System

Abstract

An object of a system according to an embodiment is to improve the fairness of personnel evaluation and to provide satisfactory feedback to employees.SOLUTION: A system includes a reception unit, an analysis unit, a support unit, and a feedback unit. The reception unit receives a self-evaluation of an employee. The analysis unit analyzes the self-evaluation received by the reception unit. The support unit supports evaluation by the evaluator on the basis of the self-evaluation analyzed by the analysis unit. The feedback unit feeds back the evaluation result supported by the support unit to the employee.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques can lead to a sense of unfairness in personnel evaluations and insufficient feedback, which can lead to a decline in employee motivation and a loss of talent.

[0005] The system according to the embodiment aims to improve the fairness of personnel evaluations and provide employees with feedback that is convincing to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a support unit, and a feedback unit. The reception unit receives the employee's self-evaluation. The analysis unit analyzes the self-evaluation received by the reception unit. The support unit supports the evaluator's evaluation based on the self-evaluation analyzed by the analysis unit. The feedback unit provides feedback to the employee on the evaluation results supported by the support unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the fairness of personnel evaluations and provide employees with convincing feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A personnel evaluation support system according to an embodiment of the present invention accepts, analyzes, and optimizes employee self-evaluations, supports evaluators' evaluations, and provides feedback. The personnel evaluation support system accepts employee self-evaluations and uses AI to analyze and optimize them. Next, the AI ​​supports the evaluators' evaluations, clearly explains the reasons for the evaluations, and provides advice for maintaining consistency in the evaluations. Finally, the AI ​​supports feedback on the evaluation results and provides detailed feedback to the employees. For example, the personnel evaluation support system may set detailed items for employees to use when conducting self-evaluations, allowing them to specifically evaluate their own performance and skills. Next, the AI ​​analyzes the self-evaluations, compares them with past evaluation data, checks the consistency and fairness of the evaluations, and suggests appropriate revisions. Furthermore, the AI ​​supports the evaluators' evaluations, clearly explains the reasons for the evaluations, and provides advice for maintaining consistency in the evaluations. Finally, the AI ​​supports feedback on the evaluation results, clearly explains the reasons for the evaluations and specific areas for improvement, and provides detailed feedback to the employees. This allows the personnel evaluation support system to alleviate employee dissatisfaction, improve motivation, and prevent the loss of talented personnel. In this way, the personnel evaluation support system can eliminate employee dissatisfaction, improve motivation, and prevent the loss of talented personnel. For example, the fairness of evaluations is improved, eliminating employee dissatisfaction. In addition, by providing comprehensive feedback on evaluation results, employee motivation is improved and the loss of talented personnel can be prevented.

[0029] A personnel evaluation support system according to an embodiment includes a reception unit, an analysis unit, a support unit, and a feedback unit. The reception unit receives employee self-evaluations. Examples of employee self-evaluations include, but are not limited to, work performance, skill improvement, and goal achievement. The reception unit receives the self-evaluations using, for example, an online form. The reception unit can also receive self-evaluations in an interview format. The reception unit can also receive self-evaluations using voice input or text input. For example, if an employee prefers voice input, the reception unit preferentially receives voice input. The analysis unit analyzes the self-evaluations received by the reception unit. The analysis is performed based on, for example, data analysis techniques and evaluation criteria, but is not limited to, examples. For example, the analysis unit checks the consistency and fairness of the self-evaluations by comparing them with past evaluation data. The analysis unit can also check the consistency of the evaluations using statistical techniques and propose appropriate revisions. For example, the analysis unit checks the consistency of the evaluations using regression analysis or analysis of variance. The support unit supports the evaluator in making an evaluation based on the self-evaluations analyzed by the analysis unit. The support may, for example, clearly indicate the basis for the evaluation and provide advice to maintain consistency in the evaluation, but is not limited to such examples. For example, the support department may clearly indicate the basis for the evaluation to the evaluator and encourage appropriate evaluation. The support department may also analyze the evaluator's past evaluation history and select the optimal support method. For example, the support department may analyze the content of evaluations previously performed by the evaluator and propose the optimal support method. The feedback department feeds back the evaluation results supported by the support department to the employee. The feedback may, for example, clearly indicate the basis for the evaluation results and specific areas for improvement and provide detailed feedback to the employee, but is not limited to such examples. For example, the feedback department may clearly indicate the basis for the evaluation results and provide convincing feedback to the employee. The feedback department may also improve the accuracy of the feedback by referring to the employee's past feedback results. For example, the feedback department may analyze the employee's past feedback results and adjust the feedback algorithm.As a result, the personnel evaluation support system according to the embodiment can improve the fairness and satisfaction of evaluations by accepting employees' self-evaluations, analyzing, optimizing, providing evaluation support, and providing feedback.

[0030] The analysis unit can check the consistency of the evaluation using statistical methods using past evaluation data and suggest corrections. Statistical methods include, but are not limited to, regression analysis and analysis of variance. For example, the analysis unit can perform regression analysis using past evaluation data to check the consistency of the evaluation. The analysis unit can also check the consistency of the evaluation using analysis of variance. The analysis unit can also check the consistency of the evaluation using past evaluation data and suggest appropriate corrections. For example, the analysis unit can suggest appropriate corrections if the self-evaluation is over or under compared to the past evaluation data. In this way, by using the past evaluation data, the consistency of the evaluation can be maintained and appropriate corrections can be suggested. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input past evaluation data into AI and have the AI ​​check the consistency of the evaluation and suggest corrections.

[0031] The support unit can clearly explain the basis of the evaluation to the evaluator and provide advice to maintain consistency in the evaluation. Examples of the basis of the evaluation include, but are not limited to, performance data and skill evaluations. For example, the support unit can clearly explain the basis of the evaluation to the evaluator based on performance data. The support unit can also clearly explain the basis of the evaluation based on skill evaluations. The support unit can also provide advice to maintain consistency in the evaluation. For example, if the evaluator gives an excessive evaluation to a specific employee, the support unit can explain the reason and encourage the evaluator to give an appropriate evaluation. This can improve the fairness of the evaluation by clearly explaining the basis of the evaluation to the evaluator and providing advice to maintain consistency in the evaluation. Some or all of the above-mentioned processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the evaluator's evaluation data into AI and have the AI ​​clarify the basis of the evaluation and provide advice.

[0032] The feedback unit can provide detailed feedback to the employee by clearly indicating the basis of the evaluation result and areas for improvement. Areas for improvement include, but are not limited to, specific actions for skill improvement. For example, the feedback unit can clearly indicate the basis of the evaluation result and provide convincing feedback to the employee. The feedback unit can also clearly indicate specific areas for improvement and provide detailed feedback to the employee. For example, the feedback unit can suggest specific actions for skill improvement. By clearly indicating the basis of the evaluation result and specific areas for improvement, it is possible to provide convincing feedback to the employee. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the evaluation result into AI and have the AI ​​generate the feedback content.

[0033] The reception unit can analyze the employee's past self-evaluation history and select a reception method. Examples of reception methods include, but are not limited to, online forms and interviews. For example, the reception unit can analyze the time period in which the employee previously completed self-evaluations and receive the self-evaluation during that time period. The reception unit can also prioritize input methods (voice, text, etc.) that the employee has previously used. The reception unit can also emphasize specific evaluation items based on the employee's past self-evaluation history. For example, the reception unit can analyze the time period in which the employee previously completed self-evaluations and receive the self-evaluation during that time period. By analyzing the employee's past self-evaluation history, the reception unit can select the optimal reception method and improve the efficiency of self-evaluations. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the employee's past self-evaluation history into AI and have the AI ​​select the optimal reception method.

