, it can suggest advice to improve sleep quality and relaxation methods for stress management to specific employees. This allows the analysis department to deeply understand the relationship between employees' health conditions and work performance and provide specific improvement measures according to individual needs.

[0033] The optimization department uses the generation AI to optimize the internal staffing and labor allocation based on the analysis results. Specifically, the generation AI calculates optimal staffing and work allocation based on the health and performance data of employees. For example, overall work efficiency can be improved by assigning less burdensome work to employees with poor sleep quality and assigning important work to employees with good sleep quality. The generation AI first simulates the optimal work allocation for each employee based on the health and performance profile of each employee. Next, based on the simulation results, an optimal staffing plan is created and reflected in the actual work schedule. In addition, the generation AI can dynamically adjust work allocation based on biometric information updated in real time. For example, if the health of a specific employee suddenly deteriorates, the generation AI immediately reviews the work allocation and redistributes work to other employees. The generation AI can also formulate long-term staffing strategies based on past data. For example, it is possible to maximize work efficiency by predicting changes in workload at a specific time and adjusting staffing in advance. This enables the Optimization Department to realize flexible and efficient personnel deployment that takes into account employees' health conditions, improving overall business efficiency.

[0034] The collection unit can record the sleep state or pulse state of the employee in real time. The collection unit, for example, uses a device worn by the employee to record sleep quality and pulse fluctuations in real time. For example, the collection unit collects data such as the time when the employee is in deep sleep and the time when the pulse is stable. This allows the employee's sleep state and pulse state to be recorded in real time, thereby collecting more accurate biometric information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the device worn by the employee to AI and cause AI to perform real-time recording.

[0035] The analysis unit can analyze the correlation between employee performance and sales based on the collected data. The analysis unit uses the generation AI to analyze the correlation between employee performance and sales based on the collected data. For example, the analysis unit can determine whether employees with high quality sleep tend to have improved performance and increased sales. This makes it possible to improve work efficiency by analyzing the correlation between employee performance and sales based on the collected data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the collected data into the generation AI to analyze the correlation between performance and sales.

[0036] The optimization unit can assign appropriate tasks to each employee based on the employee's performance data. The optimization unit uses the generation AI to assign appropriate tasks to each employee based on the employee's performance data. For example, the optimization unit can improve overall work efficiency by assigning less burdensome tasks to employees with poor sleep quality and having employees with good sleep quality take on important tasks. This makes it possible to improve work efficiency by assigning optimal tasks based on the employee's performance data. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's performance data into the generation AI and assigns optimal tasks.

[0037] The optimization unit can assign light work to employees with poor sleep quality and assign important work to employees with good sleep quality. The optimization unit uses the generation AI to assign light work to employees with poor sleep quality and assign important work to employees with good sleep quality. For example, the optimization unit can maximize work efficiency while maintaining the health of employees by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves. As a result, work allocation that takes into account the health of employees can be made by allocating work according to the quality of sleep. Some or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs employees' sleep data into the generation AI and assigns optimal work.

[0038] The optimization unit can grasp the health condition of employees and take measures to have employees take frequent breaks to refresh themselves. The optimization unit uses the generation AI to grasp the health condition of employees and take measures to have employees take frequent breaks to refresh themselves. For example, the optimization unit can maximize work efficiency while maintaining the health of employees by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves. As a result, by taking measures that take into account the health condition of employees, it is possible to improve work efficiency while maintaining the health of employees. Part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the health data of employees into the generation AI and takes measures to have employees take breaks to refresh themselves.

[0039] The collection unit can analyze the employee's past biometric information and select the optimal collection method. For example, the collection unit identifies the most effective collection timing from past data and collects data at that timing. The collection unit can also improve the accuracy of data by collecting data at a specific time period based on past data. Furthermore, the collection unit can analyze past data and optimize the collection method under specific conditions. In this way, the optimal collection method can be selected by analyzing past biometric information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs past biometric information data into AI and selects the optimal collection method.

[0040] The collection unit can filter the biometric information based on the employee's current work situation and stress level when collecting the biometric information. For example, during busy periods, the collection unit reduces the frequency of collection to reduce the burden on the employee. In addition, when the stress level is high, the collection unit can collect detailed data to identify the cause of stress. Furthermore, the collection unit can change the type of data to be collected depending on the work situation. In this way, more appropriate biometric information can be collected by filtering based on the work situation and stress level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the employee's work situation data and stress level data into AI and performs filtering.

[0041] When collecting biometric information, the collection unit can preferentially collect highly relevant data by taking into account the geographical location information of the employee. For example, when the employee is in the office, the collection unit preferentially collects biometric information related to the office environment. In addition, when the employee is on a business trip, the collection unit can also preferentially collect biometric information related to the environment of the business trip destination. Furthermore, when the employee is at home, the collection unit can also preferentially collect biometric information related to the home environment. In this way, by taking into account the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the geographical location information data of the employee into AI, and preferentially collects highly relevant data.

