System

Abstract

To unitarily perform management of motivation and mental health of employees, activation of communication, and improvement of team engagement.SOLUTION: A system according to an embodiment includes a motivation evaluation unit, a mental care providing unit, a communication activation unit, an engagement improvement unit, and an information utilization unit. The motivation evaluation unit periodically evaluates the motivation of the employee. The mental care provider monitors a mental health state of the employee. The communication activation unit proposes measures for activating communication in the workplace. The engagement improvement unit proposes measures for improving the engagement of the team. The information utilization unit collects and analyzes data related to motivation management and mental care of employees, activation of communication, and improvement of team engagement, and effectively utilizes the information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to centrally manage employee motivation and mental health, stimulate communication, and improve team engagement.

[0005] The system according to the embodiment aims to centrally manage employee motivation and mental health, stimulate communication, and improve team engagement. [Means for solving the problem]

[0006] The system according to the embodiment includes a motivation evaluation unit, a mental care provision unit, a communication activation unit, an engagement improvement unit, and an information utilization unit. The motivation evaluation unit periodically evaluates employee motivation using a generation AI. The mental care provision unit monitors the mental health status of employees using a generation AI. The communication activation unit uses a generation AI to propose measures to activate communication in the workplace. The engagement improvement unit uses a generation AI to propose measures to increase team engagement. The information utilization unit uses a generation AI to collect and analyze data related to employee motivation management, mental care, communication activation, and team engagement improvement, and effectively utilizes the information. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage employee motivation and mental health, stimulate communication, and improve team engagement. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that stimulates communication in the workplace and increases team engagement through employee motivation management and mental health care. Furthermore, the system aims to improve the performance of the entire organization by effectively utilizing the acquired information. This allows the system to manage employee motivation and mental health, stimulate communication in the workplace, and increase team engagement.

[0029] The system according to the embodiment includes a motivation evaluation unit, a mental health care provision unit, a communication activation unit, an engagement improvement unit, and an information utilization unit. The motivation evaluation unit periodically evaluates employee motivation using a generation AI. For example, the generation AI periodically conducts surveys of employees and analyzes the results. The generation AI evaluates motivation levels based on prompts containing questions about the employee's motivation. For example, the generation AI analyzes the survey results using natural language processing technology and calculates a motivation score. The generation AI can also analyze employee behavioral data to detect fluctuations in motivation. The mental health provision unit monitors the employee's mental health status using the generation AI. For example, if an employee is feeling stressed, the generation AI detects the signs and suggests relaxation methods or counseling. The generation AI evaluates the employee's mental health status based on prompts containing questions about their mental health. For example, the generation AI analyzes psychological test results and biometric data to calculate a mental health score. The communication activation unit uses the generation AI to propose measures to activate communication in the workplace. For example, the generation AI analyzes communication patterns between employees and identifies areas for improvement. The Generative AI suggests ways to improve communication based on prompts containing data on communication between employees. For example, the Generative AI might suggest holding team meetings or introducing communication tools. The Engagement Improvement Department uses the Generative AI to suggest measures to increase team engagement. For example, the Generative AI might evaluate the engagement levels of team members and suggest team building activities or training programs. The Generative AI suggests ways to improve engagement based on prompts containing data on team engagement. For example, the Generative AI might analyze survey results and behavioral data and calculate an engagement score. The Information Utilization Department uses the Generative AI to collect and analyze data on employee motivation management, mental care, stimulating communication, and improving team engagement, and effectively utilizes that information.For example, the generative AI proposes performance improvement measures for the entire organization based on data collected. The generative AI proposes performance improvement measures based on prompts containing various data about employees. The generative AI analyzes, for example, work logs and behavioral data to propose performance improvement measures. As a result, the system according to the embodiment can manage employee motivation and mental health, stimulate communication in the workplace, and increase team engagement. For example, by providing an environment where employees can work without feeling stressed and strengthening trust between team members, a more efficient and productive workplace can be realized.

[0030] The motivation evaluation unit can analyze employees' work logs and behavioral data in real time to detect fluctuations in motivation. The motivation evaluation unit, for example, uses generation AI to analyze employees' work logs and behavioral data in real time to detect fluctuations in motivation. For example, it calculates a motivation score based on work progress and break time usage and provides immediate feedback. The generation AI clarifies the specific content and collection method of the work logs, for example, analyzing work time and task progress. It clarifies the specific content and collection method of the behavioral data, for example, analyzing movement history and operation logs. It clarifies the specific definition and implementation method of real time, for example, taking into account data update frequency and delay time. This makes it possible to detect fluctuations in employee motivation in real time.