[0034] When receiving the self-evaluation, the reception unit can filter the self-evaluation based on the employee's current project and areas of interest. Specific filtering criteria include, but are not limited to, the type of project and a method for identifying the area of ​​interest. For example, the reception unit can prioritize displaying evaluation items related to the project the employee is currently working on. The reception unit can also customize the self-evaluation items based on the employee's areas of interest. The reception unit can also filter the self-evaluation items according to the employee's current work content. For example, the reception unit can prioritize displaying evaluation items related to the project the employee is currently working on. This allows for filtering the self-evaluation items based on the employee's current project and areas of interest, thereby enabling a more relevant self-evaluation. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the employee's project data and area of ​​interest data into AI and have the AI ​​perform the filtering.

[0035] When accepting the self-evaluation, the reception unit can select a reception means depending on the employee's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the employee prefers voice input, the reception unit can prioritize receiving voice input. Furthermore, if the employee prefers text input, the reception unit can also prioritize receiving text input. Furthermore, if the employee prefers image input, the reception unit can also prioritize receiving image input. For example, if the employee prefers voice input, the reception unit prioritizes receiving voice input. This allows for smooth acceptance of the self-evaluation by selecting the optimal reception means depending on the employee's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the employee's input method data into AI and have the AI ​​select the optimal reception means.

[0036] When accepting self-evaluations, the reception unit can prioritize accepting relevant self-evaluations by taking into account the employee's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, when an employee is in the office, the reception unit can prioritize accepting office-related self-evaluations. Furthermore, when an employee is on a business trip, the reception unit can prioritize accepting self-evaluations related to the business trip destination. Furthermore, when an employee is working remotely, the reception unit can prioritize accepting self-evaluations related to remote work. For example, when an employee is in the office, the reception unit prioritizes accepting office-related self-evaluations. This allows for the priority acceptance of highly relevant self-evaluations by taking into account the employee's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the employee's geographical location information into AI and have the AI ​​prioritize the relevant self-evaluations.

[0037] When receiving a self-evaluation, the reception unit may analyze the employee's social media activity and receive a related self-evaluation. Specific methods for analyzing social media activity include, but are not limited to, the content of posts and the frequency of activity. For example, the reception unit may preferentially receive a self-evaluation related to a project shared by the employee on social media. The reception unit may also analyze the employee's social media activity and suggest a related self-evaluation. The reception unit may also suggest a related self-evaluation based on the activity of the employee's friends on social media. For example, the reception unit may preferentially receive a self-evaluation related to a project shared by the employee on social media. In this way, the reception unit may analyze the employee's social media activity and receive a related self-evaluation. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit may input the employee's social media data into an AI and have the AI ​​execute the suggestion of a related self-evaluation.

[0038] When accepting self-evaluations, the reception unit can adjust the reception method by reflecting the employee's past feedback. Specific content of past feedback includes, but is not limited to, feedback history and evaluation content. For example, the reception unit customizes the self-evaluation items based on the employee's past feedback. The reception unit can also analyze the employee's past feedback and adjust the self-evaluation reception method. The reception unit can also determine the priority of self-evaluations by referring to the employee's past feedback. For example, the reception unit customizes the self-evaluation items based on the employee's past feedback. This allows the self-evaluation reception method to be customized by reflecting the employee's past feedback, resulting in more appropriate reception. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the employee's past feedback data into AI and have the AI ​​adjust the reception method.

[0039] During analysis, the analysis unit can adjust the level of detail based on the importance of the self-assessment. Specific criteria for the level of detail include, but are not limited to, the granularity of information and the presence or absence of detailed explanations. For example, the analysis unit performs detailed optimization for self-assessments with high importance. The analysis unit can also perform simple optimization for self-assessments with low importance. The analysis unit can also gradually adjust the level of detail of optimization depending on the importance. For example, the analysis unit performs detailed optimization for self-assessments with high importance. This allows for more appropriate optimization by adjusting the level of detail of optimization based on the importance of the self-assessment. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input self-assessment importance data into AI and have the AI ​​adjust the level of detail.

[0040] During analysis, the analysis unit can apply different algorithms depending on the category of self-assessment. Specific types of algorithms include, but are not limited to, machine learning algorithms and statistical algorithms. For example, the analysis unit can apply a project management optimization algorithm to self-assessment regarding project achievement. The analysis unit can also apply a team dynamics optimization algorithm to self-assessment regarding teamwork contribution. The analysis unit can also apply a skill mapping optimization algorithm to self-assessment regarding skill improvement. For example, the analysis unit can apply a project management optimization algorithm to self-assessment regarding project achievement. This allows for more appropriate optimization by applying different optimization algorithms depending on the category of self-assessment. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input self-assessment category data into AI and have the AI ​​apply the optimal algorithm.

[0041] During analysis, the analysis unit can improve accuracy by referring to the employee's past analysis results. Specific evaluation criteria for accuracy include, but are not limited to, for example, error rate and methods for improving accuracy. The analysis unit, for example, analyzes the employee's past optimization results and adjusts the optimization algorithm. The analysis unit can also improve optimization accuracy based on the employee's past optimization results. The analysis unit can also adjust optimization parameters by referring to the employee's past optimization results. For example, the analysis unit analyzes the employee's past optimization results and adjusts the optimization algorithm. In this way, the optimization accuracy can be improved by referring to the employee's past optimization results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past optimization result data into AI and have the AI ​​perform adjustments to improve accuracy.

[0042] During analysis, the analysis unit can determine priorities based on the submission dates of the self-assessments. Specific criteria for determining priorities include, but are not limited to, submission dates and importance. For example, the analysis unit prioritizes optimization of self-assessments with upcoming submission deadlines. The analysis unit can also adjust the optimization priority according to the submission dates. The analysis unit can also quickly optimize self-assessments whose submission dates have passed. For example, the analysis unit prioritizes optimization of self-assessments with upcoming submission deadlines. This allows for more efficient optimization by determining the optimization priority based on the submission dates of the self-assessments. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input self-assessment submission date data into AI and have the AI ​​determine the priorities.

[0043] During analysis, the analysis unit can adjust the order based on the relevance of the self-assessments. Specific criteria for determining the order include, but are not limited to, for example, order of relevance or order of importance. For example, the analysis unit prioritizes optimizing highly relevant self-assessments. The analysis unit can also adjust the order of optimization according to the relevance. The analysis unit can also postpone optimization of less relevant self-assessments. For example, the analysis unit prioritizes optimizing highly relevant self-assessments. In this way, by adjusting the order of optimization based on the relevance of the self-assessments, more relevant optimization can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input self-assessment relevance data into AI and have the AI ​​adjust the order.

[0044] During analysis, the analysis unit can adjust the use of technical terms according to the employee's level of expertise. Specific criteria for the use of technical terms include, but are not limited to, the definition and frequency of use of the technical terms. For example, the analysis unit can provide optimization results that use a lot of technical terms to employees with a high level of expertise. The analysis unit can also provide concise and easy-to-understand optimization results to employees with a low level of expertise. The analysis unit can also adjust the way the optimization results are presented according to the employee's level of expertise. For example, the analysis unit can provide optimization results that use a lot of technical terms to employees with a high level of expertise. In this way, by adjusting the use of technical terms in the optimization according to the employee's level of expertise, it is possible to provide more understandable optimization results. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input employee expertise level data into AI and have the AI ​​adjust the use of technical terms.

[0045] When providing evaluation support, the support unit can analyze the evaluator's past evaluation history to select a support method. Specific content of the evaluation history includes, for example, past evaluation results and evaluation trends, but is not limited to these examples. For example, the support unit analyzes the evaluator's past evaluation content and proposes an optimal support method. The support unit can also provide support to maintain consistency in evaluations based on the evaluator's past evaluation history. The support unit can also provide support to correct evaluation bias based on the evaluator's past evaluation history. For example, the support unit analyzes the evaluator's past evaluation content and proposes an optimal support method. In this way, by analyzing the evaluator's past evaluation history, an optimal support method can be selected and the consistency of evaluations can be maintained. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the evaluator's past evaluation history data into AI and have the AI ​​select a support method.

