System

Abstract

An object of a system according to an embodiment is to adjust a gap between evaluations and provide an evaluation that can satisfy an employee.SOLUTION: A system according to an embodiment includes an evaluation-data analysis unit, an evaluation adjustment unit, an evaluation support unit, and an career change assistance unit. The evaluation-data analysis unit analyzes the past evaluation date by using the generated AI. The evaluation adjustment section adjusts the evaluation gap based on the evaluation data analyzed by the evaluation data analysis section. The evaluation support unit supports the superior in evaluation based on the evaluation adjusted by the evaluation adjustment unit. The career change assistance part measures a social level on the basis of the company level and the result of the employee analyzed by the evaluation-data analysis part, and predicts an appropriate salary.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] With conventional technology, there was a problem that there could be a gap in the evaluations given by superiors, making it difficult for employees to receive evaluations that they were satisfied with.

[0005] The system according to the embodiment aims to adjust the gap in evaluations and provide evaluations that employees can accept. [Means for solving the problem]

[0006] The system according to the embodiment includes an evaluation data analysis unit, an evaluation adjustment unit, an evaluation support unit, and a job change support unit. The evaluation data analysis unit analyzes past evaluation data using a generation AI. The evaluation adjustment unit adjusts evaluation gaps based on the evaluation data analyzed by the evaluation data analysis unit. The evaluation support unit supports supervisors in conducting evaluations based on the evaluations adjusted by the evaluation adjustment unit. The job change support unit measures the employee's social level based on the company level and performance analyzed by the evaluation data analysis unit, and predicts an appropriate salary. [Effects of the Invention]

[0007] The system according to the embodiment can adjust the gap in evaluation and provide an evaluation that employees can accept. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The evaluation system according to an embodiment of the present invention uses generative AI to analyze past evaluation data and evaluate employee performance from multiple angles. This allows the evaluation system to improve employee satisfaction with their evaluation, reduce the time cost of supervisor evaluations, and provide appropriate salary predictions when changing jobs.

[0029] An evaluation system according to an embodiment includes an evaluation data analysis unit, an evaluation adjustment unit, an evaluation support unit, and a job change support unit. The evaluation data analysis unit analyzes past evaluation data using a generation AI. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to analyze employee performance data and evaluation prompts. The evaluation data analysis unit also considers employee project results, teamwork, leadership, problem-solving ability, and other evaluation criteria. For example, the generation AI analyzes an employee's project results and evaluates them based on those results. The generation AI analyzes teamwork data and evaluates the employee's contribution within the team. The generation AI analyzes leadership data and evaluates the degree of leadership demonstrated. The generation AI analyzes problem-solving ability data and evaluates problem-solving skills. The evaluation adjustment unit adjusts evaluation gaps based on the evaluation data analyzed by the evaluation data analysis unit. For example, the generation AI analyzes evaluation data from multiple supervisors and, if different evaluations are given to employees with the same performance, detects the gaps and proposes appropriate evaluations. The generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. The evaluation support unit supports supervisors when making evaluations based on the evaluations adjusted by the evaluation adjustment unit. For example, the generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. The generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. The job change support unit measures the employee's social level based on the company level and performance analyzed by the evaluation data analysis unit, and predicts an appropriate salary. For example, the generation AI analyzes the employee's past performance data and company evaluation data, and predicts what salary employees at the same level are earning. The generation AI measures the employee's social level based on the company level and performance, and predicts an appropriate salary. The generation AI measures the employee's social level based on the company level and performance, and predicts an appropriate salary. As a result, the evaluation system according to the embodiment can improve employees' satisfaction with their evaluations, reduce the time cost of supervisors' evaluations, and provide appropriate salary predictions when changing jobs.For example, the evaluation system displays the employee evaluation results through a web application or a mobile application. The evaluation system sends the evaluation results by email. The evaluation system prints the evaluation results on paper.

[0030] The evaluation data analysis unit can analyze employees' social media activities and include their social influence in the evaluation. For example, the evaluation data analysis unit uses a generative AI to analyze employees' social media activities and reflect their social influence in the evaluation. For example, it quantifies influence based on the content of employees' posts and the number of followers. The evaluation data analysis unit also collects data on employees' social media activities and integrates it with internal evaluations. This allows employees' social influence to be included in the evaluation. The evaluation data analysis unit also incorporates employees' social influence into the evaluation through social media analysis. For example, it evaluates the degree of influence an employee has within their industry. This allows employees' social influence to be reflected in the evaluation.

[0031] The evaluation data analysis unit analyzes employee health data and can reflect the impact of health status on performance in the evaluation. For example, the evaluation data analysis unit uses a generative AI to analyze data from an employee's fitness tracker and reflect the health status in the evaluation. For example, it calculates a health score based on the amount of exercise and sleep time. The evaluation data analysis unit also collects health data over a long period of time and analyzes the correlation with performance. This allows the impact of health status on performance to be incorporated into the evaluation. The evaluation data analysis unit also reflects the employee's health status in the evaluation based on the fitness tracker data. For example, if an employee with good health has high performance, this correlation is included in the evaluation. This allows the employee's health status to be reflected in the evaluation.

