system

Abstract

The system according to this embodiment aims to evaluate the appropriateness of salaries and provide employees with appropriate information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and an information provision unit. The collection unit collects market salary data and in-house salary data. The analysis unit analyzes the data collected by the collection unit and evaluates the appropriateness of the salaries. The information provision unit provides information to employees based on the evaluation results obtained 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 performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a lack of transparency regarding the validity of salaries, which may reduce the motivation of employees.

[0005] The system according to the embodiment aims to evaluate the validity of salaries and provide appropriate information to employees.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an information providing unit. The collection unit collects market salary data and in-company salary data. The analysis unit analyzes the data collected by the collection unit and evaluates the validity of salaries. The information providing unit provides information to employees based on the evaluation result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can evaluate the appropriateness of salaries and provide employees with appropriate information. [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 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 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] 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 salary transparency improvement system according to an embodiment of the present invention is a system that improves salary transparency using an AI agent. This salary transparency improvement system first automatically collects market salary data and internal salary data. Next, the AI ​​analyzes the collected data and evaluates the appropriateness of the salaries. Finally, it provides appropriate information to employees based on the evaluation results. This mechanism improves salary transparency, increases employee motivation, and enhances overall organizational trust. For example, by eliminating the feeling of unfairness in salaries, employees' motivation to work is promoted and the turnover rate is reduced. In addition, transparent operations become possible, and the overall trustworthiness of the organization is improved. Thus, the salary transparency improvement system can improve salary transparency, increase employee motivation, and enhance overall organizational trust.

[0029] The salary transparency improvement system according to the embodiment comprises a collection unit, an analysis unit, and an information provision unit. The collection unit collects market salary data and internal salary data. The collection unit collects market salary data from, for example, job postings on the internet or salary survey databases. The collection unit can also collect internal salary data from the company's internal salary system. For example, the collection unit obtains market salary data from a specific website and internal salary data from an internal database. Furthermore, the collection unit can use AI to improve the efficiency of data collection. The analysis unit analyzes the data collected by the collection unit and evaluates the appropriateness of the salaries. For example, the analysis unit compares the collected market salary data with the internal salary data and evaluates whether each employee's salary is appropriate compared to the market level. For example, the analysis unit determines whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. Furthermore, the analysis unit can use AI to improve the accuracy of data analysis. The information provision unit provides information to employees based on the evaluation results obtained by the analysis unit. For example, the information provision unit summarizes the evaluation results in an easy-to-understand manner and provides information to each employee individually. For example, the information provision department provides each employee with information showing how their salary compares to market standards. This allows employees to understand whether their salary is fair and improves their motivation. Some or all of the above processing in the information provision department may be performed using AI, or not. For example, the information provision department can input evaluation results into the AI, which can then generate and provide appropriate information to each employee. As a result, the salary transparency improvement system according to this embodiment can improve salary transparency, increase employee motivation, and enhance overall organizational confidence.

[0030] The data collection department collects market salary data and internal salary data. For example, it collects market salary data from online job postings and salary survey databases. Specifically, the department automatically extracts data from multiple job sites and industry-specific salary survey reports and integrates this data into a centralized management system. This ensures that the department always has access to the latest market salary information. The department can also collect internal salary data from the company's internal payroll system. This internal salary data includes detailed information such as each employee's base salary, bonuses, and allowances, and this data is automatically retrieved from the company's HR system and payroll management system. For example, the department can obtain market salary data from specific websites and internal salary data from internal databases. Furthermore, the department can use AI to improve the efficiency of data collection. AI automatically collects online job postings using web scraping technology, detecting and correcting data duplication and inconsistencies. AI also optimizes the data extraction process from internal databases, quickly and accurately collecting the necessary data. This allows the department to efficiently collect data from a wide range of data sources and provide up-to-date and accurate salary information. Furthermore, the data collection unit can flexibly configure the frequency and scope of data collection, enabling data collection tailored to specific industries or regions. This allows the data collection unit to achieve customized data collection that meets the needs of companies, strengthening the foundation of the payroll transparency improvement system.