[0042] The collection unit can analyze the employee's social media activity and collect related data when collecting the biometric information. For example, when the employee feels stressed on social media, the collection unit can collect stress-related biometric information. In addition, when the employee feels relaxed on social media, the collection unit can also collect biometric information related to a relaxed state. Furthermore, the collection unit can determine the biometric information to be collected during a specific time period from the employee's social media activity. In this way, related biometric information can be collected by analyzing the social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the employee's social media activity data into AI and collects related biometric information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the biometric information during analysis. The analysis unit uses the generation AI to adjust the level of detail of the analysis based on the importance of the biometric information during analysis. For example, the analysis unit performs a detailed analysis of biometric information with high importance. The analysis unit can also perform a simplified analysis of biometric information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. As a result, by adjusting the level of detail of the analysis based on the importance of the biometric information, more important information can be analyzed in detail. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the importance data of the biometric information to the generation AI and adjusts the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of biometric information during analysis. The analysis unit uses the generation AI to apply different analysis algorithms depending on the category of biometric information during analysis. For example, the analysis unit applies a sleep analysis algorithm to biometric information related to a sleep state. The analysis unit can also apply a pulse analysis algorithm to biometric information related to a pulse state. Furthermore, the analysis unit can also apply a stress analysis algorithm to biometric information related to a stress level. In this way, by applying different analysis algorithms depending on the category of biometric information, it is possible to provide more accurate analysis results. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs category data of the biometric information to the generation AI and applies different analysis algorithms.

[0045] The analysis unit can determine the priority of the analysis based on the collection time of the biometric information during analysis. The analysis unit uses the generation AI to determine the priority of the analysis based on the collection time of the biometric information during analysis. For example, the analysis unit prioritizes the analysis of recently collected biometric information. The analysis unit can also prioritize the analysis of biometric information collected during a specific time period. Furthermore, the analysis unit can also prioritize the analysis of biometric information collected before and after an important event. In this way, by determining the priority of the analysis based on the collection time of the biometric information, more important information can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the collection time data of the biometric information into the generation AI to determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the biometric information during analysis. The analysis unit adjusts the order of analysis based on the relevance of the biometric information using the generation AI during analysis. For example, the analysis unit prioritizes analysis of highly relevant biometric information. The analysis unit can also postpone analysis of less relevant biometric information. Furthermore, the analysis unit can dynamically change the order of analysis according to the relevance. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the biometric information. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs relevance data of the biometric information to the generation AI and adjusts the order of analysis.

[0047] The analysis unit can perform the analysis taking into account the employee's attribute information during the analysis. The analysis unit uses the generation AI to perform the analysis taking into account the employee's attribute information during the analysis. For example, the analysis unit performs the analysis taking into account the employee's age and gender. The analysis unit can also perform the analysis taking into account the employee's job content. Furthermore, the analysis unit can perform the analysis taking into account the employee's working hours. In this way, by taking into account the employee's attribute information, it is possible to provide more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs employee attribute information data into the generation AI and performs the analysis.

[0048] The analysis unit can perform the analysis while taking into account the geographical distribution of the biometric information. The analysis unit performs the analysis while taking into account the geographical distribution of the biometric information using the generation AI. For example, the analysis unit analyzes the biometric information based on the employee's place of work. The analysis unit can also analyze the biometric information based on the employee's business trip destination. Furthermore, the analysis unit can analyze the biometric information based on the employee's home address. In this way, by taking the geographical distribution into account, it is possible to provide more accurate analysis results. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs geographical distribution data of the biometric information into the generation AI and performs the analysis.

[0049] The analysis unit can improve the accuracy of the analysis by referring to literature related to the bioinformation during the analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to literature related to the bioinformation during the analysis. For example, the analysis unit performs an analysis that reflects the latest research results based on the related literature. The analysis unit can also improve the accuracy of the analysis by referring to the related literature. Furthermore, the analysis unit can also improve the reliability of the analysis results based on the related literature. As a result, by referring to the related literature, it is possible to provide more accurate analysis results. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs literature data related to the bioinformation into the generation AI to improve the accuracy of the analysis.

[0050] The optimization unit can select the optimal task allocation method by analyzing the employee's past performance data during optimization. The optimization unit uses the generation AI to analyze the employee's past performance data during optimization and select the optimal task allocation method. For example, the optimization unit selects the optimal task allocation method based on the past performance data. The optimization unit can also analyze the past performance data and select an employee suitable for a specific task. Furthermore, the optimization unit can improve the efficiency of task allocation based on the past performance data. In this way, the optimal task allocation method can be selected by analyzing the past performance data. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's past performance data into the generation AI and selects the optimal task allocation method.

[0051] The optimization unit can customize the task allocation means based on the employee's current health condition at the time of optimization. The optimization unit uses the generation AI to customize the task allocation means based on the employee's current health condition at the time of optimization. For example, the optimization unit assigns important tasks to employees in good health. The optimization unit can also assign less burdensome tasks to employees in poor health. Furthermore, the optimization unit can dynamically change the task allocation means according to the health condition. This enables more appropriate task allocation by customizing the task allocation means based on the employee's health condition. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's health condition data into the generation AI and customizes the task allocation means.

[0052] The optimization unit can select the optimal task allocation method by taking into account the geographic location information of the employee during optimization. The optimization unit uses the generation AI to select the optimal task allocation method by taking into account the geographic location information of the employee during optimization. For example, when the employee is in the office, the optimization unit allocates tasks suitable for the office environment. In addition, when the employee is on a business trip, the optimization unit can also allocate tasks that can be efficiently performed at the business trip destination. Furthermore, when the employee is at home, the optimization unit can also allocate tasks that can be performed at home. This makes it possible to allocate tasks more appropriately by taking into account the geographic location information. Part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the geographic location information data of the employee into the generation AI and selects the optimal task allocation method.