[0032] The evaluation support unit can analyze an employee's hobbies or interests and provide personalized evaluation feedback. For example, the evaluation support unit uses a generation AI to analyze an employee's hobbies and interests and provide personalized evaluation feedback based on that. For example, skills and knowledge related to hobbies are reflected in the evaluation. The evaluation support unit also registers an employee's hobbies and interests in a database and customizes evaluation feedback based on that. For example, it evaluates the degree of contribution to a project related to a hobby. The evaluation support unit also analyzes hobbies and interests to provide evaluation feedback that increases employee motivation. For example, it evaluates contributions to work that utilize skills related to a hobby. This makes it possible to provide evaluation feedback based on an employee's hobbies and interests.

[0033] The evaluation support unit can compare an employee's self-assessment with the assessment of others to identify the gap between self-perception and the perception of others. For example, the evaluation support unit uses a generation AI to compare an employee's self-assessment with the assessment of others to identify the gap between self-perception and the perception of others. For example, if the self-assessment is high but the assessment of others is low, the unit provides feedback on that gap. The evaluation support unit also analyzes data on self-assessment and assessment of others and builds a system to identify the gap. This provides feedback to help employees improve their self-perception. The evaluation support unit also analyzes the gap between self-assessment and assessment of others and provides evaluation feedback based on the results. For example, it provides specific advice to close the gap between self-perception and the perception of others. This identifies the gap between self-perception and the perception of others, thereby promoting self-improvement among employees.

[0034] The evaluation data analysis unit can analyze real-time performance data in addition to past evaluation data, and perform evaluations that reflect the latest performance. For example, the evaluation data analysis unit uses a generation AI to integrate an employee's past evaluation data with real-time performance data and perform evaluations that reflect the latest performance. For example, the evaluation includes the results of ongoing projects. The evaluation data analysis unit also collects real-time performance data and compares it with past evaluation data to reflect the latest performance in the evaluation. This allows for an accurate evaluation of the employee's current situation. The evaluation data analysis unit also analyzes past evaluation data and real-time performance data, and builds a system that incorporates the latest performance into the evaluation. For example, evaluations are performed based on performance data that is updated in real time. This makes it possible to perform evaluations that reflect the latest performance.

[0035] When analyzing performance data, the evaluation data analysis unit takes into account industry trends and market fluctuations, and is able to reflect the relative value of performance in the evaluation. For example, the evaluation data analysis unit uses generative AI to analyze industry trends and market fluctuations and reflect them in employee performance data. For example, evaluations are made taking into account the overall industry growth rate and market demand. The evaluation data analysis unit also builds a system that takes into account the latest industry trends and market fluctuations when analyzing performance data. This allows the relative value of performance to be incorporated into the evaluation. The evaluation data analysis unit also collects industry trends and market fluctuations in real time and reflects them in employee performance data. For example, evaluations are made based on the competitive situation in the industry and market demand. This allows the relative value of performance to be reflected in the evaluation.

[0036] When analyzing performance data, the evaluation data analysis unit can compare it with data from different industries and fields to perform cross-industry evaluations. For example, the evaluation data analysis unit uses generative AI to collect data from different industries and fields and compare it with employee performance data. For example, it can perform a cross-industry evaluation of performance in the technical and marketing fields. The evaluation data analysis unit also analyzes data from different industries and builds a system that reflects this in employee performance data. This makes cross-industry evaluations possible. The evaluation data analysis unit also compares performance data with different industries and fields and incorporates relative value into the evaluation. For example, it performs evaluations based on benchmark data from different industries. This makes it possible to perform evaluations that compare data from different industries and fields.

[0037] When analyzing performance data, the evaluation data analysis unit analyzes the correlation with the performance of the entire team, and can reflect the team's contribution in the evaluation. For example, the evaluation data analysis unit uses a generation AI to compare an employee's performance data with the performance of the entire team and analyze the correlation. For example, the evaluation data analysis unit reflects an individual's contribution to the team's success in the evaluation. The evaluation data analysis unit also builds a system that collects performance data of the entire team and integrates it with employee performance data. This allows the team's contribution to be incorporated into the evaluation. The evaluation data analysis unit also analyzes the correlation between performance data and team performance, and reflects the team's contribution in the evaluation. For example, it evaluates an individual's contribution to achieving the team's goals. This allows the team's contribution to be reflected in the evaluation.

[0038] The evaluation adjustment unit can analyze the evaluation trends for each supervisor based on the supervisor's evaluation data and correct evaluation bias. For example, the evaluation adjustment unit uses a generation AI to analyze the evaluation data for each supervisor and identify evaluation trends. For example, if a particular supervisor tends to give harsh evaluations, the evaluation adjustment unit corrects that bias. The evaluation adjustment unit also collects evaluation data for each supervisor over a long period of time and analyzes evaluation trends. This allows it to propose specific methods for correcting evaluation bias. The evaluation adjustment unit also analyzes evaluation data and builds a system that identifies the evaluation trends for each supervisor. For example, it develops an algorithm to quantify and correct evaluation bias. This makes it possible to correct the evaluation bias for each supervisor.

