system

Abstract

The system according to this embodiment aims to improve the fairness and transparency of personnel evaluations. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a feedback unit. The data collection unit collects employee work data. The analysis unit analyzes the data collected by the data collection unit. The feedback unit provides visual feedback based on the results analyzed by the analysis unit.

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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, the fairness and transparency of personnel evaluations are not sufficiently ensured, and there is a risk of evaluation bias.

[0005] The system according to the embodiment aims to improve the fairness and transparency of personnel evaluations.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects business data of employees. The analysis unit analyzes the data collected by the collection unit. The feedback unit provides visual feedback based on the results analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can improve the fairness and transparency of personnel evaluations. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 ......

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

[0020] It should be noted that there seems to be an incomplete sentence in the translation of line . Please check and correct it if necessary.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 Muscle World Evaluation System Muscle Brain, according to an embodiment of the present invention, is a system that utilizes generative AI to improve the fairness and transparency of personnel evaluations. This Muscle World Evaluation System Muscle Brain analyzes employee work data and provides visual feedback by likening achievements and skills to muscles, thereby eliminating evaluation bias and improving employee motivation. For example, for managers, the evaluation criteria become clearer, and evaluation errors are reduced through quantification and visualization, while for subordinates, evaluation gaps are eliminated. Furthermore, the company as a whole can expect improved employee motivation and improved performance. In addition, this system is targeted at companies ranging from small and medium-sized enterprises with 100 or more employees to large corporations, with IT companies and startups, in particular, being the main customers, as they are companies that are highly compatible with data-driven evaluations. This allows the system to provide an effective solution to companies that seek fair and transparent personnel evaluations. Thus, the Muscle World Evaluation System Muscle Brain can achieve fair and transparent personnel evaluations by collecting employee work data, having generative AI analyze that data, and providing visual feedback.

[0029] The Muscle World Evaluation System Muscle Brain according to this embodiment comprises a data collection unit, an analysis unit, and a feedback unit. The data collection unit collects employee work data. The data collection unit can collect data such as emails, chats, and project management tool logs. The data collection unit can also collect data such as the number of tasks completed by employees, the progress of projects, and the frequency of communication. For example, the data collection unit records the number of tasks completed by employees, tracks the progress of projects, and saves the frequency of communication as a log. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to evaluate individual outputs and communication abilities based on the employee's work data. For example, the analysis unit evaluates employees with a high number of completed tasks as "barbell curl 75kg" and employees with high project progress as "bench press 100kg". The feedback unit provides visual feedback based on the results analyzed by the analysis unit. The feedback unit uses generative AI to provide employees with feedback using muscle metaphors. For example, the feedback unit might give feedback like "Nice biceps!" to an employee with a high number of completed tasks, and "Your pectoral muscles are walking!" to an employee with a high project progress. In this way, the Muscle Brain muscle world evaluation system according to this embodiment can collect and analyze employee work data and provide visual feedback, enabling fair and transparent personnel evaluations.

[0030] The data collection unit collects employee work data. This data can include, for example, email, chat, and project management tool logs. Specifically, it includes email sending and receiving history, chat message content, and task progress and completion reports from project management tools. This data provides a detailed record of employee work activities and serves as foundational data for evaluating work efficiency and communication quality. The data collection unit can also collect data such as the number of tasks completed by employees, project progress, and communication frequency. For example, it can record the number of tasks completed by employees, track project progress, and save communication frequency as logs. This allows the data collection unit to gain a multifaceted understanding of employee work activities and provide detailed data. Furthermore, the data collection unit can centrally manage this data and integrate it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and feedback units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Using generative AI, the analysis unit evaluates individual employee output and communication skills based on their work data. Specifically, the generative AI uses natural language processing technology to analyze email and chat content to evaluate employees' communication skills and collaborative attitudes. It also analyzes data from project management tools to evaluate task completion speed and project progress. For example, the analysis unit might rate an employee with a high number of completed tasks as "75kg barbell curl" and an employee with high project progress as "100kg bench press." Based on this data, the generative AI expresses employee work performance using muscle metaphors, providing evaluation results in a visually easy-to-understand format. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term performance trends and growth rates. For example, it can evaluate employee growth trends and predict future potential based on past task completion data and project progress data. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem identification. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term performance evaluation and anomaly detection, thereby improving the reliability and transparency of the entire system.

