system

Abstract

The system according to the embodiment aims to analyze work activity data of employees and to detect employees who are likely to suffer from health problems at an early stage. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects employee work activity data. The analysis unit analyzes the data collected by the collection unit and learns conditions that make employees more likely to suffer from illness. The transmission unit sends an alert based on the results obtained by the analysis unit.

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] With conventional technology, it was difficult to quickly grasp the health status of employees in a remote work environment and take appropriate measures.

[0005] The system according to the embodiment aims to analyze work activity data of employees and to detect employees who are likely to suffer from health problems at an early stage. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects employee work activity data. The analysis unit analyzes the data collected by the collection unit and learns conditions that make employees more likely to suffer from illness. The transmission unit transmits an alert based on the results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the work activity data of employees and detect employees who are likely to suffer from health problems at an early stage. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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) A health management system according to an embodiment of the present invention collects employee work activity data, analyzes it using AI, and sends alerts. The health management system collects employee work activity data, analyzes it using AI, and learns conditions that are likely to cause illness, allowing it to identify employees with a high risk of health problems early on. Furthermore, the health management system sends alerts to the identified employees' supervisors, allowing them to provide care before they collapse. For example, the health management system obtains employees' work hours and overtime hours from a time card system and the number of communication activities from an internal chat tool. Next, the health management system uses AI to analyze the collected data and learns conditions that are likely to cause illness. For example, it learns that long hours of overtime and frequent meetings are likely to cause illness. Next, the health management system sends alerts to the identified employees' supervisors. This allows the supervisor to understand the employee's condition and provide appropriate care. For example, the supervisor can encourage the employee to take vacation or adjust the employee's work load to reduce the employee's workload. This allows the health management system to protect the physical and mental health of employees. This allows the health management system to protect the physical and mental health of employees. For example, if an employee works long hours of overtime or frequently attends meetings, their supervisor can provide appropriate care to protect their health.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects work activity data of employees. The work activity data of employees includes, but is not limited to, work hours, overtime hours, meeting hours, number of attendances, and number of communication activities. The collection unit acquires work hours and overtime hours from, for example, a time card system. The collection unit can also acquire the number of communication activities from an internal chat tool. For example, the collection unit automatically acquires work hours and overtime hours from the time card system. The collection unit can also acquire the number of messages sent and received from the internal chat tool. The collection unit can also acquire meeting times from a calendar system. For example, the collection unit acquires the start and end times of meetings from the calendar system and calculates the meeting time. The analysis unit uses AI to analyze the data collected by the collection unit and learn conditions that make employees more likely to feel unwell. For example, the analysis unit learns that long hours of overtime and frequent meetings make employees more likely to feel unwell. The analysis unit can also use AI to analyze employee work activity data and learn conditions such as stress levels and insufficient rest. For example, the analysis unit inputs work activity data into an AI model and outputs conditions that are likely to cause illness. The transmission unit transmits an alert based on the results obtained by the analysis unit. The alert may be transmitted in the form of, for example, an email notification, a pop-up message, or a voice alert, but is not limited to these examples. The transmission unit transmits an alert to, for example, a supervisor. The transmission unit can also transmit an alert to encourage the supervisor to take a vacation. For example, the transmission unit transmits an alert to the supervisor by email to notify the supervisor of the employee's status. This allows the health management system according to the embodiment to protect the physical and mental health of employees. Some or all of the above-described processing by the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can transmit an alert using an AI model that inputs the results obtained by the analysis unit and outputs an alert.

[0030] The collection unit can acquire working hours and overtime hours from a time card system. Working hours include, for example, but are not limited to, the time from the start of work to the end of work. Overtime hours include, for example, but are not limited to, the time beyond regular working hours. The collection unit, for example, automatically acquires working hours and overtime hours from the time card system. The collection unit can also acquire working hours and overtime hours from the time card system in real time. For example, the collection unit periodically acquires working hours and overtime hours from the time card system and stores them in a database. This enables accurate data collection by acquiring working hours and overtime hours from the time card system. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the working hours and overtime hours data acquired from the time card system into AI and have the AI ​​analyze the data.

[0031] The collection unit can acquire the number of communication activities from the internal chat tool. The number of communication activities includes, for example, but is not limited to, the number of times a message is sent and received. For example, the collection unit automatically acquires the number of times a message is sent and received from the internal chat tool. The collection unit can also acquire the number of times a message is sent and received from the internal chat tool in real time. For example, the collection unit periodically acquires the number of times a message is sent and received from the internal chat tool and stores it in a database. In this way, by acquiring the number of communication activities from the internal chat tool, the communication status of employees can be understood. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the number of times a message is sent and received obtained from the internal chat tool into AI and have the AI ​​analyze the data.

[0032] The analysis unit can learn that long hours of overtime work and frequent meetings are likely to cause illness. Long hours of overtime work include, for example, but are not limited to, working more than eight hours a day. Frequent meetings include, for example, but are not limited to, a certain number of meetings per week. The analysis unit can, for example, use AI to learn that long hours of overtime work and frequent meetings are likely to cause illness. The analysis unit can also use AI to analyze employees' work activity data and learn conditions such as stress levels and lack of breaks. For example, the analysis unit inputs work activity data into an AI model and outputs conditions that are likely to cause illness. This allows learning that long hours of overtime work and frequent meetings are likely to cause illness, thereby enabling early detection of employee health risks. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit inputs work activity data into AI, which learns conditions that are likely to cause illness.

