System

Abstract

An object of a system according to an embodiment is to improve the efficiency of employee performance evaluation.SOLUTION: A system includes a self-evaluation analysis part, a calendar connection part, a project evaluation part, and a target achievement evaluation part. The self-evaluation analysis unit analyzes the employee's self-evaluation. The calendar connector organizes the evaluation scores based on the self-evaluation analyzed by the self-evaluation analyzer. A project evaluation part evaluates the project on the basis of the evaluation points arranged by the calendar connection part. A target achievement evaluation part evaluates the degree of achievement to the set target on the basis of the information of the project evaluated by the project evaluation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not efficiently evaluate employee performance and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of employee performance evaluation. [Means for solving the problem]

[0006] The system according to the embodiment includes a self-assessment analysis unit, a calendar connection unit, a project evaluation unit, and a goal achievement evaluation unit. The self-assessment analysis unit analyzes the self-assessments of employees. The calendar connection unit organizes evaluation points based on the self-assessments analyzed by the self-assessment analysis unit. The project evaluation unit evaluates projects based on the evaluation points organized by the calendar connection unit. The goal achievement evaluation unit evaluates the degree of achievement of set goals based on project information evaluated by the project evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of employee performance evaluation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The employee performance evaluation system according to an embodiment of the present invention is a system that connects with employees' self-evaluations and calendars, organizes evaluation points such as projects in which the generation AI has participated, man-hours worked, and employees' selling points, and supports evaluation of the degree of achievement of initially set goals and overall performance. This enables the employee performance evaluation system to efficiently and comprehensively evaluate employees' performance.

[0029] An employee performance evaluation system according to an embodiment includes a self-evaluation analysis unit, a calendar connection unit, a project evaluation unit, and a goal achievement evaluation unit. The self-evaluation analysis unit analyzes an employee's self-evaluation. For example, it analyzes the self-evaluation data entered by the employee and organizes evaluation points. The self-evaluation analysis unit can also check the consistency of evaluations by referring to the employee's past evaluation history. For example, if an employee was previously evaluated as having demonstrated leadership, it can check whether a similar evaluation was given in the current self-evaluation. The self-evaluation analysis unit can also automatically generate feedback on the content of the self-evaluation and provide advice to the employee to improve their next self-evaluation. For example, if an employee evaluates themselves as having "demonstrated leadership," the system can advise the employee to provide specific examples of leadership. The calendar connection unit organizes evaluation points based on the self-evaluation analyzed by the self-evaluation analysis unit. For example, it can analyze schedules entered in a calendar and calculate working man-hours. The calendar connection unit can also take into account employees' break times and non-working times to more accurately calculate actual working man-hours. For example, it can calculate actual working man-hours by excluding break times entered in the calendar. Furthermore, the calendar connection unit can evaluate employees' time management skills and suggest areas for improvement. For example, if there is a lot of wasted time, it can provide advice on how to reduce that time. The project evaluation unit evaluates projects based on the evaluation points organized by the calendar connection unit. For example, it organizes information about projects in which employees have participated and evaluates them. The project evaluation unit can also include the project's progress and the quality of the deliverables in the evaluation. For example, it evaluates whether the project is progressing as planned. Furthermore, the project evaluation unit can evaluate employees' skill sets based on project information and suggest areas for skill improvement. For example, it can identify skills that were lacking in the project and provide advice on how to improve those skills. The goal achievement evaluation unit evaluates the degree of achievement of set goals based on the project information evaluated by the project evaluation unit. For example, it can analyze the degree of achievement of goals set by employees and calculate evaluation points.As a result, the employee performance evaluation system according to the embodiment can efficiently and comprehensively evaluate employee performance. For example, it is possible to accurately grasp how much time an employee has spent on which project and what results they have achieved. Furthermore, by reflecting an employee's strengths and goal achievement status in the evaluation, fairer and more accurate evaluation is possible.

[0030] The self-assessment analysis unit can refer to an employee's past evaluation history and check the consistency of the evaluation. For example, when the generation AI analyzes the self-assessment data entered by an employee, the self-assessment analysis unit refers to the past evaluation history. For example, if an employee was evaluated as having demonstrated leadership in the past, it checks whether the same evaluation has been given in the current self-assessment. The generation AI also checks the consistency of the self-assessment based on the employee's past evaluation history. For example, it analyzes whether the skills and abilities for which the employee was highly rated in the past are reflected in the current self-assessment. The generation AI also checks the consistency of the self-assessment by referring to the employee's past evaluation history. For example, it checks whether the points for which the employee was poorly rated in the past have been improved in the current self-assessment. This makes it possible to maintain the consistency of the evaluation.