[0031] The analysis department analyzes the data collected by the data collection department and evaluates the appropriateness of salaries. For example, the analysis department compares collected market salary data with internal salary data to evaluate whether each employee's salary is appropriate compared to market levels. Specifically, the analysis department determines whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. To improve the accuracy of data analysis, AI uses machine learning algorithms to learn data patterns and detect outliers and inconsistencies. For example, the analysis department calculates the median and interquartile range of market salaries for a particular job and evaluates how well each employee's salary matches these indicators. The analysis department can also use AI to improve the accuracy of data analysis. AI can analyze historical data and trends to predict future salary fluctuations. This allows the analysis department to analyze collected data quickly and accurately and grasp the surrounding risk situation in real time. Furthermore, the analysis department can also use historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical salary data, it can predict salary fluctuations in a particular job or industry and evaluate the appropriateness of future salaries. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Information Provision Department provides information to employees based on the evaluation results obtained by the Analysis Department. For example, the Information Provision Department summarizes the evaluation results in an easy-to-understand manner and provides the information individually to each employee. Specifically, the Information Provision Department provides each employee with information showing how their salary compares to market standards. This allows employees to understand whether their salary is fair and improves their motivation. Some or all of the above processing in the Information Provision Department may be performed using AI, or not. For example, the Information Provision Department can input the evaluation results into an AI, which can then generate and provide appropriate information to each employee. The AI ​​uses natural language generation technology to explain the evaluation results in easy-to-understand language and creates individually customized reports for each employee. This allows employees to concretely understand how their salary compares to market standards. Furthermore, the Information Provision Department provides dashboards and graphs to visually display the evaluation results, enabling employees to intuitively grasp the information. For example, it can provide histograms showing how each employee's salary is positioned in relation to the market salary distribution, or timeline graphs showing salary fluctuations. This allows the Information Department to provide employees with highly transparent information, increasing their understanding and satisfaction with their salaries. Furthermore, the Information Department can collect feedback from employees and continuously improve the quality of the information it provides. For example, it can implement a feedback function that allows employees to submit questions and opinions about the information provided, and use this feedback to review the methods and content of information provision. In this way, the Information Department can achieve flexible information provision that meets employee needs and maximize the effectiveness of the salary transparency improvement system.

[0033] The data collection unit can collect market salary data and internal salary data from the internet and internal systems. For example, the data collection unit can collect market salary data from job postings and salary survey databases on the internet. The data collection unit can also collect internal salary data from internal salary systems. For example, the data collection unit can obtain market salary data from a specific website and internal salary data from an internal database. This allows the unit to obtain the latest market salary data and internal salary data by collecting data from the internet and internal systems. 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 data from the internet into a generating AI, and the generating AI can collect the data.

[0034] The analysis unit can compare collected market salary data with internal salary data to evaluate whether each employee's salary is reasonable compared to market levels. For example, the analysis unit can compare collected market salary data with internal salary data to evaluate whether each employee's salary is reasonable compared to market levels. For example, the analysis unit can determine whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. In this way, the appropriateness of each employee's salary can be evaluated by comparing market salary data with internal salary 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 can input collected data into a generating AI, and the generating AI can analyze the data.

[0035] The Information Provision Department can summarize evaluation results in an easy-to-understand manner and provide information to each employee individually. For example, the Information Provision Department can summarize evaluation results in an easy-to-understand manner and provide information to each employee individually. For example, the Information Provision Department can provide each employee with information showing how their salary compares to market standards. By summarizing evaluation results in an easy-to-understand manner, appropriate information can be provided to each employee. Some or all of the above processing in the Information Provision Department may be performed using AI, or not using AI. For example, the Information Provision Department can input evaluation results into a generating AI, and the generating AI can generate and provide appropriate information to each employee.