[0033] The sending unit can send an alert to a supervisor. Examples of the supervisor include, but are not limited to, a direct supervisor or a department head. Examples of the alert include, but are not limited to, an email notification, a pop-up message, or a voice alert. For example, the sending unit can send an alert to the supervisor by email to notify them of the employee's status. The sending unit can also send an alert to the supervisor via a pop-up message. For example, the sending unit can display a pop-up message on the supervisor's computer to notify them of the employee's status. By sending the alert to the supervisor, the supervisor can take appropriate care. Some or all of the above-described processing by the sending unit can be performed using, for example, AI, or can be performed without using AI. For example, the sending unit can send an alert using an AI model that inputs the results obtained by the analysis unit and outputs an alert.

[0034] The transmission unit can send an alert to encourage a supervisor to take a vacation to an employee. Examples of encouraging a supervisor to take a vacation include, but are not limited to, email notifications and verbal instructions. For example, the transmission unit can send an alert to a supervisor by email to encourage the employee to take a vacation. The transmission unit can also send an alert to encourage the supervisor to give verbal instructions. For example, the transmission unit can display a pop-up message on the supervisor's computer to encourage the employee to take a vacation. This allows the supervisor to send an alert encouraging the employee to take a vacation, thereby protecting the employee's health. Some or all of the above-described processing by the transmission unit can be performed, for example, using AI or without AI. For example, the transmission unit can send an alert using an AI model that uses the results obtained by the analysis unit as input and outputs an alert.

[0035] The health management system includes a collection unit that analyzes an employee's past work activity data and selects the optimal collection method. For example, if an employee has worked a lot of overtime in the past, the collection unit automates the collection of work activity data. Furthermore, if an employee has frequently attended meetings in the past, the collection unit can also collect work activity data after the meetings. For example, if an employee has frequently used communication tools in the past, the collection unit collects tool usage status in real time. This allows the optimal collection method to be selected by analyzing the employee's past work activity data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input past work activity data into AI and have the AI ​​select the optimal collection method.

[0036] The health management system includes a collection unit that filters work activity data based on the employee's current projects and areas of interest when collecting the data. For example, the collection unit collects only data related to projects currently underway by the employee. The collection unit can also prioritize the collection of data related to areas of interest to the employee. For example, the collection unit collects data related to projects in which the employee has previously expressed interest. This allows highly relevant data to be collected by filtering the data based on the employee's current projects and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data related to projects and areas of interest into AI and have the AI ​​perform the filtering.

[0037] The health management system includes a collection unit that selects the optimal collection means depending on the employee's input method when collecting work activity data. For example, if the employee uses voice input, the collection unit collects voice data. The collection unit can also collect text data if the employee uses text input. For example, if the employee uses images, the collection unit collects image data. This enables efficient data collection by selecting the optimal collection means depending on the employee's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data related to the input method into AI and have the AI ​​select the optimal collection means.

[0038] The health management system includes a collection unit that, when collecting work activity data, prioritizes collecting highly relevant data by taking into account the employee's geographical location information. For example, when the employee is in the office, the collection unit prioritizes collecting work activity data at the office. In addition, when the employee is working remotely, the collection unit can also prioritize collecting work activity data at home. For example, when the employee is on a business trip, the collection unit prioritizes collecting work activity data at the business trip destination. In this way, highly relevant data can be collected preferentially by taking into account the employee's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and have the AI ​​collect highly relevant data.

[0039] The health management system includes a collection unit that analyzes employees' social media activities and collects related data when collecting work activity data. The collection unit, for example, collects communication activity data based on the number of posts made by employees on social media. The collection unit can also analyze the content of employees' posts on social media to collect work-related data. For example, the collection unit collects work activity data based on the amount of time employees spend on social media. In this way, related data can be collected by analyzing employees' social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data related to social media activities into AI and have the AI ​​collect related data.

[0040] The health management system includes a collection unit that customizes the collection method by reflecting employees' past feedback when collecting work activity data. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by employees in the past. The collection unit can also change the data collection method based on feedback provided by employees in the past. For example, the collection unit changes the type of data to be collected based on feedback provided by employees in the past. This allows the collection method to be customized by reflecting employees' past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data regarding past feedback into AI and have the AI ​​customize the collection method.

[0041] The health management system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the work activity data. The analysis unit, for example, performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the work activity data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the importance of the work activity data into AI and have the AI ​​adjust the level of detail.

[0042] The health management system includes an analysis unit that applies different analysis algorithms depending on the category of work activity data. For example, the analysis unit applies a time series analysis algorithm to work time data. The analysis unit can also apply a network analysis algorithm to communication activity data. For example, the analysis unit applies a frequency analysis algorithm to meeting time data. This enables highly accurate analysis by applying different analysis algorithms depending on the category of work activity data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of work activity data into AI and have the AI ​​select the analysis algorithm to apply.

[0043] The health management system includes an analysis unit that improves the accuracy of analysis by referring to the employee's past analysis results. The analysis unit, for example, corrects the current analysis result based on the employee's past analysis results. The analysis unit can also adjust the parameters of the analysis algorithm based on the employee's past analysis results. For example, the analysis unit creates a feedback loop to improve the accuracy of the analysis based on the employee's past analysis results. This allows the analysis accuracy to be improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into AI and have the AI ​​improve the accuracy.