[0031] The self-assessment analysis unit can automatically generate feedback for the content of the self-assessment and provide advice to help the employee improve their next self-assessment. In the self-assessment analysis unit, for example, the generation AI analyzes the content of the self-assessment and automatically generates feedback. For example, if an employee evaluates themselves as "demonstrating leadership," the generation AI provides advice to cite specific examples of leadership. The generation AI also provides advice to improve their next self-assessment based on the content of the self-assessment. For example, if an employee evaluates themselves as "emphasizing teamwork," the generation AI provides advice to cite specific teamwork results. The generation AI also analyzes the content of the self-assessment and automatically generates feedback. For example, if an employee evaluates themselves as "acquiring a new skill," the generation AI provides advice to cite specific examples of how that skill is applied. This allows the employee to receive specific advice to improve their next self-assessment.

[0032] The calendar connection unit takes into account employee break times and non-working times, allowing for more accurate calculation of actual working hours. For example, when the generation AI analyzes calendar data, the calendar connection unit also takes into account employee break times and non-working times. For example, it calculates actual working hours by excluding break times entered in the calendar. The generation AI also calculates working hours based on calendar data, taking into account employee non-working times. For example, it calculates actual working hours by excluding the time of meetings and conferences entered in the calendar. The generation AI also takes into account employee break times and non-working times when analyzing calendar data, allowing for more accurate calculation of actual working hours. For example, it calculates actual working hours by excluding the time of vacation and sick leave entered in the calendar. This allows for more accurate calculation of actual working hours.

[0033] The calendar connection unit can evaluate an employee's time management skills and suggest areas for improvement. In the calendar connection unit, for example, the generation AI evaluates an employee's time management skills based on calendar data. For example, it analyzes the time allocation of appointments entered on the calendar and evaluates whether time is being managed efficiently. The generation AI also analyzes the calendar data and suggests areas for improvement in the employee's time management skills. For example, if there is a lot of wasted time, it provides advice on how to reduce that time. The generation AI also evaluates an employee's time management skills based on calendar data and suggests areas for improvement. For example, if not enough time is being spent on important tasks, it advises the employee to spend more time on those tasks. In this way, it is possible to evaluate an employee's time management skills and suggest areas for improvement.

[0034] The calendar connection unit can evaluate work-life balance by taking into account an employee's private schedule. For example, when the generation AI analyzes calendar data, the calendar connection unit also takes into account an employee's private schedule. For example, it evaluates how much time an employee allocates to private schedules and evaluates their work-life balance. The generation AI also evaluates work-life balance by taking into account an employee's private schedule based on calendar data. For example, it evaluates how an employee allocates their time between work and private life. The generation AI also evaluates work-life balance by taking into account an employee's private schedule when analyzing calendar data. For example, it evaluates whether an employee allocates enough time to private schedules. This makes it possible to evaluate an employee's work-life balance.

[0035] The calendar connection unit can make suggestions to optimize the time allocation between employees' projects. In the calendar connection unit, for example, the generation AI makes suggestions to optimize the time allocation between employees' projects based on calendar data. For example, it provides advice to allocate more time to important projects. The generation AI also analyzes calendar data and makes suggestions to optimize the time allocation between employees' projects. For example, it provides advice to reduce wasted time and allocate time efficiently. The generation AI also makes suggestions to optimize the time allocation between employees' projects based on calendar data. For example, it provides advice to allocate time evenly between multiple projects. This makes it possible to make suggestions to optimize the time allocation between employees' projects.

[0036] The project evaluation unit can also include the progress of the project and the quality of the deliverables in the evaluation. For example, when the generation AI analyzes project information, the project evaluation unit includes the progress of the project in the evaluation. For example, it evaluates whether the project is progressing as planned. The generation AI also includes the quality of the deliverables in the evaluation based on the project information. For example, it evaluates the quality of the products or services developed in the project. The generation AI also includes the progress of the project and the quality of the deliverables in the evaluation when analyzing project information. For example, it comprehensively evaluates the progress of the project and the quality of the deliverables. This allows for a more accurate evaluation by including the progress of the project and the quality of the deliverables in the evaluation.