[0032] The Feedback Department provides visual feedback based on the results analyzed by the Analysis Department. The Feedback Department uses generative AI to provide employees with feedback using muscle metaphors. Specifically, the generative AI generates messages that liken employees' work performance to muscles based on the analysis results. For example, an employee with a high number of completed tasks might receive feedback like "Nice biceps!", while an employee with high project progress might receive feedback like "Your pectorals are walking!". This allows employees to understand their work performance visually and intuitively. Furthermore, the Feedback Department can customize the feedback content. For example, it can adjust the tone and expression of the feedback according to the employee's preferences and personality. This makes the feedback more personalized and contributes to increased employee motivation. The Feedback Department can also continuously monitor the effectiveness of the feedback and make improvements as needed. For example, it can analyze the performance data of employees who have received feedback to evaluate its effectiveness. This allows the Feedback Department to continue providing effective feedback to improve employee work performance. Additionally, the Feedback Department can collect employee reactions and opinions on the feedback to help improve the feedback content. This allows the feedback department to provide flexible and effective feedback tailored to employee needs, enabling fair and transparent performance evaluations.

[0033] The data collection unit can collect data such as emails, chats, and project management tool logs. For example, the data collection unit uses APIs from specific email services, chat applications, and project management tools to collect data. For instance, the data collection unit might use an email service API to retrieve employee email logs, a chat application API to collect chat logs, and a project management tool API to retrieve project progress. This allows for a more comprehensive evaluation by collecting business data from diverse data sources. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit could input email logs obtained using an email service API into a generating AI and have the generating AI analyze the email logs.

[0034] The analysis unit can evaluate individual output and communication skills based on employees' work data. For example, the analysis unit might evaluate an employee with a high number of completed tasks as "75kg barbell curl" and an employee with high project progress as "100kg bench press". The analysis unit can use generative AI to analyze employees' work data and evaluate individual output and communication skills. For example, the analysis unit inputs data on the number of tasks completed and project progress of employees into the generative AI, and the generative AI performs evaluations based on that data. This allows for more accurate personnel evaluations by performing detailed evaluations based on employees' work data. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs employee work data into the generative AI, and the generative AI performs evaluations based on that data.

[0035] The feedback unit can provide feedback such as "Nice biceps!" to employees who complete many tasks, or "Your pectoral muscles are walking!" to employees who are making good progress on projects. The feedback unit can, for example, use generative AI to provide employees with feedback using muscle metaphors. For example, the feedback unit inputs data on the number of tasks completed and project progress of employees into the generative AI, and the generative AI generates feedback based on that data. This can improve employee motivation by providing visual and humorous feedback. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs employee work data into the generative AI, and the generative AI generates feedback based on that data.

[0036] The feedback department can eliminate evaluation bias and improve employee motivation. The feedback department provides employees with fair and transparent feedback, for example, using generative AI. For instance, the feedback department inputs employee work data into the generative AI, which then generates unbiased feedback based on that data. This eliminates evaluation bias, enabling fair evaluations and improving employee motivation. Some or all of the above-described processes in the feedback department may be performed using AI, or without AI. For example, the feedback department inputs employee work data into the generative AI, which then generates unbiased feedback based on that data.

[0037] The data collection unit can collect data such as the number of tasks completed by employees, project progress, and communication frequency. For example, the data collection unit can record the number of tasks completed by employees, track project progress, and save communication frequency as a log. For example, the data collection unit can store the number of tasks completed by employees in a database, retrieve project progress using the API of a project management tool, and collect communication frequency using the API of a chat application. This allows for more accurate evaluation by collecting detailed work data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the number of tasks completed by employees into a generating AI, and the generating AI can perform analysis based on that data.

[0038] The data collection unit can analyze employees' past work data and select the optimal data collection method. For example, the data collection unit can identify the time of day when employees can provide data most efficiently based on past work data. For example, the data collection unit can select a data collection method preferred by employees (e.g., surveys, log analysis) based on past work data. For example, the data collection unit can analyze past work data and select a data collection method that causes the least stress to employees. This enables efficient data collection by selecting the optimal data collection method based on past work data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs past work data into a generating AI, and the generating AI selects the optimal data collection method based on that data.

[0039] The data collection unit can filter data based on employees' current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting only data related to ongoing projects. For example, the data collection unit can filter and collect highly relevant data based on employees' areas of interest. For example, the data collection unit can dynamically filter and collect necessary data according to the progress of projects. 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, for example, or without AI. For example, the data collection unit inputs data on employees' current projects and areas of interest into a generating AI, and the generating AI performs filtering based on that data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, if an employee is in the office, the data collection unit will prioritize the collection of activity data within the office. For example, if an employee is on a business trip, the data collection unit will prioritize the collection of work data at the business trip destination. For example, if an employee is working remotely, the data collection unit will prioritize the collection of work data at home. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of employees into a generating AI, and the generating AI will prioritize the collection of highly relevant data based on that data.