[0039] The evaluation adjustment department can analyze the supervisor's evaluation data, check the consistency of the evaluations, and propose standardization of evaluation criteria. For example, the evaluation adjustment department uses a generation AI to analyze the supervisor's evaluation data and check the consistency of the evaluations. For example, if different evaluations are given for the same performance, the department evaluates the consistency. The evaluation adjustment department also analyzes the evaluation data and builds a system that proposes standardization of evaluation criteria. This improves the consistency of evaluations, allowing employees to receive evaluations that they are satisfied with. The evaluation adjustment department also collects the supervisor's evaluation data over a long period of time and checks the consistency of the evaluations. This provides a specific method for proposing standardization of evaluation criteria. This makes it possible to check the consistency of evaluations and propose standardization of evaluation criteria.

[0040] When analyzing a supervisor's evaluation data, the evaluation adjustment unit can compare it with evaluation data from different departments or regions to ensure consistency in evaluation. For example, the generation AI in the evaluation adjustment unit collects evaluation data from different departments or regions and compares it with the supervisor's evaluation data. This ensures consistency in evaluation. The evaluation adjustment unit also analyzes evaluation data for each department or region and builds a system to ensure consistency in evaluation. For example, it unifies the evaluation criteria for different departments. The evaluation adjustment unit also collects evaluation data from different departments or regions in real time and compares it with the supervisor's evaluation data. This improves the consistency of evaluation. This ensures consistency in evaluation by comparing it with evaluation data from different departments or regions.

[0041] When analyzing the supervisor's evaluation data, the evaluation adjustment unit can automatically generate evaluation feedback and support the supervisor when communicating the evaluation. For example, the evaluation adjustment unit uses a generation AI to analyze the supervisor's evaluation data and automatically generate evaluation feedback. For example, it provides feedback that includes the basis for the evaluation and specific areas for improvement. The evaluation adjustment unit also builds a system that automatically generates feedback and supports the supervisor when communicating the evaluation. This reduces the burden on the supervisor. The evaluation adjustment unit also automatically generates evaluation feedback based on the supervisor's evaluation data. For example, it provides feedback that includes key points of the evaluation and specific advice. This makes it possible to automatically generate evaluation feedback and support the supervisor when communicating the evaluation.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The evaluation system can also predict an employee's career path and reflect future growth potential in the evaluation. For example, the generative AI analyzes an employee's past performance data and skill set to predict their future career path. The evaluation data analysis unit then takes into account the employee's career goals and desired job type and incorporates growth potential into the evaluation. This allows the employee's future growth potential to be reflected in the evaluation. The evaluation support unit then provides specific career advice to the employee based on the career path prediction results. For example, it can suggest specific steps for acquiring the necessary skills and experience. This creates an evaluation system that supports employees' career growth.

[0044] The evaluation data analysis unit analyzes employees' social media activities and can include their social influence in the evaluation. For example, the generative AI analyzes employees' social media activities and reflects their social influence in the evaluation. For example, it quantifies influence based on the content of employees' posts and the number of followers. The evaluation data analysis unit also collects data on employees' social media activities and integrates it with internal evaluations. This allows employees' social influence to be included in the evaluation. The evaluation data analysis unit also incorporates employees' social influence into the evaluation through social media analysis. For example, it evaluates the degree of influence an employee has within their industry. This allows employees' social influence to be reflected in the evaluation.

[0045] The evaluation data analysis unit analyzes employee health data and can reflect the impact of health status on performance in the evaluation. For example, the generative AI analyzes data from an employee's fitness tracker and reflects health status in the evaluation. For example, it calculates a health score based on the amount of exercise and sleep time. The evaluation data analysis unit also collects health data over a long period of time and analyzes the correlation with performance. This allows the impact of health status on performance to be incorporated into the evaluation. The evaluation data analysis unit also reflects employee health status in the evaluation based on fitness tracker data. For example, if an employee with good health has high performance, this correlation will be included in the evaluation. This allows the health status of an employee to be reflected in the evaluation.

[0046] The evaluation support department can analyze employees' hobbies or interests and provide personalized evaluation feedback. For example, the generative AI analyzes an employee's hobbies and interests and provides personalized evaluation feedback based on that. For example, skills and knowledge related to hobbies are reflected in the evaluation. The evaluation support department also registers employees' hobbies and interests in a database and customizes evaluation feedback based on that. For example, it evaluates the degree of contribution to a project related to a hobby. The evaluation support department also analyzes hobbies and interests to provide evaluation feedback that increases employee motivation. For example, it evaluates contributions to work that utilize skills related to a hobby. This makes it possible to provide evaluation feedback based on employees' hobbies and interests.

[0047] The evaluation support department can compare an employee's self-assessment with the assessment of others to identify the gap between their self-perception and the perception of others. For example, the generation AI compares an employee's self-assessment with the assessment of others to identify the gap between their self-perception and the perception of others. For example, if an employee's self-assessment is high but their assessment of others is low, the gap is provided as feedback. The evaluation support department also analyzes data on self-assessment and assessment of others and builds a system to identify the gap. This provides feedback to help employees improve their self-perception. The evaluation support department also analyzes the gap between self-assessment and assessment of others and provides evaluation feedback based on the results. For example, it provides specific advice to close the gap between self-perception and the perception of others. This identifies the gap between self-perception and the perception of others, thereby promoting self-improvement among employees.

