System

Abstract

An object of a system according to an embodiment is to identify a bias in working hours of employees and to quickly deal with the bias.SOLUTION: A system according to an embodiment includes a work situation monitoring unit, a work time deviation specification unit, and an alert providing unit. The working situation monitoring part collects working data of employees. A work time deviation specification part analyzes the work data collected by the work situation monitoring part, and specifies the deviation of the work time. An alert providing part provides an alert based on the deviation of the working hours specified by the working hour deviation specifying part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently identify and quickly address imbalances in employee working hours.

[0005] The system according to the embodiment aims to identify imbalances in employee working hours and to address them promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a work status monitoring unit, a work hour imbalance identification unit, and an alert providing unit. The work status monitoring unit collects employee work data. The work hour imbalance identification unit analyzes the work data collected by the work status monitoring unit and identifies work hour imbalances. The alert providing unit provides an alert based on the work hour imbalance identified by the work hour imbalance identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify imbalances in employee working hours and respond quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The working environment improvement system according to an embodiment of the present invention uses AI technology to monitor and analyze work conditions and provide alerts. This system analyzes data on imbalances in working hours and overtime, identifies problems, and encourages appropriate responses. As a result, the working environment improvement system can level out workloads, improve the working environment, and reduce the burden on managers.

[0029] A working environment improvement system according to an embodiment includes a work status monitoring unit, a work hour imbalance identification unit, and an alert providing unit. The work status monitoring unit collects employee work data, such as arrival and departure times, break times, and work content. The work status monitoring unit can also collect data in real time and monitor employee work status. The work hour imbalance identification unit analyzes the work data collected by the work status monitoring unit to identify imbalances in work hours. For example, it can detect cases where a specific employee continues to work long hours or where there is an imbalance in a specific task. The work hour imbalance identification unit can also analyze past work data to identify long-term imbalances in work hours. The alert providing unit provides an alert based on the imbalance in work hours identified by the work hour imbalance identification unit. For example, if a specific employee works long hours consecutively, it can send a notification to a manager. Furthermore, if the workload is uneven, it can provide an alert encouraging appropriate work distribution. This allows the working environment improvement system to identify imbalances in work hours and respond quickly to improve the working environment. For example, it can improve work efficiency while protecting the health of employees. In addition, by detecting problems such as uneven working hours and overtime early and taking appropriate measures, it is possible to provide a comfortable working environment.

[0030] The work status monitoring unit can monitor the employee's biometric data in real time and analyze the work status based on the employee's health condition. The work status monitoring unit, for example, monitors the employee's heart rate in real time and detects abnormal heart rate fluctuations. For example, a sudden increase in heart rate is analyzed as a sign of stress or overwork, and appropriate action is promoted. The work status monitoring unit also monitors the employee's body temperature in real time and detects abnormal body temperature fluctuations. For example, a sudden increase in body temperature is analyzed as a deterioration in health condition, and appropriate action is promoted. The work status monitoring unit also monitors the employee's blood pressure in real time and detects abnormal blood pressure fluctuations. For example, a sudden increase in blood pressure is analyzed as a sign of stress or overwork, and appropriate action is promoted. This makes it possible to analyze the work status taking the employee's health condition into consideration.

[0031] The work status monitoring unit can automatically record the work content of employees using voice recognition technology and perform a detailed analysis of the work. For example, the work status monitoring unit analyzes employee conversations using voice recognition technology and automatically records the work content. For example, remarks made during meetings are converted into text to grasp the progress of work. The work status monitoring unit also analyzes employee telephone conversations using voice recognition technology and automatically records the work content. For example, conversations with customers are converted into text to perform a detailed analysis of the work. The work status monitoring unit also analyzes employee work instructions using voice recognition technology and automatically records the work content. For example, instructions from a supervisor are converted into text to perform a detailed analysis of the work. This makes it possible to perform a detailed analysis of the work content.

[0032] The work status monitoring unit not only monitors the work status of employees, but also monitors the status of remote work at home and can perform analysis based on the influence of the home environment. The work status monitoring unit, for example, monitors the employee's remote work environment and analyzes the influence of the home environment. For example, it measures the noise level in the home and provides an alert if it affects the level of concentration. The work status monitoring unit also monitors the employee's internet connection status and analyzes the influence of the home environment. For example, it provides an alert if the internet connection is unstable. The work status monitoring unit also monitors the temperature and humidity in the employee's home and analyzes the influence of the home environment. For example, it provides an alert if the temperature or humidity is not appropriate. This makes it possible to analyze work status taking the influence of the home environment into account.