[0041] The data collection unit can analyze employees' social media activity and collect relevant data during data collection. For example, the data collection unit can extract and collect work-related information from employees' social media posts. For example, the data collection unit can analyze employees' activity on social media and collect data useful for work. For example, the data collection unit can collect data related to employees' areas of interest based on their social media activity history. This allows for the efficient collection of work-related data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee social media activity data into a generating AI, and the generating AI can collect relevant data based on that data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the business data during the analysis. For example, the analysis unit performs a detailed analysis on business data with high importance. For example, the analysis unit performs a simplified analysis on business data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the business data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the business data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the importance of the business data into a generating AI, and the generating AI adjusts the level of detail of the analysis based on that data.

[0043] The analysis unit can apply different analysis algorithms depending on the category of business data during analysis. For example, the analysis unit applies an algorithm that evaluates progress to project management data. For example, the analysis unit applies an algorithm that evaluates the quality of dialogue to communication data. For example, the analysis unit applies an algorithm that evaluates efficiency to task completion data. This enables accurate analysis by applying the appropriate analysis algorithm according to the category of business data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the categories of business data into a generating AI, and the generating AI applies an appropriate analysis algorithm based on that data.

[0044] The analysis unit can determine the priority of analysis based on the submission timing of business data during the analysis process. For example, the analysis unit may prioritize analyzing business data with an approaching submission deadline. For example, it may postpone analyzing business data with a distant submission deadline. For example, the analysis unit may adjust the level of detail of the analysis according to the submission timing. This enables efficient analysis by determining the priority of analysis based on the submission timing of business data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the submission timing of business data into a generating AI, and the generating AI may determine the priority of analysis based on that data.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the business data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant business data. For example, the analysis unit postpones the analysis of less relevant business data. For example, the analysis unit adjusts the level of detail of the analysis according to the relevance of the business data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the business data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the relevance of the business data into a generating AI, and the generating AI adjusts the order of analysis based on that data.

[0046] The feedback unit can select the optimal feedback method by referring to the employee's past evaluation history when providing feedback. For example, the feedback unit can select the feedback method that is easiest for the employee to understand from their past evaluation history. For example, the feedback unit can select the feedback method that makes the employee most motivated based on their past evaluation history. For example, the feedback unit can select the feedback method that makes it easiest for the employee to improve by referring to their past evaluation history. In this way, by selecting the optimal feedback method based on past evaluation history, feedback that is easy for employees to understand can be provided. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit inputs the employee's past evaluation history into a generating AI, and the generating AI selects the optimal feedback method based on that data.

[0047] The feedback unit can customize the content of feedback based on the employee's current work situation. For example, the feedback unit can provide feedback related to ongoing projects. For example, the feedback unit can suggest specific areas for improvement based on the current work situation. For example, the feedback unit can provide feedback at an appropriate time, taking into account the current work situation. This allows the feedback unit to provide appropriate feedback to employees by customizing the content based on the current work situation. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit inputs the employee's current work situation into a generating AI, and the generating AI customizes the content of the feedback based on that data.

[0048] The feedback unit can select the optimal feedback method when providing feedback, taking into account the employee's geographical location. For example, if the employee is in the office, the feedback unit will prioritize in-person feedback. If the employee is on a business trip, the feedback unit will prioritize remote feedback. If the employee is working remotely, the feedback unit will prioritize online feedback. This allows the feedback unit to select the most suitable feedback method for each employee by considering their geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the employee's geographical location information into a generating AI, which then selects the optimal feedback method based on that data.

[0049] The feedback department can analyze an employee's social media activity and propose feedback content when providing feedback. For example, the feedback department can analyze an employee's activity on social media and provide work-related feedback. For example, the feedback department can provide feedback related to an employee's areas of interest based on their social media activity history. For example, the feedback department can provide specific feedback by referring to the content of social media posts. In this way, work-related feedback can be provided by analyzing social media activity. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department inputs employee social media activity data into a generating AI, and the generating AI proposes feedback content based on that data.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analytics department can improve the accuracy of its analysis of employee performance data by referring to the employee's past performance evaluation history. For example, the analytics department can identify an employee's strengths and weaknesses from past performance evaluation history and use that information to analyze current performance data in more detail. Furthermore, the analytics department can also understand an employee's growth trend and predict future performance based on past performance evaluation history. This enables more accurate analysis by utilizing employees' past performance evaluation history, leading to fairer and more transparent performance evaluations.

