System

Abstract

An object of a system according to an embodiment is to fairly and objectively evaluate performance of employees.SOLUTION: A system includes an evaluation part, a recording part, and an analysis part. The evaluation unit evaluates the performance of the employee. The recording unit records the data evaluated by the evaluation unit. The analysis unit analyzes the data recorded by the recording unit.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] Previous technology had the problem that employee performance evaluations were subjective and lacked fairness and objectivity.

[0005] The system according to the embodiment aims to evaluate employee performance in a fair and objective manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an evaluation unit, a recording unit, and an analysis unit. The evaluation unit evaluates the performance of employees. The recording unit records data evaluated by the evaluation unit. The analysis unit analyzes the data recorded by the recording unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the performance of employees fairly and objectively. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI-driven comprehensive personnel evaluation system according to the embodiment of the present invention is a system for evaluating employee performance fairly and objectively. As a result, the AI-driven comprehensive personnel evaluation system can evaluate employee performance fairly and objectively based on data.

[0029] An AI-driven comprehensive personnel evaluation system according to an embodiment includes an evaluation unit, a recording unit, and an analysis unit. The evaluation unit evaluates employee performance. For example, the evaluation unit evaluates employee work efficiency. The evaluation unit can also evaluate the quality of employee deliverables. The evaluation unit can also evaluate employee communication skills. The recording unit records data evaluated by the evaluation unit. For example, the recording unit records the evaluation data as numerical data. The recording unit can also record evaluation comments. The recording unit can also record evaluation scores. The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit performs statistical analysis. The analysis unit can also perform analysis using a machine learning algorithm. The analysis unit can also analyze data trends. This allows the AI-driven comprehensive personnel evaluation system according to an embodiment to evaluate employee performance fairly and objectively based on data. For example, the evaluation results are used to determine employee promotions and bonuses. The evaluation results are also used to select training programs to improve employee skills. The evaluation results are also used to suggest career paths for employees.

[0030] The evaluation department can automatically record and analyze the time an engineer spends writing code or reviewing code. For example, the evaluation department can automatically record how much time an engineer spends writing code per day and analyze weekly or monthly trends. For example, the department can graph fluctuations in the time spent writing code for a specific project to identify improvements in efficiency or problems. The evaluation department can also automatically record and analyze how much time an engineer spends on code review. For example, the department can collect data on the frequency and duration of code reviews and evaluate the quality of the reviews. This allows for an accurate evaluation of an engineer's working time.

[0031] The evaluation unit can analyze an engineer's communication patterns and evaluate their frequency or quality. For example, the evaluation unit can analyze chat tools or email exchanges to evaluate the frequency and quality of communication. For example, it can analyze message exchanges in a specific project and measure the level of communication activity. The evaluation unit can also evaluate how an engineer communicates with team members. For example, it can collect data on the frequency of meeting participation and the content of comments, and evaluate the quality of communication. This makes it possible to evaluate an engineer's communication skills.

[0032] The evaluation unit can quantify an engineer's contribution to a project based on the number of commits or the number of bugs resolved. For example, the evaluation unit collects data such as the number of commits and the number of bugs resolved by an engineer, and evaluates the degree of contribution based on that data. For example, the number of commits is tallied for each project, and the degree of contribution is visualized. The evaluation unit also collects data such as the number of bugs resolved by an engineer, and evaluates the degree of contribution. For example, the degree of contribution is evaluated based on the number of bugs resolved in a bug tracking system. This allows an engineer's contribution to be accurately evaluated.

[0033] The evaluation unit can automatically evaluate the quality of an engineer's code and analyze the rate of bugs or code readability. The evaluation unit, for example, uses generative AI to automatically evaluate the quality of code written by an engineer. For example, it evaluates the readability and maintainability of the code and provides feedback on areas for improvement. The evaluation unit also analyzes the rate of bugs in the engineer's code. For example, it measures the number of bugs per line of code and evaluates the rate of bugs. The evaluation unit also analyzes the readability of the engineer's code. For example, it evaluates the amount of comments in the code and the appropriateness of variable names to evaluate readability. This allows for an accurate evaluation of the engineer's code quality.