[0031] The motivation evaluation unit can analyze an employee's biometric data and immediately issue an alert when it detects a decline in motivation. The motivation evaluation unit, for example, uses a generation AI to analyze an employee's biometric data and immediately issue an alert when it detects a decline in motivation. For example, it calculates a motivation score based on heart rate and electrodermal activity, and issues an alert when it detects a fluctuation below a reference value. The generation AI clarifies the specific type and collection method of biometric data, for example, analyzing heart rate and electrodermal activity. It also clarifies how a decline in motivation is defined and detected, for example, detecting fluctuations below a reference value or specific behavioral patterns. It also clarifies the specific content and method of issuing the alert, for example, by email or app notification. This makes it possible to immediately detect a decline in employee motivation and issue an alert.

[0032] The mental care provision department can analyze employees' daily work logs and evaluate their mental health status. The mental care provision department, for example, uses generation AI to analyze employees' daily work logs and evaluate their mental health status. For example, it calculates a mental health score based on work progress and communication frequency and detects abnormalities. The generation AI clarifies the specific content and collection method of the work logs, and analyzes, for example, working hours and task progress. It also clarifies the evaluation criteria and specific evaluation methods for mental health status, and analyzes, for example, stress levels and psychological test results. This makes it possible to evaluate employees' mental health status from daily work logs.

[0033] The mental care provision department can analyze employees' hobbies and interests and propose personalized mental care based on that. The mental care provision department, for example, uses generation AI to analyze employees' hobbies and interests and propose personalized mental care based on that. For example, it proposes relaxation methods and activities related to hobbies. The generation AI clarifies specific methods for collecting and analyzing hobbies, for example, by conducting questionnaire surveys or social media analysis. It clarifies specific methods for collecting and analyzing interests, for example, by conducting questionnaire surveys or social media analysis. It clarifies specific methods and criteria for personalization, for example, by providing individual feedback or customized programs. This makes it possible to propose personalized mental care based on employees' hobbies and interests.

[0034] The communication activation unit analyzes the content of communication between employees and can detect potential conflicts and misunderstandings at an early stage. The communication activation unit, for example, uses generative AI to analyze the content of communication between employees and can detect potential conflicts and misunderstandings at an early stage. For example, it analyzes the content of emails and chats to detect signs of conflict. The generative AI clarifies the specific methods for collecting and analyzing communication content, for example, analyzing email content and chat logs. It clarifies the specific definition and detection method of potential conflict, for example, detecting the frequency of negative language and signs of conflict. It clarifies the specific definition and detection method of misunderstanding, for example, detecting misleading expressions and communication gaps. This makes it possible to analyze the content of communication between employees and detect potential conflicts and misunderstandings at an early stage.

[0035] The communication activation unit can evaluate the quality of communication between employees and automatically generate a report that specifically indicates areas for improvement. The communication activation unit, for example, uses generation AI to evaluate the quality of communication between employees and automatically generate a report that specifically indicates areas for improvement. For example, it analyzes the frequency and content of communication and summarizes areas for improvement in a report. The generation AI clarifies specific evaluation criteria and methods for the quality of communication, for example, evaluating the frequency of feedback and the depth of dialogue. It clarifies specific proposal methods and criteria for areas for improvement, for example, indicating specific action suggestions and steps for improvement. It clarifies the specific content of the report and the method for automatically generating it, for example, by displaying a graph or a text summary. This makes it possible to evaluate the quality of communication between employees and automatically generate a report that specifically indicates areas for improvement.

[0036] The engagement improvement department can evaluate the engagement levels of team members in real time and immediately propose improvement measures. For example, the engagement improvement department uses generation AI to evaluate the engagement levels of team members in real time and immediately propose improvement measures. For example, it proposes improvement measures when the engagement score drops. The generation AI clarifies the specific evaluation criteria and methods for engagement levels, for example, by analyzing survey results and behavioral data. It also clarifies the specific methods and criteria for proposing improvement measures, for example, by suggesting specific action suggestions and steps for improvement. This allows the engagement levels of team members to be evaluated in real time and immediately propose improvement measures.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The system can analyze employees' health data and adjust work based on their health status. For example, it can analyze an employee's sleep data and assign lighter tasks if it detects they are not getting enough sleep. It can analyze an employee's exercise data and send notifications to encourage them to exercise if it detects they are not getting enough exercise. It can analyze an employee's dietary data and suggest healthy meals if their nutritional balance is unbalanced. This allows it to adjust work based on employees' health status and provide a healthy work environment.