[0048] The evaluation data analysis unit can analyze real-time performance data in addition to past evaluation data, and perform evaluations that reflect the latest performance. For example, the generation AI integrates an employee's past evaluation data with real-time performance data to perform evaluations that reflect the latest performance. For example, the results of ongoing projects can be included in the evaluation. The evaluation data analysis unit also collects real-time performance data and compares it with past evaluation data to reflect the latest performance in the evaluation. This allows for an accurate evaluation of the employee's current situation. The evaluation data analysis unit also analyzes past evaluation data and real-time performance data, and builds a system that incorporates the latest performance into the evaluation. For example, evaluations are performed based on performance data that is updated in real time. This makes it possible to perform evaluations that reflect the latest performance.

[0049] When analyzing performance data, the evaluation data analysis department takes into account industry trends and market fluctuations, and is able to reflect the relative value of performance in evaluations. For example, generative AI analyzes industry trends and market fluctuations and reflects them in employee performance data. For example, evaluations are made taking into account the overall industry growth rate and market demand. The evaluation data analysis department also builds a system that takes into account the latest industry trends and market fluctuations when analyzing performance data. This allows the relative value of performance to be incorporated into evaluations. The evaluation data analysis department also collects industry trends and market fluctuations in real time and reflects them in employee performance data. For example, evaluations are made based on the competitive situation in the industry and market demand. This allows the relative value of performance to be reflected in evaluations.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The evaluation data analysis unit uses the generation AI to analyze past evaluation data. For example, the generation AI uses text generation AI such as GPT-3 or BERT to analyze employee performance data and evaluation prompts. The evaluation data analysis unit also considers evaluation items such as the employee's project results, teamwork, leadership, and problem-solving ability. The generation AI analyzes this data and evaluates each item. Step 2: The evaluation adjustment unit adjusts evaluation gaps based on the evaluation data analyzed by the evaluation data analysis unit. For example, the generation AI analyzes evaluation data from multiple superiors, and if different evaluations are given to employees with the same performance, it detects the gaps and suggests appropriate evaluations. The generation AI also provides advice on evaluation criteria and methods, and supports how to communicate the evaluations. Step 3: The evaluation support unit assists the supervisor in conducting an evaluation based on the evaluation adjusted by the evaluation adjustment unit. For example, the generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. Step 4: The career change support department measures the employee's social level based on the company level and performance analyzed by the evaluation data analysis department, and predicts an appropriate salary. For example, the generation AI analyzes the employee's past performance data and company evaluation data to predict what salary employees at the same level are earning. The generation AI measures the employee's social level based on the employee's company level and performance, and predicts an appropriate salary.

[0052] (Example 2) The evaluation system according to an embodiment of the present invention uses generative AI to analyze past evaluation data and evaluate employee performance from multiple angles. This allows the evaluation system to improve employee satisfaction with their evaluation, reduce the time cost of supervisor evaluations, and provide appropriate salary predictions when changing jobs.

[0053] An evaluation system according to an embodiment includes an evaluation data analysis unit, an evaluation adjustment unit, an evaluation support unit, and a job change support unit. The evaluation data analysis unit analyzes past evaluation data using a generation AI. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to analyze employee performance data and evaluation prompts. The evaluation data analysis unit also considers employee project results, teamwork, leadership, problem-solving ability, and other evaluation criteria. For example, the generation AI analyzes an employee's project results and evaluates them based on those results. The generation AI analyzes teamwork data and evaluates the employee's contribution within the team. The generation AI analyzes leadership data and evaluates the degree of leadership demonstrated. The generation AI analyzes problem-solving ability data and evaluates problem-solving skills. The evaluation adjustment unit adjusts evaluation gaps based on the evaluation data analyzed by the evaluation data analysis unit. For example, the generation AI analyzes evaluation data from multiple supervisors and, if different evaluations are given to employees with the same performance, detects the gaps and proposes appropriate evaluations. The generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. The evaluation support unit supports supervisors when making evaluations based on the evaluations adjusted by the evaluation adjustment unit. For example, the generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. The generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. The job change support unit measures the employee's social level based on the company level and performance analyzed by the evaluation data analysis unit, and predicts an appropriate salary. For example, the generation AI analyzes the employee's past performance data and company evaluation data, and predicts what salary employees at the same level are earning. The generation AI measures the employee's social level based on the company level and performance, and predicts an appropriate salary. The generation AI measures the employee's social level based on the company level and performance, and predicts an appropriate salary. As a result, the evaluation system according to the embodiment can improve employees' satisfaction with their evaluations, reduce the time cost of supervisors' evaluations, and provide appropriate salary predictions when changing jobs.For example, the evaluation system displays the employee evaluation results through a web application or a mobile application. The evaluation system sends the evaluation results by email. The evaluation system prints the evaluation results on paper.

[0054] The evaluation data analysis unit can estimate an employee's emotional state and conduct evaluations that take emotional fluctuations into account. For example, the evaluation data analysis unit uses a generative AI to monitor an employee's emotional state in real time and reflect emotional fluctuations in the evaluation. For example, it analyzes an employee's facial expressions and voice to quantify stress levels and motivation. The evaluation data analysis unit also collects employee emotional data over a long period of time and analyzes patterns of emotional fluctuations. This allows emotional fluctuations at specific times and situations to be reflected in the evaluation. The evaluation data analysis unit also incorporates fluctuations in an employee's stress and motivation into the evaluation based on the emotion estimation data. For example, it takes into account the impact of project progress and team atmosphere on emotions. This makes it possible to conduct evaluations that take an employee's emotional state into account.