[0052] The data collection unit can customize the data collection method based on the employee's skill level when collecting employee work data. For example, it can collect detailed data from highly skilled employees and basic data from less skilled employees. Furthermore, the data collection unit can adjust the frequency of data collection according to the employee's skill level. This enables data collection tailored to the employee's skill level, resulting in more accurate evaluations.

[0053] The feedback department can propose career paths for employees based on their work data. For example, it can analyze employee work data to identify their strengths and interests and propose career paths based on that. Furthermore, the feedback department can also propose training programs for skill development based on employee work data. In this way, by supporting employees' career paths, it is possible to improve employee motivation.

[0054] The analysis unit can improve the accuracy of its analysis by considering the employee's work environment when analyzing employee work data. For example, if an employee is working remotely, the analysis unit will consider data specific to remote work. Furthermore, if an employee is working in the office, the analysis can also consider data based on the office environment. This enables analysis tailored to the employee's work environment, resulting in a more accurate evaluation.

[0055] The data collection unit can adjust the scope of data collection based on employee work objectives when collecting employee work data. For example, the collection unit prioritizes collecting data related to employee work objectives. Furthermore, it can adjust the frequency of data collection according to employee work objectives. This enables data collection aligned with employee work objectives, resulting in more accurate evaluations.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects employee work data. For example, it collects data such as emails, chats, and project management tool logs to gather information such as the number of tasks completed by employees, project progress, and communication frequency. Specifically, the data collection unit records the number of tasks completed by employees, tracks project progress, and saves communication frequency as a log. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to evaluate individual employees' output and communication skills based on their work data. For example, an employee who completes many tasks might be evaluated as "75kg barbell curl," and an employee with a high project progress might be evaluated as "100kg bench press." Step 3: The feedback unit provides visual feedback based on the results analyzed by the analysis unit. The feedback unit uses generative AI to provide employees with feedback using muscle metaphors. For example, employees who complete many tasks will receive feedback such as "Nice biceps!", and employees with high project progress will receive feedback such as "Your pectorals are walking!".

[0058] (Example of form 2) The Muscle World Evaluation System Muscle Brain, according to an embodiment of the present invention, is a system that utilizes generative AI to improve the fairness and transparency of personnel evaluations. This Muscle World Evaluation System Muscle Brain analyzes employee work data and provides visual feedback by likening achievements and skills to muscles, thereby eliminating evaluation bias and improving employee motivation. For example, for managers, the evaluation criteria become clearer, and evaluation errors are reduced through quantification and visualization, while for subordinates, evaluation gaps are eliminated. Furthermore, the company as a whole can expect improved employee motivation and improved performance. In addition, this system is targeted at companies ranging from small and medium-sized enterprises with 100 or more employees to large corporations, with IT companies and startups, in particular, being the main customers, as they are companies that are highly compatible with data-driven evaluations. This allows the system to provide an effective solution to companies that seek fair and transparent personnel evaluations. Thus, the Muscle World Evaluation System Muscle Brain can achieve fair and transparent personnel evaluations by collecting employee work data, having generative AI analyze that data, and providing visual feedback.

[0059] The Muscle World Evaluation System Muscle Brain according to this embodiment comprises a data collection unit, an analysis unit, and a feedback unit. The data collection unit collects employee work data. The data collection unit can collect data such as emails, chats, and project management tool logs. The data collection unit can also collect data such as the number of tasks completed by employees, the progress of projects, and the frequency of communication. For example, the data collection unit records the number of tasks completed by employees, tracks the progress of projects, and saves the frequency of communication as a log. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to evaluate individual outputs and communication abilities based on the employee's work data. For example, the analysis unit evaluates employees with a high number of completed tasks as "barbell curl 75kg" and employees with high project progress as "bench press 100kg". The feedback unit provides visual feedback based on the results analyzed by the analysis unit. The feedback unit uses generative AI to provide employees with feedback using muscle metaphors. For example, the feedback unit might give feedback like "Nice biceps!" to an employee with a high number of completed tasks, and "Your pectoral muscles are walking!" to an employee with a high project progress. In this way, the Muscle Brain muscle world evaluation system according to this embodiment can collect and analyze employee work data and provide visual feedback, enabling fair and transparent personnel evaluations.