[0033] When monitoring employee work status, the work status monitoring unit can compare it with data from other companies and industries to provide a benchmark. For example, the work status monitoring unit compares employee work data with data from other companies to provide a benchmark. For example, it compares it with the average work hours of companies in the same industry to identify abnormal work hours. The work status monitoring unit also compares employee work data with industry data to provide a benchmark. For example, it compares it with the industry average work hours to identify abnormal work hours. The work status monitoring unit also builds a system that compares employee work data with data from other companies and industries to provide a benchmark. For example, it compares it with industry standards to provide a performance indicator. This makes it possible to provide a benchmark compared with data from other companies and industries.

[0034] The work hour imbalance identification unit can analyze an employee's past work data and analyze long-term work hour imbalances. The work hour imbalance identification unit, for example, analyzes an employee's work data from the past year to identify long-term work hour imbalances. For example, it detects cases where long work hours are concentrated in a particular month. The work hour imbalance identification unit also analyzes an employee's work data from the past six months to identify long-term work hour imbalances. For example, it detects cases where long work hours are concentrated in a particular week. The work hour imbalance identification unit also analyzes an employee's work data from the past three months to identify long-term work hour imbalances. For example, it detects cases where long work hours are concentrated on a particular day of the week. This makes it possible to identify long-term work hour imbalances.

[0035] The work hour imbalance identification unit can take into account the influence of seasons and specific events when analyzing employee work data. The work hour imbalance identification unit, for example, analyzes employee work data and identifies seasonal work hour imbalances. For example, it analyzes fluctuations in work hours before and after the New Year holidays and summer vacation. The work hour imbalance identification unit also analyzes employee work data and considers the influence of specific events (such as project deadlines). For example, it analyzes fluctuations in work hours before and after project deadlines. The work hour imbalance identification unit also analyzes employee work data and builds a system that considers the influence of seasons and specific events. For example, it analyzes seasonal fluctuations in work hours and the influence of specific events to identify work hour imbalances. This makes it possible to identify work hour imbalances that take into account the influence of seasons and specific events.

[0036] The work hour imbalance identification unit can not only identify work hour imbalances, but also propose appropriate work assignments taking into account the type and difficulty of the work. The work hour imbalance identification unit, for example, identifies work hour imbalances among employees and proposes appropriate work assignments taking into account the type and difficulty of the work. For example, it suggests dividing highly difficult work among multiple employees. The work hour imbalance identification unit can also identify work hour imbalances among employees and propose appropriate work assignments taking into account the type and difficulty of the work. For example, it can assign work that is suitable for employees with specific skills. The work hour imbalance identification unit can also build a system that identifies work hour imbalances among employees and proposes appropriate work assignments taking into account the type and difficulty of the work. For example, it evaluates the type and difficulty of the work and proposes appropriate work assignments. This makes it possible to propose appropriate work assignments taking into account the type and difficulty of the work.

[0037] The work hour imbalance identification unit can consider employees' skill sets and career paths when identifying work hour imbalances and propose appropriate work assignments. For example, the work hour imbalance identification unit analyzes employees' skill sets, identifies work hour imbalances, and then proposes appropriate work assignments. For example, it assigns tasks that are suitable for employees with specific skills. The work hour imbalance identification unit also analyzes employees' career paths, identifies work hour imbalances, and then proposes appropriate work assignments. For example, it assigns tasks that are in line with career goals. The work hour imbalance identification unit also analyzes employees' skill sets and career paths, identifies work hour imbalances, and then builds a system that proposes appropriate work assignments. For example, it evaluates skill sets and career paths and proposes appropriate work assignments. This makes it possible to propose appropriate work assignments that take skill sets and career paths into consideration.

[0038] The alert providing unit can provide personalized alerts taking into account the individual health condition and past work history of each employee. The alert providing unit, for example, analyzes the employee's past work history and provides an alert taking into account the individual health condition. For example, an alert is provided to an employee who has previously fallen ill due to overwork, urging them to take an early break. The alert providing unit also analyzes the employee's health check results and provides an alert taking into account the individual health condition. For example, an alert is provided to an employee who has found abnormalities in their health check, urging them to take regular breaks. The alert providing unit also analyzes the employee's past work history and health check results, building a system that provides alerts taking into account the individual health condition. For example, a personalized alert is provided based on the employee's past work history and health check results. This makes it possible to provide personalized alerts taking into account the individual health condition and work history.