[0037] The project evaluation unit can evaluate an employee's skill set based on project information and suggest areas for skill improvement. In the project evaluation unit, for example, the generation AI evaluates an employee's skill set based on project information. For example, it evaluates the skills and technologies used in the project. The generation AI also analyzes project information and suggests areas for improvement in the employee's skill set. For example, it identifies skills that were lacking in the project and provides advice for improving those skills. The generation AI also evaluates an employee's skill set based on project information and suggests areas for skill improvement. For example, it evaluates the skills demonstrated in the project and makes suggestions for further improving those skills. In this way, it is possible to evaluate an employee's skill set and suggest areas for skill improvement.

[0038] The project evaluation unit can find connections with other projects when analyzing project information and perform mutual evaluations. For example, when the generation AI analyzes project information, the project evaluation unit finds connections with other projects. For example, it associates and evaluates projects in which the same technology or skills were used. The generation AI also finds connections with other projects based on the project information and performs mutual evaluations. For example, it associates and evaluates projects in which the same team members participated. The generation AI also finds connections with other projects when analyzing project information and performs mutual evaluations. For example, it associates and evaluates projects that have the same goals. This makes it possible to find connections with other projects and perform mutual evaluations, enabling a more comprehensive evaluation.

[0039] The project evaluation department can evaluate an employee's leadership skills based on project information and suggest areas for improving their leadership. In the project evaluation department, for example, the generation AI evaluates an employee's leadership skills based on project information. For example, it evaluates situations in which leadership was demonstrated in a project. The generation AI also analyzes the project information and suggests areas for improving the employee's leadership skills. For example, it provides specific advice for demonstrating leadership. The generation AI also evaluates an employee's leadership skills based on project information and suggests areas for improving their leadership. For example, it suggests a training program for demonstrating leadership. In this way, it is possible to evaluate an employee's leadership skills and suggest areas for improving their leadership.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The employee performance evaluation system can also be equipped with a health management section. The health management section can collect employee health data and reflect it in performance evaluations. For example, it can monitor employees' sleep time and exercise volume to evaluate the impact of their health on performance. The health management section can also measure employees' stress levels and suggest relaxation methods if stress levels are high. Furthermore, the health management section can analyze employees' dietary data, evaluate whether their diet is nutritionally balanced, and provide advice on dietary improvements as needed. This makes it possible to perform comprehensive performance evaluations that take employees' health into account.

[0042] The employee performance evaluation system can further include a learning management section. The learning management section can track employees' learning history and evaluate their skill improvement. For example, it can analyze the history of training and online courses that employees have taken and evaluate their learning outcomes. The learning management section can also analyze employees' learning styles and suggest optimal learning methods. Furthermore, the learning management section can monitor employees' progress toward their learning goals and provide advice on how to achieve them. This helps employees improve their skills and reflects this in their performance evaluations.

[0043] The employee performance evaluation system can further include a communication evaluation unit. The communication evaluation unit can evaluate the quality of communication between employees and suggest areas for improvement. For example, it can analyze the content of comments made at conferences and meetings attended by employees to evaluate their communication skills. The communication evaluation unit can also analyze employees' email and chat exchanges to suggest effective communication methods. Furthermore, the communication evaluation unit can collect employee feedback and identify areas for improvement in communication. This allows for improved communication between employees to be reflected in performance evaluations.

[0044] The employee performance evaluation system can further include a collaboration management section to strengthen collaboration between projects. The collaboration management section can integrate information between multiple projects and evaluate the effectiveness of collaboration. For example, it can analyze the sharing status of resources used by different projects and propose efficient resource allocation. The collaboration management section can also promote communication between projects and propose areas for improving information sharing. Furthermore, the collaboration management section can evaluate the results of collaboration between projects and provide advice on how to expand successful cases to other projects. This strengthens collaboration between projects and contributes to improving overall performance.

[0045] The employee performance evaluation system can also be equipped with a career management section that supports employees' career paths. The career management section can suggest optimal career paths based on employees' skills and experience. For example, it can analyze an employee's past project history and suggest suitable positions and duties for the future. The career management section can also provide career path options by taking into account the employee's goals and aspirations. Furthermore, the career management section can suggest training and other programs to improve employees' skills. This can support employees' career paths and contribute to improving their long-term performance.