[0053] The optimization unit can analyze the employee's social media activity during optimization and propose a means of task allocation. The optimization unit uses the generation AI to analyze the employee's social media activity during optimization and propose a means of task allocation. For example, if an employee feels stressed because of social media, the optimization unit assigns tasks that emphasize stress reduction. In addition, if an employee is relaxed because of social media, the optimization unit can also assign tasks to maintain a relaxed state. Furthermore, the optimization unit can select employees suitable for specific tasks from the employees' social media activities. This makes it possible to more appropriately assign tasks by analyzing social media activities. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's social media activity data into the generation AI and proposes a means of task allocation.

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

[0055] The system can also provide individualized health advice based on employees' biometric information. For example, the analysis unit can generate appropriate exercise and dietary advice based on employees' heart rate and sleep data. The collection unit can also monitor employees' stress levels and suggest relaxation methods to reduce stress. Furthermore, the optimization unit can recommend regular health checks and medical examinations according to employees' health conditions. This can support employees' health management and contribute to improving work efficiency.

[0056] The system can also evaluate the performance of the entire team based on the biological information of employees. For example, the analysis unit can compile the sleep data and heart rate of each employee to evaluate the health condition of the entire team. The analysis unit can also identify the strengths and weaknesses of the team based on the performance data of the entire team. Furthermore, the optimization unit can optimize the allocation of work to the team and the way projects are progressed based on the health condition and performance of the entire team. This can improve the performance of the entire team.

[0057] The system can also suggest individual career plans based on employees' biometric information. For example, the analysis department can suggest appropriate career paths based on employees' health conditions and performance data. The analysis department can also suggest training and education programs for career advancement, taking into account employees' skills and experience. Furthermore, the optimization department can assign appropriate tasks and projects based on employees' career plans. This supports employees' career growth and improves their motivation.

[0058] The system can also optimize the company's employee benefits programs based on employees' biometric information. For example, the analysis department can suggest appropriate employee benefits programs based on employees' health data. The analysis department can also improve the content of employee benefits programs by taking into account employees' needs and requests. Furthermore, the optimization department can recommend the use of employee benefit programs according to employees' health conditions and performance. This can improve employee satisfaction and maximize work efficiency.

[0059] The system can also strengthen in-house safety measures based on employees' biometric information. For example, the analysis department can detect safety risks early based on employees' health data. The analysis department can also suggest appropriate safety measures based on employees' safety data. Furthermore, the optimization department can strengthen safety measures and minimize risks according to employees' health conditions and work content. This can improve in-house safety and protect the health and safety of employees.

[0060] The system can also provide individual fitness plans based on employees' biometric information. For example, the analysis unit can generate an appropriate fitness plan based on the employee's heart rate and exercise data. The analysis unit can also evaluate the effectiveness of exercise and adjust the plan based on the employee's fitness data. Furthermore, the optimization unit can support the progress of the fitness plan according to the employee's health condition and fitness goals. This can help maintain the health of employees and improve work efficiency.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit tracks the employee's biometric information. The employee's biometric information includes heart rate, blood pressure, body temperature, etc. The collection unit uses a device worn by the employee to record sleep quality and pulse rate fluctuations in real time. For example, the collection unit collects data such as the time when the employee is in deep sleep and the time when the pulse rate is stable. Step 2: The analysis unit uses the generative AI to analyze the collected biometric information. For example, the analysis unit can analyze the correlation between employee performance and sales based on the collected data. The analysis unit can determine whether employees who have better quality sleep tend to perform better and increase sales. Step 3: The analysis department uses the generative AI to analyze the correlation between employee performance and sales based on the analysis results. For example, it can determine whether employees with good quality sleep tend to perform better and increase sales. Step 4: The optimization department uses the generation AI to optimize the placement of personnel and allocation of manpower based on the analysis results. For example, by assigning less demanding tasks to employees with poor sleep quality and having employees with good sleep quality take important tasks, overall work efficiency can be improved. Furthermore, by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves, work efficiency can be maximized while maintaining the health of employees.

[0063] (Example 2) The system according to the embodiment of the present invention is a system that uses a device that tracks biometric information to track employees' sleep and pulse states, and analyzes the correlation between employees' performance and sales based on these data. The system includes a collection unit that tracks employees' biometric information, an analysis unit that analyzes the collected biometric information, an analysis unit that analyzes the correlation between employees' performance and sales based on the analysis results, and an optimization unit that optimizes in-house personnel placement and labor allocation based on the analysis results. For example, the device worn by the employee records sleep quality and pulse rate fluctuations in real time. Next, the generation AI analyzes the collected data and analyzes the correlation between employees' performance and sales. For example, it can be determined whether employees with high sleep quality tend to improve their performance and increase their sales. Furthermore, the generation AI optimizes in-house personnel placement and labor allocation based on the analysis results. For example, by assigning less burdensome tasks to employees with low sleep quality and assigning important tasks to employees with high sleep quality, overall business efficiency can be improved. This makes it possible to grasp the health status of employees and improve business efficiency. For example, by taking measures such as having employees with poor quality sleep take more breaks to refresh themselves, it is possible to maximize work efficiency while maintaining the health of employees. In this way, the system can grasp the health status of employees and improve work efficiency.