[0046] During evaluation support, the support unit can adjust the support content based on the evaluator's current evaluation criteria. Specific content of the evaluation criteria includes, but is not limited to, evaluation items and evaluation scales. For example, the support unit customizes the support content based on the evaluation criteria currently used by the evaluator. The support unit can also adjust the evaluation support method according to the evaluator's evaluation criteria. The support unit can also provide support to maintain consistency in the evaluation based on the evaluator's evaluation criteria. For example, the support unit customizes the support content based on the evaluation criteria currently used by the evaluator. This allows for customizing the support content based on the evaluator's current evaluation criteria, thereby providing more appropriate evaluation support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the evaluator's evaluation criteria data into AI and have the AI ​​adjust the support content.

[0047] The support unit can improve the evaluation support method by reflecting the evaluator's feedback during the evaluation support. Specific content of the feedback includes, but is not limited to, the format of the feedback and the level of detail of the content. For example, the support unit improves the evaluation support method based on the feedback provided by the evaluator. The support unit can also analyze the content of the evaluator's feedback to improve the accuracy of the evaluation support. The support unit can also customize the content of the evaluation support by referring to the evaluator's feedback. For example, the support unit improves the evaluation support method based on the feedback provided by the evaluator. In this way, by reflecting the evaluator's feedback, the evaluation support method can be improved and more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the evaluator's feedback data into AI and have the AI ​​improve the support method.

[0048] When providing evaluation support, the support unit can select a support method by taking into account the evaluator's geographic location information. Specific methods for acquiring geographic location information include, but are not limited to, GPS data and IP addresses. For example, if the evaluator is in the office, the support unit can provide office-related evaluation support. Furthermore, if the evaluator is on a business trip, the support unit can provide evaluation support related to the business trip destination. Furthermore, if the evaluator is working remotely, the support unit can provide evaluation support related to the remote work. For example, if the evaluator is in the office, the support unit provides office-related evaluation support. This allows the optimal support method to be selected by taking the evaluator's geographic location information into consideration, thereby providing more appropriate evaluation support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the evaluator's geographic location information into AI and have the AI ​​select a support method.

[0049] When providing evaluation support, the support unit may analyze the evaluator's social media activity and suggest content. Specific methods for analyzing social media activity include, but are not limited to, the content of posts and the frequency of activity. For example, the support unit may provide evaluation support related to a project shared by the evaluator on social media. The support unit may also analyze the evaluator's social media activity and suggest related evaluation support. The support unit may also suggest related evaluation support based on the activity of the evaluator's friends on social media. For example, the support unit may provide evaluation support related to a project shared by the evaluator on social media. In this way, relevant evaluation support can be provided by analyzing the evaluator's social media activity. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit may input the evaluator's social media data into AI and have the AI ​​suggest content of evaluation support.

[0050] The support unit can adjust the evaluation support method by reflecting the evaluator's past feedback when providing evaluation support. Specific feedback content includes, but is not limited to, the feedback format and the level of detail. For example, the support unit customizes the evaluation support method based on feedback provided by the evaluator in the past. The support unit can also analyze the content of the evaluator's past feedback to improve the accuracy of the evaluation support. The support unit can also adjust the content of the evaluation support by referring to the evaluator's past feedback. For example, the support unit customizes the evaluation support method based on the evaluator's past feedback. In this way, by reflecting the evaluator's past feedback, the evaluation support method can be customized and more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the evaluator's past feedback data into AI and have the AI ​​adjust the support method.

[0051] The feedback unit can adjust the level of detail based on the importance of the evaluation result when providing feedback. Specific criteria for the level of detail include, but are not limited to, for example, the granularity of the information and the presence or absence of detailed explanations. For example, the feedback unit can provide detailed feedback for evaluation results with high importance. The feedback unit can also provide concise feedback for evaluation results with low importance. The feedback unit can also adjust the level of detail of the feedback in stages depending on the importance. For example, the feedback unit provides detailed feedback for evaluation results with high importance. In this way, by adjusting the level of detail of the feedback based on the importance of the evaluation result, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input importance data of the evaluation result into AI and cause the AI ​​to adjust the level of detail.

[0052] The feedback unit can apply different algorithms depending on the category of the evaluation result when providing feedback. Specific types of algorithms include, but are not limited to, machine learning algorithms and statistical algorithms. For example, the feedback unit can apply a project management feedback algorithm to the evaluation result regarding the project achievement level. The feedback unit can also apply a team dynamics feedback algorithm to the evaluation result regarding the teamwork contribution level. The feedback unit can also apply a skill mapping feedback algorithm to the evaluation result regarding skill improvement. For example, the feedback unit can apply a project management feedback algorithm to the evaluation result regarding the project achievement level. In this way, by applying different feedback algorithms depending on the category of the evaluation result, more appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input category data of the evaluation result into AI and cause the AI ​​to apply the optimal algorithm.

[0053] The feedback unit can improve the accuracy of feedback by referring to the employee's past feedback results. Specific evaluation criteria for accuracy include, but are not limited to, for example, error rate and methods for improving accuracy. For example, the feedback unit can analyze the employee's past feedback results and adjust the feedback algorithm. The feedback unit can also improve the accuracy of feedback based on the employee's past feedback results. The feedback unit can also adjust the feedback parameters by referring to the employee's past feedback results. For example, the feedback unit can analyze the employee's past feedback results and adjust the feedback algorithm. In this way, the accuracy of feedback can be improved by referring to the employee's past feedback results. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the employee's past feedback result data into AI and have the AI ​​perform adjustments to improve accuracy.

[0054] The feedback unit may determine the priority of the evaluation results based on the submission time of the evaluation results when providing feedback. Specific criteria for determining the priority include, but are not limited to, the submission time and importance. For example, the feedback unit may prioritize feedback for evaluation results whose submission deadline is approaching. The feedback unit may also adjust the priority of feedback depending on the submission time. The feedback unit may also provide prompt feedback for evaluation results whose submission time has passed. For example, the feedback unit may prioritize feedback for evaluation results whose submission deadline is approaching. This allows for more efficient feedback by determining the priority of feedback based on the submission time of the evaluation results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input data on the submission time of the evaluation results into AI and have the AI ​​determine the priority.

[0055] The feedback unit can adjust the order of the evaluation results based on the relevance of the evaluation results when providing feedback. Specific criteria for determining the order include, but are not limited to, for example, in descending order of relevance or importance. For example, the feedback unit can provide feedback preferentially to highly relevant evaluation results. The feedback unit can also adjust the order of feedback according to the relevance. The feedback unit can also provide feedback later to less relevant evaluation results. For example, the feedback unit can provide feedback preferentially to highly relevant evaluation results. In this way, by adjusting the order of feedback based on the relevance of the evaluation results, more relevant feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input relevance data of the evaluation results into AI and cause the AI ​​to adjust the order.

[0056] The feedback unit may adjust the use of technical terms when providing feedback depending on the employee's level of expertise. Specific criteria for the use of technical terms include, but are not limited to, the definition and frequency of use of technical terms. For example, the feedback unit may provide feedback that uses a lot of technical terms to employees with a high level of expertise. The feedback unit may also provide concise and easy-to-understand feedback to employees with a low level of expertise. The feedback unit may also adjust the way the feedback is expressed depending on the employee's level of expertise. For example, the feedback unit may provide feedback that uses a lot of technical terms to employees with a high level of expertise. In this way, by adjusting the use of technical terms in the feedback depending on the employee's level of expertise, it is possible to provide feedback that is easier to understand. Some or all of the above-described processing by the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input employee expertise level data into AI and have the AI ​​adjust the use of technical terms.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When accepting an employee's self-evaluation, the reception unit can analyze the employee's past evaluation history and customize the self-evaluation items. For example, it can highlight items for which the employee received a high evaluation in the past and encourage the employee to re-evaluate those items when self-evaluating. It can also clearly indicate areas for improvement for items for which the employee received a low evaluation in the past, so that the employee will focus on those items when self-evaluating. Furthermore, it can add or delete self-evaluation items based on the employee's past evaluation history. This allows for self-evaluations that reflect the employee's past evaluation history.