[0039] The system can analyze employee skill data and suggest appropriate training programs based on their skills. For example, it can analyze an employee's technical skills, identify skill gaps, and suggest the necessary training. It can analyze an employee's leadership skills and suggest leadership training. It can analyze an employee's communication skills and suggest communication training. This makes it possible to provide appropriate training programs based on employees' skills and help them improve their skills.

[0040] The system can analyze employees' career data and provide appropriate career advice based on their career path. For example, it can analyze an employee's past work experience and suggest career directions. It can analyze an employee's skill set and suggest skills to acquire for career advancement. It can analyze an employee's interests and goals and suggest career plans. This makes it possible to provide appropriate career advice based on an employee's career data and support their career growth.

[0041] The system analyzes employee project data and can optimize resources based on the progress of the project. For example, if a project is behind schedule, additional resources can be allocated. If the project is progressing smoothly, resources can be reallocated to other projects. If the project is stalled, resources can be provided to resolve the issue. This allows for resource optimization based on the project's progress, enabling efficient project management.

[0042] The system can analyze employee feedback data and propose improvements to business processes based on the feedback. For example, it can analyze employee feedback, identify bottlenecks in business processes, and propose improvements. It can analyze employee feedback and make proposals to improve the efficiency of business processes. It can analyze employee feedback and make proposals to improve the quality of business processes. This makes it possible to propose improvements to business processes based on employee feedback, thereby improving business efficiency and quality.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The motivation evaluation unit uses the generation AI to periodically evaluate employee motivation. For example, it periodically conducts surveys on employees, and the generation AI analyzes the results. The generation AI evaluates the motivation level based on prompts containing questions about employee motivation. The generation AI analyzes the survey results using, for example, natural language processing technology and calculates a motivation score. The generation AI can also analyze employee behavioral data to detect fluctuations in motivation. Step 2: The mental care provision department uses the generation AI to monitor the employee's mental health status. For example, if an employee is feeling stressed, the generation AI detects the signs and suggests relaxation methods or counseling. The generation AI evaluates the employee's mental health status based on prompts containing questions about their mental health. The generation AI calculates a mental health score, for example, by analyzing psychological test results and biometric data. Step 3: The Communication Activation Department uses generative AI to propose measures to activate communication in the workplace. For example, generative AI analyzes communication patterns between employees and identifies areas for improvement. Generative AI proposes communication improvement measures based on prompts containing data on communication between employees. For example, generative AI suggests holding team meetings or introducing communication tools. Step 4: The Engagement Improvement Department uses the Generative AI to propose measures to increase team engagement. For example, the Generative AI evaluates the engagement levels of team members and suggests team building activities and training programs. The Generative AI proposes engagement improvement measures based on prompts containing data on team engagement. For example, the Generative AI analyzes survey results and behavioral data to calculate an engagement score. Step 5: The Information Utilization Department uses the Generative AI to collect and analyze data related to employee motivation management, mental health care, stimulating communication, and improving team engagement, and then effectively utilizes that information. For example, the Generative AI proposes measures to improve performance across the organization based on the data it collects. The Generative AI proposes performance improvement measures based on prompts containing various data about employees. For example, the Generative AI analyzes work logs and behavioral data to propose performance improvement measures.

[0045] (Example 2) A system according to an embodiment of the present invention is a system that stimulates communication in the workplace and increases team engagement through employee motivation management and mental health care. Furthermore, the system aims to improve the performance of the entire organization by effectively utilizing the acquired information. This allows the system to manage employee motivation and mental health, stimulate communication in the workplace, and increase team engagement.