[0039] The alert providing unit can automatically evaluate the importance and urgency of an alert and notify it with priority. The alert providing unit, for example, automatically evaluates the importance and urgency of an alert and notifies it with priority. For example, if there is a possibility of a serious impact on health, it will notify it with the highest priority. The alert providing unit also automatically evaluates the urgency of an alert and notifies it with priority. For example, if an urgent response is required, it will notify it with the highest priority. The alert providing unit also builds a system that automatically evaluates the importance and urgency of an alert and notifies it with priority. For example, alerts are provided with priority based on the importance and urgency. This makes it possible to evaluate the importance and urgency of alerts and notify them with priority.

[0040] The alert providing unit not only provides alerts, but also tracks the employee's response to the alert and re-notifies if the response is delayed. The alert providing unit, for example, tracks the employee's response to the alert and re-notifies if the response is delayed. For example, a re-notification is made if an employee does not respond to an alert urging them to take a break. The alert providing unit also tracks the employee's response to the alert and re-notifies if the response is delayed. For example, a re-notification is made if an employee does not respond to an alert urging them to distribute their work. The alert providing unit also builds a system that tracks the employee's response to the alert and re-notifies if the response is delayed. For example, the response status is evaluated and re-notification is made. This makes it possible to track the response to an alert and re-notify.

[0041] The alert providing unit, when providing an alert, can take into account the situations of other employees and the entire team and provide an alert to balance the entire team. For example, when providing an alert, the alert providing unit takes into account the situations of other employees and provides an alert to balance the entire team. For example, when work is concentrated on a specific employee, the alert providing unit suggests distributing the work to other employees. Furthermore, when providing an alert, the alert providing unit takes into account the situation of the entire team and provides an alert to balance the work. For example, when a load is concentrated on a specific team member, the alert providing unit suggests distributing the work to other members. Furthermore, when providing an alert, the alert providing unit takes into account the situation of other employees and the entire team and builds a system to provide an alert to balance the work. For example, the alert providing unit evaluates the performance of the entire team and provides an alert to balance the work. This makes it possible to provide an alert that takes into account the balance of the entire team.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The working environment improvement system can further include a training proposal unit that analyzes employees' skill sets and proposes appropriate training programs. For example, it can propose training to improve skills for employees who lack specific skills. The training proposal unit can also consider the employee's career path and propose training to acquire the skills necessary for career advancement. Furthermore, the training proposal unit can propose training to improve work efficiency based on the employee's work content. This can support employee skill improvement and career advancement, and improve work efficiency.

[0044] The working environment improvement system can further include a vacation suggestion unit that analyzes employee work data and suggests appropriate vacation timing. For example, it can suggest appropriate vacation for an employee who has been working long hours. The vacation suggestion unit can also consider the employee's health condition and suggest vacation for maintaining health. Furthermore, the vacation suggestion unit can suggest vacation timing that avoids peak work hours based on the employee's work content. This can help maintain employee health and improve work efficiency.

[0045] The working environment improvement system can further include a feedback providing unit that analyzes employee work data and provides appropriate feedback. For example, appropriate feedback can be provided depending on the progress of work. The feedback providing unit can also take into account the employee's skill set and provide feedback for skill improvement. Furthermore, the feedback providing unit can take into account the employee's career path and provide feedback necessary for career advancement. This can support employee skill improvement and career advancement, and improve work efficiency.

[0046] The working environment improvement system can further include a career path suggestion unit that analyzes employees' work data and suggests appropriate career paths. For example, it can analyze an employee's skill set and suggest a career path for acquiring the skills necessary for career advancement. The career path suggestion unit can also suggest a career path for improving work efficiency based on the employee's work content. Furthermore, the career path suggestion unit can also consider the employee's career goals and suggest the steps necessary for career advancement. This can support employees' career advancement and improve work efficiency.

[0047] The working environment improvement system can further include a health management suggestion unit that analyzes employee work data and suggests appropriate health management. For example, it can suggest appropriate health management for employees who have been working long hours. The health management suggestion unit can also analyze employees' health conditions and make suggestions for maintaining their health. Furthermore, the health management suggestion unit can also suggest health management to reduce the burden of work based on the employee's work content. This makes it possible to maintain employee health and improve work efficiency.