[0034] The evaluation unit monitors the engineer's work environment and can propose an efficient work environment. For example, the evaluation unit monitors the tools and devices used by the engineer and evaluates work efficiency. For example, it identifies frequently used tools and devices and proposes an efficient work environment. The evaluation unit also evaluates the comfort of the engineer's work space. For example, it evaluates the desk layout and lighting status and proposes improvements to the work environment. The evaluation unit also monitors the engineer's posture and movements while working and proposes an efficient work environment. For example, it proposes a work environment based on ergonomics. This makes it possible to optimize the engineer's work environment.

[0035] The evaluation unit can track the frequency of contact between sales representatives and customers or the time it takes to conclude a contract. For example, the evaluation unit automatically records how frequently sales representatives contact customers and evaluates the frequency of contact. For example, it analyzes telephone and email exchanges and collects data on the frequency of contact. The evaluation unit also tracks how long it takes sales representatives to conclude a contract. For example, it measures and evaluates the average time from the first contact to the conclusion of a contract. The evaluation unit also evaluates the quality of the sales representative's customer service. For example, it evaluates the quality of the service based on feedback from customers. This makes it possible to accurately evaluate the sales representative's customer service.

[0036] The evaluation department can measure the activity level of information sharing within the sales team. For example, the evaluation department collects data on the frequency of meetings and information sharing within the sales team and evaluates the activity level. For example, the evaluation department measures the activity level of information sharing based on the number of meetings and the number of participants. The evaluation department also evaluates the quality of information sharing within the sales team. For example, the evaluation department evaluates the content and usefulness of the shared information. The evaluation department also evaluates the frequency of information sharing within the sales team. For example, the evaluation department collects data on the number of times information is shared per week and evaluates it. This makes it possible to accurately evaluate the activity level of information sharing within the sales team.

[0037] The evaluation unit can evaluate the degree of contribution based on the performance data of the sales representative. For example, the evaluation unit collects data on the sales revenue and number of new customers of the sales representative and evaluates the degree of contribution based on that. For example, monthly sales are tallied and the degree of contribution is visualized. The evaluation unit also collects data on the number of contracts the sales representative has and evaluates the degree of contribution. For example, the degree of contribution is evaluated based on the increase or decrease in the number of contracts. The evaluation unit also collects data on the customer satisfaction of the sales representative and evaluates the degree of contribution. For example, the degree of contribution is evaluated based on the results of a customer survey. This allows the degree of contribution of the sales representative to be accurately evaluated.

[0038] The evaluation unit can automatically evaluate the presentation skills of sales representatives and provide feedback on areas for improvement. The evaluation unit, for example, uses generative AI to automatically evaluate the presentation skills of sales representatives. For example, it analyzes the content and speaking style of the presentation and provides feedback on areas for improvement. The evaluation unit also evaluates the quality of the sales representative's presentation. For example, it evaluates the quality of the presentation based on the audience's reactions and feedback. The evaluation unit also evaluates the structure of the sales representative's presentation. For example, it evaluates the flow and logic of the presentation and suggests areas for improvement. This improves the sales representative's presentation skills.

[0039] The evaluation unit can analyze the sales representative's customer response history and propose response methods that will lead to improved customer satisfaction. The evaluation unit, for example, analyzes the sales representative's customer response history and proposes response methods that will lead to improved customer satisfaction. For example, it identifies and proposes successful response methods based on past response history. The evaluation unit also evaluates the quality of the sales representative's customer response. For example, it evaluates the quality of response based on feedback from customers. The evaluation unit also evaluates the speed of the sales representative's customer response. For example, it collects and evaluates the speed of response as data. This makes it possible to propose response methods that will improve customer satisfaction.

[0040] The evaluation unit can monitor employees' working hours or overtime hours. For example, the evaluation unit automatically records employees' arrival and departure times and monitors working hours and overtime hours. For example, it tallys up monthly working hours based on the arrival and departure data. The evaluation unit also monitors employees' working hours in real time. For example, it automatically records arrival and departure times and collects working hours as data. The evaluation unit also monitors employees' overtime hours. For example, it records working hours after regular working hours and evaluates the overtime hours. This allows employees' working hours and overtime hours to be monitored accurately.

[0041] The evaluation unit can tally the employee's task completion rate or deadline completion rate. The evaluation unit, for example, uses a task management tool to tally the employee's task completion rate or deadline completion rate. For example, the evaluation unit collects and evaluates the completion status of each task as data. The evaluation unit also tally the employee's task completion rate. For example, the evaluation unit measures and evaluates the percentage of tasks that are completed among the scheduled tasks. The evaluation unit also tally the employee's deadline completion rate. For example, the evaluation unit measures and evaluates the percentage of tasks that are completed within the deadline. This allows the employee's task completion rate and deadline completion rate to be accurately evaluated.