[0046] The employee performance evaluation system may further include a creativity evaluation unit that evaluates employees' creativity. The creativity evaluation unit may evaluate employees' ideas and proposals and suggest areas for improving creativity. For example, it may evaluate the originality and feasibility of ideas submitted by employees. The creativity evaluation unit may also analyze employees' past ideas and proposals to identify trends in creativity. Furthermore, the creativity evaluation unit may suggest workshops or brainstorming sessions to enhance employees' creativity. This allows employees' creativity to be evaluated and used to improve their performance.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The self-assessment analysis unit analyzes the employee's self-assessment. For example, it analyzes the self-assessment data entered by the employee and organizes the evaluation points. The self-assessment analysis unit can also refer to the employee's past evaluation history to check the consistency of the evaluation. Furthermore, it automatically generates feedback on the content of the self-assessment and provides advice to the employee to improve their next self-assessment. Step 2: The calendar connection unit organizes the evaluation points based on the self-evaluation analyzed by the self-evaluation analysis unit. For example, it analyzes the schedule entered in the calendar and calculates the number of man-hours worked. The calendar connection unit can also take into account employees' break times and non-working time to more accurately calculate the actual number of man-hours worked. It can also evaluate employees' time management skills and suggest areas for improvement. Step 3: The Project Evaluation Department evaluates projects based on the evaluation points organized by the Calendar Connection Department. For example, it organizes information about projects in which employees have participated and performs evaluations. The evaluation can also include the progress of the project and the quality of the deliverables. Furthermore, it can evaluate employees' skill sets based on project information and suggest areas for skill improvement. Step 4: The Goal Achievement Evaluation Department evaluates the degree of achievement of the set goals based on the project information evaluated by the Project Evaluation Department. For example, it analyzes the degree of achievement of the goals set by the employee and calculates an evaluation score. This allows for efficient and comprehensive performance evaluation of employees.

[0049] (Example 2) The employee performance evaluation system according to an embodiment of the present invention is a system that connects with employees' self-evaluations and calendars, organizes evaluation points such as projects in which the generation AI has participated, man-hours worked, and employees' selling points, and supports evaluation of the degree of achievement of initially set goals and overall performance. This enables the employee performance evaluation system to efficiently and comprehensively evaluate employees' performance.

[0050] An employee performance evaluation system according to an embodiment includes a self-evaluation analysis unit, a calendar connection unit, a project evaluation unit, and a goal achievement evaluation unit. The self-evaluation analysis unit analyzes an employee's self-evaluation. For example, it analyzes the self-evaluation data entered by the employee and organizes evaluation points. The self-evaluation analysis unit can also check the consistency of evaluations by referring to the employee's past evaluation history. For example, if an employee was previously evaluated as having demonstrated leadership, it can check whether a similar evaluation was given in the current self-evaluation. The self-evaluation analysis unit can also automatically generate feedback on the content of the self-evaluation and provide advice to the employee to improve their next self-evaluation. For example, if an employee evaluates themselves as having "demonstrated leadership," the system can advise the employee to provide specific examples of leadership. The calendar connection unit organizes evaluation points based on the self-evaluation analyzed by the self-evaluation analysis unit. For example, it can analyze schedules entered in a calendar and calculate working man-hours. The calendar connection unit can also take into account employees' break times and non-working times to more accurately calculate actual working man-hours. For example, it can calculate actual working man-hours by excluding break times entered in the calendar. Furthermore, the calendar connection unit can evaluate employees' time management skills and suggest areas for improvement. For example, if there is a lot of wasted time, it can provide advice on how to reduce that time. The project evaluation unit evaluates projects based on the evaluation points organized by the calendar connection unit. For example, it organizes information about projects in which employees have participated and evaluates them. The project evaluation unit can also include the project's progress and the quality of the deliverables in the evaluation. For example, it evaluates whether the project is progressing as planned. Furthermore, the project evaluation unit can evaluate employees' skill sets based on project information and suggest areas for skill improvement. For example, it can identify skills that were lacking in the project and provide advice on how to improve those skills. The goal achievement evaluation unit evaluates the degree of achievement of set goals based on the project information evaluated by the project evaluation unit. For example, it can analyze the degree of achievement of goals set by employees and calculate evaluation points.As a result, the employee performance evaluation system according to the embodiment can efficiently and comprehensively evaluate employee performance. For example, it is possible to accurately grasp how much time an employee has spent on which project and what results they have achieved. Furthermore, by reflecting an employee's strengths and goal achievement status in the evaluation, fairer and more accurate evaluation is possible.

[0051] The self-assessment analysis unit can refer to an employee's past evaluation history and check the consistency of the evaluation. For example, when the generation AI analyzes the self-assessment data entered by an employee, the self-assessment analysis unit refers to the past evaluation history. For example, if an employee was evaluated as having demonstrated leadership in the past, it checks whether the same evaluation has been given in the current self-assessment. The generation AI also checks the consistency of the self-assessment based on the employee's past evaluation history. For example, it analyzes whether the skills and abilities for which the employee was highly rated in the past are reflected in the current self-assessment. The generation AI also checks the consistency of the self-assessment by referring to the employee's past evaluation history. For example, it checks whether the points for which the employee was poorly rated in the past have been improved in the current self-assessment. This makes it possible to maintain the consistency of the evaluation.

