system

Abstract

The system according to the embodiment aims to detect in advance when an employee needs mental care and respond appropriately. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a response unit. The collection unit collects behavioral data or communication data of employees. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies employees who need mental care based on the results of the analysis by the analysis unit. The response unit takes action for the employees identified by the identification 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] Conventional technology has the drawback of making it difficult to detect in advance when an employee needs mental health care and to respond appropriately.

[0005] The system according to the embodiment aims to detect in advance when an employee needs mental care and respond appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a response unit. The collection unit collects behavioral data or communication data of employees. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies employees who need mental care based on the results of the analysis by the analysis unit. The response unit responds to the employees identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect in advance when an employee needs mental care and respond appropriately. [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 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 mental health support system according to an embodiment of the present invention collects, analyzes, identifies, and responds to employee behavioral and communication data. This mental health support system collects employee behavioral and communication data, identifies employees in need of mental health care based on the analysis results, and provides appropriate support, thereby improving employee satisfaction and reducing employee turnover. For example, the mental health support system collects data such as the content of employees' emails and chats, working hours, and vacation status. Next, AI analyzes the collected data to detect signs of stress and anxiety. For example, natural language processing or machine learning models are used to analyze emotions from the content of emails and chats. Based on the analysis results, employees in need of mental health care are identified. Appropriate mental health support measures are then provided to identified employees. For example, notifications encouraging consultation with a specialist may be sent, or stress management advice may be provided. This system improves employee satisfaction and reduces employee turnover. It also avoids the costs of hiring and employee training. As a result, the mental health support system can proactively detect employees' mental health needs and provide appropriate support, thereby improving employee satisfaction and reducing employee turnover.

[0029] A mental care support system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a response unit. The collection unit collects behavioral data and communication data of employees. For example, the collection unit can collect data such as the content of emails and chat messages, working hours, and vacation status of employees. The collection unit can analyze the content of emails and chat messages to perform keyword frequency and sentiment analysis. The collection unit can also collect data on working hours and record data such as arrival times, departure times, and overtime hours. The collection unit can also collect data on vacation status and record data such as paid vacation, sick leave, and special leave. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing or a machine learning model to analyze the collected data and detect signs of stress or anxiety. The analysis unit can use natural language processing technology to perform morphological analysis, grammatical analysis, semantic analysis, and the like on the content of emails and chat messages. The analysis unit can also use a machine learning model to analyze the collected data and detect signs of stress or anxiety. For example, a machine learning model such as a neural network or a support vector machine can be used. The identification unit identifies employees who need mental care based on the results of the analysis by the analysis unit. For example, the identification unit can evaluate stress levels and signs of anxiety based on the analysis results and identify employees who need mental care. The identification unit can use an algorithm to identify employees who need mental care based on stress levels and signs of anxiety. The response unit takes appropriate action for the employees identified by the identification unit. For example, the response unit can send a notification to the identified employees encouraging them to consult with a specialist. The response unit can also provide the identified employees with stress management advice. For example, the response unit can provide advice such as relaxation techniques and time management tips. As a result, the mental care support system according to the embodiment can detect employees' mental care needs in advance and take appropriate action, thereby improving employee satisfaction and reducing employee turnover.

[0030] The collection unit can collect data on email or chat content, working hours, and vacation usage status. For example, the collection unit can collect email or chat content and perform keyword frequency and sentiment analysis. For example, the collection unit can analyze email or chat content and measure the frequency of specific keywords. The collection unit can also use a sentiment analysis algorithm to analyze emotions from email or chat content. For example, the collection unit can measure the proportion of positive and negative emotions. Furthermore, the collection unit can collect data on working hours and record arrival times, departure times, overtime hours, etc. For example, the collection unit can record employees' arrival times and departure times and measure overtime hours. The collection unit can also collect vacation usage status and record data on paid leave, sick leave, special leave, etc. For example, the collection unit can record employees' vacation usage status and measure the type and number of days of vacation. In this way, by collecting detailed employee behavioral data and communication data, employees in need of mental care can be more accurately identified.

[0031] The analysis unit can analyze the collected data using natural language processing or a machine learning model to detect signs of stress or anxiety. The analysis unit can analyze the collected data using, for example, natural language processing technology. For example, the analysis unit can perform morphological analysis to analyze the structure of a sentence. The analysis unit can also perform grammatical analysis to analyze the grammatical structure of a sentence. The analysis unit can also perform semantic analysis to analyze the meaning of a sentence. For example, the analysis unit can extract keywords and phrases from a sentence and analyze their meaning. The analysis unit can also analyze the collected data using a machine learning model. For example, the analysis unit can analyze the collected data using a neural network. The analysis unit can also analyze the collected data using a support vector machine. The analysis unit can also detect signs of stress or anxiety based on the collected data. For example, the analysis unit can detect signs of stress or anxiety from the collected data using a sentiment analysis algorithm. As a result, signs of stress and anxiety can be detected with high accuracy by using natural language processing or a machine learning model.

[0032] The identification unit can identify employees who need mental care based on the analysis results. The identification unit can, for example, evaluate stress levels and signs of anxiety based on the analysis results and identify employees who need mental care. For example, the identification unit can evaluate stress levels and identify employees who need mental care if the stress levels exceed a certain standard. The identification unit can also evaluate signs of anxiety and identify employees who need mental care if the signs of anxiety exceed a certain standard. Furthermore, the identification unit can use an algorithm to identify employees who need mental care based on the analysis results. For example, the identification unit can use an algorithm to identify employees who need mental care based on stress levels and signs of anxiety. This makes it possible to identify employees who need mental care based on the analysis results and take appropriate measures.

