system

Abstract

The system according to this embodiment aims to appropriately evaluate the effects of employee behavior and reflect them in the personnel system. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, a quantification unit, and an integration unit. The data collection unit collects employee behavior data. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit evaluates the effectiveness of employee behavior based on the data analyzed by the analysis unit. The quantification unit quantifies the evaluation results obtained by the evaluation unit. The integration unit integrates the quantified results from the quantification unit into the personnel system.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to appropriately evaluate the effects of employees' actions and reflect them in the personnel system.

[0005] The system according to the embodiment aims to appropriately evaluate the effects of employees' actions and reflect them in the personnel system.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, a quantification unit, and an integration unit. The data collection unit collects employee behavior data. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit evaluates the effectiveness of employee behavior based on the data analyzed by the analysis unit. The quantification unit quantifies the evaluation results obtained by the evaluation unit. The integration unit incorporates the quantified results into the personnel system. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately evaluate the effects of employee behavior and reflect them in the personnel system. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The PIPM system according to an embodiment of the present invention is a system that evaluates employee behavior and improves the overall performance of the organization. The PIPM system collects employee behavior data, performs statistical analysis, and evaluates the effectiveness of employee behavior. For example, actions such as taking on the account of an employee who has suddenly resigned to reduce the burden on other members, or providing advice through communication tools, are evaluated. Based on these evaluation results, the employee's contribution is quantified as PIPM and incorporated into the personnel system. This allows employee behaviors that have previously been overlooked to be evaluated, leading to increased motivation and more active communication. For example, the PIPM system collects employee behavior data. In this process, detailed information about each employee's actions, such as meeting minutes from communication tools, is collected. For example, advice and comments on communication tools, and conversations with franchisees are collected. This ensures that employee behavior data is comprehensively collected. Next, the collected data is statistically analyzed by AI. The AI ​​analyzes the collected data and evaluates the effectiveness of employee behavior. For example, actions such as taking on the account of an employee who has suddenly resigned to reduce the burden on other members, or providing advice through communication tools, are evaluated. This system quantifies qualitative aspects that cannot be expressed solely through numbers. Furthermore, the AI ​​uses the evaluation results to quantify employee contributions as PIPM (Performance, Performance, and Productivity). For example, the speed of advice and decision-making provided by employees, as well as the strengthening of relationships with franchisees, are quantified as PIPM. This clearly demonstrates the level of employee contribution. Finally, the quantified PIPM is incorporated into the personnel system. This allows employee actions that were previously overlooked to be evaluated, leading to increased motivation and improved communication. For example, if an employee's actions contribute to raising the overall team's performance figures, that evaluation is reflected in the PIPM. This improves employee motivation and enhances the overall performance of the organization. Through this system, employee actions are evaluated, leading to improved overall organizational performance. For example, the speed of advice and decision-making provided by employees, as well as the strengthening of relationships with franchisees, are evaluated, leading to increased employee motivation and improved overall organizational performance.Furthermore, by specifically evaluating employee behavior, communication is stimulated, fostering a better organizational culture. This can lead to improved sales and profits across the entire organization. In short, the PIPM system can evaluate employee behavior and improve overall organizational performance.

[0029] The PIPM system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, a quantification unit, and an embedding unit. The collection unit collects employee behavioral data. The collection unit can collect behavioral data such as employee working hours, project progress, and communication frequency. The collection unit can also collect meeting minutes from communication tools. For example, the collection unit can collect minutes from emails, chats, video conferences, etc. Furthermore, the collection unit can also collect employees' social media activity. For example, the collection unit can analyze employees' social media posts and collect relevant data. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, natural language processing technology. The analysis unit can also analyze the collected data using machine learning algorithms. Furthermore, the analysis unit can also analyze the collected data using data mining technology. The evaluation unit evaluates the effectiveness of employee behavior based on the data analyzed by the analysis unit. The evaluation unit can evaluate, for example, the speed of employee advice and decision-making. Furthermore, the evaluation unit can assess whether an employee's actions contributed to raising the overall team performance. The evaluation unit can also assess the impact of an employee's actions on the overall organizational performance. The quantification unit quantifies the evaluation results obtained by the evaluation unit. For example, the quantification unit can quantify the evaluation results based on a scoring method. The quantification unit can also graph the evaluation results. Furthermore, the quantification unit can quantify the evaluation results based on percentiles. The integration unit integrates the quantified results from the quantification unit into the personnel system. For example, the integration unit can reflect the quantified results in the evaluation system. The integration unit can also reflect the quantified results in the promotion system. Furthermore, the integration unit can reflect the quantified results in the compensation system. As a result, the PIPM system according to this embodiment can evaluate employee actions and improve the overall organizational performance.

[0030] The data collection department collects employee behavioral data. For example, it can collect behavioral data such as employee working hours, project progress, and communication frequency. Specifically, it automatically retrieves task completion status and progress reports from the work management tools and project management software used by employees. It also collects data from time card systems that record employee attendance and break times. Furthermore, the data collection department can collect meeting minutes from communication tools. For example, it collects minutes from emails, chats, and video conferences. This involves analyzing logs from email servers and chat applications to extract conversation content and frequency. Video conference minutes are obtained by transcribing meeting recordings using speech recognition technology and extracting important statements and decisions. Additionally, the data collection department can collect employee social media activity. For example, it analyzes employee social media posts and collects relevant data. This includes scraping content from employees' public social media accounts and performing sentiment analysis and topic modeling using natural language processing techniques. This allows the data collection unit to gather a wide range of data related to employees' work and provide a foundation for conducting detailed behavioral analysis.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can analyze the collected data using natural language processing technology. Specifically, it can analyze the content of emails and chats to understand communication patterns and frequency among employees. By using natural language processing technology, it can extract emotions and intentions from text data and evaluate employee motivation and stress levels. The analysis department can also analyze the collected data using machine learning algorithms. For example, it can analyze project progress data to identify causes of delays and risk factors. By using machine learning algorithms, it can learn patterns from past data and predict the probability of future project success. Furthermore, the analysis department can analyze the collected data using data mining technology. By using data mining technology, it can discover useful information and hidden patterns from large amounts of data, gaining insights into employee behavior and performance. For example, it can analyze employee work hours and project progress data to identify efficient work methods and areas for improvement. This allows the analysis department to analyze the collected data from multiple perspectives and provide detailed insights into employee behavior and performance.