[0064] The system according to the embodiment includes a collection unit, an analysis unit, an analysis unit, and an optimization unit. The collection unit tracks the employee's biometric information. The employee's biometric information includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The collection unit records the quality of sleep and pulse fluctuations in real time, for example, using a device worn by the employee. For example, the collection unit collects data such as the time when the employee is in deep sleep and the time when the pulse is stable. The analysis unit analyzes the collected biometric information using a generation AI. For example, the analysis unit analyzes the correlation between the employee's performance and sales based on the collected data. For example, the analysis unit can determine whether an employee with high quality sleep tends to improve performance and increase sales. The analysis unit analyzes the correlation between the employee's performance and sales based on the analysis results using the generation AI. For example, the analysis unit can determine whether an employee with high quality sleep tends to improve performance and increase sales. The optimization unit optimizes the internal personnel placement and labor allocation based on the analysis results using the generation AI. For example, the optimization unit can improve overall work efficiency by allocating less burdensome tasks to employees with poor sleep quality and assigning important tasks to employees with good sleep quality. This allows the system according to the embodiment to grasp the health status of employees and improve work efficiency. For example, the optimization unit can maximize work efficiency while maintaining the health of employees by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves.

[0065] The collection unit tracks the employee's biological information. The employee's biological information includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. For example, the collection unit uses a device worn by the employee to record sleep quality and pulse fluctuations in real time. Specifically, the wearable device worn by the employee is equipped with a heart rate sensor, a blood pressure sensor, a body temperature sensor, and the like, and these sensors continuously collect data. The heart rate sensor records heart rate fluctuations in milliseconds, and the blood pressure sensor periodically measures systolic and diastolic blood pressure. The body temperature sensor monitors the temperature of the skin surface in real time and detects body temperature fluctuations. These data are transmitted to a central database via Bluetooth or Wi-Fi, and the collection unit centrally manages them. Furthermore, the collection unit uses an acceleration sensor and a gyro sensor to measure the frequency of turning over and movement in order to record the employee's sleep pattern in detail. This makes it possible to accurately grasp the time when the employee is in deep sleep and the time when he or she is in light sleep. The collection unit collects these data in real time and can continuously monitor the employee's health condition. This enables the collection department to track employees' biometric information in detail and detect any changes in their health status early on.

[0066] The analysis unit uses the generation AI to analyze the collected biometric information. For example, the analysis unit analyzes the correlation between employee performance and sales based on the collected data. Specifically, the generation AI multidimensionally analyzes the collected heart rate, blood pressure, body temperature, sleep data, etc., to clarify the relationship between the employee's health condition and work performance. The generation AI first preprocesses the data and complements outliers and missing values. Next, it extracts features of the data and creates a profile of the health condition of each employee. For example, it evaluates the employee's stress level and fatigue level using heart rate fluctuation patterns and sleep quality indicators. Furthermore, the generation AI integrates these health indicators with work performance data (for example, sales, project progress, task completion rate, etc.) and performs correlation analysis. Specifically, it quantitatively evaluates the relationship between health condition and work performance using regression analysis and clustering methods. For example, it can determine whether employees with high sleep quality tend to improve their performance and increase their sales. Based on these analysis results, the analysis unit can clarify the impact of employees' health conditions on work performance and provide specific guidelines for improvement.

[0067] The analysis department uses the generation AI to further analyze the correlation between employee performance and sales based on the analysis results. Specifically, based on the correlation results obtained by the analysis department, the generation AI creates a detailed performance profile for each employee. For example, the generation AI performs multivariate analysis combining multiple variables to identify under what conditions an employee's health condition most affects performance. For example, it combines heart rate stability, sleep depth, daytime activity level, etc. to identify the factors that most affect employee performance. In addition, the generation AI analyzes data along the time axis to clarify fluctuation patterns of health condition and performance. For example, it can analyze fluctuations in performance during specific times of the day or days of the week and propose optimal work schedules. Based on these detailed analysis results, the analysis department can formulate specific action plans to improve the performance of each employee. For example, it can suggest advice to improve sleep quality and relaxation methods for stress management to specific employees. This allows the analysis department to deeply understand the relationship between employees' health conditions and work performance and provide specific improvement measures according to individual needs.

[0068] The optimization department uses the generation AI to optimize the internal staffing and labor allocation based on the analysis results. Specifically, the generation AI calculates optimal staffing and work allocation based on the health and performance data of employees. For example, overall work efficiency can be improved by assigning less burdensome work to employees with poor sleep quality and assigning important work to employees with good sleep quality. The generation AI first simulates the optimal work allocation for each employee based on the health and performance profile of each employee. Next, based on the simulation results, an optimal staffing plan is created and reflected in the actual work schedule. In addition, the generation AI can dynamically adjust work allocation based on biometric information updated in real time. For example, if the health of a specific employee suddenly deteriorates, the generation AI immediately reviews the work allocation and redistributes work to other employees. The generation AI can also formulate long-term staffing strategies based on past data. For example, it is possible to maximize work efficiency by predicting changes in workload at a specific time and adjusting staffing in advance. This enables the Optimization Department to realize flexible and efficient personnel deployment that takes into account employees' health conditions, improving overall business efficiency.

[0069] The collection unit can record the sleep state or pulse state of the employee in real time. The collection unit, for example, uses a device worn by the employee to record sleep quality and pulse fluctuations in real time. For example, the collection unit collects data such as the time when the employee is in deep sleep and the time when the pulse is stable. This allows the employee's sleep state and pulse state to be recorded in real time, thereby collecting more accurate biometric information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the device worn by the employee to AI and cause AI to perform real-time recording.

[0070] The analysis unit can analyze the correlation between employee performance and sales based on the collected data. The analysis unit uses the generation AI to analyze the correlation between employee performance and sales based on the collected data. For example, the analysis unit can determine whether employees with high quality sleep tend to have improved performance and increased sales. This makes it possible to improve work efficiency by analyzing the correlation between employee performance and sales based on the collected data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the collected data into the generation AI to analyze the correlation between performance and sales.