[0052] The self-assessment analysis unit can automatically generate feedback for the content of the self-assessment and provide advice to help the employee improve their next self-assessment. In the self-assessment analysis unit, for example, the generation AI analyzes the content of the self-assessment and automatically generates feedback. For example, if an employee evaluates themselves as "demonstrating leadership," the generation AI provides advice to cite specific examples of leadership. The generation AI also provides advice to improve their next self-assessment based on the content of the self-assessment. For example, if an employee evaluates themselves as "emphasizing teamwork," the generation AI provides advice to cite specific teamwork results. The generation AI also analyzes the content of the self-assessment and automatically generates feedback. For example, if an employee evaluates themselves as "acquiring a new skill," the generation AI provides advice to cite specific examples of how that skill is applied. This allows the employee to receive specific advice to improve their next self-assessment.

[0053] The self-evaluation analysis unit can use the emotion estimation function to analyze the employee's emotions when evaluating themselves and prioritize evaluation points that indicate strong positive emotions. In the self-evaluation analysis unit, for example, the generation AI uses the emotion estimation function to analyze the employee's emotions when evaluating themselves. For example, it analyzes the employee's facial expressions and voice when entering their self-evaluation and prioritizes evaluation points that indicate strong positive emotions. The generation AI also combines and analyzes the content of the self-evaluation with the emotion estimation data and prioritizes evaluation points that indicate strong positive emotions. For example, if an employee smiles when entering their self-evaluation, it prioritizes those evaluation points. The generation AI also uses the emotion estimation function to analyze the employee's emotions when evaluating themselves and prioritizes evaluation points that indicate strong positive emotions. For example, if an employee frequently uses positive words when entering their self-evaluation, it prioritizes those evaluation points. This prioritizes evaluation points that indicate strong positive emotions, thereby increasing employee motivation.

[0054] The calendar connection unit takes into account employee break times and non-working times, allowing for more accurate calculation of actual working hours. For example, when the generation AI analyzes calendar data, the calendar connection unit also takes into account employee break times and non-working times. For example, it calculates actual working hours by excluding break times entered in the calendar. The generation AI also calculates working hours based on calendar data, taking into account employee non-working times. For example, it calculates actual working hours by excluding the time of meetings and conferences entered in the calendar. The generation AI also takes into account employee break times and non-working times when analyzing calendar data, allowing for more accurate calculation of actual working hours. For example, it calculates actual working hours by excluding the time of vacation and sick leave entered in the calendar. This allows for more accurate calculation of actual working hours.

[0055] The calendar connection unit can evaluate an employee's time management skills and suggest areas for improvement. In the calendar connection unit, for example, the generation AI evaluates an employee's time management skills based on calendar data. For example, it analyzes the time allocation of appointments entered on the calendar and evaluates whether time is being managed efficiently. The generation AI also analyzes the calendar data and suggests areas for improvement in the employee's time management skills. For example, if there is a lot of wasted time, it provides advice on how to reduce that time. The generation AI also evaluates an employee's time management skills based on calendar data and suggests areas for improvement. For example, if not enough time is being spent on important tasks, it advises the employee to spend more time on those tasks. In this way, it is possible to evaluate an employee's time management skills and suggest areas for improvement.

[0056] The calendar connection unit uses the emotion estimation function to analyze employees' emotions regarding events entered in the calendar, identify stressful events, and reflect them in the evaluation. In the calendar connection unit, for example, the generation AI uses the emotion estimation function to analyze employees' emotions regarding events entered in the calendar. For example, it identifies events that cause stress to employees and reflects those events in the evaluation. The generation AI also combines and analyzes calendar data and emotion estimation data to identify stressful events. For example, it identifies times of meetings or conferences that cause stress to employees and reflects those times in the evaluation. The generation AI also uses the emotion estimation function to analyze employees' emotions regarding events entered in the calendar, identify stressful events, and reflect those times in the evaluation. For example, it identifies times of tasks or projects that cause stress to employees and reflects those times in the evaluation. In this way, by identifying stressful events and reflecting those times in the evaluation, it is possible to manage employee stress.