[0044] The health management system includes an analysis unit that determines the priority of analysis based on the time of submission of work activity data. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data that was submitted a long time ago. For example, the analysis unit appropriately prioritizes data that was submitted a medium time ago. This enables efficient analysis by determining the priority of analysis based on the time of submission of work activity data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the time of submission into AI and have the AI ​​determine the priority.

[0045] The health management system includes an analysis unit that adjusts the order of analysis based on the relevance of work activity data. The analysis unit, for example, prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. For example, the analysis unit moderately prioritizes data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of work activity data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data regarding relevance into AI and have the AI ​​adjust the order.

[0046] The health management system includes an analysis unit that adjusts the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit uses a lot of technical terminology for employees with high levels of expertise. The analysis unit can also avoid technical terminology for employees with low levels of expertise. For example, the analysis unit uses technical terminology moderately for employees with intermediate levels of expertise. This allows for adjusting the use of technical terminology according to the employee's level of expertise, thereby providing easy-to-understand analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data regarding the level of expertise into AI and have the AI ​​execute the use of technical terminology.

[0047] The health management system includes a transmission unit that adjusts the level of detail of the alert based on the importance of the employee's work activity data when sending an alert. The transmission unit, for example, transmits a detailed alert for data of high importance. The transmission unit can also transmit a simplified alert for data of low importance. For example, the transmission unit transmits an alert with an appropriate level of detail for data of medium importance. This allows for efficient alerting by adjusting the level of detail of the alert based on the importance of the work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may input the importance of the work activity data into AI and have the AI ​​adjust the level of detail.

[0048] The health management system includes a transmission unit that applies different alert algorithms depending on the category of employee work activity data when sending an alert. For example, the transmission unit applies a time-series alert algorithm to work time data. The transmission unit can also apply a network alert algorithm to communication activity data. For example, the transmission unit applies a frequency alert algorithm to meeting time data. This enables highly accurate alerts by applying different alert algorithms depending on the category of work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the category of work activity data into AI and have the AI ​​select the alert algorithm to apply.

[0049] The health management system includes a transmission unit that, when sending an alert, improves the accuracy of the alert by referring to the employee's past alert results. The transmission unit, for example, corrects the current alert based on the employee's past alert results. The transmission unit can also adjust the parameters of the alert algorithm based on the employee's past alert results. For example, the transmission unit creates a feedback loop to improve the accuracy of the alert based on the employee's past alert results. This allows the accuracy of the alert to be improved by referring to the employee's past alert results. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may input past alert results into AI and have the AI ​​improve the accuracy.

[0050] The health management system includes a transmission unit that, when sending an alert, determines the priority of the alert based on the time of submission of the employee's work activity data. The transmission unit, for example, prioritizes sending alerts based on recently submitted data. The transmission unit can also postpone alerts based on data submitted a short time ago. For example, the transmission unit gives moderate priority to alerts based on data submitted a medium time ago. This enables efficient alerting by determining the priority of alerts based on the time of submission of work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input data regarding the time of submission into AI and have the AI ​​determine the priority.

[0051] The health management system includes a transmission unit that adjusts the order of alerts based on the relevance of employee work activity data when sending alerts. The transmission unit, for example, prioritizes sending alerts based on data with high relevance. The transmission unit can also postpone alerts based on data with low relevance. For example, the transmission unit moderately prioritizes alerts based on data with medium relevance. This enables efficient alerting by adjusting the order of alerts based on the relevance of work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input data regarding relevance into AI and have the AI ​​adjust the order.

[0052] The health management system includes a transmission unit that adjusts the use of technical terminology in an alert depending on the employee's level of expertise when sending the alert. For example, the transmission unit transmits an alert that uses a lot of technical terminology to employees with high levels of expertise. The transmission unit can also transmit alerts that avoid technical terminology to employees with low levels of expertise. For example, the transmission unit transmits an alert that uses a moderate amount of technical terminology to employees with medium levels of expertise. This allows for providing an alert that is easy to understand by adjusting the use of technical terminology depending on the employee's level of expertise. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may input data regarding the level of expertise into AI and have the AI ​​control the use of technical terminology.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The health management system may also include an evaluation unit that evaluates an employee's performance based on the employee's work activity data. The evaluation unit may score the employee's performance based on, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities. The evaluation unit may also notify the employee's supervisor of the employee's performance score. For example, the evaluation unit may send the employee's performance score to the supervisor by email, providing reference information for evaluating the employee. This allows the health management system to evaluate the employee's performance and the supervisor to provide appropriate feedback.

[0055] The health management system may also include a career proposal unit that proposes career paths for employees based on their work activity data. The career proposal unit may analyze, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities, and propose a career path suitable for the employee. The career proposal unit may also propose appropriate training programs based on the employee's skills and interests. For example, the career proposal unit may analyze the employee's skill set and propose a training program to improve those skills. In this way, the health management system can support the career growth of employees.

[0056] The health management system can also include a team evaluation unit that evaluates the performance of the entire team based on employee work activity data. The team evaluation unit analyzes, for example, team members' working hours, overtime hours, meeting hours, and number of communication activities to score the performance of the entire team. The team evaluation unit can also notify the supervisor of the team's performance score. For example, the team evaluation unit can send the team's performance score to the supervisor by email, providing reference information for evaluating the team. This allows the health management system to evaluate the performance of the entire team and the supervisor to provide appropriate feedback.