[0032] The evaluation department assesses the effectiveness of employee actions based on data analyzed by the analysis department. For example, the evaluation department can evaluate the speed of employee advice and decision-making. Specifically, it can evaluate the quality of advice provided by employees and how much that advice contributed to the success of a project. Regarding the speed of decision-making, it can evaluate how quickly employees solved problems and made appropriate judgments. The evaluation department can also evaluate whether employee actions contributed to raising the overall performance of the team. For example, it can evaluate how much an employee's actions affected the team's productivity and efficiency. Furthermore, the evaluation department can evaluate the impact of employee actions on the overall performance of the organization. For example, it can evaluate how much an employee's actions contributed to achieving the organization's overall goals. This includes evaluating how well an employee's actions align with the organization's strategic goals and vision. Based on these evaluation results, the evaluation department can identify employees' strengths and areas for improvement and provide individualized feedback. In this way, the evaluation department can comprehensively evaluate the effectiveness of employee actions and contribute to improving the overall performance of the organization.

[0033] The quantification unit quantifies the evaluation results obtained by the evaluation unit. For example, the quantification unit can quantify evaluation results based on a scoring method. Specifically, it can set certain standards for employee behavior and performance and assign scores based on those standards. For example, it can assign a 5-point scale or a 10-point scale to the quality of advice or the speed of decision-making. The quantification unit can also graph the evaluation results. This includes creating bar graphs and line graphs to visually compare employee performance. Furthermore, the quantification unit can quantify evaluation results based on percentiles. For example, it can calculate percentile ranks to show where an employee's performance stands in relation to the overall performance. This allows the quantification unit to express evaluation results objectively and visually, and to clearly understand employee performance. In addition, the quantification unit can store these quantified results in a database and use them for later analysis and reporting. This allows the quantification unit to effectively quantify evaluation results and use them to manage the performance of the entire organization.

[0034] The Integrations Department incorporates the quantified results from the Quantification Department into the personnel system. For example, the Integrations Department can reflect the quantified results in the evaluation system. Specifically, it can conduct regular evaluations and provide feedback based on employee performance scores. The Integrations Department can also reflect the quantified results in the promotion system. For example, it can consider promotions or changes in job titles for employees who achieve a certain score. Furthermore, the Integrations Department can reflect the quantified results in the compensation system. For example, it can determine bonus and salary increase amounts based on performance scores. In this way, the Integrations Department can effectively incorporate quantified results into the personnel system, contributing to improved employee motivation and overall organizational performance. In addition, the Integrations Department can design individual career plans and training programs based on the quantified results. For example, it can provide appropriate training and education to employees who lack specific skills or abilities. In this way, the Integrations Department can support employee growth and improve the overall skill level of the organization.

[0035] The data collection unit can collect meeting minutes from communication tools. For example, it can collect meeting minutes from emails. It can also collect meeting minutes from chats. Furthermore, it can collect meeting minutes from video conferences. This allows for comprehensive collection of employee behavioral data by collecting meeting minutes from communication tools. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input meeting minutes from communication tools into an AI, which can then automatically collect the minutes.

[0036] The evaluation department can assess the speed of employees' advice and decision-making. For example, it can evaluate the technical advice given by employees. It can also evaluate the suggestions for business improvements made by employees. Furthermore, it can evaluate the time it takes for employees to make decisions. This allows for a concrete assessment of employees' contributions by evaluating the speed of their advice and decision-making. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can input the speed of employees' advice and decision-making into an AI, which can then perform the evaluation automatically.

[0037] The quantification unit can graph the evaluation results. For example, the quantification unit can display the evaluation results as a bar graph. It can also display the evaluation results as a line graph. Furthermore, it can display the evaluation results as a pie chart. In this way, the evaluation results can be presented in a visually easy-to-understand manner by graphing them. Some or all of the above processing in the quantification unit may be performed using AI, or it may be performed without AI. For example, the quantification unit can input the evaluation results into AI, and the AI ​​can automatically generate a graph.

[0038] The embedded system can incorporate the quantified results into the personnel system. For example, the embedded system can reflect the quantified results in the evaluation system. It can also reflect the quantified results in the promotion system. Furthermore, it can reflect the quantified results in the compensation system. In this way, by incorporating the quantified results into the personnel system, the contributions of employees can be reflected in their evaluations. Some or all of the above processing in the embedded system may be performed using AI or not. For example, the embedded system can input the quantified results into the AI, which can then automatically reflect them in the personnel system.

[0039] The evaluation department can assess whether an employee's actions contributed to raising the overall team performance. For example, it can assess whether an employee's actions contributed to increased sales. It can also assess whether an employee's actions contributed to cost reduction. Furthermore, it can assess whether an employee's actions contributed to improved customer satisfaction. By assessing whether an employee's actions contributed to raising the overall team performance, the evaluation department contributes to improving the overall team performance. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can input employee behavior data into an AI, which can then automatically perform the evaluation.

[0040] The data collection unit can analyze employees' past behavioral data and select the optimal collection method. For example, by collecting data during specific time periods based on past behavioral data, the data collection unit can obtain more accurate data. Furthermore, based on past behavioral data, the data collection unit can collect data during specific events or meetings. In addition, by analyzing past behavioral data, the data collection unit can prioritize the collection of data related to specific projects or tasks. This allows for the collection of more accurate data through the analysis of past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past behavioral data into an AI, which can then automatically select the optimal collection method.

[0041] The data collection unit can filter behavioral data based on employees' current projects and areas of interest. For example, it can collect only data related to the current project and exclude irrelevant data. It can also prioritize the collection of relevant data based on employees' areas of interest. Furthermore, it can dynamically filter the necessary data according to the progress of the project. This allows for the collection of highly relevant data by filtering data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee project data into an AI, which can then automatically perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees when collecting behavioral data. For example, if an employee is in a specific location, the data collection unit can prioritize the collection of data related to that location. The data collection unit can also collect data related to a specific region based on geographical location information. Furthermore, the data collection unit can analyze employees' movement patterns and collect highly relevant data. In this way, highly relevant data can be collected by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of employees into AI, and the AI ​​can automatically collect data.

[0043] The data collection unit can analyze employees' social media activities and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze employees' social media posts and collect relevant data. It can also collect data related to specific topics based on social media activity patterns. Furthermore, the data collection unit can analyze social media interactions and collect relevant data. This allows for the collection of relevant data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee social media data into an AI, which can then automatically collect the data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. Conversely, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically allocate analysis resources according to the importance of the data. This allows for efficient use of resources by adjusting the level of detail of the analysis based on the importance of the behavioral data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input behavioral data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a natural language processing algorithm to communication data. It can also apply a project management algorithm to project data. Furthermore, it can apply a social network analysis algorithm to social media data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the category of behavioral data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input behavioral data into an AI, which can then automatically apply an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the submission date of behavioral data during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically allocate analysis resources based on the submission date. This allows for the prioritization of analysis based on the submission date of behavioral data, thereby prioritizing the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input behavioral data into an AI, which can then automatically determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically allocate analysis resources based on the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the behavioral data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input behavioral data into AI, which can then automatically adjust the order of analysis.