[0046] The system according to the embodiment includes a motivation evaluation unit, a mental health care provision unit, a communication activation unit, an engagement improvement unit, and an information utilization unit. The motivation evaluation unit periodically evaluates employee motivation using a generation AI. For example, the generation AI periodically conducts surveys of employees and analyzes the results. The generation AI evaluates motivation levels based on prompts containing questions about the employee's motivation. For example, the generation AI analyzes the survey results using natural language processing technology and calculates a motivation score. The generation AI can also analyze employee behavioral data to detect fluctuations in motivation. The mental health provision unit monitors the employee's mental health status using the generation AI. For example, if an employee is feeling stressed, the generation AI detects the signs and suggests relaxation methods or counseling. The generation AI evaluates the employee's mental health status based on prompts containing questions about their mental health. For example, the generation AI analyzes psychological test results and biometric data to calculate a mental health score. The communication activation unit uses the generation AI to propose measures to activate communication in the workplace. For example, the generation AI analyzes communication patterns between employees and identifies areas for improvement. The Generative AI suggests ways to improve communication based on prompts containing data on communication between employees. For example, the Generative AI might suggest holding team meetings or introducing communication tools. The Engagement Improvement Department uses the Generative AI to suggest measures to increase team engagement. For example, the Generative AI might evaluate the engagement levels of team members and suggest team building activities or training programs. The Generative AI suggests ways to improve engagement based on prompts containing data on team engagement. For example, the Generative AI might analyze survey results and behavioral data and calculate an engagement score. The Information Utilization Department uses the Generative AI to collect and analyze data on employee motivation management, mental care, stimulating communication, and improving team engagement, and effectively utilizes that information.For example, the generative AI proposes performance improvement measures for the entire organization based on data collected. The generative AI proposes performance improvement measures based on prompts containing various data about employees. The generative AI analyzes, for example, work logs and behavioral data to propose performance improvement measures. As a result, the system according to the embodiment can manage employee motivation and mental health, stimulate communication in the workplace, and increase team engagement. For example, by providing an environment where employees can work without feeling stressed and strengthening trust between team members, a more efficient and productive workplace can be realized.

[0047] The motivation evaluation unit can analyze employees' work logs and behavioral data in real time to detect fluctuations in motivation. The motivation evaluation unit, for example, uses generation AI to analyze employees' work logs and behavioral data in real time to detect fluctuations in motivation. For example, it calculates a motivation score based on work progress and break time usage and provides immediate feedback. The generation AI clarifies the specific content and collection method of the work logs, for example, analyzing work time and task progress. It clarifies the specific content and collection method of the behavioral data, for example, analyzing movement history and operation logs. It clarifies the specific definition and implementation method of real time, for example, taking into account data update frequency and delay time. This makes it possible to detect fluctuations in employee motivation in real time.

[0048] The motivation evaluation unit can analyze an employee's biometric data and immediately issue an alert when it detects a decline in motivation. The motivation evaluation unit, for example, uses a generation AI to analyze an employee's biometric data and immediately issue an alert when it detects a decline in motivation. For example, it calculates a motivation score based on heart rate and electrodermal activity, and issues an alert when it detects a fluctuation below a reference value. The generation AI clarifies the specific type and collection method of biometric data, for example, analyzing heart rate and electrodermal activity. It also clarifies how a decline in motivation is defined and detected, for example, detecting fluctuations below a reference value or specific behavioral patterns. It also clarifies the specific content and method of issuing the alert, for example, by email or app notification. This makes it possible to immediately detect a decline in employee motivation and issue an alert.

[0049] The mental care provision department can analyze employees' daily work logs and evaluate their mental health status. The mental care provision department, for example, uses generation AI to analyze employees' daily work logs and evaluate their mental health status. For example, it calculates a mental health score based on work progress and communication frequency and detects abnormalities. The generation AI clarifies the specific content and collection method of the work logs, and analyzes, for example, working hours and task progress. It also clarifies the evaluation criteria and specific evaluation methods for mental health status, and analyzes, for example, stress levels and psychological test results. This makes it possible to evaluate employees' mental health status from daily work logs.

[0050] The mental care provision department can analyze employees' hobbies and interests and propose personalized mental care based on that. The mental care provision department, for example, uses generation AI to analyze employees' hobbies and interests and propose personalized mental care based on that. For example, it proposes relaxation methods and activities related to hobbies. The generation AI clarifies specific methods for collecting and analyzing hobbies, for example, by conducting questionnaire surveys or social media analysis. It clarifies specific methods for collecting and analyzing interests, for example, by conducting questionnaire surveys or social media analysis. It clarifies specific methods and criteria for personalization, for example, by providing individual feedback or customized programs. This makes it possible to propose personalized mental care based on employees' hobbies and interests.