[0057] The health management system may also include a prediction unit that predicts an employee's health risk based on the employee's work activity data. The prediction unit may predict the employee's health risk by analyzing, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities. The prediction unit may also send an alert to the employee's supervisor based on the predicted health risk. For example, if the employee's health risk is high, the prediction unit may send an alert by email to the employee's supervisor, urging them to take appropriate care. This allows the health management system to predict an employee's health risk early, allowing the supervisor to take appropriate action.

[0058] The health management system can also include a skill matching unit that matches employees' skills based on their work activity data. The skill matching unit analyzes, for example, employees' working hours, overtime hours, meeting hours, and number of communication activities, and suggests projects and tasks that are suitable for the employee. The skill matching unit can also form appropriate project teams based on employees' skill sets. For example, the skill matching unit analyzes employees' skill sets and suggests optimal project teams. This allows the health management system to make the most of employees' skills and support efficient project execution.

[0059] The health management system can also include an evaluation unit that evaluates an employee's work-life balance based on the employee's work activity data. The evaluation unit analyzes, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities to score the employee's work-life balance. The evaluation unit can also notify the employee's supervisor of the employee's work-life balance score. For example, the evaluation unit can send the employee's work-life balance score to the supervisor by email, providing reference information for the employee's health management. This allows the health management system to evaluate the employee's work-life balance, allowing the supervisor to take appropriate action.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection department collects data on employees' work activities. Specifically, it collects data such as working hours, overtime hours, meeting hours, number of attendances, and number of communication activities. For example, it obtains working hours and overtime hours from a time card system, the number of messages sent and received from an internal chat tool, and the start and end times of meetings from a calendar system to calculate meeting duration. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the conditions under which employees are likely to feel unwell. For example, it learns that employees are more likely to feel unwell when they work long hours of overtime or have frequent meetings, and also analyzes other conditions such as stress levels and lack of rest. Work activity data is input into the AI ​​model, and the conditions under which employees are likely to feel unwell are output. Step 3: The sending unit sends an alert based on the results obtained by the analysis unit. The alert is sent in the form of an email notification, a pop-up message, a voice alert, etc. For example, an alert can be sent to a supervisor to inform them of the employee's situation. A supervisor can also send an alert to encourage an employee to take vacation. The sending unit can also send an alert using an AI model that takes the results obtained by the analysis unit as input and outputs an alert.

[0062] (Example 2) A health management system according to an embodiment of the present invention collects employee work activity data, analyzes it using AI, and sends alerts. The health management system collects employee work activity data, analyzes it using AI, and learns conditions that are likely to cause illness, allowing it to identify employees with a high risk of health problems early on. Furthermore, the health management system sends alerts to the identified employees' supervisors, allowing them to provide care before they collapse. For example, the health management system obtains employees' work hours and overtime hours from a time card system and the number of communication activities from an internal chat tool. Next, the health management system uses AI to analyze the collected data and learns conditions that are likely to cause illness. For example, it learns that long hours of overtime and frequent meetings are likely to cause illness. Next, the health management system sends alerts to the identified employees' supervisors. This allows the supervisor to understand the employee's condition and provide appropriate care. For example, the supervisor can encourage the employee to take vacation or adjust the employee's work load to reduce the employee's workload. This allows the health management system to protect the physical and mental health of employees. This allows the health management system to protect the physical and mental health of employees. For example, if an employee works long hours of overtime or frequently attends meetings, their supervisor can provide appropriate care to protect their health.

[0063] A health management system according to an embodiment includes a collection unit, an analysis unit, and a transmission unit. The collection unit collects work activity data of employees. The work activity data of employees includes, but is not limited to, work hours, overtime hours, meeting hours, number of attendances, and number of communication activities. The collection unit acquires work hours and overtime hours from, for example, a time card system. The collection unit can also acquire the number of communication activities from an internal chat tool. For example, the collection unit automatically acquires work hours and overtime hours from the time card system. The collection unit can also acquire the number of messages sent and received from the internal chat tool. The collection unit can also acquire meeting times from a calendar system. For example, the collection unit acquires the start and end times of meetings from the calendar system and calculates the meeting time. The analysis unit uses AI to analyze the data collected by the collection unit and learn conditions that make employees more likely to feel unwell. For example, the analysis unit learns that long hours of overtime and frequent meetings make employees more likely to feel unwell. The analysis unit can also use AI to analyze employee work activity data and learn conditions such as stress levels and insufficient rest. For example, the analysis unit inputs work activity data into an AI model and outputs conditions that are likely to cause illness. The transmission unit transmits an alert based on the results obtained by the analysis unit. The alert may be transmitted in the form of, for example, an email notification, a pop-up message, or a voice alert, but is not limited to these examples. The transmission unit transmits an alert to, for example, a supervisor. The transmission unit can also transmit an alert to encourage the supervisor to take a vacation. For example, the transmission unit transmits an alert to the supervisor by email to notify the supervisor of the employee's status. This allows the health management system according to the embodiment to protect the physical and mental health of employees. Some or all of the above-described processing by the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can transmit an alert using an AI model that inputs the results obtained by the analysis unit and outputs an alert.

[0064] The collection unit can acquire working hours and overtime hours from a time card system. Working hours include, for example, but are not limited to, the time from the start of work to the end of work. Overtime hours include, for example, but are not limited to, the time beyond regular working hours. The collection unit, for example, automatically acquires working hours and overtime hours from the time card system. The collection unit can also acquire working hours and overtime hours from the time card system in real time. For example, the collection unit periodically acquires working hours and overtime hours from the time card system and stores them in a database. This enables accurate data collection by acquiring working hours and overtime hours from the time card system. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the working hours and overtime hours data acquired from the time card system into AI and have the AI ​​analyze the data.