[0042] The evaluation department can analyze the quality of interactions through employee communication tools. For example, the evaluation department analyzes exchanges via chat tools or emails to evaluate the quality of interactions between employees. For example, the quality of interactions is measured based on the content and frequency of messages. The evaluation department also evaluates the usage status of communication tools by employees. For example, the frequency of use of chat tools and the content of messages are collected and evaluated as data. The evaluation department also evaluates the quality of communication between employees. For example, the quality of interactions is evaluated by evaluating the constructiveness of conversations and the depth of content. This makes it possible to accurately evaluate the quality of communication between employees.

[0043] The evaluation unit can monitor the health status of employees and make suggestions for health management. The evaluation unit monitors the health status of employees, for example, using generative AI. For example, it analyzes data from wearable devices and evaluates the health status. The evaluation unit also monitors the employee's vital signs. For example, it measures heart rate and blood pressure and evaluates the health status. The evaluation unit also evaluates the health status based on the employee's health checkup results. For example, it collects the results of the health checkup as data and evaluates the health status. This allows the employee's health status to be accurately monitored and health management suggestions to be made.

[0044] The evaluation department can automatically evaluate an employee's skill set and suggest a career path. The evaluation department, for example, uses generative AI to automatically evaluate an employee's skill set. For example, it evaluates skills based on data from past projects and tasks. The evaluation department also evaluates an employee's technical skills. For example, it evaluates programming skills and data analysis skills. The evaluation department also evaluates an employee's soft skills. For example, it evaluates communication skills and leadership skills. This allows the department to accurately evaluate an employee's skill set and suggest a career path.

[0045] The evaluation department can evaluate productivity during remote work and evaluate the effectiveness of remote work. For example, the evaluation department monitors employees' working hours and task completion rates to evaluate productivity during remote work. For example, it collects and evaluates working hours during remote work as data. The evaluation department also evaluates the frequency of communication during remote work. For example, it collects and evaluates the frequency of use of chat tools and video conferencing as data. The evaluation department also evaluates employee satisfaction during remote work. For example, it evaluates the effectiveness of remote work based on survey results. This allows for an accurate evaluation of productivity during remote work and the effectiveness of remote work.

[0046] The evaluation department can evaluate the employee's level of participation in company events or volunteer activities and evaluate the employee's level of contribution to the company culture. The evaluation department, for example, collects data on the employee's level of participation in company events and volunteer activities and reflects this in the employee's evaluation. For example, the evaluation department calculates an evaluation score based on the number of events attended or the amount of time spent volunteering. The evaluation department also evaluates the employee's level of participation in company events. For example, the evaluation department evaluates the degree of involvement in planning and running the event. The evaluation department also evaluates the employee's level of participation in volunteer activities. For example, the evaluation department evaluates the content and results of the volunteer activities. This allows for an accurate evaluation of the employee's level of contribution to the company culture.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The evaluation department can also evaluate employees' creativity. For example, it can collect data on the number of new ideas proposed by employees and the degree to which they were realized, and evaluate their creativity. The evaluation department can also evaluate the frequency and quality of brainstorming sessions that employees participate in. For example, it can evaluate employees based on the content of comments made in the sessions and the originality of their proposals. The evaluation department can also collect data on the number of prototypes and samples created by employees, and evaluate their creativity. This makes it possible to evaluate employees' creativity from multiple angles.

[0049] The evaluation department can also evaluate employees' motivation to learn. For example, it can collect data on the number of online courses employees have taken and their completion rates to evaluate their motivation to learn. The evaluation department can also evaluate the frequency and content of in-house training and seminars that employees have attended. For example, it can evaluate employees based on their attitude toward participation in training and the degree to which they have put what they learned into practice. The evaluation department can also collect data on records of employees' voluntary learning activities to evaluate their motivation to learn. This makes it possible to evaluate employees' motivation to learn from multiple angles.