[0071] The optimization unit can assign appropriate tasks to each employee based on the employee's performance data. The optimization unit uses the generation AI to assign appropriate tasks to each employee based on the employee's performance data. For example, the optimization unit can improve overall work efficiency by assigning less burdensome tasks to employees with poor sleep quality and having employees with good sleep quality take on important tasks. This makes it possible to improve work efficiency by assigning optimal tasks based on the employee's performance data. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's performance data into the generation AI and assigns optimal tasks.

[0072] The optimization unit can assign light work to employees with poor sleep quality and assign important work to employees with good sleep quality. The optimization unit uses the generation AI to assign light work to employees with poor sleep quality and assign important work to employees with good sleep quality. For example, the optimization unit can maximize work efficiency while maintaining the health of employees by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves. As a result, work allocation that takes into account the health of employees can be made by allocating work according to the quality of sleep. Some or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs employees' sleep data into the generation AI and assigns optimal work.

[0073] The optimization unit can grasp the health condition of employees and take measures to have employees take frequent breaks to refresh themselves. The optimization unit uses the generation AI to grasp the health condition of employees and take measures to have employees take frequent breaks to refresh themselves. For example, the optimization unit can maximize work efficiency while maintaining the health of employees by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves. As a result, by taking measures that take into account the health condition of employees, it is possible to improve work efficiency while maintaining the health of employees. Part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the health data of employees into the generation AI and takes measures to have employees take breaks to refresh themselves.

[0074] The collection unit can estimate the user's emotion and adjust the collection timing of the bioinformation based on the estimated user's emotion. For example, when the user is stressed, the collection unit can increase the collection timing to acquire detailed data. Also, when the user is relaxed, the collection unit can reduce the collection timing to reduce the data load. Furthermore, when the user is in a hurry, the collection unit can shorten the collection timing to quickly acquire data. This allows more appropriate bioinformation to be collected by adjusting the collection timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing.

[0075] The collection unit can analyze the employee's past biometric information and select the optimal collection method. For example, the collection unit identifies the most effective collection timing from past data and collects data at that timing. The collection unit can also improve the accuracy of data by collecting data at a specific time period based on past data. Furthermore, the collection unit can analyze past data and optimize the collection method under specific conditions. In this way, the optimal collection method can be selected by analyzing past biometric information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs past biometric information data into AI and selects the optimal collection method.

[0076] The collection unit can filter the biometric information based on the employee's current work situation and stress level when collecting the biometric information. For example, during busy periods, the collection unit reduces the frequency of collection to reduce the burden on the employee. In addition, when the stress level is high, the collection unit can collect detailed data to identify the cause of stress. Furthermore, the collection unit can change the type of data to be collected depending on the work situation. In this way, more appropriate biometric information can be collected by filtering based on the work situation and stress level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the employee's work situation data and stress level data into AI and performs filtering.

[0077] The collection unit can estimate the user's emotion and determine the priority of the biometric information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects stress-related biometric information. Also, when the user is relaxed, the collection unit can preferentially collect biometric information related to the relaxed state. Furthermore, when the user is in a hurry, the collection unit can preferentially collect biometric information that can be collected quickly. In this way, by determining the priority of the biometric information based on the user's emotion, more important information can be preferentially collected. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the user's emotion data into the generation AI and determines the priority of the biometric information to be collected.

[0078] When collecting biometric information, the collection unit can preferentially collect highly relevant data by taking into account the geographical location information of the employee. For example, when the employee is in the office, the collection unit preferentially collects biometric information related to the office environment. In addition, when the employee is on a business trip, the collection unit can also preferentially collect biometric information related to the environment of the business trip destination. Furthermore, when the employee is at home, the collection unit can also preferentially collect biometric information related to the home environment. In this way, by taking into account the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the geographical location information data of the employee into AI, and preferentially collects highly relevant data.

[0079] The collection unit can analyze the employee's social media activity and collect related data when collecting the biometric information. For example, when the employee feels stressed on social media, the collection unit can collect stress-related biometric information. In addition, when the employee feels relaxed on social media, the collection unit can also collect biometric information related to a relaxed state. Furthermore, the collection unit can determine the biometric information to be collected during a specific time period from the employee's social media activity. In this way, related biometric information can be collected by analyzing the social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the employee's social media activity data into AI and collects related biometric information.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. This makes it possible to provide a more appropriate analysis result by adjusting the expression method of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and adjusts the expression method of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the biometric information during analysis. The analysis unit uses the generation AI to adjust the level of detail of the analysis based on the importance of the biometric information during analysis. For example, the analysis unit performs a detailed analysis of biometric information with high importance. The analysis unit can also perform a simplified analysis of biometric information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. As a result, by adjusting the level of detail of the analysis based on the importance of the biometric information, more important information can be analyzed in detail. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the importance data of the biometric information to the generation AI and adjusts the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of biometric information during analysis. The analysis unit uses the generation AI to apply different analysis algorithms depending on the category of biometric information during analysis. For example, the analysis unit applies a sleep analysis algorithm to biometric information related to a sleep state. The analysis unit can also apply a pulse analysis algorithm to biometric information related to a pulse state. Furthermore, the analysis unit can also apply a stress analysis algorithm to biometric information related to a stress level. In this way, by applying different analysis algorithms depending on the category of biometric information, it is possible to provide more accurate analysis results. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs category data of the biometric information to the generation AI and applies different analysis algorithms.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows a more appropriate analysis result to be provided by adjusting the length of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and adjusts the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the collection time of the biometric information during analysis. The analysis unit uses the generation AI to determine the priority of the analysis based on the collection time of the biometric information during analysis. For example, the analysis unit prioritizes the analysis of recently collected biometric information. The analysis unit can also prioritize the analysis of biometric information collected during a specific time period. Furthermore, the analysis unit can also prioritize the analysis of biometric information collected before and after an important event. In this way, by determining the priority of the analysis based on the collection time of the biometric information, more important information can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the collection time data of the biometric information into the generation AI to determine the priority of the analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the biometric information during analysis. The analysis unit adjusts the order of analysis based on the relevance of the biometric information using the generation AI during analysis. For example, the analysis unit prioritizes analysis of highly relevant biometric information. The analysis unit can also postpone analysis of less relevant biometric information. Furthermore, the analysis unit can dynamically change the order of analysis according to the relevance. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the biometric information. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs relevance data of the biometric information to the generation AI and adjusts the order of analysis.