[0036] The Information Provision Department can provide each employee with information showing how their salary compares to market standards. For example, the Information Provision Department can provide each employee with information showing how their salary compares to market standards. This improves salary transparency by providing each employee with information showing how their salary compares to market standards. Some or all of the above processing in the Information Provision Department may be performed using AI, or not using AI. For example, the Information Provision Department can input evaluation results into a generating AI, which can then generate and provide appropriate information to each employee.

[0037] The analysis unit can determine whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. For example, the analysis unit can determine whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. This allows for the determination of the appropriateness of each employee's salary by comparing it with market salary data for the same job or industry. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history and perform data collection. For example, the data collection unit can analyze past data collection history and select a method to shorten the time required for collection. For example, the data collection unit can select a method to improve the accuracy of collection based on past data collection history. In this way, the optimal collection method can be selected by analyzing past data collection history. 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 can input past data collection history into a generating AI, and the generating AI can select the optimal collection method.

[0039] The data collection unit can filter data based on employees' current job duties and positions during data collection. For example, the data collection unit can collect only highly relevant data based on employees' job duties. For example, the data collection unit can prioritize the collection of necessary data based on employees' positions. For example, the data collection unit can adjust the scope of data to be collected, taking into account employees' job duties and positions. This allows for the collection of highly relevant data by filtering based on employees' job duties and positions. 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 job duties and position data into a generating AI, which can then perform the filtering.

[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, the data collection unit can prioritize the collection of highly relevant market salary data based on the employee's work location. For example, the data collection unit can collect region-specific salary data based on the employee's place of residence. For example, the data collection unit can collect market salary data for a business trip destination based on the employee's business trip destination. This allows for the priority collection of highly relevant data by considering the geographical location information of employees. 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 employee's geographical location information into a generating AI, which can then prioritize the collection of highly relevant 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 identify industry trends from employees' social media activity and collect relevant market salary data. For example, the data collection unit can identify job types and industries of interest from employees' social media activity and collect relevant data. For example, the data collection unit can analyze employees' social media activity and collect opinions and feedback regarding salaries. In this way, relevant data can be collected by analyzing employees' social media activity. 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 employee social media activity data into a generating AI, and the generating AI can collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the 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 can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a statistical analysis algorithm to market salary data. For example, the analysis unit can apply a machine learning algorithm to in-house salary data. The analysis unit can select and apply the most suitable analysis algorithm depending on the data category. This allows for the provision of optimal analysis results by applying different analysis algorithms depending on the data category. 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 can input the data category into a generating AI, which can then select and apply the most suitable analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the latest data while referring to past data. For example, the analysis unit may adjust the priority of analysis in stages according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may adjust the order of analysis step by step according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI, and the generating AI may adjust the order of analysis.

[0046] The information provision department can select the optimal information provision method by referring to the employee's past salary information when providing information. For example, the information provision department can select an appropriate information provision method based on the employee's past salary information. For example, the information provision department can adjust the content of the information provided by referring to the employee's past salary information. For example, the information provision department can analyze the employee's past salary information and propose the optimal information provision method. This allows the optimal information provision method to be selected by referring to the employee's past salary information. Some or all of the above processes in the information provision department may be performed using AI, for example, or without using AI. For example, the information provision department can input the employee's past salary information into a generating AI, and the generating AI can select the optimal information provision method.

[0047] The Information Provision Department can customize the means of providing information based on the employee's current job duties when providing information. For example, the Information Provision Department can select an appropriate means of providing information according to the employee's job duties. For example, the Information Provision Department can customize the content of the information provided, taking into account the employee's job duties. For example, the Information Provision Department can propose the optimal means of providing information based on the employee's job duties. This makes it possible to provide appropriate information by customizing the means of providing information based on the employee's job duties. Some or all of the above processes in the Information Provision Department may be performed using AI, for example, or without AI. For example, the Information Provision Department can input employee job content data into a generating AI, and the generating AI can customize the means of providing information.