[0055] The evaluation data analysis unit can analyze employees' social media activities and include their social influence in the evaluation. For example, the evaluation data analysis unit uses a generative AI to analyze employees' social media activities and reflect their social influence in the evaluation. For example, it quantifies influence based on the content of employees' posts and the number of followers. The evaluation data analysis unit also collects data on employees' social media activities and integrates it with internal evaluations. This allows employees' social influence to be included in the evaluation. The evaluation data analysis unit also incorporates employees' social influence into the evaluation through social media analysis. For example, it evaluates the degree of influence an employee has within their industry. This allows employees' social influence to be reflected in the evaluation.

[0056] The evaluation data analysis unit analyzes employee health data and can reflect the impact of health status on performance in the evaluation. For example, the evaluation data analysis unit uses a generative AI to analyze data from an employee's fitness tracker and reflect the health status in the evaluation. For example, it calculates a health score based on the amount of exercise and sleep time. The evaluation data analysis unit also collects health data over a long period of time and analyzes the correlation with performance. This allows the impact of health status on performance to be incorporated into the evaluation. The evaluation data analysis unit also reflects the employee's health status in the evaluation based on the fitness tracker data. For example, if an employee with good health has high performance, this correlation is included in the evaluation. This allows the employee's health status to be reflected in the evaluation.

[0057] The evaluation support unit can analyze an employee's hobbies or interests and provide personalized evaluation feedback. For example, the evaluation support unit uses a generation AI to analyze an employee's hobbies and interests and provide personalized evaluation feedback based on that. For example, skills and knowledge related to hobbies are reflected in the evaluation. The evaluation support unit also registers an employee's hobbies and interests in a database and customizes evaluation feedback based on that. For example, it evaluates the degree of contribution to a project related to a hobby. The evaluation support unit also analyzes hobbies and interests to provide evaluation feedback that increases employee motivation. For example, it evaluates contributions to work that utilize skills related to a hobby. This makes it possible to provide evaluation feedback based on an employee's hobbies and interests.

[0058] The evaluation support unit can compare an employee's self-assessment with the assessment of others to identify the gap between self-perception and the perception of others. For example, the evaluation support unit uses a generation AI to compare an employee's self-assessment with the assessment of others to identify the gap between self-perception and the perception of others. For example, if the self-assessment is high but the assessment of others is low, the unit provides feedback on that gap. The evaluation support unit also analyzes data on self-assessment and assessment of others and builds a system to identify the gap. This provides feedback to help employees improve their self-perception. The evaluation support unit also analyzes the gap between self-assessment and assessment of others and provides evaluation feedback based on the results. For example, it provides specific advice to close the gap between self-perception and the perception of others. This identifies the gap between self-perception and the perception of others, thereby promoting self-improvement among employees.

[0059] The evaluation support unit can use the emotion estimation function to make suggestions that will elicit positive emotions when providing evaluation feedback. For example, the generation AI uses the emotion estimation function to make suggestions that will elicit positive emotions when providing evaluation feedback. For example, the evaluation support unit includes positive expressions and encouraging words in the feedback. The evaluation support unit also adjusts the content of the evaluation feedback based on the emotion estimation data. This provides feedback that makes employees feel positive emotions. The evaluation support unit also uses the emotion estimation function to make specific suggestions that will elicit positive emotions when providing evaluation feedback. For example, it provides feedback that emphasizes success stories and a sense of accomplishment. This elicits positive emotions when providing evaluation feedback, thereby improving employee motivation.

[0060] The evaluation data analysis unit can analyze real-time performance data in addition to past evaluation data, and perform evaluations that reflect the latest performance. For example, the evaluation data analysis unit uses a generation AI to integrate an employee's past evaluation data with real-time performance data and perform evaluations that reflect the latest performance. For example, the evaluation includes the results of ongoing projects. The evaluation data analysis unit also collects real-time performance data and compares it with past evaluation data to reflect the latest performance in the evaluation. This allows for an accurate evaluation of the employee's current situation. The evaluation data analysis unit also analyzes past evaluation data and real-time performance data, and builds a system that incorporates the latest performance into the evaluation. For example, evaluations are performed based on performance data that is updated in real time. This makes it possible to perform evaluations that reflect the latest performance.

[0061] When analyzing performance data, the evaluation data analysis unit takes into account industry trends and market fluctuations, and is able to reflect the relative value of performance in the evaluation. For example, the evaluation data analysis unit uses generative AI to analyze industry trends and market fluctuations and reflect them in employee performance data. For example, evaluations are made taking into account the overall industry growth rate and market demand. The evaluation data analysis unit also builds a system that takes into account the latest industry trends and market fluctuations when analyzing performance data. This allows the relative value of performance to be incorporated into the evaluation. The evaluation data analysis unit also collects industry trends and market fluctuations in real time and reflects them in employee performance data. For example, evaluations are made based on the competitive situation in the industry and market demand. This allows the relative value of performance to be reflected in the evaluation.

[0062] When analyzing performance data, the evaluation data analysis unit can compare it with data from different industries and fields to perform cross-industry evaluations. For example, the evaluation data analysis unit uses generative AI to collect data from different industries and fields and compare it with employee performance data. For example, it can perform a cross-industry evaluation of performance in the technical and marketing fields. The evaluation data analysis unit also analyzes data from different industries and builds a system that reflects this in employee performance data. This makes cross-industry evaluations possible. The evaluation data analysis unit also compares performance data with different industries and fields and incorporates relative value into the evaluation. For example, it performs evaluations based on benchmark data from different industries. This makes it possible to perform evaluations that compare data from different industries and fields.