[0059] When analyzing an employee's self-evaluation, the analysis unit can adjust the analysis algorithm taking into account the employee's current work situation. For example, the analysis results of the self-evaluation can be adjusted based on the progress of the project the employee is currently working on. The analysis results of the self-evaluation can also be optimized taking into account the employee's workload and team performance. Furthermore, the parameters of the analysis algorithm can be dynamically adjusted according to the employee's work situation. This allows for self-evaluation analysis that reflects the employee's current work situation.

[0060] When clearly explaining the basis of an evaluation to an evaluator, the support department can analyze the evaluator's past evaluation trends and provide advice to maintain consistency in evaluations. For example, if an evaluator has over-evaluated a particular employee in the past, the support department can point out this tendency and encourage appropriate evaluations. The support department can also provide advice to correct evaluation bias based on the evaluator's past evaluation trends. Furthermore, the support department can suggest specific methods to maintain consistency in evaluations based on the evaluator's past evaluation history. This allows for evaluation support that reflects the evaluator's past evaluation trends.

[0061] When providing feedback to an employee, the feedback department can analyze the employee's past feedback history and customize the content of the feedback. For example, it can provide detailed feedback to the employee by specifying specific areas for improvement based on feedback the employee has received in the past. It can also adjust the format and content of the feedback based on the employee's past feedback history. Furthermore, it can determine the priority of feedback by referring to the employee's past feedback history. This allows feedback to be provided that reflects the employee's past feedback history.

[0062] When analyzing an employee's self-evaluation, the analysis unit can adjust the analysis algorithm based on the employee's past evaluation data. For example, for items for which the employee received a high evaluation in the past, the analysis results can be emphasized, highlighting the employee's strengths. Also, for items for which the employee received a low evaluation in the past, it can highlight areas for improvement and suggest specific actions. Furthermore, it can dynamically adjust the parameters of the analysis algorithm based on the employee's past evaluation data. This makes it possible to analyze self-evaluations that reflect the employee's past evaluation data.

[0063] When accepting an employee's self-evaluation, the reception department can customize the self-evaluation items based on the employee's current projects and areas of interest. For example, it can prioritize and display evaluation items related to the project the employee is currently working on. It can also add or delete self-evaluation items based on the employee's areas of interest. Furthermore, it can filter the self-evaluation items according to the employee's current work content. This allows the self-evaluation to reflect the employee's current projects and areas of interest.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception department accepts employee self-evaluations. Employee self-evaluations include work performance, skill improvement, and goal achievement. The reception department can accept self-evaluations via online forms, face-to-face meetings, voice input, or text input. For example, if an employee prefers voice input, voice input is given priority. Step 2: The analysis unit analyzes the self-assessments received by the reception unit. The analysis is based on data analysis methods and evaluation criteria. For example, the analysis unit checks the consistency and fairness of the self-assessments by comparing them with past evaluation data, and uses statistical methods to check the consistency of the evaluations and propose appropriate corrections. Specifically, regression analysis and analysis of variance can be used. Step 3: The support unit supports the evaluator's evaluation based on the self-evaluation analyzed by the analysis unit. The support unit clarifies the basis for the evaluation and provides advice to maintain consistency in the evaluation. For example, it can clarify the basis for the evaluation to the evaluator and encourage appropriate evaluation. It can also analyze the evaluator's past evaluation history and select the most appropriate support method. Step 4: The feedback department provides the employee with the evaluation results supported by the support department. The feedback provides detailed feedback to the employee, clearly stating the reasons for the evaluation results and specific areas for improvement. For example, the feedback can be provided in a way that clearly explains the reasons for the evaluation results and provides the employee with feedback that is convincing. The accuracy of the feedback can also be improved by referring to the employee's past feedback results.

[0066] (Example 2) A personnel evaluation support system according to an embodiment of the present invention accepts, analyzes, and optimizes employee self-evaluations, supports evaluators' evaluations, and provides feedback. The personnel evaluation support system accepts employee self-evaluations and uses AI to analyze and optimize them. Next, the AI ​​supports the evaluators' evaluations, clearly explains the reasons for the evaluations, and provides advice for maintaining consistency in the evaluations. Finally, the AI ​​supports feedback on the evaluation results and provides detailed feedback to the employees. For example, the personnel evaluation support system may set detailed items for employees to use when conducting self-evaluations, allowing them to specifically evaluate their own performance and skills. Next, the AI ​​analyzes the self-evaluations, compares them with past evaluation data, checks the consistency and fairness of the evaluations, and suggests appropriate revisions. Furthermore, the AI ​​supports the evaluators' evaluations, clearly explains the reasons for the evaluations, and provides advice for maintaining consistency in the evaluations. Finally, the AI ​​supports feedback on the evaluation results, clearly explains the reasons for the evaluations and specific areas for improvement, and provides detailed feedback to the employees. This allows the personnel evaluation support system to alleviate employee dissatisfaction, improve motivation, and prevent the loss of talented personnel. In this way, the personnel evaluation support system can eliminate employee dissatisfaction, improve motivation, and prevent the loss of talented personnel. For example, the fairness of evaluations is improved, eliminating employee dissatisfaction. In addition, by providing comprehensive feedback on evaluation results, employee motivation is improved and the loss of talented personnel can be prevented.

[0067] A personnel evaluation support system according to an embodiment includes a reception unit, an analysis unit, a support unit, and a feedback unit. The reception unit receives employee self-evaluations. Examples of employee self-evaluations include, but are not limited to, work performance, skill improvement, and goal achievement. The reception unit receives the self-evaluations using, for example, an online form. The reception unit can also receive self-evaluations in an interview format. The reception unit can also receive self-evaluations using voice input or text input. For example, if an employee prefers voice input, the reception unit preferentially receives voice input. The analysis unit analyzes the self-evaluations received by the reception unit. The analysis is performed based on, for example, data analysis techniques and evaluation criteria, but is not limited to, examples. For example, the analysis unit checks the consistency and fairness of the self-evaluations by comparing them with past evaluation data. The analysis unit can also check the consistency of the evaluations using statistical techniques and propose appropriate revisions. For example, the analysis unit checks the consistency of the evaluations using regression analysis or analysis of variance. The support unit supports the evaluator in making an evaluation based on the self-evaluations analyzed by the analysis unit. The support may, for example, clearly indicate the basis for the evaluation and provide advice to maintain consistency in the evaluation, but is not limited to such examples. For example, the support department may clearly indicate the basis for the evaluation to the evaluator and encourage appropriate evaluation. The support department may also analyze the evaluator's past evaluation history and select the optimal support method. For example, the support department may analyze the content of evaluations previously performed by the evaluator and propose the optimal support method. The feedback department feeds back the evaluation results supported by the support department to the employee. The feedback may, for example, clearly indicate the basis for the evaluation results and specific areas for improvement and provide detailed feedback to the employee, but is not limited to such examples. For example, the feedback department may clearly indicate the basis for the evaluation results and provide convincing feedback to the employee. The feedback department may also improve the accuracy of the feedback by referring to the employee's past feedback results. For example, the feedback department may analyze the employee's past feedback results and adjust the feedback algorithm.As a result, the personnel evaluation support system according to the embodiment can improve the fairness and satisfaction of evaluations by accepting employees' self-evaluations, analyzing, optimizing, providing evaluation support, and providing feedback.

[0068] The analysis unit can check the consistency of the evaluation using statistical methods using past evaluation data and suggest corrections. Statistical methods include, but are not limited to, regression analysis and analysis of variance. For example, the analysis unit can perform regression analysis using past evaluation data to check the consistency of the evaluation. The analysis unit can also check the consistency of the evaluation using analysis of variance. The analysis unit can also check the consistency of the evaluation using past evaluation data and suggest appropriate corrections. For example, the analysis unit can suggest appropriate corrections if the self-evaluation is over or under compared to the past evaluation data. In this way, by using the past evaluation data, the consistency of the evaluation can be maintained and appropriate corrections can be suggested. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input past evaluation data into AI and have the AI ​​check the consistency of the evaluation and suggest corrections.