[0048] The information provision department can select the most appropriate method of providing information by considering the geographical location of employees. For example, the information provision department can provide highly relevant information based on the employee's work location. For example, the information provision department can provide region-specific information based on the employee's place of residence. For example, the information provision department can provide relevant information about an employee's business trip destination based on their business trip destination. This allows the department to select the most appropriate method of providing information by considering the employee's geographical location. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input the employee's geographical location information into a generating AI, which can then select the most appropriate method of providing information.

[0049] The Information Provision Department can analyze employees' social media activity and propose methods for providing information when providing information. For example, the Information Provision Department can provide information of interest based on employees' social media activity. For example, the Information Provision Department can analyze employees' social media activity and propose the most suitable method for providing information. For example, the Information Provision Department can customize the content of information provided by referring to employees' social media activity. In this way, by analyzing employees' social media activity, the most suitable method for providing information can be proposed. Some or all of the above processing in the Information Provision Department may be performed using AI, for example, or without AI. For example, the Information Provision Department can input employee social media activity data into a generating AI, and the generating AI can propose methods for providing information.

[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 salary transparency improvement system can also evaluate the appropriateness of salaries by considering employees' career paths. For example, the analysis department analyzes an employee's past work history and skill set and compares it to the salaries of other employees with similar career paths. This allows for the evaluation of appropriate salaries in line with the employee's career growth. The information provision department can also provide salary information based on career paths, showing what kind of salary employees can expect in the future. Furthermore, the data collection department can collect industry career path data and compare it with employees' career paths to make more accurate evaluations.

[0052] The salary transparency improvement system can also collect employee performance data and evaluate the appropriateness of salaries. For example, the data collection department collects employee work results and evaluation data, and the analysis department evaluates the appropriateness of salaries based on this data. This enables fair salary evaluations based on employee performance. In addition, the information provision department provides salary information based on performance data, allowing employees to understand the appropriateness of their salary in relation to their performance. Furthermore, the data collection department can collect industry performance standard data and compare it with employee performance to make more accurate evaluations.

[0053] The salary transparency improvement system can also evaluate the appropriateness of salaries by considering employees' life events. For example, the analysis department analyzes employee life event data such as marriage, childbirth, and relocation, and evaluates the appropriateness of salaries based on these events. This enables appropriate salary evaluation according to the employee's life stage. In addition, the information provision department provides salary information based on life events, allowing employees to understand the appropriateness of their salary in relation to their life stage. Furthermore, the collection department collects employee life event data, and the analysis department can evaluate the appropriateness of salaries based on this data.

[0054] The salary transparency improvement system can also collect employee health data and evaluate the appropriateness of their salaries. For example, the data collection unit collects employee health checkup results and fitness data, and the analysis unit evaluates the appropriateness of their salaries based on this data. This makes it possible to evaluate salaries while taking into account the health status of employees. In addition, the information provision unit provides salary information based on health data, allowing employees to understand the appropriateness of their salary in relation to their health status. Furthermore, the data collection unit can collect industry health standard data and compare it with employees' health status to make more accurate evaluations.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects market salary data and internal salary data. For example, it collects market salary data from online job postings and salary survey databases, and internal salary data from the company's internal payroll system. The data collection unit can obtain market salary data from specific websites and internal salary data from internal databases. Furthermore, the data collection unit can use AI to improve the efficiency of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the appropriateness of salaries. For example, it compares the collected market salary data with the company's internal salary data to evaluate whether each employee's salary is appropriate compared to market levels. The analysis unit determines whether each employee's salary is appropriate by comparing it with market salary data for the same job type or industry. The analysis unit can also use AI to improve the accuracy of data analysis. Step 3: The Information Provision Department provides information to employees based on the evaluation results obtained by the Analysis Department. For example, it summarizes the evaluation results in an easy-to-understand format and provides the information individually to each employee. The Information Provision Department provides each employee with information showing how their salary compares to market standards. This helps employees understand whether their salary is fair and improves their motivation. Some or all of the processing in the Information Provision Department may be performed using AI or not.