[0060] The data collection unit collects employee work data. This data can include, for example, email, chat, and project management tool logs. Specifically, it includes email sending and receiving history, chat message content, and task progress and completion reports from project management tools. This data provides a detailed record of employee work activities and serves as foundational data for evaluating work efficiency and communication quality. The data collection unit can also collect data such as the number of tasks completed by employees, project progress, and communication frequency. For example, it can record the number of tasks completed by employees, track project progress, and save communication frequency as logs. This allows the data collection unit to gain a multifaceted understanding of employee work activities and provide detailed data. Furthermore, the data collection unit can centrally manage this data and integrate it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and feedback units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the data collected by the data collection unit. Using generative AI, the analysis unit evaluates individual employee output and communication skills based on their work data. Specifically, the generative AI uses natural language processing technology to analyze email and chat content to evaluate employees' communication skills and collaborative attitudes. It also analyzes data from project management tools to evaluate task completion speed and project progress. For example, the analysis unit might rate an employee with a high number of completed tasks as "75kg barbell curl" and an employee with high project progress as "100kg bench press." Based on this data, the generative AI expresses employee work performance using muscle metaphors, providing evaluation results in a visually easy-to-understand format. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term performance trends and growth rates. For example, it can evaluate employee growth trends and predict future potential based on past task completion data and project progress data. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem identification. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term performance evaluation and anomaly detection, thereby improving the reliability and transparency of the entire system.

[0062] The Feedback Department provides visual feedback based on the results analyzed by the Analysis Department. The Feedback Department uses generative AI to provide employees with feedback using muscle metaphors. Specifically, the generative AI generates messages that liken employees' work performance to muscles based on the analysis results. For example, an employee with a high number of completed tasks might receive feedback like "Nice biceps!", while an employee with high project progress might receive feedback like "Your pectorals are walking!". This allows employees to understand their work performance visually and intuitively. Furthermore, the Feedback Department can customize the feedback content. For example, it can adjust the tone and expression of the feedback according to the employee's preferences and personality. This makes the feedback more personalized and contributes to increased employee motivation. The Feedback Department can also continuously monitor the effectiveness of the feedback and make improvements as needed. For example, it can analyze the performance data of employees who have received feedback to evaluate its effectiveness. This allows the Feedback Department to continue providing effective feedback to improve employee work performance. Additionally, the Feedback Department can collect employee reactions and opinions on the feedback to help improve the feedback content. This allows the feedback department to provide flexible and effective feedback tailored to employee needs, enabling fair and transparent performance evaluations.

[0063] The data collection unit can collect data such as emails, chats, and project management tool logs. For example, the data collection unit uses APIs from specific email services, chat applications, and project management tools to collect data. For instance, the data collection unit might use an email service API to retrieve employee email logs, a chat application API to collect chat logs, and a project management tool API to retrieve project progress. This allows for a more comprehensive evaluation by collecting business data from diverse data sources. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit could input email logs obtained using an email service API into a generating AI and have the generating AI analyze the email logs.

[0064] The analysis unit can evaluate individual output and communication skills based on employees' work data. For example, the analysis unit might evaluate an employee with a high number of completed tasks as "75kg barbell curl" and an employee with high project progress as "100kg bench press". The analysis unit can use generative AI to analyze employees' work data and evaluate individual output and communication skills. For example, the analysis unit inputs data on the number of tasks completed and project progress of employees into the generative AI, and the generative AI performs evaluations based on that data. This allows for more accurate personnel evaluations by performing detailed evaluations based on employees' work data. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs employee work data into the generative AI, and the generative AI performs evaluations based on that data.

[0065] The feedback unit can provide feedback such as "Nice biceps!" to employees who complete many tasks, or "Your pectoral muscles are walking!" to employees who are making good progress on projects. The feedback unit can, for example, use generative AI to provide employees with feedback using muscle metaphors. For example, the feedback unit inputs data on the number of tasks completed and project progress of employees into the generative AI, and the generative AI generates feedback based on that data. This can improve employee motivation by providing visual and humorous feedback. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs employee work data into the generative AI, and the generative AI generates feedback based on that data.

[0066] The feedback department can eliminate evaluation bias and improve employee motivation. The feedback department provides employees with fair and transparent feedback, for example, using generative AI. For instance, the feedback department inputs employee work data into the generative AI, which then generates unbiased feedback based on that data. This eliminates evaluation bias, enabling fair evaluations and improving employee motivation. Some or all of the above-described processes in the feedback department may be performed using AI, or without AI. For example, the feedback department inputs employee work data into the generative AI, which then generates unbiased feedback based on that data.