[0069] The support unit can clearly explain the basis of the evaluation to the evaluator and provide advice to maintain consistency in the evaluation. Examples of the basis of the evaluation include, but are not limited to, performance data and skill evaluations. For example, the support unit can clearly explain the basis of the evaluation to the evaluator based on performance data. The support unit can also clearly explain the basis of the evaluation based on skill evaluations. The support unit can also provide advice to maintain consistency in the evaluation. For example, if the evaluator gives an excessive evaluation to a specific employee, the support unit can explain the reason and encourage the evaluator to give an appropriate evaluation. This can improve the fairness of the evaluation by clearly explaining the basis of the evaluation to the evaluator and providing advice to maintain consistency in the evaluation. Some or all of the above-mentioned processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the evaluator's evaluation data into AI and have the AI ​​clarify the basis of the evaluation and provide advice.

[0070] The feedback unit can provide detailed feedback to the employee by clearly indicating the basis of the evaluation result and areas for improvement. Areas for improvement include, but are not limited to, specific actions for skill improvement. For example, the feedback unit can clearly indicate the basis of the evaluation result and provide convincing feedback to the employee. The feedback unit can also clearly indicate specific areas for improvement and provide detailed feedback to the employee. For example, the feedback unit can suggest specific actions for skill improvement. By clearly indicating the basis of the evaluation result and specific areas for improvement, it is possible to provide convincing feedback to the employee. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the evaluation result into AI and have the AI ​​generate the feedback content.

[0071] The reception unit can estimate the employee's emotions and adjust the timing of receiving the self-evaluation based on the estimated employee emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if an employee is feeling stressed, the reception unit can receive the self-evaluation during a time when the employee is able to relax. Furthermore, if an employee is concentrating, the reception unit can also encourage the employee to submit the self-evaluation at that time. Furthermore, if an employee is tired, the reception unit can receive the self-evaluation after a break. For example, the reception unit can capture the employee's facial expression with a camera and estimate the employee's emotions using an emotion estimation algorithm. This allows the timing of receiving the self-evaluation to be adjusted according to the employee's emotions, thereby allowing the self-evaluation to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception department can input facial expression data of employees into the generation AI and have the generation AI estimate their emotions.

[0072] The reception unit can analyze the employee's past self-evaluation history and select a reception method. Examples of reception methods include, but are not limited to, online forms and interviews. For example, the reception unit can analyze the time period in which the employee previously completed self-evaluations and receive the self-evaluation during that time period. The reception unit can also prioritize input methods (voice, text, etc.) that the employee has previously used. The reception unit can also emphasize specific evaluation items based on the employee's past self-evaluation history. For example, the reception unit can analyze the time period in which the employee previously completed self-evaluations and receive the self-evaluation during that time period. By analyzing the employee's past self-evaluation history, the reception unit can select the optimal reception method and improve the efficiency of self-evaluations. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the employee's past self-evaluation history into AI and have the AI ​​select the optimal reception method.

[0073] When receiving the self-evaluation, the reception unit can filter the self-evaluation based on the employee's current project and areas of interest. Specific filtering criteria include, but are not limited to, the type of project and a method for identifying the area of ​​interest. For example, the reception unit can prioritize displaying evaluation items related to the project the employee is currently working on. The reception unit can also customize the self-evaluation items based on the employee's areas of interest. The reception unit can also filter the self-evaluation items according to the employee's current work content. For example, the reception unit can prioritize displaying evaluation items related to the project the employee is currently working on. This allows for filtering the self-evaluation items based on the employee's current project and areas of interest, thereby enabling a more relevant self-evaluation. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the employee's project data and area of ​​interest data into AI and have the AI ​​perform the filtering.

[0074] When accepting the self-evaluation, the reception unit can select a reception means depending on the employee's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the employee prefers voice input, the reception unit can prioritize receiving voice input. Furthermore, if the employee prefers text input, the reception unit can also prioritize receiving text input. Furthermore, if the employee prefers image input, the reception unit can also prioritize receiving image input. For example, if the employee prefers voice input, the reception unit prioritizes receiving voice input. This allows for smooth acceptance of the self-evaluation by selecting the optimal reception means depending on the employee's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the employee's input method data into AI and have the AI ​​select the optimal reception means.

[0075] The reception unit can estimate the employee's emotions and prioritize the self-evaluations to be received based on the estimated employee emotions. Specific criteria for determining the priorities include, but are not limited to, the intensity of emotions and the importance of work. For example, if the employee is feeling stressed, the reception unit can postpone self-evaluations of lower importance. Furthermore, if the employee is relaxed, the reception unit can also prioritize self-evaluations of higher importance. Furthermore, if the employee is in a hurry, the reception unit can prioritize simple self-evaluations. For example, if the employee is feeling stressed, the reception unit can postpone self-evaluations of lower importance. Thus, by prioritizing the self-evaluations based on the employee's emotions, the self-evaluations can be received in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception department can input employee emotion data into the generation AI and have the generation AI set priorities.

[0076] When accepting self-evaluations, the reception unit can prioritize accepting relevant self-evaluations by taking into account the employee's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, when an employee is in the office, the reception unit can prioritize accepting office-related self-evaluations. Furthermore, when an employee is on a business trip, the reception unit can prioritize accepting self-evaluations related to the business trip destination. Furthermore, when an employee is working remotely, the reception unit can prioritize accepting self-evaluations related to remote work. For example, when an employee is in the office, the reception unit prioritizes accepting office-related self-evaluations. This allows for the priority acceptance of highly relevant self-evaluations by taking into account the employee's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the employee's geographical location information into AI and have the AI ​​prioritize the relevant self-evaluations.

[0077] When receiving a self-evaluation, the reception unit may analyze the employee's social media activity and receive a related self-evaluation. Specific methods for analyzing social media activity include, but are not limited to, the content of posts and the frequency of activity. For example, the reception unit may preferentially receive a self-evaluation related to a project shared by the employee on social media. The reception unit may also analyze the employee's social media activity and suggest a related self-evaluation. The reception unit may also suggest a related self-evaluation based on the activity of the employee's friends on social media. For example, the reception unit may preferentially receive a self-evaluation related to a project shared by the employee on social media. In this way, the reception unit may analyze the employee's social media activity and receive a related self-evaluation. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit may input the employee's social media data into an AI and have the AI ​​execute the suggestion of a related self-evaluation.

[0078] When accepting self-evaluations, the reception unit can adjust the reception method by reflecting the employee's past feedback. Specific content of past feedback includes, but is not limited to, feedback history and evaluation content. For example, the reception unit customizes the self-evaluation items based on the employee's past feedback. The reception unit can also analyze the employee's past feedback and adjust the self-evaluation reception method. The reception unit can also determine the priority of self-evaluations by referring to the employee's past feedback. For example, the reception unit customizes the self-evaluation items based on the employee's past feedback. This allows the self-evaluation reception method to be customized by reflecting the employee's past feedback, resulting in more appropriate reception. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the employee's past feedback data into AI and have the AI ​​adjust the reception method.

[0079] The analysis unit can estimate the employee's emotions and adjust the expression method based on the estimated employee emotions. Specific types of expression methods include, but are not limited to, text format and graph format. For example, if the employee is relaxed, the analysis unit can provide detailed optimization results. Furthermore, if the employee is stressed, the analysis unit can provide concise optimization results. Furthermore, if the employee is in a hurry, the analysis unit can provide optimization results that focus on the main points. For example, if the employee is relaxed, the analysis unit can provide detailed optimization results. By adjusting the optimization expression method based on the employee's emotions, more appropriate optimization results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the employee's emotion data into the generation AI and have the generation AI adjust the expression method.