[0050] The evaluation department can also evaluate employees' leadership skills. For example, it can collect data such as the number of projects an employee has been in charge of as a leader and the results of those projects to evaluate their leadership skills. The evaluation department can also evaluate an employee's leadership skills based on feedback the employee has received from team members. For example, it can evaluate based on the content of the feedback and the evaluation score. The evaluation department can also collect data such as how often an employee has participated in leadership training and the results of that training to evaluate their leadership skills. This makes it possible to evaluate an employee's leadership skills from multiple angles.

[0051] The evaluation department can also evaluate employees' problem-solving abilities. For example, it can collect data on the number of problems solved by employees and their difficulty, and evaluate their problem-solving abilities. The evaluation department can also evaluate employees based on the effectiveness and implementation of solutions proposed by employees. For example, it can collect data on the results of implementing the proposals and their impact, and evaluate them. The evaluation department can also collect data on the frequency and results of problem-solving workshops and training that employees participate in, and evaluate their problem-solving abilities. This makes it possible to evaluate employees' problem-solving abilities from multiple perspectives.

[0052] The evaluation department can also evaluate employees' teamwork skills. For example, the evaluation department can collect data such as the number of projects an employee has completed as a team and the results of those projects to evaluate teamwork skills. The evaluation department can also evaluate teamwork skills based on feedback an employee has received from team members. For example, the evaluation can be based on the content of the feedback and the evaluation score. The evaluation department can also evaluate teamwork skills by collecting data such as how often an employee has participated in team building activities and the results of those activities. This makes it possible to evaluate employees' teamwork skills from multiple angles.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The evaluation department evaluates the employee's performance. For example, the evaluation department evaluates the employee's work efficiency, quality of work, and communication skills. Step 2: The recording unit records the data evaluated by the evaluation unit. For example, the recording unit records the evaluation data as numerical data, evaluation comments, and evaluation scores. Step 3: The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit performs statistical analysis, analyzes using machine learning algorithms, and analyzes trends in the data.

[0055] (Example 2) The AI-driven comprehensive personnel evaluation system according to the embodiment of the present invention is a system for evaluating employee performance fairly and objectively. As a result, the AI-driven comprehensive personnel evaluation system can evaluate employee performance fairly and objectively based on data.

[0056] An AI-driven comprehensive personnel evaluation system according to an embodiment includes an evaluation unit, a recording unit, and an analysis unit. The evaluation unit evaluates employee performance. For example, the evaluation unit evaluates employee work efficiency. The evaluation unit can also evaluate the quality of employee deliverables. The evaluation unit can also evaluate employee communication skills. The recording unit records data evaluated by the evaluation unit. For example, the recording unit records the evaluation data as numerical data. The recording unit can also record evaluation comments. The recording unit can also record evaluation scores. The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit performs statistical analysis. The analysis unit can also perform analysis using a machine learning algorithm. The analysis unit can also analyze data trends. This allows the AI-driven comprehensive personnel evaluation system according to an embodiment to evaluate employee performance fairly and objectively based on data. For example, the evaluation results are used to determine employee promotions and bonuses. The evaluation results are also used to select training programs to improve employee skills. The evaluation results are also used to suggest career paths for employees.

[0057] The evaluation department can automatically record and analyze the time an engineer spends writing code or reviewing code. For example, the evaluation department can automatically record how much time an engineer spends writing code per day and analyze weekly or monthly trends. For example, the department can graph fluctuations in the time spent writing code for a specific project to identify improvements in efficiency or problems. The evaluation department can also automatically record and analyze how much time an engineer spends on code review. For example, the department can collect data on the frequency and duration of code reviews and evaluate the quality of the reviews. This allows for an accurate evaluation of an engineer's working time.

[0058] The evaluation unit can analyze an engineer's communication patterns and evaluate their frequency or quality. For example, the evaluation unit can analyze chat tools or email exchanges to evaluate the frequency and quality of communication. For example, it can analyze message exchanges in a specific project and measure the level of communication activity. The evaluation unit can also evaluate how an engineer communicates with team members. For example, it can collect data on the frequency of meeting participation and the content of comments, and evaluate the quality of communication. This makes it possible to evaluate an engineer's communication skills.

[0059] The evaluation unit can quantify an engineer's contribution to a project based on the number of commits or the number of bugs resolved. For example, the evaluation unit collects data such as the number of commits and the number of bugs resolved by an engineer, and evaluates the degree of contribution based on that data. For example, the number of commits is tallied for each project, and the degree of contribution is visualized. The evaluation unit also collects data such as the number of bugs resolved by an engineer, and evaluates the degree of contribution. For example, the degree of contribution is evaluated based on the number of bugs resolved in a bug tracking system. This allows an engineer's contribution to be accurately evaluated.