[0067] The data collection unit can collect data such as the number of tasks completed by employees, project progress, and communication frequency. For example, the data collection unit can record the number of tasks completed by employees, track project progress, and save communication frequency as a log. For example, the data collection unit can store the number of tasks completed by employees in a database, retrieve project progress using the API of a project management tool, and collect communication frequency using the API of a chat application. This allows for more accurate evaluation by collecting detailed work data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the number of tasks completed by employees into a generating AI, and the generating AI can perform analysis based on that data.

[0068] 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 reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is concentrating, the data collection unit can adjust the timing of data collection to avoid interfering with their work. In this way, the user's burden can be reduced by adjusting the timing of data collection according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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, for example, or without AI. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotion based on that data.

[0069] The data collection unit can analyze employees' past work data and select the optimal data collection method. For example, the data collection unit can identify the time of day when employees can provide data most efficiently based on past work data. For example, the data collection unit can select a data collection method preferred by employees (e.g., surveys, log analysis) based on past work data. For example, the data collection unit can analyze past work data and select a data collection method that causes the least stress to employees. This enables efficient data collection by selecting the optimal data collection method based on past work data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs past work data into a generating AI, and the generating AI selects the optimal data collection method based on that data.

[0070] The data collection unit can filter data based on employees' current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting only data related to ongoing projects. For example, the data collection unit can filter and collect highly relevant data based on employees' areas of interest. For example, the data collection unit can dynamically filter and collect necessary data according to the progress of projects. 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, for example, or without AI. For example, the data collection unit inputs data on employees' current projects and areas of interest into a generating AI, and the generating AI performs filtering based on that data.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will prioritize the collection of detailed data. For example, if the user is focused, the data collection unit will prioritize the collection of important data related to the work. This enables efficient data collection by prioritizing data according to 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 using AI. For example, the data collection unit inputs user emotion data into a generative AI, and the generative AI estimates the emotions based on that data.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, if an employee is in the office, the data collection unit will prioritize the collection of activity data within the office. For example, if an employee is on a business trip, the data collection unit will prioritize the collection of work data at the business trip destination. For example, if an employee is working remotely, the data collection unit will prioritize the collection of work data at home. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of employees into a generating AI, and the generating AI will prioritize the collection of highly relevant data based on that data.

[0073] The data collection unit can analyze employees' social media activity and collect relevant data during data collection. For example, the data collection unit can extract and collect work-related information from employees' social media posts. For example, the data collection unit can analyze employees' activity on social media and collect data useful for work. For example, the data collection unit can collect data related to employees' areas of interest based on their social media activity history. This allows for the efficient collection of work-related data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee social media activity data into a generating AI, and the generating AI can collect relevant data based on that data.

[0074] 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 relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is focused, the analysis unit provides visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotion based on that data.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the business data during the analysis. For example, the analysis unit performs a detailed analysis on business data with high importance. For example, the analysis unit performs a simplified analysis on business data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the business data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the business data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the importance of the business data into a generating AI, and the generating AI adjusts the level of detail of the analysis based on that data.

[0076] The analysis unit can apply different analysis algorithms depending on the category of business data during analysis. For example, the analysis unit applies an algorithm that evaluates progress to project management data. For example, the analysis unit applies an algorithm that evaluates the quality of dialogue to communication data. For example, the analysis unit applies an algorithm that evaluates efficiency to task completion data. This enables accurate analysis by applying the appropriate analysis algorithm according to the category of business data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the categories of business data into a generating AI, and the generating AI applies an appropriate analysis algorithm based on that data.

[0077] 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 provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is focused, the analysis unit provides a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. 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-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions based on that data.

[0078] The analysis unit can determine the priority of analysis based on the submission timing of business data during the analysis process. For example, the analysis unit may prioritize analyzing business data with an approaching submission deadline. For example, it may postpone analyzing business data with a distant submission deadline. For example, the analysis unit may adjust the level of detail of the analysis according to the submission timing. This enables efficient analysis by determining the priority of analysis based on the submission timing of business data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the submission timing of business data into a generating AI, and the generating AI may determine the priority of analysis based on that data.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the business data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant business data. For example, the analysis unit postpones the analysis of less relevant business data. For example, the analysis unit adjusts the level of detail of the analysis according to the relevance of the business data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the business data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the relevance of the business data into a generating AI, and the generating AI adjusts the order of analysis based on that data.