[0080] During analysis, the analysis unit can adjust the level of detail based on the importance of the self-assessment. Specific criteria for the level of detail include, but are not limited to, the granularity of information and the presence or absence of detailed explanations. For example, the analysis unit performs detailed optimization for self-assessments with high importance. The analysis unit can also perform simple optimization for self-assessments with low importance. The analysis unit can also gradually adjust the level of detail of optimization depending on the importance. For example, the analysis unit performs detailed optimization for self-assessments with high importance. This allows for more appropriate optimization by adjusting the level of detail of optimization based on the importance of the self-assessment. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input self-assessment importance data into AI and have the AI ​​adjust the level of detail.

[0081] During analysis, the analysis unit can apply different algorithms depending on the category of self-assessment. Specific types of algorithms include, but are not limited to, machine learning algorithms and statistical algorithms. For example, the analysis unit can apply a project management optimization algorithm to self-assessment regarding project achievement. The analysis unit can also apply a team dynamics optimization algorithm to self-assessment regarding teamwork contribution. The analysis unit can also apply a skill mapping optimization algorithm to self-assessment regarding skill improvement. For example, the analysis unit can apply a project management optimization algorithm to self-assessment regarding project achievement. This allows for more appropriate optimization by applying different optimization algorithms depending on the category of self-assessment. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input self-assessment category data into AI and have the AI ​​apply the optimal algorithm.

[0082] During analysis, the analysis unit can improve accuracy by referring to the employee's past analysis results. Specific evaluation criteria for accuracy include, but are not limited to, for example, error rate and methods for improving accuracy. The analysis unit, for example, analyzes the employee's past optimization results and adjusts the optimization algorithm. The analysis unit can also improve optimization accuracy based on the employee's past optimization results. The analysis unit can also adjust optimization parameters by referring to the employee's past optimization results. For example, the analysis unit analyzes the employee's past optimization results and adjusts the optimization algorithm. In this way, the optimization accuracy can be improved by referring to the employee's past optimization results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past optimization result data into AI and have the AI ​​perform adjustments to improve accuracy.

[0083] The analysis unit can estimate the employee's emotions and adjust the length based on the estimated employee emotions. Specific criteria for length include, but are not limited to, the length of the sentences and the level of detail in the explanation. For example, if the employee is relaxed, the analysis unit can provide a detailed optimization result. Furthermore, if the employee is stressed, the analysis unit can provide a concise optimization result. Furthermore, if the employee is in a hurry, the analysis unit can provide a concise optimization result. For example, if the employee is relaxed, the analysis unit can provide a detailed optimization result. By adjusting the optimization length based on the employee's emotions, more appropriate optimization results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI. For example, the analysis unit can input the employee's emotion data into the generation AI and have the generation AI adjust the length.

[0084] During analysis, the analysis unit can determine priorities based on the submission dates of the self-assessments. Specific criteria for determining priorities include, but are not limited to, submission dates and importance. For example, the analysis unit prioritizes optimization of self-assessments with upcoming submission deadlines. The analysis unit can also adjust the optimization priority according to the submission dates. The analysis unit can also quickly optimize self-assessments whose submission dates have passed. For example, the analysis unit prioritizes optimization of self-assessments with upcoming submission deadlines. This allows for more efficient optimization by determining the optimization priority based on the submission dates of the self-assessments. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input self-assessment submission date data into AI and have the AI ​​determine the priorities.

[0085] During analysis, the analysis unit can adjust the order based on the relevance of the self-assessments. Specific criteria for determining the order include, but are not limited to, for example, order of relevance or order of importance. For example, the analysis unit prioritizes optimizing highly relevant self-assessments. The analysis unit can also adjust the order of optimization according to the relevance. The analysis unit can also postpone optimization of less relevant self-assessments. For example, the analysis unit prioritizes optimizing highly relevant self-assessments. In this way, by adjusting the order of optimization based on the relevance of the self-assessments, more relevant optimization can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input self-assessment relevance data into AI and have the AI ​​adjust the order.

[0086] During analysis, the analysis unit can adjust the use of technical terms according to the employee's level of expertise. Specific criteria for the use of technical terms include, but are not limited to, the definition and frequency of use of the technical terms. For example, the analysis unit can provide optimization results that use a lot of technical terms to employees with a high level of expertise. The analysis unit can also provide concise and easy-to-understand optimization results to employees with a low level of expertise. The analysis unit can also adjust the way the optimization results are presented according to the employee's level of expertise. For example, the analysis unit can provide optimization results that use a lot of technical terms to employees with a high level of expertise. In this way, by adjusting the use of technical terms in the optimization according to the employee's level of expertise, it is possible to provide more understandable optimization results. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input employee expertise level data into AI and have the AI ​​adjust the use of technical terms.

[0087] The support unit can estimate the employee's emotions and adjust the support method based on the estimated employee emotions. Specific types of support methods include, but are not limited to, the form of advice and the content of support. For example, the support unit can provide detailed evaluation support when the employee is relaxed. The support unit can also provide concise evaluation support when the employee is stressed. The support unit can also provide evaluation support that focuses on the key points when the employee is in a hurry. For example, the support unit can provide detailed evaluation support when the employee is relaxed. This allows for more appropriate evaluation support by adjusting the evaluation support method based on the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the support unit can be performed using, for example, AI. For example, the support unit can input the employee's emotion data into the generation AI and have the generation AI adjust the support method.

[0088] When providing evaluation support, the support unit can analyze the evaluator's past evaluation history to select a support method. Specific content of the evaluation history includes, for example, past evaluation results and evaluation trends, but is not limited to these examples. For example, the support unit analyzes the evaluator's past evaluation content and proposes an optimal support method. The support unit can also provide support to maintain consistency in evaluations based on the evaluator's past evaluation history. The support unit can also provide support to correct evaluation bias based on the evaluator's past evaluation history. For example, the support unit analyzes the evaluator's past evaluation content and proposes an optimal support method. In this way, by analyzing the evaluator's past evaluation history, an optimal support method can be selected and the consistency of evaluations can be maintained. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the evaluator's past evaluation history data into AI and have the AI ​​select a support method.

[0089] During evaluation support, the support unit can adjust the support content based on the evaluator's current evaluation criteria. Specific content of the evaluation criteria includes, but is not limited to, evaluation items and evaluation scales. For example, the support unit customizes the support content based on the evaluation criteria currently used by the evaluator. The support unit can also adjust the evaluation support method according to the evaluator's evaluation criteria. The support unit can also provide support to maintain consistency in the evaluation based on the evaluator's evaluation criteria. For example, the support unit customizes the support content based on the evaluation criteria currently used by the evaluator. This allows for customizing the support content based on the evaluator's current evaluation criteria, thereby providing more appropriate evaluation support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the evaluator's evaluation criteria data into AI and have the AI ​​adjust the support content.

[0090] The support unit can improve the evaluation support method by reflecting the evaluator's feedback during the evaluation support. Specific content of the feedback includes, but is not limited to, the format of the feedback and the level of detail of the content. For example, the support unit improves the evaluation support method based on the feedback provided by the evaluator. The support unit can also analyze the content of the evaluator's feedback to improve the accuracy of the evaluation support. The support unit can also customize the content of the evaluation support by referring to the evaluator's feedback. For example, the support unit improves the evaluation support method based on the feedback provided by the evaluator. In this way, by reflecting the evaluator's feedback, the evaluation support method can be improved and more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the evaluator's feedback data into AI and have the AI ​​improve the support method.