[0048] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships of behavioral data during the evaluation process. For example, the evaluation unit can analyze the interrelationships of behavioral data to improve the accuracy of the evaluation. The evaluation unit can also prioritize the evaluation of data with strong interrelationships. Furthermore, the evaluation unit can dynamically allocate evaluation resources based on these interrelationships. This improves the accuracy of the evaluation by considering the interrelationships of behavioral data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input behavioral data into an AI, which can then automatically analyze the interrelationships and improve the accuracy of the evaluation.

[0049] The evaluation unit can consider the attribute information of the submitter of the behavioral data when conducting evaluations. For example, the evaluation unit can consider the submitter's job title and years of experience. It can also consider the submitter's area of ​​expertise and skill set. Furthermore, the evaluation unit can consider the submitter's past evaluation history. This allows for a fairer and more accurate evaluation by considering the submitter's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the submitter's attribute information into an AI, which can then automatically perform the evaluation.

[0050] The evaluation unit can perform evaluations while considering the geographical distribution of behavioral data. For example, the evaluation unit can integrate geographically dispersed data for evaluation. It can also prioritize the evaluation of data related to specific regions. Furthermore, the evaluation unit can dynamically allocate evaluation resources based on geographical distribution. This makes it possible to perform evaluations that reflect the characteristics of each region by considering geographical distribution. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input behavioral data into AI, and the AI ​​can automatically perform evaluations while considering geographical distribution.

[0051] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature on behavioral data during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature. The evaluation unit can also adjust the evaluation criteria based on the relevant literature. Furthermore, the evaluation unit can dynamically allocate evaluation resources based on the relevant literature. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input behavioral data into AI, which can then automatically refer to relevant literature and perform the evaluation.

[0052] The quantification unit can adjust the level of detail of the quantification based on the importance of the evaluation results. For example, the quantification unit can perform detailed quantification for evaluation results of high importance. It can also perform simplified quantification for evaluation results of low importance. Furthermore, the quantification unit can dynamically allocate quantification resources according to the importance of the evaluation results. This allows for efficient use of resources by adjusting the level of detail of the quantification based on the importance of the evaluation results. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input the evaluation results into AI, and the AI ​​can automatically adjust the level of detail of the quantification.

[0053] The quantification unit can apply different quantification algorithms depending on the category of the evaluation result during the quantification process. For example, the quantification unit can apply a specific quantification algorithm to communication data. It can also apply a different quantification algorithm to project data. Furthermore, it can apply yet another quantification algorithm to social media data. This improves the accuracy of the quantification by applying different quantification algorithms depending on the category of the evaluation result. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input the evaluation results into an AI, which can then automatically apply an appropriate quantification algorithm.

[0054] The quantification unit can adjust the order of quantification based on the submission date of the evaluation results. For example, the quantification unit can prioritize the quantification of the most recent evaluation results. It can also postpone the quantification of older evaluation results. Furthermore, the quantification unit can dynamically allocate quantification resources based on the submission date. This allows for the prioritization of the most recent quantified results by adjusting the order of quantification based on the submission date of the evaluation results. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input evaluation results into AI, which can then automatically adjust the order of quantification.

[0055] The quantification unit can adjust the order of quantification based on the relevance of the evaluation results during the quantification process. For example, the quantification unit can prioritize the quantification of highly relevant evaluation results. It can also postpone the quantification of less relevant evaluation results. Furthermore, the quantification unit can dynamically allocate quantification resources based on the relevance of the evaluation results. This allows for the priority provision of highly relevant quantification results by adjusting the order of quantification based on the relevance of the evaluation results. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input evaluation results into AI, which can then automatically adjust the order of quantification.

[0056] The embedded unit can adjust the level of detail of the embedded data based on the importance of the quantified results during the embedding process. For example, the embedded unit can perform detailed embedding for highly important quantified results. Conversely, it can perform simplified embedding for less important quantified results. Furthermore, the embedded unit can dynamically allocate embedding resources according to the importance of the quantified results. This allows for efficient resource utilization by adjusting the level of detail of the embedded data based on the importance of the quantified results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the quantified results into the AI, which can then automatically adjust the level of detail of the embedded data.

[0057] The embedded unit can apply different embedded algorithms depending on the category of the digitized results during the embedding process. For example, the embedded unit can apply a specific embedded algorithm to communication data. It can also apply a different embedded algorithm to project data. Furthermore, it can apply yet another embedded algorithm to social media data. This improves the accuracy of the embedding by applying different embedded algorithms depending on the category of the digitized results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the digitized results into an AI, which can then automatically apply the appropriate embedded algorithm.

[0058] The embedded unit can adjust the embedding order based on the submission timing of the digitized results during embedding. For example, the embedded unit can prioritize embedding the most recent digitized results. It can also postpone embedding older digitized results. Furthermore, the embedded unit can dynamically allocate embedding resources based on the submission timing. This allows for prioritizing the embedding of the most recent digitized results by adjusting the embedding order based on the submission timing of the digitized results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the digitized results into the AI, which can then automatically adjust the embedding order.

[0059] The embedded unit can adjust the embedding order based on the relevance of the digitized results during embedding. For example, the embedded unit can prioritize embedding highly relevant digitized results. It can also postpone embedding less relevant digitized results. Furthermore, the embedded unit can dynamically allocate embedding resources based on the relevance of the digitized results. This allows for prioritizing the embedding of highly relevant digitized results by adjusting the embedding order based on the relevance of the digitized results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the digitized results into AI, which can then automatically adjust the embedding order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can collect employee health data in addition to behavioral data. For example, it can collect data such as employee heart rate, steps taken, and sleep duration, and analyze this data in combination with behavioral data. This allows for the evaluation of the relationship between employee health status and work performance. Furthermore, the data collection unit can evaluate the impact of employee health status on work performance based on employee health data. In addition, the data collection unit can provide advice for health improvement based on employee health data. This enables evaluation that takes employee health status into account, and is expected to improve employee health management and work performance.

[0062] The analysis unit can refer to employees' past evaluation data when analyzing employee behavior data. For example, it can adjust the evaluation criteria for current behavior data based on past evaluation data. Furthermore, the analysis unit can assess an employee's growth based on past evaluation data. In addition, the analysis unit can predict employee behavior patterns and future behavior based on past evaluation data. This allows for more accurate evaluations by utilizing past evaluation data, thereby promoting employee growth.

[0063] The data collection unit can collect employee behavioral data while considering the employee's skill set. For example, it can prioritize the collection of behavioral data from employees with specific skills and evaluate the impact of those skills on their work. Furthermore, the data collection unit can collect data related to specific projects or tasks based on the employee's skill set. In addition, the data collection unit can collect data that helps improve skills based on the employee's skill set. This enables data collection that considers the employee's skill set, leading to expected improvements in skills and work performance.