[0065] The collection unit can acquire the number of communication activities from the internal chat tool. The number of communication activities includes, for example, but is not limited to, the number of times a message is sent and received. For example, the collection unit automatically acquires the number of times a message is sent and received from the internal chat tool. The collection unit can also acquire the number of times a message is sent and received from the internal chat tool in real time. For example, the collection unit periodically acquires the number of times a message is sent and received from the internal chat tool and stores it in a database. In this way, by acquiring the number of communication activities from the internal chat tool, the communication status of employees can be understood. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the number of times a message is sent and received obtained from the internal chat tool into AI and have the AI ​​analyze the data.

[0066] The analysis unit can learn that long hours of overtime work and frequent meetings are likely to cause illness. Long hours of overtime work include, for example, but are not limited to, working more than eight hours a day. Frequent meetings include, for example, but are not limited to, a certain number of meetings per week. The analysis unit can, for example, use AI to learn that long hours of overtime work and frequent meetings are likely to cause illness. The analysis unit can also use AI to analyze employees' work activity data and learn conditions such as stress levels and lack of breaks. For example, the analysis unit inputs work activity data into an AI model and outputs conditions that are likely to cause illness. This allows learning that long hours of overtime work and frequent meetings are likely to cause illness, thereby enabling early detection of employee health risks. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit inputs work activity data into AI, which learns conditions that are likely to cause illness.

[0067] The sending unit can send an alert to a supervisor. Examples of the supervisor include, but are not limited to, a direct supervisor or a department head. Examples of the alert include, but are not limited to, an email notification, a pop-up message, or a voice alert. For example, the sending unit can send an alert to the supervisor by email to notify them of the employee's status. The sending unit can also send an alert to the supervisor via a pop-up message. For example, the sending unit can display a pop-up message on the supervisor's computer to notify them of the employee's status. By sending the alert to the supervisor, the supervisor can take appropriate care. Some or all of the above-described processing by the sending unit can be performed using, for example, AI, or can be performed without using AI. For example, the sending unit can send an alert using an AI model that inputs the results obtained by the analysis unit and outputs an alert.

[0068] The transmission unit can send an alert to encourage a supervisor to take a vacation to an employee. Examples of encouraging a supervisor to take a vacation include, but are not limited to, email notifications and verbal instructions. For example, the transmission unit can send an alert to a supervisor by email to encourage the employee to take a vacation. The transmission unit can also send an alert to encourage the supervisor to give verbal instructions. For example, the transmission unit can display a pop-up message on the supervisor's computer to encourage the employee to take a vacation. This allows the supervisor to send an alert encouraging the employee to take a vacation, thereby protecting the employee's health. Some or all of the above-described processing by the transmission unit can be performed, for example, using AI or without AI. For example, the transmission unit can send an alert using an AI model that uses the results obtained by the analysis unit as input and outputs an alert.

[0069] The health management system includes a collection unit that estimates an employee's emotions and adjusts the timing of work activity data collection based on the estimated employee emotions. For example, the collection unit temporarily delays the collection of work activity data when the employee is stressed. The collection unit can also collect work activity data at a normal timing when the employee is relaxed. For example, the collection unit shortens the collection of work activity data when the employee is tired. This allows for more appropriate data collection by adjusting the timing of data collection based on the employee's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input the employee's emotion data into the generation AI and have the generation AI adjust the collection timing based on the emotion.

[0070] The health management system includes a collection unit that analyzes an employee's past work activity data and selects the optimal collection method. For example, if an employee has worked a lot of overtime in the past, the collection unit automates the collection of work activity data. Furthermore, if an employee has frequently attended meetings in the past, the collection unit can also collect work activity data after the meetings. For example, if an employee has frequently used communication tools in the past, the collection unit collects tool usage status in real time. This allows the optimal collection method to be selected by analyzing the employee's past work activity data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input past work activity data into AI and have the AI ​​select the optimal collection method.

[0071] The health management system includes a collection unit that filters work activity data based on the employee's current projects and areas of interest when collecting the data. For example, the collection unit collects only data related to projects currently underway by the employee. The collection unit can also prioritize the collection of data related to areas of interest to the employee. For example, the collection unit collects data related to projects in which the employee has previously expressed interest. This allows highly relevant data to be collected by filtering the data based on the employee's current projects and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data related to projects and areas of interest into AI and have the AI ​​perform the filtering.

[0072] The health management system includes a collection unit that selects the optimal collection means depending on the employee's input method when collecting work activity data. For example, if the employee uses voice input, the collection unit collects voice data. The collection unit can also collect text data if the employee uses text input. For example, if the employee uses images, the collection unit collects image data. This enables efficient data collection by selecting the optimal collection means depending on the employee's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data related to the input method into AI and have the AI ​​select the optimal collection means.

[0073] The health management system includes a collection unit that estimates an employee's emotions and prioritizes data to be collected based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit prioritizes collecting stress-related data. Furthermore, if the employee is relaxed, the collection unit can also perform normal data collection. For example, if the employee is tired, the collection unit prioritizes collecting fatigue-related data. By prioritizing data based on the employee's emotions, important data can be collected preferentially. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input the employee's emotion data into the generation AI and have the generation AI prioritize the data based on the emotions.