[0063] When analyzing performance data, the evaluation data analysis unit analyzes the correlation with the performance of the entire team, and can reflect the team's contribution in the evaluation. For example, the evaluation data analysis unit uses a generation AI to compare an employee's performance data with the performance of the entire team and analyze the correlation. For example, the evaluation data analysis unit reflects an individual's contribution to the team's success in the evaluation. The evaluation data analysis unit also builds a system that collects performance data of the entire team and integrates it with employee performance data. This allows the team's contribution to be incorporated into the evaluation. The evaluation data analysis unit also analyzes the correlation between performance data and team performance, and reflects the team's contribution in the evaluation. For example, it evaluates an individual's contribution to achieving the team's goals. This allows the team's contribution to be reflected in the evaluation.

[0064] The evaluation data analysis unit can use the emotion estimation function to monitor emotional fluctuations based on performance data in real time and reflect them in evaluations. For example, the evaluation data analysis unit uses the emotion estimation function to monitor emotional fluctuations based on performance data in real time. For example, emotional fluctuations according to the progress of a project are reflected in evaluations. The evaluation data analysis unit also integrates performance data and emotion estimation data in real time to build a system that incorporates emotional fluctuations into evaluations. This allows emotional fluctuations to be reflected in evaluations. The evaluation data analysis unit also uses the emotion estimation function to monitor emotional fluctuations based on performance data in real time and reflect them in evaluations. For example, employees with high emotional stability are given high ratings. This allows emotional fluctuations to be monitored in real time and reflected in evaluations.

[0065] The evaluation adjustment unit can analyze the evaluation trends for each supervisor based on the supervisor's evaluation data and correct evaluation bias. For example, the evaluation adjustment unit uses a generation AI to analyze the evaluation data for each supervisor and identify evaluation trends. For example, if a particular supervisor tends to give harsh evaluations, the evaluation adjustment unit corrects that bias. The evaluation adjustment unit also collects evaluation data for each supervisor over a long period of time and analyzes evaluation trends. This allows it to propose specific methods for correcting evaluation bias. The evaluation adjustment unit also analyzes evaluation data and builds a system that identifies the evaluation trends for each supervisor. For example, it develops an algorithm to quantify and correct evaluation bias. This makes it possible to correct the evaluation bias for each supervisor.

[0066] The evaluation adjustment unit can estimate the emotional state of the superior and perform an evaluation that eliminates the influence of emotions. For example, the evaluation adjustment unit uses a generation AI to estimate the emotional state of the superior and perform an evaluation that eliminates the influence of emotions. For example, it quantifies and corrects the impact of the superior's emotions on the evaluation. The evaluation adjustment unit also collects the superior's emotional data and integrates it with the evaluation data to build a system that performs an evaluation that eliminates the influence of emotions. This makes it possible to perform an objective evaluation. The evaluation adjustment unit also uses an emotion estimation function to monitor the superior's emotional state in real time and perform an evaluation that eliminates the influence of emotions. For example, it analyzes the impact that emotional fluctuations have on the evaluation. This makes it possible to perform an evaluation that eliminates the influence of the superior's emotions.

[0067] The evaluation adjustment department can analyze the supervisor's evaluation data, check the consistency of the evaluations, and propose standardization of evaluation criteria. For example, the evaluation adjustment department uses a generation AI to analyze the supervisor's evaluation data and check the consistency of the evaluations. For example, if different evaluations are given for the same performance, the department evaluates the consistency. The evaluation adjustment department also analyzes the evaluation data and builds a system that proposes standardization of evaluation criteria. This improves the consistency of evaluations, allowing employees to receive evaluations that they are satisfied with. The evaluation adjustment department also collects the supervisor's evaluation data over a long period of time and checks the consistency of the evaluations. This provides a specific method for proposing standardization of evaluation criteria. This makes it possible to check the consistency of evaluations and propose standardization of evaluation criteria.

[0068] When analyzing a supervisor's evaluation data, the evaluation adjustment unit can compare it with evaluation data from different departments or regions to ensure consistency in evaluation. For example, the generation AI in the evaluation adjustment unit collects evaluation data from different departments or regions and compares it with the supervisor's evaluation data. This ensures consistency in evaluation. The evaluation adjustment unit also analyzes evaluation data for each department or region and builds a system to ensure consistency in evaluation. For example, it unifies the evaluation criteria for different departments. The evaluation adjustment unit also collects evaluation data from different departments or regions in real time and compares it with the supervisor's evaluation data. This improves the consistency of evaluation. This ensures consistency in evaluation by comparing it with evaluation data from different departments or regions.

[0069] When analyzing the supervisor's evaluation data, the evaluation adjustment unit can automatically generate evaluation feedback and support the supervisor when communicating the evaluation. For example, the evaluation adjustment unit uses a generation AI to analyze the supervisor's evaluation data and automatically generate evaluation feedback. For example, it provides feedback that includes the basis for the evaluation and specific areas for improvement. The evaluation adjustment unit also builds a system that automatically generates feedback and supports the supervisor when communicating the evaluation. This reduces the burden on the supervisor. The evaluation adjustment unit also automatically generates evaluation feedback based on the supervisor's evaluation data. For example, it provides feedback that includes key points of the evaluation and specific advice. This makes it possible to automatically generate evaluation feedback and support the supervisor when communicating the evaluation.