[0091] The support unit can estimate the employee's emotions and determine the priority of support based on the estimated employee emotions. Specific criteria for determining the priority include, but are not limited to, the intensity of the emotion and the importance of the task. For example, if the employee is stressed, the support unit can postpone providing evaluation support of a lower importance. Furthermore, if the employee is relaxed, the support unit can prioritize providing evaluation support of a higher importance. Furthermore, if the employee is in a hurry, the support unit can prioritize providing brief evaluation support. For example, if the employee is stressed, the support unit can postpone evaluation support of a lower importance. By determining the priority of evaluation support based on the employee's emotions, the support unit can provide evaluation support in a more appropriate order. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI. For example, the support department can input employee emotional data into the generation AI and have the generation AI determine priorities.

[0092] When providing evaluation support, the support unit can select a support method by taking into account the evaluator's geographic location information. Specific methods for acquiring geographic location information include, but are not limited to, GPS data and IP addresses. For example, if the evaluator is in the office, the support unit can provide office-related evaluation support. Furthermore, if the evaluator is on a business trip, the support unit can provide evaluation support related to the business trip destination. Furthermore, if the evaluator is working remotely, the support unit can provide evaluation support related to the remote work. For example, if the evaluator is in the office, the support unit provides office-related evaluation support. This allows the optimal support method to be selected by taking the evaluator's geographic location information into consideration, thereby providing more appropriate evaluation support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the evaluator's geographic location information into AI and have the AI ​​select a support method.

[0093] When providing evaluation support, the support unit may analyze the evaluator's social media activity and suggest content. Specific methods for analyzing social media activity include, but are not limited to, the content of posts and the frequency of activity. For example, the support unit may provide evaluation support related to a project shared by the evaluator on social media. The support unit may also analyze the evaluator's social media activity and suggest related evaluation support. The support unit may also suggest related evaluation support based on the activity of the evaluator's friends on social media. For example, the support unit may provide evaluation support related to a project shared by the evaluator on social media. In this way, relevant evaluation support can be provided by analyzing the evaluator's social media activity. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit may input the evaluator's social media data into AI and have the AI ​​suggest content of evaluation support.

[0094] The support unit can adjust the evaluation support method by reflecting the evaluator's past feedback when providing evaluation support. Specific content of the feedback includes, but is not limited to, the format of the feedback and the level of detail of the content. For example, the support unit customizes the evaluation support method based on feedback provided by the evaluator in the past. The support unit can also analyze the content of the evaluator's past feedback to improve the accuracy of the evaluation support. The support unit can also adjust the content of the evaluation support by referring to the evaluator's past feedback. For example, the support unit customizes the evaluation support method based on the evaluator's past feedback. By reflecting the evaluator's past feedback, the evaluation support method can be customized and more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the evaluator's past feedback data into AI and have the AI ​​adjust the support method.

[0095] The feedback unit can estimate the employee's emotions and adjust the feedback method based on the estimated employee emotions. Specific types of feedback methods include, but are not limited to, the format of feedback and the level of detail of the feedback content. For example, the feedback unit can provide detailed feedback when the employee is relaxed. The feedback unit can also provide concise feedback when the employee is stressed. The feedback unit can also provide feedback that focuses on the key points when the employee is in a hurry. For example, the feedback unit can provide detailed feedback when the employee is relaxed. This allows for adjusting the feedback method based on the employee's emotions to provide more appropriate feedback. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input employee emotion data into the generation AI and have the generation AI adjust the feedback method.

[0096] The feedback unit can adjust the level of detail based on the importance of the evaluation result when providing feedback. Specific criteria for the level of detail include, but are not limited to, for example, the granularity of the information and the presence or absence of detailed explanations. For example, the feedback unit can provide detailed feedback for evaluation results with high importance. The feedback unit can also provide concise feedback for evaluation results with low importance. The feedback unit can also adjust the level of detail of the feedback in stages depending on the importance. For example, the feedback unit provides detailed feedback for evaluation results with high importance. In this way, by adjusting the level of detail of the feedback based on the importance of the evaluation result, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input importance data of the evaluation result into AI and cause the AI ​​to adjust the level of detail.

[0097] The feedback unit can apply different algorithms depending on the category of the evaluation result when providing feedback. Specific types of algorithms include, but are not limited to, machine learning algorithms and statistical algorithms. For example, the feedback unit can apply a project management feedback algorithm to the evaluation result regarding the project achievement level. The feedback unit can also apply a team dynamics feedback algorithm to the evaluation result regarding the teamwork contribution level. The feedback unit can also apply a skill mapping feedback algorithm to the evaluation result regarding skill improvement. For example, the feedback unit can apply a project management feedback algorithm to the evaluation result regarding the project achievement level. In this way, by applying different feedback algorithms depending on the category of the evaluation result, more appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input category data of the evaluation result into AI and cause the AI ​​to apply the optimal algorithm.

[0098] The feedback unit can improve the accuracy of feedback by referring to the employee's past feedback results. Specific evaluation criteria for accuracy include, but are not limited to, for example, error rate and methods for improving accuracy. For example, the feedback unit can analyze the employee's past feedback results and adjust the feedback algorithm. The feedback unit can also improve the accuracy of feedback based on the employee's past feedback results. The feedback unit can also adjust the feedback parameters by referring to the employee's past feedback results. For example, the feedback unit can analyze the employee's past feedback results and adjust the feedback algorithm. In this way, the accuracy of feedback can be improved by referring to the employee's past feedback results. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the employee's past feedback result data into AI and have the AI ​​perform adjustments to improve accuracy.

[0099] The feedback unit can estimate the employee's emotions and adjust the length of the feedback based on the estimated employee emotions. Specific criteria for length include, but are not limited to, the length of the sentence and the level of detail in the explanation. For example, the feedback unit can provide detailed feedback when the employee is relaxed. The feedback unit can also provide concise feedback when the employee is stressed. The feedback unit can also provide feedback that focuses on the main points when the employee is in a hurry. For example, the feedback unit can provide detailed feedback when the employee is relaxed. This allows for adjusting the length of the feedback based on the employee's emotions, thereby providing more appropriate feedback. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the employee's emotion data into the generation AI and have the generation AI adjust the length.

[0100] The feedback unit may determine the priority of the evaluation results based on the submission time of the evaluation results when providing feedback. Specific criteria for determining the priority include, but are not limited to, the submission time and importance. For example, the feedback unit may prioritize feedback for evaluation results whose submission deadline is approaching. The feedback unit may also adjust the priority of feedback depending on the submission time. The feedback unit may also provide prompt feedback for evaluation results whose submission time has passed. For example, the feedback unit may prioritize feedback for evaluation results whose submission deadline is approaching. This allows for more efficient feedback by determining the priority of feedback based on the submission time of the evaluation results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input data on the submission time of the evaluation results into AI and have the AI ​​determine the priority.

[0101] The feedback unit can adjust the order of the evaluation results based on the relevance of the evaluation results when providing feedback. Specific criteria for determining the order include, but are not limited to, for example, in descending order of relevance or importance. For example, the feedback unit can provide feedback preferentially to highly relevant evaluation results. The feedback unit can also adjust the order of feedback according to the relevance. The feedback unit can also provide feedback later to less relevant evaluation results. For example, the feedback unit can provide feedback preferentially to highly relevant evaluation results. In this way, by adjusting the order of feedback based on the relevance of the evaluation results, more relevant feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input relevance data of the evaluation results into AI and cause the AI ​​to adjust the order.