[0086] The analysis unit can estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit performs analysis by focusing on stress-related criteria. Also, when the user is relaxed, the analysis unit can also perform analysis by focusing on criteria related to a relaxed state. Furthermore, when the user is in a hurry, the analysis unit can also emphasize criteria for quickly providing analysis results. In this way, by adjusting the analysis criteria based on the user's emotion, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and adjusts the analysis criteria.

[0087] The analysis unit can perform the analysis taking into account the employee's attribute information during the analysis. The analysis unit uses the generation AI to perform the analysis taking into account the employee's attribute information during the analysis. For example, the analysis unit performs the analysis taking into account the employee's age and gender. The analysis unit can also perform the analysis taking into account the employee's job content. Furthermore, the analysis unit can perform the analysis taking into account the employee's working hours. In this way, by taking into account the employee's attribute information, it is possible to provide more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs employee attribute information data into the generation AI and performs the analysis.

[0088] The analysis unit can estimate the user's emotion and adjust the order of displaying the analysis results based on the estimated user's emotion. For example, when the user is stressed, the analysis unit displays important results first. Also, when the user is relaxed, the analysis unit can display detailed results in an orderly manner. Furthermore, when the user is in a hurry, the analysis unit can display results with the main points first. This allows for more appropriate analysis results to be provided by adjusting the display order of the results based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and adjusts the display order of the results.

[0089] The analysis unit can perform the analysis while taking into account the geographical distribution of the biometric information. The analysis unit performs the analysis while taking into account the geographical distribution of the biometric information using the generation AI. For example, the analysis unit analyzes the biometric information based on the employee's place of work. The analysis unit can also analyze the biometric information based on the employee's business trip destination. Furthermore, the analysis unit can analyze the biometric information based on the employee's home address. In this way, by taking the geographical distribution into account, it is possible to provide more accurate analysis results. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs geographical distribution data of the biometric information into the generation AI and performs the analysis.

[0090] The analysis unit can improve the accuracy of the analysis by referring to literature related to the bioinformation during the analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to literature related to the bioinformation during the analysis. For example, the analysis unit performs an analysis that reflects the latest research results based on the related literature. The analysis unit can also improve the accuracy of the analysis by referring to the related literature. Furthermore, the analysis unit can also improve the reliability of the analysis results based on the related literature. As a result, by referring to the related literature, it is possible to provide more accurate analysis results. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs literature data related to the bioinformation into the generation AI to improve the accuracy of the analysis.

[0091] The optimization unit can estimate the user's emotion and adjust the optimization method based on the estimated user's emotion. For example, when the user is feeling stressed, the optimization unit performs optimization with emphasis on stress reduction. Also, when the user is relaxed, the optimization unit can perform optimization to maintain a relaxed state. Furthermore, when the user is in a hurry, the optimization unit can quickly provide an optimization result. As a result, by adjusting the optimization method based on the user's emotion, a more appropriate optimization result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the user's emotion data into the generation AI and adjusts the optimization method.

[0092] The optimization unit can select the optimal task allocation method by analyzing the employee's past performance data during optimization. The optimization unit uses the generation AI to analyze the employee's past performance data during optimization and select the optimal task allocation method. For example, the optimization unit selects the optimal task allocation method based on the past performance data. The optimization unit can also analyze the past performance data and select an employee suitable for a specific task. Furthermore, the optimization unit can improve the efficiency of task allocation based on the past performance data. In this way, the optimal task allocation method can be selected by analyzing the past performance data. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's past performance data into the generation AI and selects the optimal task allocation method.

[0093] The optimization unit can customize the task allocation means based on the employee's current health condition at the time of optimization. The optimization unit uses the generation AI to customize the task allocation means based on the employee's current health condition at the time of optimization. For example, the optimization unit assigns important tasks to employees in good health. The optimization unit can also assign less burdensome tasks to employees in poor health. Furthermore, the optimization unit can dynamically change the task allocation means according to the health condition. This enables more appropriate task allocation by customizing the task allocation means based on the employee's health condition. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's health condition data into the generation AI and customizes the task allocation means.

[0094] The optimization unit can estimate the user's emotion and determine the priority of optimization based on the estimated user's emotion. For example, when the user is stressed, the optimization unit performs optimization with a priority on stress reduction. Also, when the user is relaxed, the optimization unit can perform optimization to maintain the relaxed state. Furthermore, when the user is in a hurry, the optimization unit can quickly provide the optimization result. As a result, by determining the priority of optimization based on the user's emotion, a more appropriate optimization result can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the user's emotion data into the generation AI and determines the priority of optimization.