[0048] The working environment improvement system can further include a work allocation proposal unit that analyzes employee work data and proposes appropriate work allocation. For example, if there is a bias towards a particular task, it can propose an appropriate work allocation. The work allocation proposal unit can also take into account the employee's skill set and propose work allocation that matches the skills. Furthermore, the work allocation proposal unit can also take into account the employee's career path and propose work allocation necessary for career advancement. This reduces the workload of employees and improves work efficiency.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The work status monitoring unit collects employee work data, such as arrival and departure times, break times, and work content. The work status monitoring unit also collects data in real time and can monitor employee work status. Step 2: The work hour imbalance identification unit analyzes the work data collected by the work status monitoring unit and identifies imbalances in work hours. For example, it detects cases where a particular employee continues to work long hours or where there is a bias in a particular task. The work hour imbalance identification unit can also analyze past work data to identify long-term imbalances in work hours. Step 3: The alert provision unit provides an alert based on the work hour imbalance identified by the work hour imbalance identification unit. For example, if a specific employee is working long hours consecutively, it sends a notification to the manager. Also, if the workload is uneven, it provides an alert to encourage appropriate work distribution.

[0051] (Example 2) The working environment improvement system according to an embodiment of the present invention uses AI technology to monitor and analyze work conditions and provide alerts. This system analyzes data on imbalances in working hours and overtime, identifies problems, and encourages appropriate responses. As a result, the working environment improvement system can level out workloads, improve the working environment, and reduce the burden on managers.

[0052] A working environment improvement system according to an embodiment includes a work status monitoring unit, a work hour imbalance identification unit, and an alert providing unit. The work status monitoring unit collects employee work data, such as arrival and departure times, break times, and work content. The work status monitoring unit can also collect data in real time and monitor employee work status. The work hour imbalance identification unit analyzes the work data collected by the work status monitoring unit to identify imbalances in work hours. For example, it can detect cases where a specific employee continues to work long hours or where there is an imbalance in a specific task. The work hour imbalance identification unit can also analyze past work data to identify long-term imbalances in work hours. The alert providing unit provides an alert based on the imbalance in work hours identified by the work hour imbalance identification unit. For example, if a specific employee works long hours consecutively, it can send a notification to a manager. Furthermore, if the workload is uneven, it can provide an alert encouraging appropriate work distribution. This allows the working environment improvement system to identify imbalances in work hours and respond quickly to improve the working environment. For example, it can improve work efficiency while protecting the health of employees. In addition, by detecting problems such as uneven working hours and overtime early and taking appropriate measures, it is possible to provide a comfortable working environment.

[0053] The work status monitoring unit can monitor the employee's biometric data in real time and analyze the work status based on the employee's health condition. The work status monitoring unit, for example, monitors the employee's heart rate in real time and detects abnormal heart rate fluctuations. For example, a sudden increase in heart rate is analyzed as a sign of stress or overwork, and appropriate action is promoted. The work status monitoring unit also monitors the employee's body temperature in real time and detects abnormal body temperature fluctuations. For example, a sudden increase in body temperature is analyzed as a deterioration in health condition, and appropriate action is promoted. The work status monitoring unit also monitors the employee's blood pressure in real time and detects abnormal blood pressure fluctuations. For example, a sudden increase in blood pressure is analyzed as a sign of stress or overwork, and appropriate action is promoted. This makes it possible to analyze the work status taking the employee's health condition into consideration.

[0054] The work status monitoring unit can automatically record the work content of employees using voice recognition technology and perform a detailed analysis of the work. For example, the work status monitoring unit analyzes employee conversations using voice recognition technology and automatically records the work content. For example, remarks made during meetings are converted into text to grasp the progress of work. The work status monitoring unit also analyzes employee telephone conversations using voice recognition technology and automatically records the work content. For example, conversations with customers are converted into text to perform a detailed analysis of the work. The work status monitoring unit also analyzes employee work instructions using voice recognition technology and automatically records the work content. For example, instructions from a supervisor are converted into text to perform a detailed analysis of the work. This makes it possible to perform a detailed analysis of the work content.

[0055] The work status monitoring unit uses the emotion estimation function to analyze the emotional state of employees and detect signs of stress and fatigue early. The work status monitoring unit, for example, analyzes the facial expressions of employees to estimate their emotional state. For example, it detects signs of stress and fatigue from facial expressions and encourages appropriate responses. The work status monitoring unit also analyzes the voice of employees to estimate their emotional state. For example, it detects signs of stress and fatigue from the tone and speed of voice and encourages appropriate responses. The work status monitoring unit also analyzes the biometric data of employees to estimate their emotional state. For example, it detects signs of stress and fatigue from fluctuations in heart rate and body temperature and encourages appropriate responses. This makes it possible to detect signs of stress and fatigue early.