[0033] The response unit can send the identified employee a notification recommending that they consult with a specialist. The response unit can, for example, send the identified employee a notification recommending that they consult with a specialist. For example, the response unit can send an email notification recommending that they consult with a specialist. The response unit can also send an app notification recommending that they consult with a specialist. Furthermore, the response unit can customize the content of the notification recommending that they consult with a specialist for the identified employee. For example, the response unit can customize the content of the notification depending on the situation of the identified employee and recommend that they consult with a specialist. In this way, by sending a notification encouraging that they consult with a specialist, mental care for the employee can be provided quickly.

[0034] The support department can provide stress management advice to the identified employee. The support department can, for example, provide stress management advice to the identified employee. For example, the support department can suggest relaxation techniques. For example, the support department can suggest relaxation techniques such as deep breathing and meditation. The support department can also provide tips on time management. For example, the support department can suggest methods for prioritizing tasks and methods for efficient schedule management. Furthermore, the support department can customize the stress management advice for the identified employee. For example, the support department can customize the content of the advice according to the situation of the identified employee and provide stress management advice. In this way, by providing stress management advice, it is possible to support the mental care of employees.

[0035] The collection unit can analyze the employee's past behavioral data and select an appropriate data collection method. The collection unit can, for example, analyze the employee's past behavioral data and select the optimal data collection method. For example, the collection unit can prioritize communication tools that the employee has frequently used in the past as data collection targets. The collection unit can also select the most efficient data collection timing based on the employee's past working time data. Furthermore, the collection unit can analyze the employee's past vacation status and adjust the vacation data collection method. For example, the collection unit can select the vacation data collection method based on the type and number of vacation days the employee has taken in the past. In this way, the optimal data collection method can be selected by analyzing the past behavioral data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past behavioral data into a generation AI and have the generation AI select the optimal data collection method.

[0036] The collection unit may filter data based on the employee's current project or job content when collecting data. For example, the collection unit may filter data based on the employee's current project or job content when collecting data. For example, the collection unit may prioritize collecting data related to the employee's current project. The collection unit may also collect only necessary data and exclude unnecessary data depending on the employee's job content. Furthermore, if an employee is in charge of multiple projects, the collection unit may collect data related to each project separately. For example, the collection unit may filter data based on keywords or phrases related to a specific project. This allows data to be filtered based on the current project or job content, thereby collecting only necessary 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 have the generation AI perform filtering based on the current project or job content.

[0037] The collection unit can prioritize collecting highly relevant data based on the employee's geographic location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking the employee's geographic location information into consideration when collecting data. For example, when the employee is in the office, the collection unit can prioritize collecting behavioral data within the office. Furthermore, when the employee is on a business trip, the collection unit can prioritize collecting behavioral data at the business trip destination. Furthermore, when the employee is working remotely, the collection unit can prioritize collecting behavioral data at home. For example, the collection unit can obtain the employee's geographic location information using GPS data or a location information service and set priorities for data collection based on that information. This allows highly relevant data to be collected preferentially by taking the geographic location information into consideration. Some or all of the above-described 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 cause the generation AI to set priorities for data collection based on the geographic location information.

[0038] The collection unit can analyze employees' social media activities during data collection and collect related data. For example, the collection unit can analyze employees' social media activities during data collection and collect related data. For example, the collection unit can analyze employees' social media posts to detect signs of stress or anxiety. The collection unit can also estimate an employee's mental state based on the frequency of the employee's social media activity. Furthermore, the collection unit can analyze employees' social media interactions to understand interpersonal relationships in the workplace. For example, the collection unit can analyze social media posts to measure the frequency of specific keywords or phrases. This allows for the analysis of social media activity to collect related data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can have a generation AI perform the analysis of social media activity.

[0039] The analysis unit can set the level of analysis detail based on the importance of the data during analysis. The analysis unit can, for example, adjust the level of analysis detail based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can evaluate the importance of the data based on the impact on business operations and the freshness of the data, and set the level of analysis detail. This enables efficient analysis by adjusting the level of analysis detail according to the importance of the data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can cause the generation AI to set the level of analysis detail based on the importance of the data.

[0040] The analysis unit can use different analysis algorithms depending on the data category during analysis. The analysis unit can apply different analysis algorithms depending on the data category during analysis, for example. For example, the analysis unit can apply a natural language processing algorithm to communication data. Furthermore, the analysis unit can apply a machine learning algorithm to behavioral data. Furthermore, the analysis unit can apply a statistical analysis algorithm to working time data. For example, the analysis unit can apply a text analysis algorithm to text data and a numerical analysis algorithm to numerical data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. 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 cause a generation AI to apply an analysis algorithm depending on the data category.

[0041] The analysis unit can set analysis priorities based on the time of data submission during analysis. The analysis unit can, for example, determine analysis priorities based on the time of data submission during analysis. For example, the analysis unit can prioritize analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule according to the time of submission. For example, the analysis unit can evaluate the time of data submission based on the submission date and submission time and set analysis priorities. This enables efficient analysis by determining analysis priorities according to the time of data submission. 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 cause the generation AI to set analysis priorities based on the time of submission.

[0042] The analysis unit can set the order of analysis based on the relevance of the data during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the data. For example, the analysis unit can evaluate the relevance of the data based on correlations and causal relationships between the data and set the order of analysis. This enables efficient analysis by adjusting the order of analysis according to the relevance of the 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 cause the generation AI to set the order of analysis based on the relevance.

[0043] The identification unit can improve the accuracy of identification based on the interrelationships of data during identification. For example, the identification unit can improve the accuracy of identification by taking into account the interrelationships of data during identification. For example, the identification unit can analyze the interrelationships between communication data and behavioral data to improve the accuracy of identification. The identification unit can also analyze the interrelationships between working hours data and vacation acquisition data to improve the accuracy of identification. Furthermore, the identification unit can analyze the interrelationships between social media activity data and business data to improve the accuracy of identification. For example, the identification unit can use an algorithm that improves the accuracy of identification based on correlations and causal relationships between data. This improves the accuracy of identification by taking into account the interrelationships of data. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can cause a generation AI to improve the accuracy of identification based on the interrelationships.