[0074] The health management system includes a collection unit that, when collecting work activity data, prioritizes collecting highly relevant data by taking into account the employee's geographical location information. For example, when the employee is in the office, the collection unit prioritizes collecting work activity data at the office. In addition, when the employee is working remotely, the collection unit can also prioritize collecting work activity data at home. For example, when the employee is on a business trip, the collection unit prioritizes collecting work activity data at the business trip destination. In this way, highly relevant data can be collected preferentially by taking into account the employee's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and have the AI ​​collect highly relevant data.

[0075] The health management system includes a collection unit that analyzes employees' social media activities and collects related data when collecting work activity data. The collection unit, for example, collects communication activity data based on the number of posts made by employees on social media. The collection unit can also analyze the content of employees' posts on social media to collect work-related data. For example, the collection unit collects work activity data based on the amount of time employees spend on social media. In this way, related data can be collected by analyzing employees' social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data related to social media activities into AI and have the AI ​​collect related data.

[0076] The health management system includes a collection unit that customizes the collection method by reflecting employees' past feedback when collecting work activity data. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by employees in the past. The collection unit can also change the data collection method based on feedback provided by employees in the past. For example, the collection unit changes the type of data to be collected based on feedback provided by employees in the past. This allows the collection method to be customized by reflecting employees' past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data regarding past feedback into AI and have the AI ​​customize the collection method.

[0077] The health management system includes an analysis unit that estimates an employee's emotions and adjusts the analysis presentation method based on the estimated employee emotions. For example, if the employee is stressed, the analysis unit presents the analysis results in a simple presentation. Alternatively, if the employee is relaxed, the analysis unit can present the analysis results in a detailed presentation. For example, if the employee is tired, the analysis unit presents the analysis results in a visually easy-to-understand presentation. This allows for more appropriate analysis results to be provided by adjusting the analysis presentation method based on the employee's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input the employee's emotion data into the generative AI and have the generative AI adjust the presentation method based on the emotion.

[0078] The health management system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the work activity data. The analysis unit, for example, performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the work activity data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the importance of the work activity data into AI and have the AI ​​adjust the level of detail.

[0079] The health management system includes an analysis unit that applies different analysis algorithms depending on the category of work activity data. For example, the analysis unit applies a time series analysis algorithm to work time data. The analysis unit can also apply a network analysis algorithm to communication activity data. For example, the analysis unit applies a frequency analysis algorithm to meeting time data. This enables highly accurate analysis by applying different analysis algorithms depending on the category of work activity data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of work activity data into AI and have the AI ​​select the analysis algorithm to apply.

[0080] The health management system includes an analysis unit that improves the accuracy of analysis by referring to the employee's past analysis results. The analysis unit, for example, corrects the current analysis result based on the employee's past analysis results. The analysis unit can also adjust the parameters of the analysis algorithm based on the employee's past analysis results. For example, the analysis unit creates a feedback loop to improve the accuracy of the analysis based on the employee's past analysis results. This allows the analysis accuracy to be improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into AI and have the AI ​​improve the accuracy.

[0081] The health management system includes an analysis unit that estimates an employee's emotions and adjusts the length of the analysis based on the estimated employee emotions. For example, the analysis unit provides a short analysis result when the employee is stressed. The analysis unit can also provide a detailed analysis result when the employee is relaxed. For example, the analysis unit provides a concise analysis result when the employee is tired. By adjusting the length of the analysis based on the employee's emotions, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input the employee's emotion data into the generative AI and have the generative AI adjust the length of the analysis based on the emotion.

[0082] The health management system includes an analysis unit that determines the priority of analysis based on the time of submission of work activity data. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data that was submitted a long time ago. For example, the analysis unit appropriately prioritizes data that was submitted a medium time ago. This enables efficient analysis by determining the priority of analysis based on the time of submission of work activity data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the time of submission into AI and have the AI ​​determine the priority.

[0083] The health management system includes an analysis unit that adjusts the order of analysis based on the relevance of work activity data. The analysis unit, for example, prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. For example, the analysis unit moderately prioritizes data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of work activity data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data regarding relevance into AI and have the AI ​​adjust the order.

[0084] The health management system includes an analysis unit that adjusts the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit uses a lot of technical terminology for employees with high levels of expertise. The analysis unit can also avoid technical terminology for employees with low levels of expertise. For example, the analysis unit uses technical terminology moderately for employees with intermediate levels of expertise. This allows for adjusting the use of technical terminology according to the employee's level of expertise, thereby providing easy-to-understand analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data regarding the level of expertise into AI and have the AI ​​execute the use of technical terminology.

[0085] The health management system includes a transmission unit that estimates an employee's emotions and adjusts the way an alert is expressed based on the estimated employee's emotions. For example, if the employee is feeling stressed, the transmission unit transmits the alert using calm language. Furthermore, if the employee is relaxed, the transmission unit can also transmit the alert using normal language. For example, if the employee is tired, the transmission unit transmits the alert using simple language. This allows for adjusting the way the alert is expressed based on the employee's emotions, thereby providing a more appropriate alert. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or without AI. For example, the transmission unit may input the employee's emotion data into the generation AI and have the generation AI adjust the way the alert is expressed based on the emotion.