[0057] The calendar connection unit can evaluate work-life balance by taking into account an employee's private schedule. For example, when the generation AI analyzes calendar data, the calendar connection unit also takes into account an employee's private schedule. For example, it evaluates how much time an employee allocates to private schedules and evaluates their work-life balance. The generation AI also evaluates work-life balance by taking into account an employee's private schedule based on calendar data. For example, it evaluates how an employee allocates their time between work and private life. The generation AI also evaluates work-life balance by taking into account an employee's private schedule when analyzing calendar data. For example, it evaluates whether an employee allocates enough time to private schedules. This makes it possible to evaluate an employee's work-life balance.

[0058] The calendar connection unit can make suggestions to optimize the time allocation between employees' projects. In the calendar connection unit, for example, the generation AI makes suggestions to optimize the time allocation between employees' projects based on calendar data. For example, it provides advice to allocate more time to important projects. The generation AI also analyzes calendar data and makes suggestions to optimize the time allocation between employees' projects. For example, it provides advice to reduce wasted time and allocate time efficiently. The generation AI also makes suggestions to optimize the time allocation between employees' projects based on calendar data. For example, it provides advice to allocate time evenly between multiple projects. This makes it possible to make suggestions to optimize the time allocation between employees' projects.

[0059] The calendar connection unit uses the emotion estimation function to monitor employees' emotions regarding events entered in the calendar in real time, and can prioritize events that are associated with strong positive emotions. In the calendar connection unit, for example, the generation AI uses the emotion estimation function to monitor employees' emotions regarding events entered in the calendar in real time. For example, it prioritizes organizing events that employees have positive emotions about. The generation AI also combines and analyzes calendar data and emotion estimation data to prioritize events that are associated with strong positive emotions. For example, it prioritizes organizing times for conferences and meetings that employees look forward to. The generation AI also uses the emotion estimation function to monitor employees' emotions regarding events entered in the calendar in real time, and prioritizes organizing events that are associated with strong positive emotions. For example, it prioritizes organizing times for tasks or projects that employees have positive emotions about. In this way, by prioritizing organizing events that are associated with strong positive emotions, employee motivation can be increased.

[0060] The project evaluation unit can also include the progress of the project and the quality of the deliverables in the evaluation. For example, when the generation AI analyzes project information, the project evaluation unit includes the progress of the project in the evaluation. For example, it evaluates whether the project is progressing as planned. The generation AI also includes the quality of the deliverables in the evaluation based on the project information. For example, it evaluates the quality of the products or services developed in the project. The generation AI also includes the progress of the project and the quality of the deliverables in the evaluation when analyzing project information. For example, it comprehensively evaluates the progress of the project and the quality of the deliverables. This allows for a more accurate evaluation by including the progress of the project and the quality of the deliverables in the evaluation.

[0061] The project evaluation unit can evaluate an employee's skill set based on project information and suggest areas for skill improvement. In the project evaluation unit, for example, the generation AI evaluates an employee's skill set based on project information. For example, it evaluates the skills and technologies used in the project. The generation AI also analyzes project information and suggests areas for improvement in the employee's skill set. For example, it identifies skills that were lacking in the project and provides advice for improving those skills. The generation AI also evaluates an employee's skill set based on project information and suggests areas for skill improvement. For example, it evaluates the skills demonstrated in the project and makes suggestions for further improving those skills. In this way, it is possible to evaluate an employee's skill set and suggest areas for skill improvement.

[0062] The project evaluation unit can use the emotion estimation function to analyze employees' emotions when participating in a project, and prioritize evaluation of projects for which employees have strong positive emotions. In the project evaluation unit, for example, the generation AI uses the emotion estimation function to analyze employees' emotions when participating in a project. For example, it prioritizes evaluation of projects for which employees have positive emotions. The generation AI also combines and analyzes project information and emotion estimation data to prioritize evaluation of projects for which employees have strong positive emotions. For example, it prioritizes evaluation of projects that employees enjoyed working on. The generation AI also uses the emotion estimation function to analyze employees' emotions when participating in a project, and prioritizes evaluation of projects for which employees have strong positive emotions. For example, it prioritizes evaluation of the results of projects for which employees have positive emotions. In this way, by prioritizing evaluation of projects for which employees have strong positive emotions, employee motivation can be increased.

[0063] The project evaluation unit can find connections with other projects when analyzing project information and perform mutual evaluations. For example, when the generation AI analyzes project information, the project evaluation unit finds connections with other projects. For example, it associates and evaluates projects in which the same technology or skills were used. The generation AI also finds connections with other projects based on the project information and performs mutual evaluations. For example, it associates and evaluates projects in which the same team members participated. The generation AI also finds connections with other projects when analyzing project information and performs mutual evaluations. For example, it associates and evaluates projects that have the same goals. This makes it possible to find connections with other projects and perform mutual evaluations, enabling a more comprehensive evaluation.