[0044] The identification unit can perform identification based on the employee's attribute information during identification. For example, the identification unit can perform identification taking into account the employee's attribute information during identification. For example, the identification unit can perform identification taking into account the employee's age and gender. The identification unit can also perform identification taking into account the employee's job title and position. The identification unit can also perform identification taking into account the employee's years of service and experience. For example, the identification unit can use an algorithm that improves the accuracy of identification based on the employee's attribute information. This improves the accuracy of identification by taking into account the employee's attribute information. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can cause a generation AI to perform identification based on the attribute information.

[0045] The identification unit can perform identification based on the geographic distribution of the data during identification. For example, the identification unit can perform identification taking the geographic distribution of the data into consideration during identification. For example, when an employee is in the office, the identification unit can prioritize identifying data within the office. Furthermore, when an employee is on a business trip, the identification unit can prioritize identifying data at the business trip destination. Furthermore, when an employee is working remotely, the identification unit can prioritize identifying data at home. For example, the identification unit can evaluate the geographic distribution of the data based on data by region or data by country, and improve the accuracy of identification. In this way, the accuracy of identification is improved by taking the geographic distribution of the data into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can cause a generation AI to perform identification based on the geographic distribution.

[0046] The identification unit can improve the accuracy of identification based on related literature during identification. For example, the identification unit can improve the accuracy of identification by referring to related literature during identification. For example, the identification unit can improve the accuracy of identification by referring to the latest research papers. The identification unit can also improve the accuracy of identification by referring to industry best practices. Furthermore, the identification unit can improve the accuracy of identification by referring to past cases. For example, the identification unit can use an algorithm that improves the accuracy of identification based on academic papers and technical reports. In this way, the accuracy of identification is improved by referring to related literature. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can cause the generation AI to improve the accuracy of identification based on related literature.

[0047] The response unit can select an appropriate response method based on the employee's past response history when responding. For example, the response unit can select the optimal response method by referring to the employee's past response history when responding. For example, the response unit can re-propose a response method that was effective for the employee in the past. The response unit can also analyze the employee's past response history and select the optimal response method. Furthermore, the response unit can propose a new response method based on response methods that the employee has tried in the past. For example, the response unit can select the optimal response method based on past counseling records and response results. In this way, the optimal response method can be selected by referring to the past response history. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can have a generation AI select a response method based on the past response history.

[0048] The response unit can set a response measure based on the employee's current situation when responding. The response unit can, for example, customize a response measure based on the employee's current situation when responding. For example, the response unit can suggest a response measure related to a project the employee is currently working on. The response unit can also suggest the optimal response measure based on the employee's current work content. Furthermore, the response unit can customize the response measure based on the employee's current work situation. For example, the response unit can select the optimal response measure based on the employee's current work content and the progress of the project. This enables more effective response by customizing the response measure based on the current situation. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can cause a generation AI to customize the response measure based on the current situation.

[0049] The response unit can select an appropriate response method based on the employee's geographic location information when responding. For example, the response unit can select the optimal response method by taking the employee's geographic location information into consideration when responding. For example, if the employee is in the office, the response unit can suggest a response method to be used in the office. Furthermore, if the employee is on a business trip, the response unit can suggest a response method to be used at the business trip destination. Furthermore, if the employee is working remotely, the response unit can suggest a response method to be used at home. For example, the response unit can obtain the employee's geographic location information using GPS data or a location information service and select a response method based on that information. This allows the optimal response method to be selected by taking the geographic location information into consideration. Some or all of the above-described processing in the response unit can be performed using, for example, AI, or can be performed without using AI. For example, the response unit can cause a generation AI to select a response method based on the geographic location information.

[0050] The response unit can analyze the employee's social media activity and suggest response measures when responding. For example, the response unit can analyze the employee's social media activity and suggest response measures when responding. For example, the response unit can analyze the employee's social media posts and suggest response measures based on signs of stress or anxiety. The response unit can also determine the need for mental health care based on the frequency of the employee's social media activity and suggest response measures. Furthermore, the response unit can analyze the employee's social media interactions and suggest response measures based on workplace relationships. For example, the response unit can analyze the content of social media posts and measure the frequency of specific keywords and phrases. By analyzing social media activity, appropriate response measures can be suggested. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without AI. For example, the response unit can have a generation AI perform the analysis of social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can collect biometric data of employees and monitor their health status. For example, the collection unit can collect biometric data such as employees' heart rate, blood pressure, and body temperature. The collection unit can also monitor employees' sleep patterns and evaluate their sleep quality. Furthermore, the collection unit can record employees' exercise volume and understand their daily activity level. This allows for comprehensive monitoring of employees' health status, making it possible to more accurately identify the need for mental care.

[0053] The analysis unit can analyze employees' biometric data and predict health risks. For example, the analysis unit can analyze fluctuations in heart rate and blood pressure to evaluate stress levels. The analysis unit can also analyze sleep data to detect signs of sleep deprivation and insomnia. Furthermore, the analysis unit can analyze exercise data to evaluate the risk of insufficient exercise or excessive exercise. This allows for early prediction of employees' health risks and allows appropriate measures to be taken.

[0054] The identification department can evaluate the health risks of employees and identify the necessary health management programs. For example, the identification department can suggest a stress management program to an employee with a high stress level. The identification department can also suggest a sleep improvement program to an employee who is sleep deprived. The identification department can also suggest an exercise program to an employee who is not getting enough exercise. This makes it possible to identify an appropriate health management program according to the employee's health risks.

[0055] The corresponding department can provide a health management program according to the employee's health risk. For example, the corresponding department can provide relaxation techniques and stress relief methods as a stress management program. The corresponding department can also provide guidance on improving the sleep environment and improving sleep habits as a sleep improvement program. Furthermore, the corresponding department can provide appropriate exercise menus and guidance on exercise habits as an exercise program. In this way, it is possible to provide an appropriate health management program according to the employee's health risk.