[0056] The work status monitoring unit not only monitors the work status of employees, but also monitors the status of remote work at home and can perform analysis based on the influence of the home environment. The work status monitoring unit, for example, monitors the employee's remote work environment and analyzes the influence of the home environment. For example, it measures the noise level in the home and provides an alert if it affects the level of concentration. The work status monitoring unit also monitors the employee's internet connection status and analyzes the influence of the home environment. For example, it provides an alert if the internet connection is unstable. The work status monitoring unit also monitors the temperature and humidity in the employee's home and analyzes the influence of the home environment. For example, it provides an alert if the temperature or humidity is not appropriate. This makes it possible to analyze work status taking the influence of the home environment into account.

[0057] When monitoring employee work status, the work status monitoring unit can compare it with data from other companies and industries to provide a benchmark. For example, the work status monitoring unit compares employee work data with data from other companies to provide a benchmark. For example, it compares it with the average work hours of companies in the same industry to identify abnormal work hours. The work status monitoring unit also compares employee work data with industry data to provide a benchmark. For example, it compares it with the industry average work hours to identify abnormal work hours. The work status monitoring unit also builds a system that compares employee work data with data from other companies and industries to provide a benchmark. For example, it compares it with industry standards to provide a performance indicator. This makes it possible to provide a benchmark compared with data from other companies and industries.

[0058] The work status monitoring unit can use the emotion estimation function to analyze the emotions felt by employees while they are working in real time and provide suggestions to bring out positive emotions. The work status monitoring unit, for example, analyzes the emotional state of employees in real time and makes suggestions to bring out positive emotions. For example, it may suggest relaxing music when stress levels are high. The work status monitoring unit also analyzes the emotional state of employees in real time and makes suggestions to bring out positive emotions. For example, it may suggest taking a break when fatigue is accumulating. The work status monitoring unit also analyzes the emotional state of employees in real time and makes suggestions to bring out positive emotions. For example, it may provide an encouraging message when motivation is low. This makes it possible to make suggestions to bring out positive emotions.

[0059] The work hour imbalance identification unit can analyze an employee's past work data and analyze long-term work hour imbalances. The work hour imbalance identification unit, for example, analyzes an employee's work data from the past year to identify long-term work hour imbalances. For example, it detects cases where long work hours are concentrated in a particular month. The work hour imbalance identification unit also analyzes an employee's work data from the past six months to identify long-term work hour imbalances. For example, it detects cases where long work hours are concentrated in a particular week. The work hour imbalance identification unit also analyzes an employee's work data from the past three months to identify long-term work hour imbalances. For example, it detects cases where long work hours are concentrated on a particular day of the week. This makes it possible to identify long-term work hour imbalances.

[0060] The work hour imbalance identification unit can take into account the influence of seasons and specific events when analyzing employee work data. The work hour imbalance identification unit, for example, analyzes employee work data and identifies seasonal work hour imbalances. For example, it analyzes fluctuations in work hours before and after the New Year holidays and summer vacation. The work hour imbalance identification unit also analyzes employee work data and considers the influence of specific events (such as project deadlines). For example, it analyzes fluctuations in work hours before and after project deadlines. The work hour imbalance identification unit also analyzes employee work data and builds a system that considers the influence of seasons and specific events. For example, it analyzes seasonal fluctuations in work hours and the influence of specific events to identify work hour imbalances. This makes it possible to identify work hour imbalances that take into account the influence of seasons and specific events.

[0061] The work hour imbalance identification unit can use the emotion estimation function to analyze the impact of work hour imbalances on employees' emotions and provide suggestions for reducing the emotional burden. The work hour imbalance identification unit, for example, analyzes the emotional state of employees and identifies the impact of work hour imbalances on emotions. For example, it analyzes that continued long working hours increase stress and suggests appropriate breaks. The work hour imbalance identification unit also analyzes the emotional state of employees and identifies the impact of work hour imbalances on emotions. For example, it analyzes that continued work imbalances decrease motivation and suggests work distribution. The work hour imbalance identification unit also analyzes the emotional state of employees and builds a system that identifies the impact of work hour imbalances on emotions. For example, it identifies work hour imbalances based on emotion scores and makes suggestions for reducing the emotional burden. This makes it possible to make suggestions for reducing the emotional burden.