[0057] (Example of form 2) The salary transparency improvement system according to an embodiment of the present invention is a system that improves salary transparency using an AI agent. This salary transparency improvement system first automatically collects market salary data and internal salary data. Next, the AI ​​analyzes the collected data and evaluates the appropriateness of the salaries. Finally, it provides appropriate information to employees based on the evaluation results. This mechanism improves salary transparency, increases employee motivation, and enhances overall organizational trust. For example, by eliminating the feeling of unfairness in salaries, employees' motivation to work is promoted and the turnover rate is reduced. In addition, transparent operations become possible, and the overall trustworthiness of the organization is improved. Thus, the salary transparency improvement system can improve salary transparency, increase employee motivation, and enhance overall organizational trust.

[0058] The salary transparency improvement system according to the embodiment comprises a collection unit, an analysis unit, and an information provision unit. The collection unit collects market salary data and internal salary data. The collection unit collects market salary data from, for example, job postings on the internet or salary survey databases. The collection unit can also collect internal salary data from the company's internal salary system. For example, the collection unit obtains market salary data from a specific website and internal salary data from an internal database. Furthermore, the collection unit can use AI to improve the efficiency of data collection. The analysis unit analyzes the data collected by the collection unit and evaluates the appropriateness of the salaries. For example, the analysis unit compares the collected market salary data with the internal salary data and evaluates whether each employee's salary is appropriate compared to the market level. For example, the analysis unit determines whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. Furthermore, the analysis unit can use AI to improve the accuracy of data analysis. The information provision unit provides information to employees based on the evaluation results obtained by the analysis unit. For example, the information provision unit summarizes the evaluation results in an easy-to-understand manner and provides information to each employee individually. For example, the information provision department provides each employee with information showing how their salary compares to market standards. This allows employees to understand whether their salary is fair and improves their motivation. Some or all of the above processing in the information provision department may be performed using AI, or not. For example, the information provision department can input evaluation results into the AI, which can then generate and provide appropriate information to each employee. As a result, the salary transparency improvement system according to this embodiment can improve salary transparency, increase employee motivation, and enhance overall organizational confidence.

[0059] The data collection department collects market salary data and internal salary data. For example, it collects market salary data from online job postings and salary survey databases. Specifically, the department automatically extracts data from multiple job sites and industry-specific salary survey reports and integrates this data into a centralized management system. This ensures that the department always has access to the latest market salary information. The department can also collect internal salary data from the company's internal payroll system. This internal salary data includes detailed information such as each employee's base salary, bonuses, and allowances, and this data is automatically retrieved from the company's HR system and payroll management system. For example, the department can obtain market salary data from specific websites and internal salary data from internal databases. Furthermore, the department can use AI to improve the efficiency of data collection. AI automatically collects online job postings using web scraping technology, detecting and correcting data duplication and inconsistencies. AI also optimizes the data extraction process from internal databases, quickly and accurately collecting the necessary data. This allows the department to efficiently collect data from a wide range of data sources and provide up-to-date and accurate salary information. Furthermore, the data collection unit can flexibly configure the frequency and scope of data collection, enabling data collection tailored to specific industries or regions. This allows the data collection unit to achieve customized data collection that meets the needs of companies, strengthening the foundation of the payroll transparency improvement system.