[0056] The collection department can analyze employees' social media activities to understand interpersonal relationships in the workplace. For example, the collection department can analyze what employees say on social media to detect signs of stress or anxiety. The collection department can also estimate an employee's mental state based on the frequency of their social media activity. Furthermore, the collection department can analyze what employees interact with on social media to understand interpersonal relationships in the workplace. By analyzing social media activity, it is possible to understand interpersonal relationships in the workplace and identify the need for mental care.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects employee behavioral data and communication data. For example, the collection unit can collect data such as the content of employees' emails and chats, working hours, and vacation usage status. The collection unit can, for example, analyze the content of emails and chats and perform keyword frequency and sentiment analysis. The collection unit can also collect data on working hours and record arrival times, departure times, overtime hours, etc. The collection unit can also collect vacation usage status and record data such as paid vacation, sick leave, and special leave. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing or a machine learning model to analyze the collected data and detect signs of stress or anxiety. For example, the analysis unit can use natural language processing technology to perform morphological analysis, grammatical analysis, semantic analysis, etc. on the content of emails and chats. The analysis unit can also use a machine learning model to analyze the collected data and detect signs of stress or anxiety. For example, a machine learning model such as a neural network or a support vector machine can be used. Step 3: The identification unit identifies employees who need mental care based on the results of the analysis by the analysis unit. For example, the identification unit can evaluate stress levels and signs of anxiety based on the analysis results and identify employees who need mental care. For example, the identification unit can use an algorithm to identify employees who need mental care based on stress levels and signs of anxiety. Step 4: The response department takes appropriate action for the employees identified by the identification department. For example, the response department can send a notification to the identified employees urging them to consult with a specialist. The response department can also provide the identified employees with advice on stress management. For example, the response department can provide advice such as relaxation techniques and time management tips.

[0059] (Example 2) A mental health support system according to an embodiment of the present invention collects, analyzes, identifies, and responds to employee behavioral and communication data. This mental health support system collects employee behavioral and communication data, identifies employees in need of mental health care based on the analysis results, and provides appropriate support, thereby improving employee satisfaction and reducing employee turnover. For example, the mental health support system collects data such as the content of employees' emails and chats, working hours, and vacation status. Next, AI analyzes the collected data to detect signs of stress and anxiety. For example, natural language processing or machine learning models are used to analyze emotions from the content of emails and chats. Based on the analysis results, employees in need of mental health care are identified. Appropriate mental health support measures are then provided to identified employees. For example, notifications encouraging consultation with a specialist may be sent, or stress management advice may be provided. This system improves employee satisfaction and reduces employee turnover. It also avoids the costs of hiring and employee training. As a result, the mental health support system can proactively detect employees' mental health needs and provide appropriate support, thereby improving employee satisfaction and reducing employee turnover.

[0060] A mental care support system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a response unit. The collection unit collects behavioral data and communication data of employees. For example, the collection unit can collect data such as the content of emails and chat messages, working hours, and vacation status of employees. The collection unit can analyze the content of emails and chat messages to perform keyword frequency and sentiment analysis. The collection unit can also collect data on working hours and record data such as arrival times, departure times, and overtime hours. The collection unit can also collect data on vacation status and record data such as paid vacation, sick leave, and special leave. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing or a machine learning model to analyze the collected data and detect signs of stress or anxiety. The analysis unit can use natural language processing technology to perform morphological analysis, grammatical analysis, semantic analysis, and the like on the content of emails and chat messages. The analysis unit can also use a machine learning model to analyze the collected data and detect signs of stress or anxiety. For example, a machine learning model such as a neural network or a support vector machine can be used. The identification unit identifies employees who need mental care based on the results of the analysis by the analysis unit. For example, the identification unit can evaluate stress levels and signs of anxiety based on the analysis results and identify employees who need mental care. The identification unit can use an algorithm to identify employees who need mental care based on stress levels and signs of anxiety. The response unit takes appropriate action for the employees identified by the identification unit. For example, the response unit can send a notification to the identified employees encouraging them to consult with a specialist. The response unit can also provide the identified employees with stress management advice. For example, the response unit can provide advice such as relaxation techniques and time management tips. As a result, the mental care support system according to the embodiment can detect employees' mental care needs in advance and take appropriate action, thereby improving employee satisfaction and reducing employee turnover.

[0061] The collection unit can collect data on email or chat content, working hours, and vacation usage status. For example, the collection unit can collect email or chat content and perform keyword frequency and sentiment analysis. For example, the collection unit can analyze email or chat content and measure the frequency of specific keywords. The collection unit can also use a sentiment analysis algorithm to analyze emotions from email or chat content. For example, the collection unit can measure the proportion of positive and negative emotions. Furthermore, the collection unit can collect data on working hours and record arrival times, departure times, overtime hours, etc. For example, the collection unit can record employees' arrival times and departure times and measure overtime hours. The collection unit can also collect vacation usage status and record data on paid leave, sick leave, special leave, etc. For example, the collection unit can record employees' vacation usage status and measure the type and number of days of vacation. In this way, by collecting detailed employee behavioral data and communication data, employees in need of mental care can be more accurately identified.

[0062] The analysis unit can analyze the collected data using natural language processing or a machine learning model to detect signs of stress or anxiety. The analysis unit can analyze the collected data using, for example, natural language processing technology. For example, the analysis unit can perform morphological analysis to analyze the structure of a sentence. The analysis unit can also perform grammatical analysis to analyze the grammatical structure of a sentence. The analysis unit can also perform semantic analysis to analyze the meaning of a sentence. For example, the analysis unit can extract keywords and phrases from a sentence and analyze their meaning. The analysis unit can also analyze the collected data using a machine learning model. For example, the analysis unit can analyze the collected data using a neural network. The analysis unit can also analyze the collected data using a support vector machine. The analysis unit can also detect signs of stress or anxiety based on the collected data. For example, the analysis unit can detect signs of stress or anxiety from the collected data using a sentiment analysis algorithm. As a result, signs of stress and anxiety can be detected with high accuracy by using natural language processing or a machine learning model.