[0064] The analytics department can analyze employee behavioral data while considering employees' career goals. For example, it can prioritize the analysis of specific behavioral data based on an employee's career goals and provide advice to help them achieve those goals. Furthermore, the analytics department can identify necessary skills and experience based on employees' career goals and recommend actions to acquire them. In addition, based on employees' career goals, the analytics department can predict future career paths and propose appropriate career plans. This enables analysis that considers employees' career goals, leading to expected employee growth and career development.

[0065] The performance evaluation department can assess an employee's contribution to teamwork when evaluating employee behavioral data. For example, it can evaluate the contribution of an employee when they collaborate with team members on a project. The department can also evaluate the effectiveness of support and advice provided by an employee to team members. Furthermore, it can assess the impact of employee teamwork on the project's success. By evaluating the contribution of employee teamwork, the department can contribute to improving the overall performance of the team.

[0066] The quantification unit can quantify evaluation results while taking into account the progress of employees' projects. For example, it can prioritize the quantification of specific evaluation results based on project progress and provide advice for project success. Furthermore, the quantification unit can identify and recommend necessary resources and support based on project progress. It can also predict the probability of future project success based on project progress and propose appropriate countermeasures. This enables quantification that takes project progress into account, contributing to project success.

[0067] The integration unit can incorporate quantified results into the personnel system while considering the career stage of each employee. For example, it can integrate opportunities for growth for employees in the early stages of their careers. It can also integrate opportunities for leadership for employees in the middle stages of their careers. Furthermore, it can integrate opportunities for sharing expertise and mentorship for employees in the later stages of their careers. This enables integration that takes into account the career stage of each employee, and is expected to promote employee growth and career development.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection department collects employee behavioral data. The data collection department can collect behavioral data such as employees' working hours, project progress, and communication frequency. The data collection department can also collect meeting minutes from communication tools. For example, the data collection department can collect minutes from emails, chats, and video conferences. Furthermore, the data collection department can collect employees' social media activity. For example, the data collection department can analyze employees' social media posts and collect relevant data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, natural language processing technology. It can also analyze the collected data using machine learning algorithms. Furthermore, the analysis unit can analyze the collected data using data mining technology. Step 3: The evaluation department assesses the effectiveness of employee actions based on the data analyzed by the analysis department. For example, the evaluation department can evaluate the speed of employee advice and decision-making. It can also evaluate whether employee actions contributed to raising the overall team performance. Furthermore, the evaluation department can assess the impact of employee actions on the overall organizational performance. Step 4: The quantification unit quantifies the evaluation results obtained by the evaluation unit. The quantification unit can, for example, quantify the evaluation results based on a scoring method. The quantification unit can also graph the evaluation results. Furthermore, the quantification unit can also quantify the evaluation results based on percentiles. Step 5: The Integration Department integrates the quantified results from the Quantification Department into the personnel system. For example, the Integration Department can reflect the quantified results in the performance evaluation system. It can also reflect the quantified results in the promotion system. Furthermore, the Integration Department can reflect the quantified results in the compensation system.

[0070] (Example of form 2) The PIPM system according to an embodiment of the present invention is a system that evaluates employee behavior and improves the overall performance of the organization. The PIPM system collects employee behavior data, performs statistical analysis, and evaluates the effectiveness of employee behavior. For example, actions such as taking on the account of an employee who has suddenly resigned to reduce the burden on other members, or providing advice through communication tools, are evaluated. Based on these evaluation results, the employee's contribution is quantified as PIPM and incorporated into the personnel system. This allows employee behaviors that have previously been overlooked to be evaluated, leading to increased motivation and more active communication. For example, the PIPM system collects employee behavior data. In this process, detailed information about each employee's actions, such as meeting minutes from communication tools, is collected. For example, advice and comments on communication tools, and conversations with franchisees are collected. This ensures that employee behavior data is comprehensively collected. Next, the collected data is statistically analyzed by AI. The AI ​​analyzes the collected data and evaluates the effectiveness of employee behavior. For example, actions such as taking on the account of an employee who has suddenly resigned to reduce the burden on other members, or providing advice through communication tools, are evaluated. This system quantifies qualitative aspects that cannot be expressed solely through numbers. Furthermore, the AI ​​uses the evaluation results to quantify employee contributions as PIPM (Performance, Performance, and Productivity). For example, the speed of advice and decision-making provided by employees, as well as the strengthening of relationships with franchisees, are quantified as PIPM. This clearly demonstrates the level of employee contribution. Finally, the quantified PIPM is incorporated into the personnel system. This allows employee actions that were previously overlooked to be evaluated, leading to increased motivation and improved communication. For example, if an employee's actions contribute to raising the overall team's performance figures, that evaluation is reflected in the PIPM. This improves employee motivation and enhances the overall performance of the organization. Through this system, employee actions are evaluated, leading to improved overall organizational performance. For example, the speed of advice and decision-making provided by employees, as well as the strengthening of relationships with franchisees, are evaluated, leading to increased employee motivation and improved overall organizational performance.Furthermore, by specifically evaluating employee behavior, communication is stimulated, fostering a better organizational culture. This can lead to improved sales and profits across the entire organization. In short, the PIPM system can evaluate employee behavior and improve overall organizational performance.

[0071] The PIPM system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, a quantification unit, and an embedding unit. The collection unit collects employee behavioral data. The collection unit can collect behavioral data such as employee working hours, project progress, and communication frequency. The collection unit can also collect meeting minutes from communication tools. For example, the collection unit can collect minutes from emails, chats, video conferences, etc. Furthermore, the collection unit can also collect employees' social media activity. For example, the collection unit can analyze employees' social media posts and collect relevant data. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, natural language processing technology. The analysis unit can also analyze the collected data using machine learning algorithms. Furthermore, the analysis unit can also analyze the collected data using data mining technology. The evaluation unit evaluates the effectiveness of employee behavior based on the data analyzed by the analysis unit. The evaluation unit can evaluate, for example, the speed of employee advice and decision-making. Furthermore, the evaluation unit can assess whether an employee's actions contributed to raising the overall team performance. The evaluation unit can also assess the impact of an employee's actions on the overall organizational performance. The quantification unit quantifies the evaluation results obtained by the evaluation unit. For example, the quantification unit can quantify the evaluation results based on a scoring method. The quantification unit can also graph the evaluation results. Furthermore, the quantification unit can quantify the evaluation results based on percentiles. The integration unit integrates the quantified results from the quantification unit into the personnel system. For example, the integration unit can reflect the quantified results in the evaluation system. The integration unit can also reflect the quantified results in the promotion system. Furthermore, the integration unit can reflect the quantified results in the compensation system. As a result, the PIPM system according to this embodiment can evaluate employee actions and improve the overall organizational performance.