[0062] The work hour imbalance identification unit can not only identify work hour imbalances, but also propose appropriate work assignments taking into account the type and difficulty of the work. The work hour imbalance identification unit, for example, identifies work hour imbalances among employees and proposes appropriate work assignments taking into account the type and difficulty of the work. For example, it suggests dividing highly difficult work among multiple employees. The work hour imbalance identification unit can also identify work hour imbalances among employees and propose appropriate work assignments taking into account the type and difficulty of the work. For example, it can assign work that is suitable for employees with specific skills. The work hour imbalance identification unit can also build a system that identifies work hour imbalances among employees and proposes appropriate work assignments taking into account the type and difficulty of the work. For example, it evaluates the type and difficulty of the work and proposes appropriate work assignments. This makes it possible to propose appropriate work assignments taking into account the type and difficulty of the work.

[0063] The work hour imbalance identification unit can consider employees' skill sets and career paths when identifying work hour imbalances and propose appropriate work assignments. For example, the work hour imbalance identification unit analyzes employees' skill sets, identifies work hour imbalances, and then proposes appropriate work assignments. For example, it assigns tasks that are suitable for employees with specific skills. The work hour imbalance identification unit also analyzes employees' career paths, identifies work hour imbalances, and then proposes appropriate work assignments. For example, it assigns tasks that are in line with career goals. The work hour imbalance identification unit also analyzes employees' skill sets and career paths, identifies work hour imbalances, and then builds a system that proposes appropriate work assignments. For example, it evaluates skill sets and career paths and proposes appropriate work assignments. This makes it possible to propose appropriate work assignments that take skill sets and career paths into consideration.

[0064] The work hour imbalance identification unit uses the emotion estimation function to analyze the impact of work hour imbalances on employee motivation and can provide suggestions for maintaining motivation. The work hour imbalance identification unit, for example, analyzes the emotional state of employees and identifies the impact of work hour imbalances on motivation. For example, it analyzes that continued long working hours decrease motivation and suggests appropriate breaks. The work hour imbalance identification unit also analyzes the emotional state of employees and identifies the impact of work hour imbalances on motivation. For example, it analyzes that continued work imbalances decrease motivation and suggests work distribution. The work hour imbalance identification unit also analyzes the emotional state of employees and builds a system for identifying the impact of work hour imbalances on motivation. For example, it identifies work hour imbalances based on emotion scores and makes suggestions for maintaining motivation. This makes it possible to make suggestions for maintaining motivation.

[0065] The alert providing unit can provide personalized alerts taking into account the individual health condition and past work history of each employee. The alert providing unit, for example, analyzes the employee's past work history and provides an alert taking into account the individual health condition. For example, an alert is provided to an employee who has previously fallen ill due to overwork, urging them to take an early break. The alert providing unit also analyzes the employee's health check results and provides an alert taking into account the individual health condition. For example, an alert is provided to an employee who has found abnormalities in their health check, urging them to take regular breaks. The alert providing unit also analyzes the employee's past work history and health check results, building a system that provides alerts taking into account the individual health condition. For example, a personalized alert is provided based on the employee's past work history and health check results. This makes it possible to provide personalized alerts taking into account the individual health condition and work history.

[0066] The alert providing unit can automatically evaluate the importance and urgency of an alert and notify it with priority. The alert providing unit, for example, automatically evaluates the importance and urgency of an alert and notifies it with priority. For example, if there is a possibility of a serious impact on health, it will notify it with the highest priority. The alert providing unit also automatically evaluates the urgency of an alert and notifies it with priority. For example, if an urgent response is required, it will notify it with the highest priority. The alert providing unit also builds a system that automatically evaluates the importance and urgency of an alert and notifies it with priority. For example, alerts are provided with priority based on the importance and urgency. This makes it possible to evaluate the importance and urgency of alerts and notify them with priority.

[0067] The alert providing unit can use the emotion estimation function to analyze the emotional impact of an alert on an employee and adjust the alert content to reduce stress. The alert providing unit, for example, analyzes the emotional state of an employee and identifies the emotional impact of an alert. For example, it adjusts the alert content that increases stress to reduce stress. The alert providing unit also analyzes the emotional state of an employee and identifies the emotional impact of an alert. For example, it provides alert content to relax when fatigue is accumulating. The alert providing unit also builds a system that analyzes the emotional state of an employee and identifies the emotional impact of an alert. For example, it adjusts the alert content based on the emotion score to reduce stress. This makes it possible to analyze the emotional impact of an alert and adjust the alert content to reduce stress.