[0064] The project evaluation department can evaluate an employee's leadership skills based on project information and suggest areas for improving their leadership. In the project evaluation department, for example, the generation AI evaluates an employee's leadership skills based on project information. For example, it evaluates situations in which leadership was demonstrated in a project. The generation AI also analyzes the project information and suggests areas for improving the employee's leadership skills. For example, it provides specific advice for demonstrating leadership. The generation AI also evaluates an employee's leadership skills based on project information and suggests areas for improving their leadership. For example, it suggests a training program for demonstrating leadership. In this way, it is possible to evaluate an employee's leadership skills and suggest areas for improving their leadership.

[0065] The project evaluation department uses the emotion estimation function to monitor employees' emotions in real time when participating in a project, and can send encouraging messages if employees have strong negative emotions. In the project evaluation department, for example, the generation AI uses the emotion estimation function to monitor employees' emotions in real time when participating in a project. For example, it sends encouraging messages to projects that employees have negative emotions about. The generation AI also combines and analyzes project information and emotion estimation data, and sends encouraging messages if employees have strong negative emotions. For example, it sends encouraging messages to projects that cause employees stress. The generation AI also uses the emotion estimation function to monitor employees' emotions in real time when participating in a project, and can send encouraging messages if employees have strong negative emotions. For example, it sends encouraging messages to projects that cause employees anxiety. In this way, by sending encouraging messages if employees have strong negative emotions, it is possible to maintain employee motivation.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The employee performance evaluation system can also be equipped with a health management section. The health management section can collect employee health data and reflect it in performance evaluations. For example, it can monitor employees' sleep time and exercise volume to evaluate the impact of their health on performance. The health management section can also measure employees' stress levels and suggest relaxation methods if stress levels are high. Furthermore, the health management section can analyze employees' dietary data, evaluate whether their diet is nutritionally balanced, and provide advice on dietary improvements as needed. This makes it possible to perform comprehensive performance evaluations that take employees' health into account.

[0068] The employee performance evaluation system can further include a learning management section. The learning management section can track employees' learning history and evaluate their skill improvement. For example, it can analyze the history of training and online courses that employees have taken and evaluate their learning outcomes. The learning management section can also analyze employees' learning styles and suggest optimal learning methods. Furthermore, the learning management section can monitor employees' progress toward their learning goals and provide advice on how to achieve them. This helps employees improve their skills and reflects this in their performance evaluations.

[0069] The employee performance evaluation system can further include a communication evaluation unit. The communication evaluation unit can evaluate the quality of communication between employees and suggest areas for improvement. For example, it can analyze the content of comments made at conferences and meetings attended by employees to evaluate their communication skills. The communication evaluation unit can also analyze employees' email and chat exchanges to suggest effective communication methods. Furthermore, the communication evaluation unit can collect employee feedback and identify areas for improvement in communication. This allows for improved communication between employees to be reflected in performance evaluations.

[0070] The employee performance evaluation system can also use an emotion estimation function to evaluate employee motivation. For example, it can analyze the emotions employees express when entering their self-evaluations and evaluate their motivation levels. It can also use the emotion estimation function to monitor employees' emotions when working on projects and evaluate fluctuations in their motivation. It can also use the emotion estimation function to provide advice for improving motivation based on employees' emotional data. This can help evaluate employee motivation and improve performance.

[0071] The employee performance evaluation system can further use an emotion estimation function to evaluate an employee's stress level. For example, the emotion estimation function can be used to analyze the emotion an employee expresses when entering their self-evaluation and evaluate their stress level. The emotion estimation function can also be used to monitor the emotion an employee expresses when working on a project and evaluate fluctuations in their stress level. The emotion estimation function can also be used to provide advice for reducing stress based on the employee's emotional data. This can help evaluate an employee's stress level and improve their performance.

[0072] The employee performance evaluation system can also use an emotion estimation function to monitor employees' emotions in real time and suggest relaxation methods if negative emotions are strong. For example, the emotions of employees when they enter their self-evaluations can be analyzed in real time, and relaxation methods can be suggested if negative emotions are strong. The emotion estimation function can also be used to monitor employees' emotions in real time when they work on a project and suggest relaxation methods if negative emotions are strong. The emotion estimation function can also be used to suggest relaxation methods based on employees' emotional data. This can help reduce employees' negative emotions and improve their performance.