[0051] The communication activation unit analyzes the content of communication between employees and can detect potential conflicts and misunderstandings at an early stage. The communication activation unit, for example, uses generative AI to analyze the content of communication between employees and can detect potential conflicts and misunderstandings at an early stage. For example, it analyzes the content of emails and chats to detect signs of conflict. The generative AI clarifies the specific methods for collecting and analyzing communication content, for example, analyzing email content and chat logs. It clarifies the specific definition and detection method of potential conflict, for example, detecting the frequency of negative language and signs of conflict. It clarifies the specific definition and detection method of misunderstanding, for example, detecting misleading expressions and communication gaps. This makes it possible to analyze the content of communication between employees and detect potential conflicts and misunderstandings at an early stage.

[0052] The communication activation unit can evaluate the quality of communication between employees and automatically generate a report that specifically indicates areas for improvement. The communication activation unit, for example, uses generation AI to evaluate the quality of communication between employees and automatically generate a report that specifically indicates areas for improvement. For example, it analyzes the frequency and content of communication and summarizes areas for improvement in a report. The generation AI clarifies specific evaluation criteria and methods for the quality of communication, for example, evaluating the frequency of feedback and the depth of dialogue. It clarifies specific proposal methods and criteria for areas for improvement, for example, indicating specific action suggestions and steps for improvement. It clarifies the specific content of the report and the method for automatically generating it, for example, by displaying a graph or a text summary. This makes it possible to evaluate the quality of communication between employees and automatically generate a report that specifically indicates areas for improvement.

[0053] The engagement improvement department can evaluate the engagement levels of team members in real time and immediately propose improvement measures. For example, the engagement improvement department uses generation AI to evaluate the engagement levels of team members in real time and immediately propose improvement measures. For example, it proposes improvement measures when the engagement score drops. The generation AI clarifies the specific evaluation criteria and methods for engagement levels, for example, by analyzing survey results and behavioral data. It also clarifies the specific methods and criteria for proposing improvement measures, for example, by suggesting specific action suggestions and steps for improvement. This allows the engagement levels of team members to be evaluated in real time and immediately propose improvement measures.

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

[0055] The system can estimate an employee's emotions and provide personalized feedback based on the estimated emotions. For example, if an employee is feeling stressed, it can suggest relaxation techniques or counseling. If an employee is highly motivated, it can suggest more challenging tasks. If an employee is feeling fatigued, it can send a notification encouraging them to take a break. This allows the system to provide appropriate feedback according to the employee's emotions and address individual needs.

[0056] The system can analyze employees' health data and adjust work based on their health status. For example, it can analyze an employee's sleep data and assign lighter tasks if it detects they are not getting enough sleep. It can analyze an employee's exercise data and send notifications to encourage them to exercise if it detects they are not getting enough exercise. It can analyze an employee's dietary data and suggest healthy meals if their nutritional balance is unbalanced. This allows it to adjust work based on employees' health status and provide a healthy work environment.

[0057] The system can estimate an employee's emotions and suggest team building activities based on the estimated emotions. For example, if an employee feels isolated, it can suggest a team lunch meeting. If an employee feels anxious, it can suggest a relaxation workshop. If an employee feels excited, it can suggest a team sporting event. This makes it possible to suggest team building activities according to employees' emotions and increase team engagement.

[0058] The system can analyze employee skill data and suggest appropriate training programs based on their skills. For example, it can analyze an employee's technical skills, identify skill gaps, and suggest the necessary training. It can analyze an employee's leadership skills and suggest leadership training. It can analyze an employee's communication skills and suggest communication training. This makes it possible to provide appropriate training programs based on employees' skills and help them improve their skills.

[0059] The system can estimate an employee's emotions and suggest improvements to the work environment based on the estimated emotions. For example, if an employee is feeling stressed, it can suggest setting up a relaxation space. If an employee is lacking concentration, it can suggest providing a quiet workspace. If an employee is feeling tired, it can suggest improvements to the break room. In this way, it is possible to suggest improvements to the work environment according to the employee's emotions and provide a comfortable working environment.

[0060] The system can analyze employees' career data and provide appropriate career advice based on their career path. For example, it can analyze an employee's past work experience and suggest career directions. It can analyze an employee's skill set and suggest skills to acquire for career advancement. It can analyze an employee's interests and goals and suggest career plans. This makes it possible to provide appropriate career advice based on an employee's career data and support their career growth.