[0068] The alert providing unit not only provides alerts, but also tracks the employee's response to the alert and re-notifies if the response is delayed. The alert providing unit, for example, tracks the employee's response to the alert and re-notifies if the response is delayed. For example, a re-notification is made if an employee does not respond to an alert urging them to take a break. The alert providing unit also tracks the employee's response to the alert and re-notifies if the response is delayed. For example, a re-notification is made if an employee does not respond to an alert urging them to distribute their work. The alert providing unit also builds a system that tracks the employee's response to the alert and re-notifies if the response is delayed. For example, the response status is evaluated and re-notification is made. This makes it possible to track the response to an alert and re-notify.

[0069] The alert providing unit, when providing an alert, can take into account the situations of other employees and the entire team and provide an alert to balance the entire team. For example, when providing an alert, the alert providing unit takes into account the situations of other employees and provides an alert to balance the entire team. For example, when work is concentrated on a specific employee, the alert providing unit suggests distributing the work to other employees. Furthermore, when providing an alert, the alert providing unit takes into account the situation of the entire team and provides an alert to balance the work. For example, when a load is concentrated on a specific team member, the alert providing unit suggests distributing the work to other members. Furthermore, when providing an alert, the alert providing unit takes into account the situation of other employees and the entire team and builds a system to provide an alert to balance the work. For example, the alert providing unit evaluates the performance of the entire team and provides an alert to balance the work. This makes it possible to provide an alert that takes into account the balance of the entire team.

[0070] The alert providing unit can use the emotion estimation function to monitor the emotional impact of alerts on employees in real time and optimize the content and timing of the alerts. The alert providing unit, for example, monitors the emotional state of employees in real time and optimizes the content and timing of alerts. For example, it provides alerts that avoid times when stress is high. The alert providing unit also monitors the emotional state of employees in real time and optimizes the content and timing of alerts. For example, it provides alert content to encourage relaxation when fatigue is accumulating. The alert providing unit also builds a system that monitors the emotional state of employees in real time and optimizes the content and timing of alerts. For example, it adjusts the content and timing of alerts based on the emotion score to reduce stress. This makes it possible to monitor the emotional impact of alerts in real time and optimize the content and timing of alerts.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The working environment improvement system can further include a training proposal unit that analyzes employees' skill sets and proposes appropriate training programs. For example, it can propose training to improve skills for employees who lack specific skills. The training proposal unit can also consider the employee's career path and propose training to acquire the skills necessary for career advancement. Furthermore, the training proposal unit can propose training to improve work efficiency based on the employee's work content. This can support employee skill improvement and career advancement, and improve work efficiency.

[0073] The working environment improvement system can further include a vacation suggestion unit that analyzes employee work data and suggests appropriate vacation timing. For example, it can suggest appropriate vacation for an employee who has been working long hours. The vacation suggestion unit can also consider the employee's health condition and suggest vacation for maintaining health. Furthermore, the vacation suggestion unit can suggest vacation timing that avoids peak work hours based on the employee's work content. This can help maintain employee health and improve work efficiency.

[0074] The working environment improvement system can further include a feedback providing unit that analyzes employee work data and provides appropriate feedback. For example, appropriate feedback can be provided depending on the progress of work. The feedback providing unit can also take into account the employee's skill set and provide feedback for skill improvement. Furthermore, the feedback providing unit can take into account the employee's career path and provide feedback necessary for career advancement. This can support employee skill improvement and career advancement, and improve work efficiency.

[0075] The working environment improvement system can further include a mental health care provider that analyzes employee work data and provides appropriate mental health care. For example, it can suggest mental health care to employees who have been working long hours. The mental health care provider can also analyze employees' emotional states, detect signs of stress and fatigue early, and provide appropriate mental health care. Furthermore, the mental health care provider can provide mental health care to reduce the burden of work based on the employee's work content. This can help maintain employees' mental health and improve work efficiency.

[0076] The working environment improvement system can further include a reward provision unit that analyzes employee work data and provides appropriate rewards. For example, appropriate rewards are provided according to work results. The reward provision unit can also analyze the emotional state of employees and provide rewards to improve their motivation. Furthermore, the reward provision unit can take into account the employee's career path and provide rewards necessary for career advancement. This can improve employee motivation and improve work efficiency.