[0072] The data collection department collects employee behavioral data. For example, it can collect behavioral data such as employee working hours, project progress, and communication frequency. Specifically, it automatically retrieves task completion status and progress reports from the work management tools and project management software used by employees. It also collects data from time card systems that record employee attendance and break times. Furthermore, the data collection department can collect meeting minutes from communication tools. For example, it collects minutes from emails, chats, and video conferences. This involves analyzing logs from email servers and chat applications to extract conversation content and frequency. Video conference minutes are obtained by transcribing meeting recordings using speech recognition technology and extracting important statements and decisions. Additionally, the data collection department can collect employee social media activity. For example, it analyzes employee social media posts and collects relevant data. This includes scraping content from employees' public social media accounts and performing sentiment analysis and topic modeling using natural language processing techniques. This allows the data collection unit to gather a wide range of data related to employees' work and provide a foundation for conducting detailed behavioral analysis.

[0073] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can analyze the collected data using natural language processing technology. Specifically, it can analyze the content of emails and chats to understand communication patterns and frequency among employees. By using natural language processing technology, it can extract emotions and intentions from text data and evaluate employee motivation and stress levels. The analysis department can also analyze the collected data using machine learning algorithms. For example, it can analyze project progress data to identify causes of delays and risk factors. By using machine learning algorithms, it can learn patterns from past data and predict the probability of future project success. Furthermore, the analysis department can analyze the collected data using data mining technology. By using data mining technology, it can discover useful information and hidden patterns from large amounts of data, gaining insights into employee behavior and performance. For example, it can analyze employee work hours and project progress data to identify efficient work methods and areas for improvement. This allows the analysis department to analyze the collected data from multiple perspectives and provide detailed insights into employee behavior and performance.

[0074] The evaluation department assesses the effectiveness of employee actions based on data analyzed by the analysis department. For example, the evaluation department can evaluate the speed of employee advice and decision-making. Specifically, it can evaluate the quality of advice provided by employees and how much that advice contributed to the success of a project. Regarding the speed of decision-making, it can evaluate how quickly employees solved problems and made appropriate judgments. The evaluation department can also evaluate whether employee actions contributed to raising the overall performance of the team. For example, it can evaluate how much an employee's actions affected the team's productivity and efficiency. Furthermore, the evaluation department can evaluate the impact of employee actions on the overall performance of the organization. For example, it can evaluate how much an employee's actions contributed to achieving the organization's overall goals. This includes evaluating how well an employee's actions align with the organization's strategic goals and vision. Based on these evaluation results, the evaluation department can identify employees' strengths and areas for improvement and provide individualized feedback. In this way, the evaluation department can comprehensively evaluate the effectiveness of employee actions and contribute to improving the overall performance of the organization.

[0075] The quantification unit quantifies the evaluation results obtained by the evaluation unit. For example, the quantification unit can quantify evaluation results based on a scoring method. Specifically, it can set certain standards for employee behavior and performance and assign scores based on those standards. For example, it can assign a 5-point scale or a 10-point scale to the quality of advice or the speed of decision-making. The quantification unit can also graph the evaluation results. This includes creating bar graphs and line graphs to visually compare employee performance. Furthermore, the quantification unit can quantify evaluation results based on percentiles. For example, it can calculate percentile ranks to show where an employee's performance stands in relation to the overall performance. This allows the quantification unit to express evaluation results objectively and visually, and to clearly understand employee performance. In addition, the quantification unit can store these quantified results in a database and use them for later analysis and reporting. This allows the quantification unit to effectively quantify evaluation results and use them to manage the performance of the entire organization.

[0076] The Integrations Department incorporates the quantified results from the Quantification Department into the personnel system. For example, the Integrations Department can reflect the quantified results in the evaluation system. Specifically, it can conduct regular evaluations and provide feedback based on employee performance scores. The Integrations Department can also reflect the quantified results in the promotion system. For example, it can consider promotions or changes in job titles for employees who achieve a certain score. Furthermore, the Integrations Department can reflect the quantified results in the compensation system. For example, it can determine bonus and salary increase amounts based on performance scores. In this way, the Integrations Department can effectively incorporate quantified results into the personnel system, contributing to improved employee motivation and overall organizational performance. In addition, the Integrations Department can design individual career plans and training programs based on the quantified results. For example, it can provide appropriate training and education to employees who lack specific skills or abilities. In this way, the Integrations Department can support employee growth and improve the overall skill level of the organization.

[0077] The data collection unit can collect meeting minutes from communication tools. For example, it can collect meeting minutes from emails. It can also collect meeting minutes from chats. Furthermore, it can collect meeting minutes from video conferences. This allows for comprehensive collection of employee behavioral data by collecting meeting minutes from communication tools. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input meeting minutes from communication tools into an AI, which can then automatically collect the minutes.

[0078] The evaluation department can assess the speed of employees' advice and decision-making. For example, it can evaluate the technical advice given by employees. It can also evaluate the suggestions for business improvements made by employees. Furthermore, it can evaluate the time it takes for employees to make decisions. This allows for a concrete assessment of employees' contributions by evaluating the speed of their advice and decision-making. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can input the speed of employees' advice and decision-making into an AI, which can then perform the evaluation automatically.

[0079] The quantification unit can graph the evaluation results. For example, the quantification unit can display the evaluation results as a bar graph. It can also display the evaluation results as a line graph. Furthermore, it can display the evaluation results as a pie chart. In this way, the evaluation results can be presented in a visually easy-to-understand manner by graphing them. Some or all of the above processing in the quantification unit may be performed using AI, or it may be performed without AI. For example, the quantification unit can input the evaluation results into AI, and the AI ​​can automatically generate a graph.

[0080] The embedded system can incorporate the quantified results into the personnel system. For example, the embedded system can reflect the quantified results in the evaluation system. It can also reflect the quantified results in the promotion system. Furthermore, it can reflect the quantified results in the compensation system. In this way, by incorporating the quantified results into the personnel system, the contributions of employees can be reflected in their evaluations. Some or all of the above processing in the embedded system may be performed using AI or not. For example, the embedded system can input the quantified results into the AI, which can then automatically reflect them in the personnel system.

[0081] The evaluation department can assess whether an employee's actions contributed to raising the overall team performance. For example, it can assess whether an employee's actions contributed to increased sales. It can also assess whether an employee's actions contributed to cost reduction. Furthermore, it can assess whether an employee's actions contributed to improved customer satisfaction. By assessing whether an employee's actions contributed to raising the overall team performance, the evaluation department contributes to improving the overall team performance. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can input employee behavior data into an AI, which can then automatically perform the evaluation.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. Conversely, if the user is relaxed, the data collection unit can advance the collection timing to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can optimize the collection timing to quickly collect only the necessary data. This reduces the user's burden and allows for the collection of detailed data by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then automatically adjust the collection timing.