[0095] The optimization unit can select the optimal task allocation method by taking into account the geographic location information of the employee during optimization. The optimization unit uses the generation AI to select the optimal task allocation method by taking into account the geographic location information of the employee during optimization. For example, when the employee is in the office, the optimization unit allocates tasks suitable for the office environment. In addition, when the employee is on a business trip, the optimization unit can also allocate tasks that can be efficiently performed at the business trip destination. Furthermore, when the employee is at home, the optimization unit can also allocate tasks that can be performed at home. This makes it possible to allocate tasks more appropriately by taking into account the geographic location information. Part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the geographic location information data of the employee into the generation AI and selects the optimal task allocation method.

[0096] The optimization unit can analyze the employee's social media activity during optimization and propose a means of task allocation. The optimization unit uses the generation AI to analyze the employee's social media activity during optimization and propose a means of task allocation. For example, if an employee feels stressed because of social media, the optimization unit assigns tasks that emphasize stress reduction. In addition, if an employee is relaxed because of social media, the optimization unit can also assign tasks to maintain a relaxed state. Furthermore, the optimization unit can select employees suitable for specific tasks from the employees' social media activities. This makes it possible to more appropriately assign tasks by analyzing social media activities. A part or all of the above-mentioned processing in the optimization unit is performed using the generation AI. For example, the optimization unit inputs the employee's social media activity data into the generation AI and proposes a means of task allocation.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The system can also provide individualized health advice based on employees' biometric information. For example, the analysis unit can generate appropriate exercise and dietary advice based on employees' heart rate and sleep data. The collection unit can also monitor employees' stress levels and suggest relaxation methods to reduce stress. Furthermore, the optimization unit can recommend regular health checks and medical examinations according to employees' health conditions. This can support employees' health management and contribute to improving work efficiency.

[0099] The system can also evaluate the performance of the entire team based on the biological information of employees. For example, the analysis unit can compile the sleep data and heart rate of each employee to evaluate the health condition of the entire team. The analysis unit can also identify the strengths and weaknesses of the team based on the performance data of the entire team. Furthermore, the optimization unit can optimize the allocation of work to the team and the way projects are progressed based on the health condition and performance of the entire team. This can improve the performance of the entire team.

[0100] The system can also suggest individual career plans based on employees' biometric information. For example, the analysis department can suggest appropriate career paths based on employees' health conditions and performance data. The analysis department can also suggest training and education programs for career advancement, taking into account employees' skills and experience. Furthermore, the optimization department can assign appropriate tasks and projects based on employees' career plans. This supports employees' career growth and improves their motivation.

[0101] The system can also improve internal communication based on employees' biometric information. For example, the analysis unit can monitor employees' stress levels and emotional states to identify areas for improvement in communication. The analysis unit can also suggest effective communication methods based on communication data between employees. Furthermore, the optimization unit can adjust the timing and method of communication according to employees' emotional states. This can facilitate internal communication and improve teamwork.

[0102] The system can also provide individualized mental health support based on employees' biometric information. For example, the analysis department can monitor employees' stress levels and emotional states to detect mental health risks early. The analysis department can also suggest appropriate counseling and support programs based on employees' mental health data. Furthermore, the optimization department can adjust the workload and recommend breaks for refreshing according to the employees' mental health status. This can support employees' mental health and improve work efficiency.

[0103] The system can also optimize the company's employee benefits programs based on employees' biometric information. For example, the analysis department can suggest appropriate employee benefits programs based on employees' health data. The analysis department can also improve the content of employee benefits programs by taking into account employees' needs and requests. Furthermore, the optimization department can recommend the use of employee benefit programs according to employees' health conditions and performance. This can improve employee satisfaction and maximize work efficiency.

[0104] The system can also provide individual stress management plans based on employees' biometric information. For example, the analysis unit can monitor employees' stress levels and identify the causes of stress. The analysis unit can also suggest appropriate stress management methods based on employees' stress data. Furthermore, the optimization unit can adjust the workload according to the employees' stress levels and recommend relaxation methods to reduce stress. This makes it possible to effectively manage employees' stress and improve work efficiency.

[0105] The system can also strengthen in-house safety measures based on employees' biometric information. For example, the analysis department can detect safety risks early based on employees' health data. The analysis department can also suggest appropriate safety measures based on employees' safety data. Furthermore, the optimization department can strengthen safety measures and minimize risks according to employees' health conditions and work content. This can improve in-house safety and protect the health and safety of employees.

[0106] The system can also provide individual fitness plans based on employees' biometric information. For example, the analysis unit can generate an appropriate fitness plan based on the employee's heart rate and exercise data. The analysis unit can also evaluate the effectiveness of exercise and adjust the plan based on the employee's fitness data. Furthermore, the optimization unit can support the progress of the fitness plan according to the employee's health condition and fitness goals. This can help maintain the health of employees and improve work efficiency.