[0102] The feedback unit may adjust the use of technical terms when providing feedback depending on the employee's level of expertise. Specific criteria for the use of technical terms include, but are not limited to, the definition and frequency of use of technical terms. For example, the feedback unit may provide feedback that uses a lot of technical terms to employees with a high level of expertise. The feedback unit may also provide concise and easy-to-understand feedback to employees with a low level of expertise. The feedback unit may also adjust the way the feedback is expressed depending on the employee's level of expertise. For example, the feedback unit may provide feedback that uses a lot of technical terms to employees with a high level of expertise. In this way, by adjusting the use of technical terms in the feedback depending on the employee's level of expertise, it is possible to provide feedback that is easier to understand. Some or all of the above-described processing by the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input employee expertise level data into AI and have the AI ​​adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, support unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive the employee's self-evaluation using the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the self-evaluation data. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports the evaluator's evaluation. The feedback unit is realized, for example, by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides detailed feedback to the employee. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, support unit, and feedback unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive the employee's self-evaluation using the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the self-evaluation data. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports the evaluator's evaluation. The feedback unit is realized, for example, by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides detailed feedback to the employee. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, support unit, and feedback unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive the employee's self-evaluation using the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the self-evaluation data. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports the evaluator's evaluation. The feedback unit is realized, for example, by the speaker 240 of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides detailed feedback to the employee. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, support unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive the employee's self-evaluation using the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the self-evaluation data. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the evaluator's evaluation. The feedback unit is realized, for example, by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides detailed feedback to the employee.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] When accepting an employee's self-evaluation, the reception unit can analyze the employee's past evaluation history and customize the self-evaluation items. For example, it can highlight items for which the employee received a high evaluation in the past and encourage the employee to re-evaluate those items when self-evaluating. It can also clearly indicate areas for improvement for items for which the employee received a low evaluation in the past, so that the employee will focus on those items when self-evaluating. Furthermore, it can add or delete self-evaluation items based on the employee's past evaluation history. This allows for self-evaluations that reflect the employee's past evaluation history.

[0105] When analyzing an employee's self-evaluation, the analysis unit can adjust the analysis algorithm taking into account the employee's current work situation. For example, the analysis results of the self-evaluation can be adjusted based on the progress of the project the employee is currently working on. The analysis results of the self-evaluation can also be optimized taking into account the employee's workload and team performance. Furthermore, the parameters of the analysis algorithm can be dynamically adjusted according to the employee's work situation. This allows for self-evaluation analysis that reflects the employee's current work situation.

[0106] When clearly explaining the basis of an evaluation to an evaluator, the support department can analyze the evaluator's past evaluation trends and provide advice to maintain consistency in evaluations. For example, if an evaluator has over-evaluated a particular employee in the past, the support department can point out this tendency and encourage appropriate evaluations. The support department can also provide advice to correct evaluation bias based on the evaluator's past evaluation trends. Furthermore, the support department can suggest specific methods to maintain consistency in evaluations based on the evaluator's past evaluation history. This allows for evaluation support that reflects the evaluator's past evaluation trends.

[0107] When providing feedback to an employee, the feedback department can analyze the employee's past feedback history and customize the content of the feedback. For example, it can provide detailed feedback to the employee by specifying specific areas for improvement based on feedback the employee has received in the past. It can also adjust the format and content of the feedback based on the employee's past feedback history. Furthermore, it can determine the priority of feedback by referring to the employee's past feedback history. This allows feedback to be provided that reflects the employee's past feedback history.

[0108] The reception unit can estimate the employee's emotions and adjust the method for receiving self-evaluations based on the estimated employee emotions. For example, if an employee is feeling stressed, the reception unit can encourage the employee to perform the self-evaluation in a relaxing environment. Also, if the employee is concentrating, the reception unit can be adjusted to have the employee perform the self-evaluation at that time. Furthermore, if the employee is tired, the reception unit can be adjusted to have the employee perform the self-evaluation after a break. In this way, the reception unit can receive self-evaluations according to the employee's emotions.

[0109] When analyzing an employee's self-evaluation, the analysis unit can adjust the analysis algorithm based on the employee's past evaluation data. For example, for items for which the employee received a high evaluation in the past, the analysis results can be emphasized, highlighting the employee's strengths. Also, for items for which the employee received a low evaluation in the past, it can highlight areas for improvement and suggest specific actions. Furthermore, it can dynamically adjust the parameters of the analysis algorithm based on the employee's past evaluation data. This makes it possible to analyze self-evaluations that reflect the employee's past evaluation data.

[0110] When clearly explaining the basis of the evaluation to the evaluator, the support unit can estimate the evaluator's emotions and adjust the method of evaluation support based on the estimated emotions of the evaluator. For example, if the evaluator is relaxed, detailed evaluation support can be provided. If the evaluator is stressed, brief evaluation support can be provided. Furthermore, if the evaluator is in a hurry, evaluation support that focuses on the main points can be provided. In this way, evaluation support can be provided according to the evaluator's emotions.

[0111] When providing feedback to an employee, the feedback unit can estimate the employee's emotions and adjust the content of the feedback based on the estimated employee emotions. For example, if the employee is relaxed, detailed feedback can be provided. If the employee is stressed, brief feedback can be provided. Furthermore, if the employee is in a hurry, feedback that focuses on the main points can be provided. In this way, feedback can be provided according to the employee's emotions.

[0112] When accepting an employee's self-evaluation, the reception department can customize the self-evaluation items based on the employee's current projects and areas of interest. For example, it can prioritize and display evaluation items related to the project the employee is currently working on. It can also add or delete self-evaluation items based on the employee's areas of interest. Furthermore, it can filter the self-evaluation items according to the employee's current work content. This allows the self-evaluation to reflect the employee's current projects and areas of interest.

[0113] When analyzing an employee's self-evaluation, the analysis unit can estimate the employee's emotions and adjust the way the analysis results are presented based on the estimated employee emotions. For example, if the employee is relaxed, detailed analysis results can be provided. If the employee is feeling stressed, concise analysis results can be provided. Furthermore, if the employee is in a hurry, analysis results that focus on the main points can be provided. This allows the analysis results to be presented according to the employee's emotions.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception department accepts employee self-evaluations. Employee self-evaluations include work performance, skill improvement, and goal achievement. The reception department can accept self-evaluations via online forms, face-to-face meetings, voice input, or text input. For example, if an employee prefers voice input, voice input is given priority. Step 2: The analysis unit analyzes the self-assessments received by the reception unit. The analysis is based on data analysis methods and evaluation criteria. For example, the analysis unit checks the consistency and fairness of the self-assessments by comparing them with past evaluation data, and uses statistical methods to check the consistency of the evaluations and propose appropriate corrections. Specifically, regression analysis and analysis of variance can be used. Step 3: The support unit supports the evaluator's evaluation based on the self-evaluation analyzed by the analysis unit. The support unit clarifies the basis for the evaluation and provides advice to maintain consistency in the evaluation. For example, it can clarify the basis for the evaluation to the evaluator and encourage appropriate evaluation. It can also analyze the evaluator's past evaluation history and select the most appropriate support method. Step 4: The feedback department provides the employee with the evaluation results supported by the support department. The feedback provides detailed feedback to the employee, clearly stating the reasons for the evaluation results and specific areas for improvement. For example, the feedback can be provided in a way that clearly explains the reasons for the evaluation results and provides the employee with feedback that is convincing. The accuracy of the feedback can also be improved by referring to the employee's past feedback results.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts employee self-evaluations; an analysis unit that analyzes the self-assessment received by the reception unit; a support unit that supports the evaluator's evaluation based on the self-evaluation analyzed by the analysis unit; a feedback unit that feeds back to the employee the evaluation results supported by the support unit. A system characterized by:

2. The analysis unit Use statistical methods to check the consistency of evaluations using past evaluation data and suggest corrections 2. The system of claim 1.

3. The support unit Explain the basis of the evaluation to the evaluator and provide advice on how to maintain consistency in evaluation.

2. The system of claim 1.

4. The feedback unit Provide detailed feedback to employees, clearly explaining the reasons for the evaluation results and areas for improvement.

2. The system of claim 1.

5. The reception unit Estimate employee emotions and adjust the timing of self-evaluation submissions based on the estimated employee emotions.

2. The system of claim 1.

6. The reception unit Analyze employees' past self-evaluation history and select the reception method 2. The system of claim 1.

7. The reception unit When accepting self-assessments, filter them based on employees' current projects and areas of interest 2. The system of claim 1.

8. The reception unit When accepting self-evaluations, select the method of acceptance depending on the employee's input method.

2. The system of claim 1.

Images