[0063] The identification unit can identify employees who need mental care based on the analysis results. The identification unit can, for example, evaluate stress levels and signs of anxiety based on the analysis results and identify employees who need mental care. For example, the identification unit can evaluate stress levels and identify employees who need mental care if the stress levels exceed a certain standard. The identification unit can also evaluate signs of anxiety and identify employees who need mental care if the signs of anxiety exceed a certain standard. Furthermore, the identification unit can use an algorithm to identify employees who need mental care based on the analysis results. For example, the identification unit can use an algorithm to identify employees who need mental care based on stress levels and signs of anxiety. This makes it possible to identify employees who need mental care based on the analysis results and take appropriate measures.

[0064] The response unit can send the identified employee a notification recommending that they consult with a specialist. The response unit can, for example, send the identified employee a notification recommending that they consult with a specialist. For example, the response unit can send an email notification recommending that they consult with a specialist. The response unit can also send an app notification recommending that they consult with a specialist. Furthermore, the response unit can customize the content of the notification recommending that they consult with a specialist for the identified employee. For example, the response unit can customize the content of the notification depending on the situation of the identified employee and recommend that they consult with a specialist. In this way, by sending a notification encouraging that they consult with a specialist, mental care for the employee can be provided quickly.

[0065] The support department can provide stress management advice to the identified employee. The support department can, for example, provide stress management advice to the identified employee. For example, the support department can suggest relaxation techniques. For example, the support department can suggest relaxation techniques such as deep breathing and meditation. The support department can also provide tips on time management. For example, the support department can suggest methods for prioritizing tasks and methods for efficient schedule management. Furthermore, the support department can customize the stress management advice for the identified employee. For example, the support department can customize the content of the advice according to the situation of the identified employee and provide stress management advice. In this way, by providing stress management advice, it is possible to support the mental care of employees.

[0066] The collection unit can estimate the employee's emotions and set the frequency of data collection based on the estimated employee emotions. The collection unit can, for example, estimate the employee's emotions and adjust the frequency of data collection based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit can increase the frequency of data collection and collect detailed data. Furthermore, if the employee is relaxed, the collection unit can reduce the frequency of data collection and collect the minimum amount of data necessary. Furthermore, if the employee is in a hurry, the collection unit can temporarily reduce the frequency of data collection to allow the employee to focus on their work. This allows for more appropriate data collection by adjusting the frequency of data collection according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The collection unit can analyze the employee's past behavioral data and select an appropriate data collection method. The collection unit can, for example, analyze the employee's past behavioral data and select the optimal data collection method. For example, the collection unit can prioritize communication tools that the employee has frequently used in the past as data collection targets. The collection unit can also select the most efficient data collection timing based on the employee's past working time data. Furthermore, the collection unit can analyze the employee's past vacation status and adjust the vacation data collection method. For example, the collection unit can select the vacation data collection method based on the type and number of vacation days the employee has taken in the past. In this way, the optimal data collection method can be selected by analyzing the past behavioral data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past behavioral data into a generation AI and have the generation AI select the optimal data collection method.

[0068] The collection unit may filter data based on the employee's current project or job content when collecting data. For example, the collection unit may filter data based on the employee's current project or job content when collecting data. For example, the collection unit may prioritize collecting data related to the employee's current project. The collection unit may also collect only necessary data and exclude unnecessary data depending on the employee's job content. Furthermore, if an employee is in charge of multiple projects, the collection unit may collect data related to each project separately. For example, the collection unit may filter data based on keywords or phrases related to a specific project. This allows data to be filtered based on the current project or job content, thereby collecting only necessary 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 have the generation AI perform filtering based on the current project or job content.

[0069] The collection unit can estimate the employee's emotions and prioritize the data to be collected based on the estimated employee emotions. The collection unit can, for example, estimate the employee's emotions and prioritize the data to be collected based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, if the employee is relaxed, the collection unit can prioritize collecting work-related data. Furthermore, if the employee is in a hurry, the collection unit can prioritize collecting work-related data. This allows important data to be collected preferentially by prioritizing data based on the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can cause the generation AI to prioritize data based on emotions.

[0070] The collection unit can prioritize collecting highly relevant data based on the employee's geographic location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking the employee's geographic location information into consideration when collecting data. For example, when the employee is in the office, the collection unit can prioritize collecting behavioral data within the office. Furthermore, when the employee is on a business trip, the collection unit can prioritize collecting behavioral data at the business trip destination. Furthermore, when the employee is working remotely, the collection unit can prioritize collecting behavioral data at home. For example, the collection unit can obtain the employee's geographic location information using GPS data or a location information service and set priorities for data collection based on that information. This allows highly relevant data to be collected preferentially by taking the geographic location information into consideration. Some or all of the above-described 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 cause the generation AI to set priorities for data collection based on the geographic location information.

[0071] The collection unit can analyze employees' social media activities during data collection and collect related data. For example, the collection unit can analyze employees' social media activities during data collection and collect related data. For example, the collection unit can analyze employees' social media posts to detect signs of stress or anxiety. The collection unit can also estimate an employee's mental state based on the frequency of the employee's social media activity. Furthermore, the collection unit can analyze employees' social media interactions to understand interpersonal relationships in the workplace. For example, the collection unit can analyze social media posts to measure the frequency of specific keywords or phrases. This allows for the analysis of social media activity to collect related data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can have a generation AI perform the analysis of social media activity.