[0107] The system can also provide individual sleep improvement plans based on employees' biometric information. For example, the analysis unit can generate advice for improving sleep quality based on employees' sleep data. The analysis unit can also suggest improvements to sleep environment and lifestyle habits based on employees' sleep data. Furthermore, the optimization unit can adjust the workload according to the employees' sleep state and take measures to improve sleep quality. This can improve employees' sleep quality and maximize work efficiency.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection unit tracks the employee's biometric information. The employee's biometric information includes heart rate, blood pressure, body temperature, etc. The collection unit uses a device worn by the employee to record sleep quality and pulse rate fluctuations in real time. For example, the collection unit collects data such as the time when the employee is in deep sleep and the time when the pulse rate is stable. Step 2: The analysis unit uses the generative AI to analyze the collected biometric information. For example, the analysis unit can analyze the correlation between employee performance and sales based on the collected data. The analysis unit can determine whether employees who have better quality sleep tend to perform better and increase sales. Step 3: The analysis department uses the generative AI to analyze the correlation between employee performance and sales based on the analysis results. For example, it can determine whether employees with good quality sleep tend to perform better and increase sales. Step 4: The optimization department uses the generation AI to optimize the placement of personnel and allocation of manpower based on the analysis results. For example, by assigning less demanding tasks to employees with poor sleep quality and having employees with good sleep quality take important tasks, overall work efficiency can be improved. Furthermore, by taking measures such as having employees with poor sleep quality take more breaks to refresh themselves, work efficiency can be maximized while maintaining the health of employees.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned collection unit, analysis unit, analysis unit, and optimization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the smart device 14, and tracks biometric information such as the employee's heart rate, blood pressure, and body temperature using a sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected biometric information using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the correlation between the employee's performance and sales based on the analysis results. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the personnel placement and labor allocation within the company based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned collection unit, analysis unit, analysis unit, and optimization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the smart glasses 214, and tracks biometric information such as the employee's heart rate, blood pressure, and body temperature using a sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected biometric information using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the correlation between the employee's performance and sales based on the analysis result. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the personnel placement and labor allocation within the company based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned collection unit, analysis unit, analysis unit, and optimization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the headset type terminal 314, and tracks biometric information such as the employee's heart rate, blood pressure, and body temperature using a sensor of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected biometric information using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the correlation between the employee's performance and sales based on the analysis results. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the personnel placement and labor allocation within the company based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned collection unit, analysis unit, analysis unit, and optimization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the robot 414, and tracks biometric information such as the employee's heart rate, blood pressure, and body temperature using a sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected biometric information using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the correlation between the employee's performance and sales based on the analysis results. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the personnel placement and labor allocation within the company based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A collection department that tracks employees' biometric information; an analysis unit that analyzes the biological information collected by the collection unit; an analysis unit that analyzes the correlation between employee performance and sales based on the analysis results obtained by the analysis unit; An optimization unit that optimizes in-house personnel placement and man-hour allocation based on the analysis results obtained by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Record employees' sleep status or pulse status in real time 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the correlation between employee performance and sales based on collected data 2. The system of claim 1, (Appendix 4) The optimization unit is Assign appropriate tasks to each employee based on employee performance data 2. The system of claim 1, (Appendix 5) The optimization unit is Assign lighter tasks to employees who get poor quality sleep, and assign more important tasks to employees who get good quality sleep. 2. The system of claim 1, (Appendix 6) The optimization unit is Monitor employee health and take steps to ensure frequent refreshment breaks 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of biometric information based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze employees' past biometric information and choose the most appropriate collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting biometric data, it will be filtered based on the employee's current work situation and stress level. 2. The system of claim 1, (Appendix 10) The collecting unit includes: A user's emotion is estimated, and a priority order of biometric information to be collected is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting biometric data, consider the geographic location of employees to prioritize the collection of more relevant data 2. The system of claim 1, (Appendix 12) The collecting unit includes: When biometric data is collected, employees' social media activity is analyzed to collect relevant data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the level of detail of the analysis is adjusted based on the importance of the vital information 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of biometric information. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when vital signs were collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of biometric information 2. The system of claim 1, (Appendix 19) The analysis unit includes: Infer user sentiment and adjust analysis criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The analysis unit includes: When conducting analysis, consider employee attribute information. 2. The system of claim 1, (Appendix 21) The analysis unit includes: Infer user sentiment and adjust the order in which analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The analysis unit includes: When conducting analysis, the geographical distribution of vital signs is taken into account. 2. The system of claim 1, (Appendix 23) The analysis unit includes: During analysis, refer to relevant literature on bioinformation to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 24) The optimization unit is Infer user sentiment and adjust optimization methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 25) The optimization unit is During optimization, analyze employees' past performance data to select the best work allocation method 2. The system of claim 1, (Appendix 26) The optimization unit is During optimization, customize work allocation methods based on employees' current health status 2. The system of claim 1, (Appendix 27) The optimization unit is Infer user sentiment and prioritize optimizations based on the inferred user sentiment 2. The system of claim 1, (Appendix 28) The optimization unit is During optimization, consider employees' geographic locations to select the best work allocation method 2. The system of claim 1, (Appendix 29) The optimization unit is During optimization, analyze employees' social media activity to suggest work allocation measures 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that tracks employees' biometric information; an analysis unit that analyzes the biological information collected by the collection unit; an analysis unit that analyzes the correlation between employee performance and sales based on the analysis results obtained by the analysis unit; An optimization unit that optimizes in-house personnel placement and man-hour allocation based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit includes: Record employees' sleep status or pulse status in real time 2. The system of claim 1 .

3. The optimization unit is Assign appropriate tasks to each employee based on employee performance data 2. The system of claim 1 .

4. The optimization unit is Employees who get poor quality sleep are assigned lighter tasks, while those who get good quality sleep are assigned more important tasks.

2. The system of claim 1 .

5. The optimization unit is Monitor employee health and take steps to ensure frequent refreshment breaks 2. The system of claim 1 .

6. The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of biometric information based on the estimated emotion of the user.

2. The system of claim 1 .

7. The collecting unit includes: Analyze employees' past biometric information and choose the most appropriate collection method 2. The system of claim 1 .

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