[0077] The working environment improvement system can further include a communication promotion unit that analyzes employee work data and promotes appropriate communication. For example, it promotes appropriate communication based on the progress of work. The communication promotion unit can also analyze employees' emotional states, detect signs of stress or fatigue early, and promote appropriate communication. Furthermore, the communication promotion unit can promote communication to reduce work burdens based on the employee's work content. This facilitates employee communication and improves work efficiency.

[0078] The working environment improvement system can further include a work-life balance suggestion unit that analyzes employees' work data and suggests an appropriate work-life balance. For example, it can suggest an appropriate work-life balance to an employee who has been working long hours. The work-life balance suggestion unit can also analyze the employee's emotional state, detect signs of stress or fatigue early, and suggest an appropriate work-life balance. Furthermore, the work-life balance suggestion unit can suggest a work-life balance that reduces the employee's workload based on the employee's work content. This makes it possible to maintain employees' work-life balance and improve work efficiency.

[0079] The working environment improvement system can further include a career path suggestion unit that analyzes employees' work data and suggests appropriate career paths. For example, it can analyze an employee's skill set and suggest a career path for acquiring the skills necessary for career advancement. The career path suggestion unit can also suggest a career path for improving work efficiency based on the employee's work content. Furthermore, the career path suggestion unit can also consider the employee's career goals and suggest the steps necessary for career advancement. This can support employees' career advancement and improve work efficiency.

[0080] The working environment improvement system can further include a health management suggestion unit that analyzes employee work data and suggests appropriate health management. For example, it can suggest appropriate health management for employees who have been working long hours. The health management suggestion unit can also analyze employees' health conditions and make suggestions for maintaining their health. Furthermore, the health management suggestion unit can also suggest health management to reduce the burden of work based on the employee's work content. This makes it possible to maintain employee health and improve work efficiency.

[0081] The working environment improvement system can further include a work allocation proposal unit that analyzes employee work data and proposes appropriate work allocation. For example, if there is a bias towards a particular task, it can propose an appropriate work allocation. The work allocation proposal unit can also take into account the employee's skill set and propose work allocation that matches the skills. Furthermore, the work allocation proposal unit can also take into account the employee's career path and propose work allocation necessary for career advancement. This reduces the workload of employees and improves work efficiency.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The work status monitoring unit collects employee work data, such as arrival and departure times, break times, and work content. The work status monitoring unit also collects data in real time and can monitor employee work status. Step 2: The work hour imbalance identification unit analyzes the work data collected by the work status monitoring unit and identifies imbalances in work hours. For example, it detects cases where a particular employee continues to work long hours or where there is a bias in a particular task. The work hour imbalance identification unit can also analyze past work data to identify long-term imbalances in work hours. Step 3: The alert provision unit provides an alert based on the work hour imbalance identified by the work hour imbalance identification unit. For example, if a specific employee is working long hours consecutively, it sends a notification to the manager. Also, if the workload is uneven, it provides an alert to encourage appropriate work distribution.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] 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.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] 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.

[0110] 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.

[0111] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0120] 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.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] 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.

[0126] 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.

[0127] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] 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.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 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 work status monitoring unit that collects employee work data; a work hour imbalance identification unit that analyzes the work data collected by the work status monitoring unit and identifies imbalances in work hours; an alert providing unit that provides an alert based on the work hour imbalance identified by the work hour imbalance identifying unit. A system characterized by:

2. The work status monitoring unit Monitor the employee's biometric data in real time and analyze their working status based on their health status.

2. The system of claim 1.

3. The work status monitoring unit In addition to monitoring the employee's work status, the company also monitors the remote work situation at home and conducts analysis based on the impact of the home environment.

2. The system of claim 1.

4. The working hour imbalance identification unit Analyze the employee's past work data and analyze long-term working hour imbalances.

2. The system of claim 1.

5. The alert providing unit Providing personalized alerts that take into account the employee's individual health status and past work history 2. The system of claim 1.

6. The work status monitoring unit Analyzing the employee's emotional state and detecting early signs of stress and fatigue 2. The system of claim 1.

7. The working hour imbalance identification unit Analyze the impact of the imbalance in working hours on the emotions of the employees and provide suggestions to reduce the emotional burden.

2. The system of claim 1.

8. The alert providing unit Analyzing the emotional impact of the alert on the employee and adjusting the alert content to reduce stress.

2. The system of claim 1.

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