[0061] The system can estimate an employee's emotions and evaluate their performance based on the estimated emotions. For example, if an employee is highly motivated, the system can give them a higher performance evaluation. If an employee is feeling stressed, the system can adjust the evaluation criteria. If an employee is feeling tired, the system can change the timing of the evaluation. This makes it possible to perform flexible performance evaluations based on the employee's emotions and provide fair evaluations.

[0062] The system analyzes employee project data and can optimize resources based on the progress of the project. For example, if a project is behind schedule, additional resources can be allocated. If the project is progressing smoothly, resources can be reallocated to other projects. If the project is stalled, resources can be provided to resolve the issue. This allows for resource optimization based on the project's progress, enabling efficient project management.

[0063] The system can estimate an employee's emotions and suggest communication improvement measures based on the estimated emotions. For example, if an employee feels isolated, it can suggest a team-wide lunch meeting. If an employee feels anxious, it can suggest a relaxation workshop. If an employee feels excited, it can suggest a team sporting event. This makes it possible to suggest communication improvement measures based on an employee's emotions and increase team engagement.

[0064] The system can analyze employee feedback data and propose improvements to business processes based on the feedback. For example, it can analyze employee feedback, identify bottlenecks in business processes, and propose improvements. It can analyze employee feedback and make proposals to improve the efficiency of business processes. It can analyze employee feedback and make proposals to improve the quality of business processes. This makes it possible to propose improvements to business processes based on employee feedback, thereby improving business efficiency and quality.

[0065] The processing flow of the second embodiment will be briefly explained below.

[0066] Step 1: The motivation evaluation unit uses the generation AI to periodically evaluate employee motivation. For example, it periodically conducts surveys on employees, and the generation AI analyzes the results. The generation AI evaluates the motivation level based on prompts containing questions about employee motivation. The generation AI analyzes the survey results using, for example, natural language processing technology and calculates a motivation score. The generation AI can also analyze employee behavioral data to detect fluctuations in motivation. Step 2: The mental care provision department uses the generation AI to monitor the employee's mental health status. For example, if an employee is feeling stressed, the generation AI detects the signs and suggests relaxation methods or counseling. The generation AI evaluates the employee's mental health status based on prompts containing questions about their mental health. The generation AI calculates a mental health score, for example, by analyzing psychological test results and biometric data. Step 3: The Communication Activation Department uses generative AI to propose measures to activate communication in the workplace. For example, generative AI analyzes communication patterns between employees and identifies areas for improvement. Generative AI proposes communication improvement measures based on prompts containing data on communication between employees. For example, generative AI suggests holding team meetings or introducing communication tools. Step 4: The Engagement Improvement Department uses the Generative AI to propose measures to increase team engagement. For example, the Generative AI evaluates the engagement levels of team members and suggests team building activities and training programs. The Generative AI proposes engagement improvement measures based on prompts containing data on team engagement. For example, the Generative AI analyzes survey results and behavioral data to calculate an engagement score. Step 5: The Information Utilization Department uses the Generative AI to collect and analyze data related to employee motivation management, mental health care, stimulating communication, and improving team engagement, and then effectively utilizes that information. For example, the Generative AI proposes measures to improve performance across the organization based on the data it collects. The Generative AI proposes performance improvement measures based on prompts containing various data about employees. For example, the Generative AI analyzes work logs and behavioral data to propose performance improvement measures.

[0067] 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.

[0068] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0069] 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.

[0070] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0071] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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).

[0076] 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.

[0077] 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.

[0078] 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.

[0079] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0080] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0081] 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.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0121] 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."

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0133] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0134] 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 motivation evaluation department that periodically evaluates employee motivation using generative AI; A mental health care department that uses generative AI to monitor the mental health status of employees, The Communication Activation Department proposes measures to activate communication in the workplace using generative AI, and The Engagement Improvement Department proposes measures to increase team engagement using generative AI, The company will have an information utilization department that will use generative AI to collect and analyze data related to employee motivation management, mental care, stimulating communication, and improving team engagement, and will utilize that information effectively. A system characterized by:

2. The motivation evaluation unit Analyze the employee's work logs and behavioral data in real time to detect fluctuations in motivation.

2. The system of claim 1.

3. The mental care providing department Analyzing the employee's daily work log and assessing the employee's mental health status 2. The system of claim 1.

4. The communication activation unit Analyze the content of communications between employees to identify potential conflicts and misunderstandings early on 2. The system of claim 1.

5. The engagement improvement unit Evaluate team member engagement levels in real time and instantly suggest improvements 2. The system of claim 1.

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