[0070] The evaluation adjustment unit uses the emotion estimation function to monitor the emotional state of the superior in real time during the evaluation, thereby ensuring the fairness of the evaluation. For example, the evaluation adjustment unit uses a generation AI to monitor the emotional state of the superior in real time and ensure the fairness of the evaluation. For example, it eliminates the influence of the superior's emotions on the evaluation. The evaluation adjustment unit also uses the emotion estimation function to build a system that monitors the emotional state of the superior. This improves the fairness of the evaluation. The evaluation adjustment unit also collects the superior's emotional data in real time and provides a specific method for ensuring the fairness of the evaluation. For example, it analyzes the influence of emotional fluctuations on the evaluation. This makes it possible to monitor the superior's emotional state in real time and ensure the fairness of the evaluation.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The evaluation system can also predict an employee's career path and reflect future growth potential in the evaluation. For example, the generative AI analyzes an employee's past performance data and skill set to predict their future career path. The evaluation data analysis unit then takes into account the employee's career goals and desired job type and incorporates growth potential into the evaluation. This allows the employee's future growth potential to be reflected in the evaluation. The evaluation support unit then provides specific career advice to the employee based on the career path prediction results. For example, it can suggest specific steps for acquiring the necessary skills and experience. This creates an evaluation system that supports employees' career growth.

[0073] The evaluation data analysis unit can estimate an employee's emotional state and conduct evaluations that take emotional fluctuations into account. For example, the generative AI monitors an employee's emotional state in real time and reflects emotional fluctuations in the evaluation. For example, it analyzes an employee's facial expressions and voice to quantify stress levels and motivation. The evaluation data analysis unit also collects employee emotional data over a long period of time and analyzes patterns of emotional fluctuations. This allows emotional fluctuations at specific times and in specific situations to be reflected in the evaluation. The evaluation data analysis unit also incorporates fluctuations in an employee's stress and motivation into the evaluation based on the emotion estimation data. For example, it takes into account the impact of project progress and team atmosphere on emotions. This makes it possible to conduct evaluations that take an employee's emotional state into account.

[0074] The evaluation data analysis unit analyzes employees' social media activities and can include their social influence in the evaluation. For example, the generative AI analyzes employees' social media activities and reflects their social influence in the evaluation. For example, it quantifies influence based on the content of employees' posts and the number of followers. The evaluation data analysis unit also collects data on employees' social media activities and integrates it with internal evaluations. This allows employees' social influence to be included in the evaluation. The evaluation data analysis unit also incorporates employees' social influence into the evaluation through social media analysis. For example, it evaluates the degree of influence an employee has within their industry. This allows employees' social influence to be reflected in the evaluation.

[0075] The evaluation data analysis unit analyzes employee health data and can reflect the impact of health status on performance in the evaluation. For example, the generative AI analyzes data from an employee's fitness tracker and reflects health status in the evaluation. For example, it calculates a health score based on the amount of exercise and sleep time. The evaluation data analysis unit also collects health data over a long period of time and analyzes the correlation with performance. This allows the impact of health status on performance to be incorporated into the evaluation. The evaluation data analysis unit also reflects employee health status in the evaluation based on fitness tracker data. For example, if an employee with good health has high performance, this correlation will be included in the evaluation. This allows the health status of an employee to be reflected in the evaluation.

[0076] The evaluation support department can analyze employees' hobbies or interests and provide personalized evaluation feedback. For example, the generative AI analyzes an employee's hobbies and interests and provides personalized evaluation feedback based on that. For example, skills and knowledge related to hobbies are reflected in the evaluation. The evaluation support department also registers employees' hobbies and interests in a database and customizes evaluation feedback based on that. For example, it evaluates the degree of contribution to a project related to a hobby. The evaluation support department also analyzes hobbies and interests to provide evaluation feedback that increases employee motivation. For example, it evaluates contributions to work that utilize skills related to a hobby. This makes it possible to provide evaluation feedback based on employees' hobbies and interests.

[0077] The evaluation support department can compare an employee's self-assessment with the assessment of others to identify the gap between their self-perception and the perception of others. For example, the generation AI compares an employee's self-assessment with the assessment of others to identify the gap between their self-perception and the perception of others. For example, if an employee's self-assessment is high but their assessment of others is low, the gap is provided as feedback. The evaluation support department also analyzes data on self-assessment and assessment of others and builds a system to identify the gap. This provides feedback to help employees improve their self-perception. The evaluation support department also analyzes the gap between self-assessment and assessment of others and provides evaluation feedback based on the results. For example, it provides specific advice to close the gap between self-perception and the perception of others. This identifies the gap between self-perception and the perception of others, thereby promoting self-improvement among employees.