[0060] The evaluation unit can automatically evaluate the quality of an engineer's code and analyze the rate of bugs or code readability. The evaluation unit, for example, uses generative AI to automatically evaluate the quality of code written by an engineer. For example, it evaluates the readability and maintainability of the code and provides feedback on areas for improvement. The evaluation unit also analyzes the rate of bugs in the engineer's code. For example, it measures the number of bugs per line of code and evaluates the rate of bugs. The evaluation unit also analyzes the readability of the engineer's code. For example, it evaluates the amount of comments in the code and the appropriateness of variable names to evaluate readability. This allows for an accurate evaluation of the engineer's code quality.

[0061] The evaluation unit monitors the engineer's work environment and can propose an efficient work environment. For example, the evaluation unit monitors the tools and devices used by the engineer and evaluates work efficiency. For example, it identifies frequently used tools and devices and proposes an efficient work environment. The evaluation unit also evaluates the comfort of the engineer's work space. For example, it evaluates the desk layout and lighting status and proposes improvements to the work environment. The evaluation unit also monitors the engineer's posture and movements while working and proposes an efficient work environment. For example, it proposes a work environment based on ergonomics. This makes it possible to optimize the engineer's work environment.

[0062] The evaluation unit can evaluate the stress level of the engineer while he or she is working and make suggestions for reducing stress. The evaluation unit, for example, uses an emotion estimation function to evaluate the stress level of the engineer while he or she is working in real time. For example, the evaluation unit measures the stress level by analyzing facial expressions and tone of voice. The evaluation unit also evaluates the stress level using the engineer's biometric data. For example, the evaluation unit measures the heart rate and electrodermal activity to evaluate the stress level. The evaluation unit also evaluates the stress level based on the engineer's self-report. For example, the evaluation unit conducts periodic surveys to evaluate the stress level. This reduces the engineer's stress and improves work efficiency.

[0063] The evaluation unit can track the frequency of contact between sales representatives and customers or the time it takes to conclude a contract. For example, the evaluation unit automatically records how frequently sales representatives contact customers and evaluates the frequency of contact. For example, it analyzes telephone and email exchanges and collects data on the frequency of contact. The evaluation unit also tracks how long it takes sales representatives to conclude a contract. For example, it measures and evaluates the average time from the first contact to the conclusion of a contract. The evaluation unit also evaluates the quality of the sales representative's customer service. For example, it evaluates the quality of the service based on feedback from customers. This makes it possible to accurately evaluate the sales representative's customer service.

[0064] The evaluation department can measure the activity level of information sharing within the sales team. For example, the evaluation department collects data on the frequency of meetings and information sharing within the sales team and evaluates the activity level. For example, the evaluation department measures the activity level of information sharing based on the number of meetings and the number of participants. The evaluation department also evaluates the quality of information sharing within the sales team. For example, the evaluation department evaluates the content and usefulness of the shared information. The evaluation department also evaluates the frequency of information sharing within the sales team. For example, the evaluation department collects data on the number of times information is shared per week and evaluates it. This makes it possible to accurately evaluate the activity level of information sharing within the sales team.

[0065] The evaluation unit can evaluate the degree of contribution based on the performance data of the sales representative. For example, the evaluation unit collects data on the sales revenue and number of new customers of the sales representative and evaluates the degree of contribution based on that. For example, monthly sales are tallied and the degree of contribution is visualized. The evaluation unit also collects data on the number of contracts the sales representative has and evaluates the degree of contribution. For example, the degree of contribution is evaluated based on the increase or decrease in the number of contracts. The evaluation unit also collects data on the customer satisfaction of the sales representative and evaluates the degree of contribution. For example, the degree of contribution is evaluated based on the results of a customer survey. This allows the degree of contribution of the sales representative to be accurately evaluated.