[0086] The health management system includes a transmission unit that adjusts the level of detail of the alert based on the importance of the employee's work activity data when sending an alert. The transmission unit, for example, transmits a detailed alert for data of high importance. The transmission unit can also transmit a simplified alert for data of low importance. For example, the transmission unit transmits an alert with an appropriate level of detail for data of medium importance. This allows for efficient alerting by adjusting the level of detail of the alert based on the importance of the work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may input the importance of the work activity data into AI and have the AI ​​adjust the level of detail.

[0087] The health management system includes a transmission unit that applies different alert algorithms depending on the category of employee work activity data when sending an alert. For example, the transmission unit applies a time-series alert algorithm to work time data. The transmission unit can also apply a network alert algorithm to communication activity data. For example, the transmission unit applies a frequency alert algorithm to meeting time data. This enables highly accurate alerts by applying different alert algorithms depending on the category of work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the category of work activity data into AI and have the AI ​​select the alert algorithm to apply.

[0088] The health management system includes a transmission unit that, when sending an alert, improves the accuracy of the alert by referring to the employee's past alert results. The transmission unit, for example, corrects the current alert based on the employee's past alert results. The transmission unit can also adjust the parameters of the alert algorithm based on the employee's past alert results. For example, the transmission unit creates a feedback loop to improve the accuracy of the alert based on the employee's past alert results. This allows the accuracy of the alert to be improved by referring to the employee's past alert results. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may input past alert results into AI and have the AI ​​improve the accuracy.

[0089] The health management system includes a transmission unit that estimates an employee's emotions and adjusts the length of an alert based on the estimated employee's emotions. For example, the transmission unit transmits a short alert when the employee is stressed. The transmission unit can also transmit a detailed alert when the employee is relaxed. For example, the transmission unit transmits a concise alert when the employee is tired. This allows for adjusting the length of the alert based on the employee's emotions, thereby providing a more appropriate alert. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using AI, or may be performed without AI. For example, the transmission unit may input employee emotion data into the generation AI and cause the generation AI to adjust the length of the alert based on the emotion.

[0090] The health management system includes a transmission unit that, when sending an alert, determines the priority of the alert based on the time of submission of the employee's work activity data. The transmission unit, for example, prioritizes sending alerts based on recently submitted data. The transmission unit can also postpone alerts based on data submitted a short time ago. For example, the transmission unit gives moderate priority to alerts based on data submitted a medium time ago. This enables efficient alerting by determining the priority of alerts based on the time of submission of work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input data regarding the time of submission into AI and have the AI ​​determine the priority.

[0091] The health management system includes a transmission unit that adjusts the order of alerts based on the relevance of employee work activity data when sending alerts. The transmission unit, for example, prioritizes sending alerts based on data with high relevance. The transmission unit can also postpone alerts based on data with low relevance. For example, the transmission unit moderately prioritizes alerts based on data with medium relevance. This enables efficient alerting by adjusting the order of alerts based on the relevance of work activity data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input data regarding relevance into AI and have the AI ​​adjust the order.

[0092] The health management system includes a transmission unit that adjusts the use of technical terminology in an alert depending on the employee's level of expertise when sending the alert. For example, the transmission unit transmits an alert that uses a lot of technical terminology to employees with high levels of expertise. The transmission unit can also transmit alerts that avoid technical terminology to employees with low levels of expertise. For example, the transmission unit transmits an alert that uses a moderate amount of technical terminology to employees with medium levels of expertise. This allows for providing an alert that is easy to understand by adjusting the use of technical terminology depending on the employee's level of expertise. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may input data regarding the level of expertise into AI and have the AI ​​control the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and transmission unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and acquires data from a time card system or an in-house chat tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using AI to learn conditions that are likely to cause illness. The transmission unit is realized, for example, by the control unit 46A of the smart device 14 and sends an alert to a supervisor. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and transmission unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and acquires data from a time card system or an in-house chat tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using AI to learn conditions that are likely to cause illness. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends an alert to a supervisor. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and transmission unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset terminal 314 and acquires data from a time card system or an in-house chat tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using AI to learn conditions that are likely to cause illness. The transmission unit is realized, for example, by the control unit 46A of the headset terminal 314 and sends an alert to a supervisor. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and transmission unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and acquires data from a time card system or an in-house chat tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using AI to learn conditions that are likely to cause illness. The transmission unit is realized, for example, by the control unit 46A of the robot 414 and sends an alert to a supervisor.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The health management system may also include an evaluation unit that evaluates an employee's performance based on the employee's work activity data. The evaluation unit may score the employee's performance based on, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities. The evaluation unit may also notify the employee's supervisor of the employee's performance score. For example, the evaluation unit may send the employee's performance score to the supervisor by email, providing reference information for evaluating the employee. This allows the health management system to evaluate the employee's performance and the supervisor to provide appropriate feedback.

[0095] The health management system may also include a career proposal unit that proposes career paths for employees based on their work activity data. The career proposal unit may analyze, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities, and propose a career path suitable for the employee. The career proposal unit may also propose appropriate training programs based on the employee's skills and interests. For example, the career proposal unit may analyze the employee's skill set and propose a training program to improve those skills. In this way, the health management system can support the career growth of employees.

[0096] The health management system may also include a suggestion unit that estimates an employee's emotions and suggests a refreshing activity based on the estimated employee's emotions. For example, if an employee is feeling stressed, the suggestion unit may suggest a relaxing activity. Also, if an employee is relaxed, the suggestion unit may suggest an activity that will increase motivation. For example, if an employee is tired, the suggestion unit may suggest a short break or light exercise. In this way, by suggesting an appropriate refreshing activity based on the employee's emotions, it is possible to improve the health and performance of the employee.