[0072] The analysis unit can estimate the employee's emotions and set an analysis algorithm based on the estimated employee emotions. The analysis unit can, for example, estimate the employee's emotions and adjust the analysis algorithm based on the estimated employee emotions. For example, if the employee is feeling stressed, the analysis unit can apply an algorithm that emphasizes stress-related data. Furthermore, if the employee is relaxed, the analysis unit can apply an algorithm that emphasizes data related to work efficiency. Furthermore, if the employee is in a hurry, the analysis unit can apply an algorithm that quickly produces analysis results. This enables more appropriate analysis by adjusting the analysis algorithm according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can cause the generation AI to adjust the analysis algorithm based on emotions.

[0073] The analysis unit can set the level of analysis detail based on the importance of the data during analysis. The analysis unit can, for example, adjust the level of analysis detail based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can evaluate the importance of the data based on the impact on business operations and the freshness of the data, and set the level of analysis detail. This enables efficient analysis by adjusting the level of analysis detail according to the importance of the data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can cause the generation AI to set the level of analysis detail based on the importance of the data.

[0074] The analysis unit can use different analysis algorithms depending on the data category during analysis. The analysis unit can apply different analysis algorithms depending on the data category during analysis, for example. For example, the analysis unit can apply a natural language processing algorithm to communication data. Furthermore, the analysis unit can apply a machine learning algorithm to behavioral data. Furthermore, the analysis unit can apply a statistical analysis algorithm to working time data. For example, the analysis unit can apply a text analysis algorithm to text data and a numerical analysis algorithm to numerical data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. 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 cause a generation AI to apply an analysis algorithm depending on the data category.

[0075] The analysis unit can estimate the employee's emotions and set a display method for the analysis results based on the estimated employee emotions. The analysis unit can, for example, estimate the employee's emotions and adjust the display method for the analysis results based on the estimated employee emotions. For example, if the employee is feeling stressed, the analysis unit can provide a simple, highly visible display method. If the employee is relaxed, the analysis unit can provide a display method that includes detailed information. If the employee is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method for the analysis results according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can cause the generation AI to adjust the display method for the analysis results based on emotions.

[0076] The analysis unit can set analysis priorities based on the time of data submission during analysis. The analysis unit can, for example, determine analysis priorities based on the time of data submission during analysis. For example, the analysis unit can prioritize analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule according to the time of submission. For example, the analysis unit can evaluate the time of data submission based on the submission date and submission time and set analysis priorities. This enables efficient analysis by determining analysis priorities according to the time of data submission. 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 cause the generation AI to set analysis priorities based on the time of submission.

[0077] The analysis unit can set the order of analysis based on the relevance of the data during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the data. For example, the analysis unit can evaluate the relevance of the data based on correlations and causal relationships between the data and set the order of analysis. This enables efficient analysis by adjusting the order of analysis according to the relevance of the 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 cause the generation AI to set the order of analysis based on the relevance.

[0078] The identification unit can estimate the employee's emotions and set specific criteria based on the estimated employee emotions. For example, the identification unit can estimate the employee's emotions and adjust the specific criteria based on the estimated employee emotions. For example, if the employee is feeling stressed, the identification unit can set stricter stress-related criteria. Furthermore, if the employee is relaxed, the identification unit can emphasize criteria related to work efficiency. Furthermore, if the employee is in a hurry, the identification unit can set criteria that quickly produce identification results. This enables more appropriate identification by adjusting the specific criteria according to the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can cause the generation AI to set specific criteria based on emotions.

[0079] The identification unit can improve the accuracy of identification based on the interrelationships of data during identification. For example, the identification unit can improve the accuracy of identification by taking into account the interrelationships of data during identification. For example, the identification unit can analyze the interrelationships between communication data and behavioral data to improve the accuracy of identification. The identification unit can also analyze the interrelationships between working hours data and vacation acquisition data to improve the accuracy of identification. Furthermore, the identification unit can analyze the interrelationships between social media activity data and business data to improve the accuracy of identification. For example, the identification unit can use an algorithm that improves the accuracy of identification based on correlations and causal relationships between data. This improves the accuracy of identification by taking into account the interrelationships of data. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can cause a generation AI to improve the accuracy of identification based on the interrelationships.

[0080] The identification unit can perform identification based on the employee's attribute information during identification. For example, the identification unit can perform identification taking into account the employee's attribute information during identification. For example, the identification unit can perform identification taking into account the employee's age and gender. The identification unit can also perform identification taking into account the employee's job title and position. The identification unit can also perform identification taking into account the employee's years of service and experience. For example, the identification unit can use an algorithm that improves the accuracy of identification based on the employee's attribute information. This improves the accuracy of identification by taking into account the employee's attribute information. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can cause a generation AI to perform identification based on the attribute information.

[0081] The identification unit can estimate the employee's emotions and set the order in which specific results are displayed based on the estimated employee emotions. The identification unit can, for example, estimate the employee's emotions and adjust the order in which specific results are displayed based on the estimated employee emotions. For example, if the employee is feeling stressed, the identification unit can prioritize displaying stress-related results. Furthermore, if the employee is relaxed, the identification unit can prioritize displaying results related to work efficiency. Furthermore, if the employee is in a hurry, the identification unit can prioritize displaying results requiring a quick response. This allows important information to be prioritized by adjusting the order in which specific results are displayed based on the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit can be performed using, for example, an AI, or without an AI. For example, the identification unit can cause the generation AI to adjust the display order of specific results based on emotions.

[0082] The identification unit can perform identification based on the geographic distribution of the data during identification. For example, the identification unit can perform identification taking the geographic distribution of the data into consideration during identification. For example, when an employee is in the office, the identification unit can prioritize identifying data within the office. Furthermore, when an employee is on a business trip, the identification unit can prioritize identifying data at the business trip destination. Furthermore, when an employee is working remotely, the identification unit can prioritize identifying data at home. For example, the identification unit can evaluate the geographic distribution of the data based on data by region or data by country, and improve the accuracy of identification. In this way, the accuracy of identification is improved by taking the geographic distribution of the data into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can cause a generation AI to perform identification based on the geographic distribution.