[0066] The evaluation unit can automatically evaluate the presentation skills of sales representatives and provide feedback on areas for improvement. The evaluation unit, for example, uses generative AI to automatically evaluate the presentation skills of sales representatives. For example, it analyzes the content and speaking style of the presentation and provides feedback on areas for improvement. The evaluation unit also evaluates the quality of the sales representative's presentation. For example, it evaluates the quality of the presentation based on the audience's reactions and feedback. The evaluation unit also evaluates the structure of the sales representative's presentation. For example, it evaluates the flow and logic of the presentation and suggests areas for improvement. This improves the sales representative's presentation skills.

[0067] The evaluation unit can analyze the sales representative's customer response history and propose response methods that will lead to improved customer satisfaction. The evaluation unit, for example, analyzes the sales representative's customer response history and proposes response methods that will lead to improved customer satisfaction. For example, it identifies and proposes successful response methods based on past response history. The evaluation unit also evaluates the quality of the sales representative's customer response. For example, it evaluates the quality of response based on feedback from customers. The evaluation unit also evaluates the speed of the sales representative's customer response. For example, it collects and evaluates the speed of response as data. This makes it possible to propose response methods that will improve customer satisfaction.

[0068] The evaluation unit can monitor employees' working hours or overtime hours. For example, the evaluation unit automatically records employees' arrival and departure times and monitors working hours and overtime hours. For example, it tallys up monthly working hours based on the arrival and departure data. The evaluation unit also monitors employees' working hours in real time. For example, it automatically records arrival and departure times and collects working hours as data. The evaluation unit also monitors employees' overtime hours. For example, it records working hours after regular working hours and evaluates the overtime hours. This allows employees' working hours and overtime hours to be monitored accurately.

[0069] The evaluation unit can tally the employee's task completion rate or deadline completion rate. The evaluation unit, for example, uses a task management tool to tally the employee's task completion rate or deadline completion rate. For example, the evaluation unit collects and evaluates the completion status of each task as data. The evaluation unit also tally the employee's task completion rate. For example, the evaluation unit measures and evaluates the percentage of tasks that are completed among the scheduled tasks. The evaluation unit also tally the employee's deadline completion rate. For example, the evaluation unit measures and evaluates the percentage of tasks that are completed within the deadline. This allows the employee's task completion rate and deadline completion rate to be accurately evaluated.

[0070] The evaluation department can analyze the quality of interactions through employee communication tools. For example, the evaluation department analyzes exchanges via chat tools or emails to evaluate the quality of interactions between employees. For example, the quality of interactions is measured based on the content and frequency of messages. The evaluation department also evaluates the usage status of communication tools by employees. For example, the frequency of use of chat tools and the content of messages are collected and evaluated as data. The evaluation department also evaluates the quality of communication between employees. For example, the quality of interactions is evaluated by evaluating the constructiveness of conversations and the depth of content. This makes it possible to accurately evaluate the quality of communication between employees.

[0071] The evaluation unit can monitor the health status of employees and make suggestions for health management. The evaluation unit monitors the health status of employees, for example, using generative AI. For example, it analyzes data from wearable devices and evaluates the health status. The evaluation unit also monitors the employee's vital signs. For example, it measures heart rate and blood pressure and evaluates the health status. The evaluation unit also evaluates the health status based on the employee's health checkup results. For example, it collects the results of the health checkup as data and evaluates the health status. This allows the employee's health status to be accurately monitored and health management suggestions to be made.

[0072] The evaluation department can automatically evaluate an employee's skill set and suggest a career path. The evaluation department, for example, uses generative AI to automatically evaluate an employee's skill set. For example, it evaluates skills based on data from past projects and tasks. The evaluation department also evaluates an employee's technical skills. For example, it evaluates programming skills and data analysis skills. The evaluation department also evaluates an employee's soft skills. For example, it evaluates communication skills and leadership skills. This allows the department to accurately evaluate an employee's skill set and suggest a career path.

[0073] The evaluation unit can evaluate employees' workplace satisfaction and make suggestions to improve it. The evaluation unit, for example, uses an emotion estimation function to evaluate employees' workplace satisfaction in real time. For example, it measures satisfaction by analyzing facial expressions and voice tones. The evaluation unit also evaluates workplace satisfaction based on employee survey results. For example, it conducts periodic surveys and evaluates satisfaction. The evaluation unit also evaluates workplace satisfaction based on employee turnover rates. For example, it collects fluctuations in turnover rates as data and evaluates satisfaction. This allows for an accurate evaluation of employees' workplace satisfaction and makes suggestions to improve satisfaction.