[0083] The data collection unit can analyze employees' past behavioral data and select the optimal collection method. For example, by collecting data during specific time periods based on past behavioral data, the data collection unit can obtain more accurate data. Furthermore, based on past behavioral data, the data collection unit can collect data during specific events or meetings. In addition, by analyzing past behavioral data, the data collection unit can prioritize the collection of data related to specific projects or tasks. This allows for the collection of more accurate data through the analysis of past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past behavioral data into an AI, which can then automatically select the optimal collection method.

[0084] The data collection unit can filter behavioral data based on employees' current projects and areas of interest. For example, it can collect only data related to the current project and exclude irrelevant data. It can also prioritize the collection of relevant data based on employees' areas of interest. Furthermore, it can dynamically filter the necessary data according to the progress of the project. This allows for the collection of highly relevant data by filtering data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee project data into an AI, which can then automatically perform the filtering.

[0085] The data collection unit can estimate the user's emotions and prioritize the behavioral data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, important data can be prioritized by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then automatically determine the data priority.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees when collecting behavioral data. For example, if an employee is in a specific location, the data collection unit can prioritize the collection of data related to that location. The data collection unit can also collect data related to a specific region based on geographical location information. Furthermore, the data collection unit can analyze employees' movement patterns and collect highly relevant data. In this way, highly relevant data can be collected by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of employees into AI, and the AI ​​can automatically collect data.

[0087] The data collection unit can analyze employees' social media activities and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze employees' social media posts and collect relevant data. It can also collect data related to specific topics based on social media activity patterns. Furthermore, the data collection unit can analyze social media interactions and collect relevant data. This allows for the collection of relevant data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee social media data into an AI, which can then automatically collect the data.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be made easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can automatically adjust the presentation of the analysis.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. Conversely, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically allocate analysis resources according to the importance of the data. This allows for efficient use of resources by adjusting the level of detail of the analysis based on the importance of the behavioral data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input behavioral data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a natural language processing algorithm to communication data. It can also apply a project management algorithm to project data. Furthermore, it can apply a social network analysis algorithm to social media data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the category of behavioral data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input behavioral data into an AI, which can then automatically apply an appropriate analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can then automatically adjust the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the submission date of behavioral data during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically allocate analysis resources based on the submission date. This allows for the prioritization of analysis based on the submission date of behavioral data, thereby prioritizing the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input behavioral data into an AI, which can then automatically determine the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically allocate analysis resources based on the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the behavioral data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input behavioral data into AI, which can then automatically adjust the order of analysis.

[0094] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide simple and easy-to-understand evaluation criteria. If the user is relaxed, the evaluation unit can provide detailed evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can provide concise evaluation criteria. In this way, by adjusting the evaluation criteria based on the user's emotions, the evaluation unit can provide evaluation criteria that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into AI, and the AI ​​can automatically adjust the evaluation criteria.

[0095] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships of behavioral data during the evaluation process. For example, the evaluation unit can analyze the interrelationships of behavioral data to improve the accuracy of the evaluation. The evaluation unit can also prioritize the evaluation of data with strong interrelationships. Furthermore, the evaluation unit can dynamically allocate evaluation resources based on these interrelationships. This improves the accuracy of the evaluation by considering the interrelationships of behavioral data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input behavioral data into an AI, which can then automatically analyze the interrelationships and improve the accuracy of the evaluation.

[0096] The evaluation unit can consider the attribute information of the submitter of the behavioral data when conducting evaluations. For example, the evaluation unit can consider the submitter's job title and years of experience. It can also consider the submitter's area of ​​expertise and skill set. Furthermore, the evaluation unit can consider the submitter's past evaluation history. This allows for a fairer and more accurate evaluation by considering the submitter's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the submitter's attribute information into an AI, which can then automatically perform the evaluation.

[0097] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated emotions. For example, if the user is nervous, the evaluation unit can display important evaluation results first. If the user is relaxed, the evaluation unit can also display detailed evaluation results sequentially. Furthermore, if the user is in a hurry, the evaluation unit can display concise evaluation results first. In this way, by adjusting the display order of evaluation results based on the user's emotions, the evaluation results can be provided to the user in the most optimal order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into AI, and the AI ​​can automatically adjust the display order of the evaluation results.

[0098] The evaluation unit can perform evaluations while considering the geographical distribution of behavioral data. For example, the evaluation unit can integrate geographically dispersed data for evaluation. It can also prioritize the evaluation of data related to specific regions. Furthermore, the evaluation unit can dynamically allocate evaluation resources based on geographical distribution. This makes it possible to perform evaluations that reflect the characteristics of each region by considering geographical distribution. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input behavioral data into AI, and the AI ​​can automatically perform evaluations while considering geographical distribution.

[0099] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature on behavioral data during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature. The evaluation unit can also adjust the evaluation criteria based on the relevant literature. Furthermore, the evaluation unit can dynamically allocate evaluation resources based on the relevant literature. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input behavioral data into AI, which can then automatically refer to relevant literature and perform the evaluation.

[0100] The quantification unit can estimate the user's emotions and adjust the quantification method based on the estimated emotions. For example, if the user is nervous, the quantification unit can provide a simple and highly visual quantification method. If the user is relaxed, the quantification unit can provide a more detailed quantification method. Furthermore, if the user is in a hurry, the quantification unit can provide a concise quantification method. By adjusting the quantification method based on the user's emotions, the system can provide quantification results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input user emotion data into an AI, which can then automatically adjust the quantification method.

[0101] The quantification unit can adjust the level of detail of the quantification based on the importance of the evaluation results. For example, the quantification unit can perform detailed quantification for evaluation results of high importance. It can also perform simplified quantification for evaluation results of low importance. Furthermore, the quantification unit can dynamically allocate quantification resources according to the importance of the evaluation results. This allows for efficient use of resources by adjusting the level of detail of the quantification based on the importance of the evaluation results. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input the evaluation results into AI, and the AI ​​can automatically adjust the level of detail of the quantification.

[0102] The quantification unit can apply different quantification algorithms depending on the category of the evaluation result during the quantification process. For example, the quantification unit can apply a specific quantification algorithm to communication data. It can also apply a different quantification algorithm to project data. Furthermore, it can apply yet another quantification algorithm to social media data. This improves the accuracy of the quantification by applying different quantification algorithms depending on the category of the evaluation result. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input the evaluation results into an AI, which can then automatically apply an appropriate quantification algorithm.