[0083] The identification unit can improve the accuracy of identification based on related literature during identification. For example, the identification unit can improve the accuracy of identification by referring to related literature during identification. For example, the identification unit can improve the accuracy of identification by referring to the latest research papers. The identification unit can also improve the accuracy of identification by referring to industry best practices. Furthermore, the identification unit can improve the accuracy of identification by referring to past cases. For example, the identification unit can use an algorithm that improves the accuracy of identification based on academic papers and technical reports. In this way, the accuracy of identification is improved by referring to related literature. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can cause the generation AI to improve the accuracy of identification based on related literature.

[0084] The response unit can estimate the employee's emotions and set a response method based on the estimated employee emotions. The response unit can, for example, estimate the employee's emotions and adjust the response method based on the estimated employee emotions. For example, if the employee is feeling stressed, the response unit can suggest a way to help the employee relax. Furthermore, if the employee is relaxed, the response unit can suggest a way to improve work efficiency. Furthermore, if the employee is in a hurry, the response unit can suggest a way to respond quickly. This enables a more appropriate response by adjusting the response method based on the employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the response unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the response unit can cause the generation AI to adjust the response method based on the emotion.

[0085] The response unit can select an appropriate response method based on the employee's past response history when responding. For example, the response unit can select the optimal response method by referring to the employee's past response history when responding. For example, the response unit can re-propose a response method that was effective for the employee in the past. The response unit can also analyze the employee's past response history and select the optimal response method. Furthermore, the response unit can propose a new response method based on response methods that the employee has tried in the past. For example, the response unit can select the optimal response method based on past counseling records and response results. In this way, the optimal response method can be selected by referring to the past response history. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can have a generation AI select a response method based on the past response history.

[0086] The response unit can set a response measure based on the employee's current situation when responding. The response unit can, for example, customize a response measure based on the employee's current situation when responding. For example, the response unit can suggest a response measure related to a project the employee is currently working on. The response unit can also suggest the optimal response measure based on the employee's current work content. Furthermore, the response unit can customize the response measure based on the employee's current work situation. For example, the response unit can select the optimal response measure based on the employee's current work content and the progress of the project. This enables more effective response by customizing the response measure based on the current situation. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can cause a generation AI to customize the response measure based on the current situation.

[0087] The response unit can estimate the employee's emotions and set response priorities based on the estimated employee emotions. The response unit can, for example, estimate the employee's emotions and determine response priorities based on the estimated employee emotions. For example, if the employee is feeling stressed, the response unit can prioritize responses to reduce stress. Furthermore, if the employee is relaxed, the response unit can prioritize responses to improve work efficiency. Furthermore, if the employee is in a hurry, the response unit can prioritize matters that require a quick response. Thus, by determining response priorities based on the employee's emotions, important responses can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, 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 response unit can be performed using, for example, an AI, or without an AI. For example, the response unit can cause the generation AI to set response priorities based on emotions.

[0088] The response unit can select an appropriate response method based on the employee's geographic location information when responding. For example, the response unit can select the optimal response method by taking the employee's geographic location information into consideration when responding. For example, if the employee is in the office, the response unit can suggest a response method to be used in the office. Furthermore, if the employee is on a business trip, the response unit can suggest a response method to be used at the business trip destination. Furthermore, if the employee is working remotely, the response unit can suggest a response method to be used at home. For example, the response unit can obtain the employee's geographic location information using GPS data or a location information service and select a response method based on that information. This allows the optimal response method to be selected by taking the geographic location information into consideration. Some or all of the above-described processing in the response unit can be performed using, for example, AI, or can be performed without using AI. For example, the response unit can cause a generation AI to select a response method based on the geographic location information.

[0089] The response unit can analyze the employee's social media activity and suggest response measures when responding. For example, the response unit can analyze the employee's social media activity and suggest response measures when responding. For example, the response unit can analyze the employee's social media posts and suggest response measures based on signs of stress or anxiety. The response unit can also determine the need for mental health care based on the frequency of the employee's social media activity and suggest response measures. Furthermore, the response unit can analyze the employee's social media interactions and suggest response measures based on workplace relationships. For example, the response unit can analyze the content of social media posts and measure the frequency of specific keywords and phrases. By analyzing social media activity, appropriate response measures can be suggested. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without AI. For example, the response unit can have a generation AI perform the analysis of social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and response 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 collects behavioral data and communication data of employees using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to detect signs of stress and anxiety. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies employees who need mental care based on the analysis results. The response unit is realized, for example, by the control unit 46A of the smart device 14, and provides appropriate mental care to the identified employees. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, and response 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 collects employee behavioral data and communication data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, realized, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data and detects signs of stress and anxiety. The identification unit, realized, for example, by the identification processing unit 290 of the data processing device 12, identifies employees who need mental care based on the analysis results. The response unit, realized, for example, by the control unit 46A of the smart glasses 214, provides appropriate mental care to the identified employees. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and response unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects behavioral data and communication data of employees using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to detect signs of stress and anxiety. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies employees who need mental care based on the analysis results. The response unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides appropriate mental care to the identified employees. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and response 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 collects behavioral data and communication data of employees using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to detect signs of stress and anxiety. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies employees who need mental care based on the analysis results. The response unit is realized, for example, by the control unit 46A of the robot 414, and provides appropriate mental care to the identified employees.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The collection unit can collect biometric data of employees and monitor their health status. For example, the collection unit can collect biometric data such as employees' heart rate, blood pressure, and body temperature. The collection unit can also monitor employees' sleep patterns and evaluate their sleep quality. Furthermore, the collection unit can record employees' exercise volume and understand their daily activity level. This allows for comprehensive monitoring of employees' health status, making it possible to more accurately identify the need for mental care.

[0092] The analysis unit can analyze employees' biometric data and predict health risks. For example, the analysis unit can analyze fluctuations in heart rate and blood pressure to evaluate stress levels. The analysis unit can also analyze sleep data to detect signs of sleep deprivation and insomnia. Furthermore, the analysis unit can analyze exercise data to evaluate the risk of insufficient exercise or excessive exercise. This allows for early prediction of employees' health risks and allows appropriate measures to be taken.