[0074] The evaluation department can evaluate productivity during remote work and evaluate the effectiveness of remote work. For example, the evaluation department monitors employees' working hours and task completion rates to evaluate productivity during remote work. For example, it collects and evaluates working hours during remote work as data. The evaluation department also evaluates the frequency of communication during remote work. For example, it collects and evaluates the frequency of use of chat tools and video conferencing as data. The evaluation department also evaluates employee satisfaction during remote work. For example, it evaluates the effectiveness of remote work based on survey results. This allows for an accurate evaluation of productivity during remote work and the effectiveness of remote work.

[0075] The evaluation department can evaluate the employee's level of participation in company events or volunteer activities and evaluate the employee's level of contribution to the company culture. The evaluation department, for example, collects data on the employee's level of participation in company events and volunteer activities and reflects this in the employee's evaluation. For example, the evaluation department calculates an evaluation score based on the number of events attended or the amount of time spent volunteering. The evaluation department also evaluates the employee's level of participation in company events. For example, the evaluation department evaluates the degree of involvement in planning and running the event. The evaluation department also evaluates the employee's level of participation in volunteer activities. For example, the evaluation department evaluates the content and results of the volunteer activities. This allows for an accurate evaluation of the employee's level of contribution to the company culture.

[0076] The evaluation unit can evaluate the emotional connection within an employee's team and make suggestions for team building. The evaluation unit, for example, uses an emotion estimation function to evaluate the emotional connection within an employee's team in real time. For example, it measures the emotional connection by analyzing facial expressions and tone of voice. The evaluation unit also evaluates the employee's level of trust within the team. For example, it collects and evaluates trust between team members as data. The evaluation unit also evaluates the degree of cooperation within the employee's team. For example, it evaluates the degree of cooperation based on the frequency and results of collaborative work. This allows the system to accurately evaluate the emotional connection within an employee's team and make team building suggestions.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The evaluation department can also evaluate employees' creativity. For example, it can collect data on the number of new ideas proposed by employees and the degree to which they were realized, and evaluate their creativity. The evaluation department can also evaluate the frequency and quality of brainstorming sessions that employees participate in. For example, it can evaluate employees based on the content of comments made in the sessions and the originality of their proposals. The evaluation department can also collect data on the number of prototypes and samples created by employees, and evaluate their creativity. This makes it possible to evaluate employees' creativity from multiple angles.

[0079] The evaluation department can also evaluate employees' motivation to learn. For example, it can collect data on the number of online courses employees have taken and their completion rates to evaluate their motivation to learn. The evaluation department can also evaluate the frequency and content of in-house training and seminars that employees have attended. For example, it can evaluate employees based on their attitude toward participation in training and the degree to which they have put what they learned into practice. The evaluation department can also collect data on records of employees' voluntary learning activities to evaluate their motivation to learn. This makes it possible to evaluate employees' motivation to learn from multiple angles.

[0080] The evaluation department can also evaluate employees' leadership skills. For example, it can collect data such as the number of projects an employee has been in charge of as a leader and the results of those projects to evaluate their leadership skills. The evaluation department can also evaluate an employee's leadership skills based on feedback the employee has received from team members. For example, it can evaluate based on the content of the feedback and the evaluation score. The evaluation department can also collect data such as how often an employee has participated in leadership training and the results of that training to evaluate their leadership skills. This makes it possible to evaluate an employee's leadership skills from multiple angles.

[0081] The evaluation department can also evaluate employees' problem-solving abilities. For example, it can collect data on the number of problems solved by employees and their difficulty, and evaluate their problem-solving abilities. The evaluation department can also evaluate employees based on the effectiveness and implementation of solutions proposed by employees. For example, it can collect data on the results of implementing the proposals and their impact, and evaluate them. The evaluation department can also collect data on the frequency and results of problem-solving workshops and training that employees participate in, and evaluate their problem-solving abilities. This makes it possible to evaluate employees' problem-solving abilities from multiple perspectives.

[0082] The evaluation department can also evaluate employees' teamwork skills. For example, the evaluation department can collect data such as the number of projects an employee has completed as a team and the results of those projects to evaluate teamwork skills. The evaluation department can also evaluate teamwork skills based on feedback an employee has received from team members. For example, the evaluation can be based on the content of the feedback and the evaluation score. The evaluation department can also evaluate teamwork skills by collecting data such as how often an employee has participated in team building activities and the results of those activities. This makes it possible to evaluate employees' teamwork skills from multiple angles.