[0103] The quantification unit can estimate the user's emotions and determine the priority of quantification based on the estimated emotions. For example, if the user is nervous, the quantification unit can prioritize quantifying important evaluation results. If the user is relaxed, the quantification unit can prioritize quantifying detailed evaluation results. Furthermore, if the user is in a hurry, the quantification unit can prioritize quantifying evaluation results that can be quantified quickly. In this way, by determining the priority of quantification based on the user's emotions, important quantification results can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input user emotion data into an AI, and the AI ​​can automatically determine the priority of quantification.

[0104] The quantification unit can adjust the order of quantification based on the submission date of the evaluation results. For example, the quantification unit can prioritize the quantification of the most recent evaluation results. It can also postpone the quantification of older evaluation results. Furthermore, the quantification unit can dynamically allocate quantification resources based on the submission date. This allows for the prioritization of the most recent quantified results by adjusting the order of quantification based on the submission date of the evaluation results. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input evaluation results into AI, which can then automatically adjust the order of quantification.

[0105] The quantification unit can adjust the order of quantification based on the relevance of the evaluation results during the quantification process. For example, the quantification unit can prioritize the quantification of highly relevant evaluation results. It can also postpone the quantification of less relevant evaluation results. Furthermore, the quantification unit can dynamically allocate quantification resources based on the relevance of the evaluation results. This allows for the priority provision of highly relevant quantification results by adjusting the order of quantification based on the relevance of the evaluation results. Some or all of the above processing in the quantification unit may be performed using AI or not. For example, the quantification unit can input evaluation results into AI, which can then automatically adjust the order of quantification.

[0106] The embedded unit can estimate the user's emotions and adjust the embedded method based on the estimated emotions. For example, if the user is tense, the embedded unit can provide a simple and highly visible embedded method. If the user is relaxed, the embedded unit can provide a detailed embedded method. Furthermore, if the user is in a hurry, the embedded unit can provide a concise embedded method. In this way, by adjusting the embedded method based on the user's emotions, an easy-to-understand embedded method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input user emotion data into the AI, and the AI ​​can automatically adjust the embedded method.

[0107] The embedded unit can adjust the level of detail of the embedded data based on the importance of the quantified results during the embedding process. For example, the embedded unit can perform detailed embedding for highly important quantified results. Conversely, it can perform simplified embedding for less important quantified results. Furthermore, the embedded unit can dynamically allocate embedding resources according to the importance of the quantified results. This allows for efficient resource utilization by adjusting the level of detail of the embedded data based on the importance of the quantified results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the quantified results into the AI, which can then automatically adjust the level of detail of the embedded data.

[0108] The embedded unit can apply different embedded algorithms depending on the category of the digitized results during the embedding process. For example, the embedded unit can apply a specific embedded algorithm to communication data. It can also apply a different embedded algorithm to project data. Furthermore, it can apply yet another embedded algorithm to social media data. This improves the accuracy of the embedding by applying different embedded algorithms depending on the category of the digitized results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the digitized results into an AI, which can then automatically apply the appropriate embedded algorithm.

[0109] The embedded unit can estimate the user's emotions and determine the priority of embedding based on the estimated user emotions. For example, if the user is tense, the embedded unit can prioritize embedding important quantified results. Similarly, if the user is relaxed, the embedded unit can prioritize embedding detailed quantified results. Furthermore, if the user is in a hurry, the embedded unit can prioritize embedding quantified results that can be quickly embedded. This allows for the prioritization of important quantified results by determining the embedding priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input user emotion data into an AI, which can then automatically determine the embedding priority.

[0110] The embedded unit can adjust the embedding order based on the submission timing of the digitized results during embedding. For example, the embedded unit can prioritize embedding the most recent digitized results. It can also postpone embedding older digitized results. Furthermore, the embedded unit can dynamically allocate embedding resources based on the submission timing. This allows for prioritizing the embedding of the most recent digitized results by adjusting the embedding order based on the submission timing of the digitized results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the digitized results into the AI, which can then automatically adjust the embedding order.

[0111] The embedded unit can adjust the embedding order based on the relevance of the digitized results during embedding. For example, the embedded unit can prioritize embedding highly relevant digitized results. It can also postpone embedding less relevant digitized results. Furthermore, the embedded unit can dynamically allocate embedding resources based on the relevance of the digitized results. This allows for prioritizing the embedding of highly relevant digitized results by adjusting the embedding order based on the relevance of the digitized results. Some or all of the above processing in the embedded unit may be performed using AI or not. For example, the embedded unit can input the digitized results into AI, which can then automatically adjust the embedding order.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can collect employee health data in addition to behavioral data. For example, it can collect data such as employee heart rate, steps taken, and sleep duration, and analyze this data in combination with behavioral data. This allows for the evaluation of the relationship between employee health status and work performance. Furthermore, the data collection unit can evaluate the impact of employee health status on work performance based on employee health data. In addition, the data collection unit can provide advice for health improvement based on employee health data. This enables evaluation that takes employee health status into account, and is expected to improve employee health management and work performance.

[0114] The analysis unit can refer to employees' past evaluation data when analyzing employee behavior data. For example, it can adjust the evaluation criteria for current behavior data based on past evaluation data. Furthermore, the analysis unit can assess an employee's growth based on past evaluation data. In addition, the analysis unit can predict employee behavior patterns and future behavior based on past evaluation data. This allows for more accurate evaluations by utilizing past evaluation data, thereby promoting employee growth.

[0115] The evaluation department can estimate employees' emotions when evaluating their behavioral data and adjust evaluation criteria based on those estimated emotions. For example, if an employee is stressed, the evaluation criteria can be relaxed to reduce their burden. Conversely, if an employee is relaxed, the evaluation criteria can be made stricter to demand higher performance. Furthermore, if an employee is highly motivated, the evaluation criteria can be adjusted to maintain that motivation. In this way, by adjusting evaluation criteria based on employees' emotions, it is possible to maximize employee performance.

[0116] The quantification unit can estimate employees' emotions when quantifying evaluation results and adjust the quantification method based on those estimated emotions. For example, if an employee is nervous, it can provide a simple and highly visual quantification method. If an employee is relaxed, it can provide a more detailed quantification method. Furthermore, if an employee is in a hurry, it can provide a concise quantification method. By adjusting the quantification method based on employees' emotions, it is possible to provide quantification results that are easy for employees to understand.

[0117] The integration unit can estimate employee emotions when incorporating quantified results into the personnel system and adjust the integration method based on those emotions. For example, if an employee is stressed, it can provide a simple and highly visual integration method. If an employee is relaxed, it can provide a more detailed integration method. Furthermore, if an employee is in a hurry, it can provide a concise integration method. By adjusting the integration method based on employee emotions, it can provide an integration method that is easy for employees to understand.