[0078] The evaluation support unit can use the emotion estimation function to make suggestions that will elicit positive emotions when providing evaluation feedback. For example, the generation AI uses the emotion estimation function to make suggestions that will elicit positive emotions when providing evaluation feedback. For example, positive expressions and encouraging words may be included in the feedback. The evaluation support unit also adjusts the content of the evaluation feedback based on the emotion estimation data. This allows the provision of feedback that will make employees feel positive emotions. The evaluation support unit also uses the emotion estimation function to make specific suggestions that will elicit positive emotions when providing evaluation feedback. For example, it may provide feedback that emphasizes success stories and a sense of accomplishment. This elicits positive emotions when providing evaluation feedback, thereby improving employee motivation.

[0079] The evaluation data analysis unit can analyze real-time performance data in addition to past evaluation data, and perform evaluations that reflect the latest performance. For example, the generation AI integrates an employee's past evaluation data with real-time performance data to perform evaluations that reflect the latest performance. For example, the results of ongoing projects can be included in the evaluation. The evaluation data analysis unit also collects real-time performance data and compares it with past evaluation data to reflect the latest performance in the evaluation. This allows for an accurate evaluation of the employee's current situation. The evaluation data analysis unit also analyzes past evaluation data and real-time performance data, and builds a system that incorporates the latest performance into the evaluation. For example, evaluations are performed based on performance data that is updated in real time. This makes it possible to perform evaluations that reflect the latest performance.

[0080] When analyzing performance data, the evaluation data analysis department takes into account industry trends and market fluctuations, and is able to reflect the relative value of performance in evaluations. For example, generative AI analyzes industry trends and market fluctuations and reflects them in employee performance data. For example, evaluations are made taking into account the overall industry growth rate and market demand. The evaluation data analysis department also builds a system that takes into account the latest industry trends and market fluctuations when analyzing performance data. This allows the relative value of performance to be incorporated into evaluations. The evaluation data analysis department also collects industry trends and market fluctuations in real time and reflects them in employee performance data. For example, evaluations are made based on the competitive situation in the industry and market demand. This allows the relative value of performance to be reflected in evaluations.

[0081] The evaluation data analysis unit uses the emotion estimation function to monitor emotional fluctuations based on performance data in real time and reflect them in evaluations. For example, the generation AI uses the emotion estimation function to monitor emotional fluctuations based on performance data in real time. For example, emotional fluctuations according to the progress of a project are reflected in evaluations. The evaluation data analysis unit also integrates performance data and emotion estimation data in real time to build a system that incorporates emotional fluctuations into evaluations. This allows emotional fluctuations to be reflected in evaluations. The evaluation data analysis unit also uses the emotion estimation function to monitor emotional fluctuations based on performance data in real time and reflect them in evaluations. For example, employees with high emotional stability are given high ratings. This allows emotional fluctuations to be monitored in real time and reflected in evaluations.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The evaluation data analysis unit uses the generation AI to analyze past evaluation data. For example, the generation AI uses text generation AI such as GPT-3 or BERT to analyze employee performance data and evaluation prompts. The evaluation data analysis unit also considers evaluation items such as the employee's project results, teamwork, leadership, and problem-solving ability. The generation AI analyzes this data and evaluates each item. Step 2: The evaluation adjustment unit adjusts evaluation gaps based on the evaluation data analyzed by the evaluation data analysis unit. For example, the generation AI analyzes evaluation data from multiple superiors, and if different evaluations are given to employees with the same performance, it detects the gaps and suggests appropriate evaluations. The generation AI also provides advice on evaluation criteria and methods, and supports how to communicate the evaluations. Step 3: The evaluation support unit assists the supervisor in conducting an evaluation based on the evaluation adjusted by the evaluation adjustment unit. For example, the generation AI provides advice on evaluation criteria and methods, and also supports how to communicate the evaluation. Step 4: The career change support department measures the employee's social level based on the company level and performance analyzed by the evaluation data analysis department, and predicts an appropriate salary. For example, the generation AI analyzes the employee's past performance data and company evaluation data to predict what salary employees at the same level are earning. The generation AI measures the employee's social level based on the employee's company level and performance, and predicts an appropriate salary.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An evaluation data analysis unit that analyzes past evaluation data using generation AI; an evaluation adjustment unit that adjusts the evaluation gap based on the evaluation data analyzed by the evaluation data analysis unit; an evaluation support unit that supports a superior when making an evaluation based on the evaluation adjusted by the evaluation adjustment unit; a job change support unit that measures the employee's social level based on the company level and performance analyzed by the evaluation data analysis unit and predicts an appropriate salary. A system characterized by:

2. The evaluation data analysis unit Estimate the employee's emotional state and make an evaluation that takes into account emotional fluctuations.

2. The system of claim 1.

3. The evaluation data analysis unit Analyze the employee's social media activity and include it in the evaluation of their social influence 2. The system of claim 1.

4. The evaluation data analysis unit Analyze the employee's health data and reflect the impact of their health on performance in their evaluation.

2. The system of claim 1.

5. The evaluation support unit Analyzing the employee's hobbies or interests and providing personalized evaluation feedback 2. The system of claim 1.

6. The evaluation support unit Compare the employee's self-assessment with the assessment of others to identify the gap between self-perception and the perception of others.

2. The system of claim 1.

7. The evaluation support unit Providing suggestions to elicit positive emotions during evaluation feedback 2. The system of claim 1.

8. The evaluation data analysis unit In addition to the past evaluation data, real-time performance data is analyzed to perform the evaluation that reflects the latest performance.

2. The system of claim 1.

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