[0083] The evaluation department can estimate the emotions of employees and evaluate their motivation based on the estimated emotions. For example, it can analyze the facial expressions and tone of voice of employees to evaluate their level of motivation. The evaluation department can also evaluate motivation based on employees' self-reports. For example, it can conduct periodic surveys to evaluate fluctuations in motivation. The evaluation department can also evaluate motivation based on employees' work efficiency and the quality of their deliverables. This makes it possible to evaluate employee motivation from multiple angles.

[0084] The evaluation unit can estimate the employee's emotions and evaluate the employee's stress level based on the estimated emotions. For example, the evaluation unit can analyze the employee's facial expressions and voice tone to evaluate the stress level. The evaluation unit can also evaluate the employee's stress level using the employee's biometric data. For example, the evaluation unit can measure the heart rate and electrodermal activity to evaluate the stress level. The evaluation unit can also evaluate the stress level based on the employee's self-report. For example, the evaluation unit can conduct periodic surveys to evaluate the stress level. This makes it possible to evaluate the employee's stress level from multiple angles.

[0085] The evaluation unit can estimate an employee's emotions and evaluate the employee's workplace satisfaction based on the estimated emotions. For example, the evaluation unit can analyze the employee's facial expressions and tone of voice to evaluate satisfaction. The evaluation unit can also evaluate workplace satisfaction based on the results of employee surveys. For example, the evaluation unit can conduct periodic surveys to evaluate satisfaction. The evaluation unit can also evaluate workplace satisfaction based on employee turnover rates. For example, the evaluation unit can collect data on fluctuations in turnover rates and evaluate satisfaction. This makes it possible to evaluate employee workplace satisfaction from multiple angles.

[0086] The evaluation unit can estimate an employee's emotions and evaluate the employee's emotional connection within the team based on the estimated emotions. For example, the evaluation unit can analyze the employee's facial expressions and tone of voice to evaluate the emotional connection. The evaluation unit can also evaluate the employee's trust within the team. For example, the evaluation unit can collect and evaluate trust between team members as data. The evaluation unit can also evaluate the degree of cooperation within the employee's team. For example, the degree of cooperation can be evaluated based on the frequency and results of joint work. This makes it possible to evaluate the emotional connection within the team from multiple angles.

[0087] The evaluation department can estimate an employee's emotions and suggest a career path for the employee based on the estimated emotions. For example, it can analyze an employee's facial expressions and tone of voice to evaluate the suitability of a career path. The evaluation department can also suggest a career path based on an employee's skill set and past performance. For example, it can suggest an appropriate career path based on skill evaluation results and performance data. The evaluation department can also suggest a career path based on an employee's self-report. For example, it can conduct regular surveys to collect and suggest career path preferences. This makes it possible to suggest a multifaceted career path for an employee.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The evaluation department evaluates the employee's performance. For example, the evaluation department evaluates the employee's work efficiency, quality of work, and communication skills. Step 2: The recording unit records the data evaluated by the evaluation unit. For example, the recording unit records the evaluation data as numerical data, evaluation comments, and evaluation scores. Step 3: The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit performs statistical analysis, analyzes using machine learning algorithms, and analyzes trends in the data.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] 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]

[0157] 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. an appraisal department to evaluate employee performance; a recording unit that records the data evaluated by the evaluation unit; an analysis unit that analyzes the data recorded by the recording unit; A system characterized by:

2. The evaluation unit Automatically assess the quality of an engineer's code and analyze the bug rate or readability of said code 2. The system of claim 1.

3. The evaluation unit Track how often your sales reps contact customers or close deals 2. The system of claim 1.

4. The evaluation unit Automatically evaluate sales representatives' presentation skills and provide feedback on areas for improvement 2. The system of claim 1.

5. The evaluation unit Monitor the health status of said employees and provide health management suggestions 2. The system of claim 1.

6. The evaluation unit Evaluate productivity during remote work and evaluate the effectiveness of remote work 2. The system of claim 1.

7. The evaluation unit Evaluate engineers' stress levels at work and provide suggestions for stress reduction 2. The system of claim 1.

8. The evaluation unit Evaluate salespeople's emotional responses to customers and provide suggestions to strengthen customer relationships 2. The system of claim 1.

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