[0097] The health management system can also include a team evaluation unit that evaluates the performance of the entire team based on employee work activity data. The team evaluation unit analyzes, for example, team members' working hours, overtime hours, meeting hours, and number of communication activities to score the performance of the entire team. The team evaluation unit can also notify the supervisor of the team's performance score. For example, the team evaluation unit can send the team's performance score to the supervisor by email, providing reference information for evaluating the team. This allows the health management system to evaluate the performance of the entire team and the supervisor to provide appropriate feedback.

[0098] The health management system may also include a priority adjustment unit that estimates an employee's emotions and adjusts the priority of work based on the estimated employee emotions. For example, if an employee is feeling stressed, the priority adjustment unit postpones less important work. The priority adjustment unit may also prioritize more important work when the employee is relaxed. For example, if an employee is tired, the priority adjustment unit prioritizes easier work. In this way, adjusting the priority of work based on the employee's emotions can reduce the burden on employees and support efficient work performance.

[0099] The health management system may also include a prediction unit that predicts an employee's health risk based on the employee's work activity data. The prediction unit may predict the employee's health risk by analyzing, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities. The prediction unit may also send an alert to the employee's supervisor based on the predicted health risk. For example, if the employee's health risk is high, the prediction unit may send an alert by email to the employee's supervisor, urging them to take appropriate care. This allows the health management system to predict an employee's health risk early, allowing the supervisor to take appropriate action.

[0100] The health management system may also include a communication adjustment unit that estimates an employee's emotions and adjusts the communication method based on the estimated employee's emotions. For example, if the employee is feeling stressed, the communication adjustment unit may communicate in a calm tone. Alternatively, if the employee is relaxed, the communication adjustment unit may communicate in a normal tone. For example, if the employee is tired, the communication adjustment unit may send a concise message. This allows for more effective communication by adjusting the communication method based on the employee's emotions.

[0101] The health management system can also include a skill matching unit that matches employees' skills based on their work activity data. The skill matching unit analyzes, for example, employees' working hours, overtime hours, meeting hours, and number of communication activities, and suggests projects and tasks that are suitable for the employee. The skill matching unit can also form appropriate project teams based on employees' skill sets. For example, the skill matching unit analyzes employees' skill sets and suggests optimal project teams. This allows the health management system to make the most of employees' skills and support efficient project execution.

[0102] The health management system may also include a feedback adjustment unit that estimates an employee's emotions and adjusts the content of feedback based on the estimated employee emotions. For example, if an employee is feeling stressed, the feedback adjustment unit prioritizes positive feedback. The feedback adjustment unit may also provide constructive feedback if the employee is relaxed. For example, if an employee is tired, the feedback adjustment unit provides concise, positive feedback. This allows for more effective feedback to be provided by adjusting the content of feedback based on the employee's emotions.

[0103] The health management system can also include an evaluation unit that evaluates an employee's work-life balance based on the employee's work activity data. The evaluation unit analyzes, for example, the employee's working hours, overtime hours, meeting hours, and number of communication activities to score the employee's work-life balance. The evaluation unit can also notify the employee's supervisor of the employee's work-life balance score. For example, the evaluation unit can send the employee's work-life balance score to the supervisor by email, providing reference information for the employee's health management. This allows the health management system to evaluate the employee's work-life balance, allowing the supervisor to take appropriate action.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection department collects data on employees' work activities. Specifically, it collects data such as working hours, overtime hours, meeting hours, number of attendances, and number of communication activities. For example, it obtains working hours and overtime hours from a time card system, the number of messages sent and received from an internal chat tool, and the start and end times of meetings from a calendar system to calculate meeting duration. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the conditions under which employees are likely to feel unwell. For example, it learns that employees are more likely to feel unwell when they work long hours of overtime or have frequent meetings, and also analyzes other conditions such as stress levels and lack of rest. Work activity data is input into the AI ​​model, and the conditions under which employees are likely to feel unwell are output. Step 3: The sending unit sends an alert based on the results obtained by the analysis unit. The alert is sent in the form of an email notification, a pop-up message, a voice alert, etc. For example, an alert can be sent to a supervisor to inform them of the employee's situation. A supervisor can also send an alert to encourage an employee to take vacation. The sending unit can also send an alert using an AI model that takes the results obtained by the analysis unit as input and outputs an alert.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects employee work activity data; an analysis unit that analyzes the data collected by the collection unit and learns conditions under which malfunctions are likely to occur; a transmitter that transmits an alert based on the results obtained by the analyzer. A system characterized by:

2. The collecting unit Obtaining working hours and overtime hours from the time card system 2. The system of claim 1.

3. The collecting unit Obtain the number of communication activities from the internal chat tool 2. The system of claim 1.

4. The analysis unit Learn that long hours of overtime and frequent meetings can lead to poor health 2. The system of claim 1.

5. The transmission unit Send an alert to your manager 2. The system of claim 1.

6. The transmission unit A manager sends an alert to employees to take time off.

2. The system of claim 1.

7. The collecting unit Estimate employee emotions and adjust the timing of work activity data collection based on the estimated employee emotions.

2. The system of claim 1.

8. The collecting unit Analyze employees' past work activity data and select the optimal collection method 2. The system of claim 1.

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