[0118] The data collection unit can collect employee behavioral data while considering the employee's skill set. For example, it can prioritize the collection of behavioral data from employees with specific skills and evaluate the impact of those skills on their work. Furthermore, the data collection unit can collect data related to specific projects or tasks based on the employee's skill set. In addition, the data collection unit can collect data that helps improve skills based on the employee's skill set. This enables data collection that considers the employee's skill set, leading to expected improvements in skills and work performance.

[0119] The analytics department can analyze employee behavioral data while considering employees' career goals. For example, it can prioritize the analysis of specific behavioral data based on an employee's career goals and provide advice to help them achieve those goals. Furthermore, the analytics department can identify necessary skills and experience based on employees' career goals and recommend actions to acquire them. In addition, based on employees' career goals, the analytics department can predict future career paths and propose appropriate career plans. This enables analysis that considers employees' career goals, leading to expected employee growth and career development.

[0120] The performance evaluation department can assess an employee's contribution to teamwork when evaluating employee behavioral data. For example, it can evaluate the contribution of an employee when they collaborate with team members on a project. The department can also evaluate the effectiveness of support and advice provided by an employee to team members. Furthermore, it can assess the impact of employee teamwork on the project's success. By evaluating the contribution of employee teamwork, the department can contribute to improving the overall performance of the team.

[0121] The quantification unit can quantify evaluation results while taking into account the progress of employees' projects. For example, it can prioritize the quantification of specific evaluation results based on project progress and provide advice for project success. Furthermore, the quantification unit can identify and recommend necessary resources and support based on project progress. It can also predict the probability of future project success based on project progress and propose appropriate countermeasures. This enables quantification that takes project progress into account, contributing to project success.

[0122] The integration unit can incorporate quantified results into the personnel system while considering the career stage of each employee. For example, it can integrate opportunities for growth for employees in the early stages of their careers. It can also integrate opportunities for leadership for employees in the middle stages of their careers. Furthermore, it can integrate opportunities for sharing expertise and mentorship for employees in the later stages of their careers. This enables integration that takes into account the career stage of each employee, and is expected to promote employee growth and career development.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection department collects employee behavioral data. The data collection department can collect behavioral data such as employees' working hours, project progress, and communication frequency. The data collection department can also collect meeting minutes from communication tools. For example, the data collection department can collect minutes from emails, chats, and video conferences. Furthermore, the data collection department can collect employees' social media activity. For example, the data collection department can analyze employees' social media posts and collect relevant data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, natural language processing technology. It can also analyze the collected data using machine learning algorithms. Furthermore, the analysis unit can analyze the collected data using data mining technology. Step 3: The evaluation department assesses the effectiveness of employee actions based on the data analyzed by the analysis department. For example, the evaluation department can evaluate the speed of employee advice and decision-making. It can also evaluate whether employee actions contributed to raising the overall team performance. Furthermore, the evaluation department can assess the impact of employee actions on the overall organizational performance. Step 4: The quantification unit quantifies the evaluation results obtained by the evaluation unit. The quantification unit can, for example, quantify the evaluation results based on a scoring method. The quantification unit can also graph the evaluation results. Furthermore, the quantification unit can also quantify the evaluation results based on percentiles. Step 5: The Integration Department integrates the quantified results from the Quantification Department into the personnel system. For example, the Integration Department can reflect the quantified results in the performance evaluation system. It can also reflect the quantified results in the promotion system. Furthermore, the Integration Department can reflect the quantified results in the compensation system.

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, quantification unit, and integration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects employee behavior data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of employee behavior based on the analysis results. The quantification unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the evaluation results. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and incorporates the quantified results into the personnel system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, quantification unit, and integration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee behavior data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of employee behavior based on the analysis results. The quantification unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the evaluation results. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and incorporates the quantified results into the personnel system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, quantification unit, and integration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects employee behavior data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of employee behavior based on the analysis results. The quantification unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the evaluation results. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and incorporates the quantified results into the personnel system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, quantification unit, and integration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects employee behavior data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of employee behavior based on the analysis results. The quantification unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the evaluation results. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and incorporates the quantified results into the personnel system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The data collection department collects employee behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates the effectiveness of employee actions based on the data analyzed by the aforementioned analysis unit, A digitization unit that quantifies the evaluation results obtained by the evaluation unit, The system includes an integration unit that incorporates the results quantified by the quantification unit into the personnel system. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect meeting minutes from communication tools. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, We evaluate the speed of employee advice and decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 4) The digitization unit is, Graph the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned integrated part is, Incorporate quantified results into the personnel system. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, Evaluate whether an employee's actions contributed to raising the overall team performance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral data, filter it based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering employees' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the behavioral data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When evaluating, consider the interrelationships between behavioral data to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation, the attribute information of the person submitting the behavioral data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation, the geographical distribution of behavioral data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During evaluation, we refer to relevant literature on behavioral data to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The digitization unit is, It estimates the user's emotions and adjusts the quantification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The digitization unit is, When quantifying, adjust the level of detail based on the importance of the evaluation result. The system described in Appendix 1, characterized by the features described herein. (Note 27) The digitization unit is, When quantifying the results, different quantification algorithms are applied depending on the category of the evaluation result. The system described in Appendix 1, characterized by the features described herein. (Note 28) The digitization unit is, The system estimates the user's emotions and determines the priority of quantification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The digitization unit is, When quantifying the results, the order of quantification will be adjusted based on the submission date of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 30) The digitization unit is, When quantifying the results, adjust the order of quantification based on the relevance of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned integrated part is, We estimate the user's emotions and adjust the implementation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned integrated part is, During implementation, adjust the level of detail based on the importance of the quantified results. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned integrated part is, During implementation, different built-in algorithms are applied depending on the category of the quantified result. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned integrated part is, It estimates the user's emotions and determines built-in priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned integrated part is, During integration, the integration order is adjusted based on when the quantified results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned integrated part is, During integration, the integration order is adjusted based on the relevance of the quantified results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects employee behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates the effectiveness of employee actions based on the data analyzed by the aforementioned analysis unit, A digitization unit that quantifies the evaluation results obtained by the evaluation unit, The system includes an integration unit that incorporates the results quantified by the quantification unit into the personnel system. A system characterized by the following features.

2. The aforementioned collection unit is Collect meeting minutes from communication tools. The system according to feature 1.

3. The evaluation unit described above, We evaluate the speed of employee advice and decision-making. The system according to feature 1.

4. The digitization unit is, Graph the evaluation results. The system according to feature 1.

5. The aforementioned integrated part is, Incorporate quantified results into the personnel system. The system according to feature 1.

6. The evaluation unit described above, Evaluate whether an employee's actions contributed to raising the overall team performance. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze employees' past behavioral data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting behavioral data, filter it based on the employee's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system according to feature 1.

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