[0073] The employee performance evaluation system can further use the emotion estimation function to analyze employees' emotions and send them a message of praise if their emotions are strong positive. For example, the emotion estimation function can be used to analyze the emotions employees have when entering their self-evaluation, and if their emotions are strong positive, a message of praise can be sent. The emotion estimation function can also be used to analyze the emotions employees have when working on a project, and if their emotions are strong positive, a message of praise can be sent. The emotion estimation function can also be used to send them a message of praise based on their emotional data. This can reinforce employees' positive emotions and help improve their performance.

[0074] The employee performance evaluation system can further include a collaboration management section to strengthen collaboration between projects. The collaboration management section can integrate information between multiple projects and evaluate the effectiveness of collaboration. For example, it can analyze the sharing status of resources used by different projects and propose efficient resource allocation. The collaboration management section can also promote communication between projects and propose areas for improving information sharing. Furthermore, the collaboration management section can evaluate the results of collaboration between projects and provide advice on how to expand successful cases to other projects. This strengthens collaboration between projects and contributes to improving overall performance.

[0075] The employee performance evaluation system can also be equipped with a career management section that supports employees' career paths. The career management section can suggest optimal career paths based on employees' skills and experience. For example, it can analyze an employee's past project history and suggest suitable positions and duties for the future. The career management section can also provide career path options by taking into account the employee's goals and aspirations. Furthermore, the career management section can suggest training and other programs to improve employees' skills. This can support employees' career paths and contribute to improving their long-term performance.

[0076] The employee performance evaluation system may further include a creativity evaluation unit that evaluates employees' creativity. The creativity evaluation unit may evaluate employees' ideas and proposals and suggest areas for improving creativity. For example, it may evaluate the originality and feasibility of ideas submitted by employees. The creativity evaluation unit may also analyze employees' past ideas and proposals to identify trends in creativity. Furthermore, the creativity evaluation unit may suggest workshops or brainstorming sessions to enhance employees' creativity. This allows employees' creativity to be evaluated and used to improve their performance.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The self-assessment analysis unit analyzes the employee's self-assessment. For example, it analyzes the self-assessment data entered by the employee and organizes the evaluation points. The self-assessment analysis unit can also refer to the employee's past evaluation history to check the consistency of the evaluation. Furthermore, it automatically generates feedback on the content of the self-assessment and provides advice to the employee to improve their next self-assessment. Step 2: The calendar connection unit organizes the evaluation points based on the self-evaluation analyzed by the self-evaluation analysis unit. For example, it analyzes the schedule entered in the calendar and calculates the number of man-hours worked. The calendar connection unit can also take into account employees' break times and non-working time to more accurately calculate the actual number of man-hours worked. It can also evaluate employees' time management skills and suggest areas for improvement. Step 3: The Project Evaluation Department evaluates projects based on the evaluation points organized by the Calendar Connection Department. For example, it organizes information about projects in which employees have participated and performs evaluations. The evaluation can also include the progress of the project and the quality of the deliverables. Furthermore, it can evaluate employees' skill sets based on project information and suggest areas for skill improvement. Step 4: The Goal Achievement Evaluation Department evaluates the degree of achievement of the set goals based on the project information evaluated by the Project Evaluation Department. For example, it analyzes the degree of achievement of the goals set by the employee and calculates an evaluation score. This allows for efficient and comprehensive performance evaluation of employees.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 system equipped with a generative AI, The generated AI is Self-evaluation analysis department that analyzes employees' self-evaluations; a calendar connection unit that organizes evaluation points based on the self-evaluation analyzed by the self-evaluation analysis unit; a project evaluation unit that evaluates the project based on the evaluation points organized by the calendar connection unit; a goal achievement evaluation unit that evaluates the degree of achievement of a set goal based on the information on the project evaluated by the project evaluation unit; A system characterized by:

2. The self-evaluation analysis unit Review the employee's past evaluation history to check for consistency in evaluations 2. The system of claim 1.

3. The self-evaluation analysis unit Automatically generate feedback on the self-assessment and provide advice to the employee to improve their next self-assessment 2. The system of claim 1.

4. The self-evaluation analysis unit Analyze the employee's emotions during self-evaluation and prioritize evaluation points that evoke strong positive emotions.

2. The system of claim 1.

5. The calendar connection portion is Taking into account the employee's break time and non-working time, the actual working hours are calculated more accurately.

2. The system of claim 1.

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