[0080] The feedback unit can estimate the user's emotions and adjust the way it presents the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit provides detailed feedback. If the user is stressed, the feedback unit provides concise and to-the-point feedback. If the user is focused, the feedback unit provides visually easy-to-understand feedback. By adjusting the way it presents the feedback according to the user's emotions, it is possible to provide feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotion based on that data.

[0081] The feedback unit can select the optimal feedback method by referring to the employee's past evaluation history when providing feedback. For example, the feedback unit can select the feedback method that is easiest for the employee to understand from their past evaluation history. For example, the feedback unit can select the feedback method that makes the employee most motivated based on their past evaluation history. For example, the feedback unit can select the feedback method that makes it easiest for the employee to improve by referring to their past evaluation history. In this way, by selecting the optimal feedback method based on past evaluation history, feedback that is easy for employees to understand can be provided. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit inputs the employee's past evaluation history into a generating AI, and the generating AI selects the optimal feedback method based on that data.

[0082] The feedback unit can customize the content of feedback based on the employee's current work situation. For example, the feedback unit can provide feedback related to ongoing projects. For example, the feedback unit can suggest specific areas for improvement based on the current work situation. For example, the feedback unit can provide feedback at an appropriate time, taking into account the current work situation. This allows the feedback unit to provide appropriate feedback to employees by customizing the content based on the current work situation. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit inputs the employee's current work situation into a generating AI, and the generating AI customizes the content of the feedback based on that data.

[0083] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will postpone less important feedback. For example, if the user is relaxed, the feedback unit will prioritize providing detailed feedback. For example, if the user is focused, the feedback unit will prioritize providing important feedback related to the work. This enables efficient feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback unit may be performed using AI or not using AI. For example, the feedback unit inputs user emotion data into the generative AI, and the generative AI estimates the emotions based on that data.

[0084] The feedback unit can select the optimal feedback method when providing feedback, taking into account the employee's geographical location. For example, if the employee is in the office, the feedback unit will prioritize in-person feedback. If the employee is on a business trip, the feedback unit will prioritize remote feedback. If the employee is working remotely, the feedback unit will prioritize online feedback. This allows the feedback unit to select the most suitable feedback method for each employee by considering their geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the employee's geographical location information into a generating AI, which then selects the optimal feedback method based on that data.

[0085] The feedback department can analyze an employee's social media activity and propose feedback content when providing feedback. For example, the feedback department can analyze an employee's activity on social media and provide work-related feedback. For example, the feedback department can provide feedback related to an employee's areas of interest based on their social media activity history. For example, the feedback department can provide specific feedback by referring to the content of social media posts. In this way, work-related feedback can be provided by analyzing social media activity. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department inputs employee social media activity data into a generating AI, and the generating AI proposes feedback content based on that data.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analytics department can improve the accuracy of its analysis of employee performance data by referring to the employee's past performance evaluation history. For example, the analytics department can identify an employee's strengths and weaknesses from past performance evaluation history and use that information to analyze current performance data in more detail. Furthermore, the analytics department can also understand an employee's growth trend and predict future performance based on past performance evaluation history. This enables more accurate analysis by utilizing employees' past performance evaluation history, leading to fairer and more transparent performance evaluations.

[0088] The feedback system can estimate an employee's emotions and adjust the timing of feedback based on those estimates. For example, if an employee is stressed, the feedback can be delayed until they are relaxed. Furthermore, if an employee is concentrating, the timing of feedback can be adjusted to avoid interrupting their work. This allows for more effective feedback by adjusting the timing of feedback according to the employee's emotions.

[0089] The data collection unit can customize the data collection method based on the employee's skill level when collecting employee work data. For example, it can collect detailed data from highly skilled employees and basic data from less skilled employees. Furthermore, the data collection unit can adjust the frequency of data collection according to the employee's skill level. This enables data collection tailored to the employee's skill level, resulting in more accurate evaluations.

[0090] The analysis unit can estimate employees' emotions and prioritize analysis based on those estimates. For example, if an employee is stressed, the analysis of less important data can be postponed. Conversely, if an employee is relaxed, detailed analysis can be prioritized. This allows for more efficient analysis by prioritizing analysis according to employees' emotions.