[0093] The identification department can evaluate the health risks of employees and identify the necessary health management programs. For example, the identification department can suggest a stress management program to an employee with a high stress level. The identification department can also suggest a sleep improvement program to an employee who is sleep deprived. The identification department can also suggest an exercise program to an employee who is not getting enough exercise. This makes it possible to identify an appropriate health management program according to the employee's health risks.

[0094] The corresponding department can provide a health management program according to the employee's health risk. For example, the corresponding department can provide relaxation techniques and stress relief methods as a stress management program. The corresponding department can also provide guidance on improving the sleep environment and improving sleep habits as a sleep improvement program. Furthermore, the corresponding department can provide appropriate exercise menus and guidance on exercise habits as an exercise program. In this way, it is possible to provide an appropriate health management program according to the employee's health risk.

[0095] The collection unit can estimate the employee's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the employee is feeling stressed, the collection unit can increase the frequency of data collection and collect detailed data. Also, if the employee is relaxed, the collection unit can reduce the frequency of data collection and collect the minimum amount of data necessary. Furthermore, if the employee is in a hurry, the collection unit can adjust the timing of data collection so as not to disrupt work. This makes it possible to collect more appropriate data by adjusting the timing of data collection according to the employee's emotions.

[0096] The analysis unit can estimate the employee's emotions and set analysis priorities based on the estimated emotions. For example, if an employee is feeling stressed, the analysis unit can prioritize analyzing stress-related data. Alternatively, if an employee is relaxed, the analysis unit can prioritize analyzing data related to work efficiency. Furthermore, if an employee is in a hurry, an algorithm can be applied to quickly produce analysis results. This allows for more appropriate analysis by setting analysis priorities according to the employee's emotions.

[0097] The identification department can estimate the employee's emotions and set identification criteria based on the estimated emotions. For example, if an employee is feeling stressed, the identification department can set stricter stress-related criteria. If an employee is relaxed, the identification department can emphasize criteria related to work efficiency. Furthermore, if an employee is in a hurry, the identification department can set criteria that will quickly produce identification results. This allows for more appropriate identification by setting identification criteria according to the employee's emotions.

[0098] The response department can estimate the employee's emotions and set a response method based on the estimated emotions. For example, if an employee is feeling stressed, the response department can suggest ways to help the employee relax. If the employee is relaxed, the response department can suggest ways to improve work efficiency. Furthermore, if the employee is in a hurry, the response department can suggest ways to respond quickly. This makes it possible to set a response method according to the employee's emotions, enabling more appropriate responses.

[0099] The response department can estimate the employee's emotions and set response priorities based on the estimated emotions. For example, if an employee is feeling stressed, the response department can prioritize responses to reduce stress. Also, if an employee is relaxed, the response department can prioritize responses to improve work efficiency. Furthermore, if an employee is in a hurry, the response department can prioritize matters that require a quick response. In this way, by setting response priorities according to the employee's emotions, important responses can be prioritized.

[0100] The collection department can analyze employees' social media activities to understand interpersonal relationships in the workplace. For example, the collection department can analyze what employees say on social media to detect signs of stress or anxiety. The collection department can also estimate an employee's mental state based on the frequency of their social media activity. Furthermore, the collection department can analyze what employees interact with on social media to understand interpersonal relationships in the workplace. By analyzing social media activity, it is possible to understand interpersonal relationships in the workplace and identify the need for mental care.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects employee behavioral data and communication data. For example, the collection unit can collect data such as the content of employees' emails and chats, working hours, and vacation usage status. The collection unit can, for example, analyze the content of emails and chats and perform keyword frequency and sentiment analysis. The collection unit can also collect data on working hours and record arrival times, departure times, overtime hours, etc. The collection unit can also collect vacation usage status and record data such as paid vacation, sick leave, and special leave. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing or a machine learning model to analyze the collected data and detect signs of stress or anxiety. For example, the analysis unit can use natural language processing technology to perform morphological analysis, grammatical analysis, semantic analysis, etc. on the content of emails and chats. The analysis unit can also use a machine learning model to analyze the collected data and detect signs of stress or anxiety. For example, a machine learning model such as a neural network or a support vector machine can be used. Step 3: The identification unit identifies employees who need mental care based on the results of the analysis by the analysis unit. For example, the identification unit can evaluate stress levels and signs of anxiety based on the analysis results and identify employees who need mental care. For example, the identification unit can use an algorithm to identify employees who need mental care based on stress levels and signs of anxiety. Step 4: The response department takes appropriate action for the employees identified by the identification department. For example, the response department can send a notification to the identified employees urging them to consult with a specialist. The response department can also provide the identified employees with advice on stress management. For example, the response department can provide advice such as relaxation techniques and time management tips.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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, in order to avoid confusion and to 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 behavioral data or communication data; an analysis unit that analyzes the data collected by the collection unit; an identification unit that identifies employees who need mental care based on the results of the analysis by the analysis unit; a response unit that responds to the employee identified by the identification unit. A system characterized by:

2. The collecting unit Collect data on email or chat content, working hours, and vacation status The system of claim 1 .

3. The analysis unit Use natural language processing or machine learning models to analyze the collected data and detect signs of stress or anxiety The system of claim 1 .

4. The identification unit Identify employees who need mental health care based on the analysis results The system of claim 1 .

5. The corresponding part is Sending notifications to identified employees recommending they seek professional advice The system of claim 1 .

6. The corresponding part is Providing stress management advice to identified employees The system of claim 1 .

7. The collecting unit Estimate employee sentiment and set data collection frequency based on the estimated employee sentiment The system of claim 1 .

8. The collecting unit Analyze employee past behavioral data and select appropriate data collection methods The system of claim 1 .

Images