[0091] The feedback department can propose career paths for employees based on their work data. For example, it can analyze employee work data to identify their strengths and interests and propose career paths based on that. Furthermore, the feedback department can also propose training programs for skill development based on employee work data. In this way, by supporting employees' career paths, it is possible to improve employee motivation.

[0092] The data collection unit can estimate employees' emotions and adjust the data collection method based on the estimated emotions. For example, if an employee is stressed, the data collection method can be simplified to reduce the burden. Furthermore, if an employee is relaxed, detailed data can be collected. This allows for efficient data collection by adjusting the data collection method according to the employee's emotions.

[0093] The analysis unit can improve the accuracy of its analysis by considering the employee's work environment when analyzing employee work data. For example, if an employee is working remotely, the analysis unit will consider data specific to remote work. Furthermore, if an employee is working in the office, the analysis can also consider data based on the office environment. This enables analysis tailored to the employee's work environment, resulting in a more accurate evaluation.

[0094] The feedback system can estimate an employee's emotions and customize the feedback based on those estimates. For example, if an employee is stressed, it prioritizes providing positive feedback. Furthermore, if an employee is relaxed, it can provide more detailed feedback. This allows for more effective feedback by tailoring the content to each employee's emotions.

[0095] The data collection unit can adjust the scope of data collection based on employee work objectives when collecting employee work data. For example, the collection unit prioritizes collecting data related to employee work objectives. Furthermore, it can adjust the frequency of data collection according to employee work objectives. This enables data collection aligned with employee work objectives, resulting in more accurate evaluations.

[0096] The analysis unit can estimate employees' emotions and adjust the analysis method based on those estimates. For example, if an employee is stressed, it will perform a concise and to-the-point analysis. Furthermore, if an employee is relaxed, it can perform a more detailed analysis. This allows for more effective analysis by adjusting the analysis method according to the employee's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects employee work data. For example, it collects data such as emails, chats, and project management tool logs to gather information such as the number of tasks completed by employees, project progress, and communication frequency. Specifically, the data collection unit records the number of tasks completed by employees, tracks project progress, and saves communication frequency as a log. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to evaluate individual employees' output and communication skills based on their work data. For example, an employee who completes many tasks might be evaluated as "75kg barbell curl," and an employee with a high project progress might be evaluated as "100kg bench press." Step 3: The feedback unit provides visual feedback based on the results analyzed by the analysis unit. The feedback unit uses generative AI to provide employees with feedback using muscle metaphors. For example, employees who complete many tasks will receive feedback such as "Nice biceps!", and employees with high project progress will receive feedback such as "Your pectorals are walking!".

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects data such as emails, chats, and project management tool logs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes employee work data using generating AI. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides visual feedback using a muscle metaphor. 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.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data such as emails, chats, and project management tool logs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes employee work data using generating AI. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides visual feedback using a muscle metaphor. 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.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data such as emails, chats, and project management tool logs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes employee work data using generating AI. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides visual feedback using a muscle metaphor. 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.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects data such as emails, chats, and project management tool logs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes employee work data using generated AI. The feedback unit is implemented by the control unit 46A of the robot 414 and provides visual feedback using a muscle metaphor. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The data collection department collects employee work data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a feedback unit that provides visual feedback based on the results analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as emails, chats, and project management tool logs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We evaluate individual output and communication skills based on employee work data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Employees who complete many tasks receive feedback such as "Nice biceps!", and employees who are making good progress on projects receive feedback such as "Your pectoral muscles are walking around!" The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Eliminate evaluation bias and improve employee motivation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system collects data such as the number of tasks completed by employees, the progress of projects, and the frequency of communication. 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 data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past work 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 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 prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, 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 business 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 business 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 the analysis is determined based on the submission timing of the business data. 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 business data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, refer to the employee's past performance review history to select the most appropriate feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the employee's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, select the most appropriate feedback method by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we analyze employees' social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 work data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a feedback unit that provides visual feedback based on the results analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as emails, chats, and project management tool logs. The system according to feature 1.

3. The aforementioned analysis unit, We evaluate individual output and communication skills based on employee work data. The system according to feature 1.

4. The aforementioned feedback unit is Provide feedback to employees who complete a large number of tasks. The system according to feature 1.

5. The aforementioned feedback unit is Eliminate evaluation bias and improve employee motivation. The system according to feature 1.

6. The aforementioned collection unit is The system collects data such as the number of tasks completed by employees, the progress of projects, and the frequency of communication. The system according to feature 1.

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

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

9. The aforementioned collection unit is When collecting 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 prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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