[0060] The analysis department analyzes the data collected by the data collection department and evaluates the appropriateness of salaries. For example, the analysis department compares collected market salary data with internal salary data to evaluate whether each employee's salary is appropriate compared to market levels. Specifically, the analysis department determines whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. To improve the accuracy of data analysis, AI uses machine learning algorithms to learn data patterns and detect outliers and inconsistencies. For example, the analysis department calculates the median and interquartile range of market salaries for a particular job and evaluates how well each employee's salary matches these indicators. The analysis department can also use AI to improve the accuracy of data analysis. AI can analyze historical data and trends to predict future salary fluctuations. This allows the analysis department to analyze collected data quickly and accurately and grasp the surrounding risk situation in real time. Furthermore, the analysis department can also use historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical salary data, it can predict salary fluctuations in a particular job or industry and evaluate the appropriateness of future salaries. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0061] The Information Provision Department provides information to employees based on the evaluation results obtained by the Analysis Department. For example, the Information Provision Department summarizes the evaluation results in an easy-to-understand manner and provides the information individually to each employee. Specifically, the Information Provision Department provides each employee with information showing how their salary compares to market standards. This allows employees to understand whether their salary is fair and improves their motivation. Some or all of the above processing in the Information Provision Department may be performed using AI, or not. For example, the Information Provision Department can input the evaluation results into an AI, which can then generate and provide appropriate information to each employee. The AI ​​uses natural language generation technology to explain the evaluation results in easy-to-understand language and creates individually customized reports for each employee. This allows employees to concretely understand how their salary compares to market standards. Furthermore, the Information Provision Department provides dashboards and graphs to visually display the evaluation results, enabling employees to intuitively grasp the information. For example, it can provide histograms showing how each employee's salary is positioned in relation to the market salary distribution, or timeline graphs showing salary fluctuations. This allows the Information Department to provide employees with highly transparent information, increasing their understanding and satisfaction with their salaries. Furthermore, the Information Department can collect feedback from employees and continuously improve the quality of the information it provides. For example, it can implement a feedback function that allows employees to submit questions and opinions about the information provided, and use this feedback to review the methods and content of information provision. In this way, the Information Department can achieve flexible information provision that meets employee needs and maximize the effectiveness of the salary transparency improvement system.

[0062] The data collection unit can collect market salary data and internal salary data from the internet and internal systems. For example, the data collection unit can collect market salary data from job postings and salary survey databases on the internet. The data collection unit can also collect internal salary data from internal salary systems. For example, the data collection unit can obtain market salary data from a specific website and internal salary data from an internal database. This allows the unit to obtain the latest market salary data and internal salary data by collecting data from the internet and internal systems. 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 data from the internet into a generating AI, and the generating AI can collect the data.

[0063] The analysis unit can compare collected market salary data with internal salary data to evaluate whether each employee's salary is reasonable compared to market levels. For example, the analysis unit can compare collected market salary data with internal salary data to evaluate whether each employee's salary is reasonable compared to market levels. For example, the analysis unit can determine whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. In this way, the appropriateness of each employee's salary can be evaluated by comparing market salary data with internal salary 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 can input collected data into a generating AI, and the generating AI can analyze the data.

[0064] The Information Provision Department can summarize evaluation results in an easy-to-understand manner and provide information to each employee individually. For example, the Information Provision Department can summarize evaluation results in an easy-to-understand manner and provide information to each employee individually. For example, the Information Provision Department can provide each employee with information showing how their salary compares to market standards. By summarizing evaluation results in an easy-to-understand manner, appropriate information can be provided to each employee. Some or all of the above processing in the Information Provision Department may be performed using AI, or not using AI. For example, the Information Provision Department can input evaluation results into a generating AI, and the generating AI can generate and provide appropriate information to each employee.

[0065] The Information Provision Department can provide each employee with information showing how their salary compares to market standards. For example, the Information Provision Department can provide each employee with information showing how their salary compares to market standards. This improves salary transparency by providing each employee with information showing how their salary compares to market standards. Some or all of the above processing in the Information Provision Department may be performed using AI, or not using AI. For example, the Information Provision Department can input evaluation results into a generating AI, which can then generate and provide appropriate information to each employee.

[0066] The analysis unit can determine whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. For example, the analysis unit can determine whether each employee's salary is appropriate by comparing it with market salary data for the same job or industry. This allows for the determination of the appropriateness of each employee's salary by comparing it with market salary data for the same job or industry. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data.

[0067] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay the timing of data collection to collect it when the employee is relaxed. For example, if an employee is relaxed, the data collection unit can advance the timing of data collection to collect it efficiently. For example, if an employee is busy, the data collection unit can adjust the timing of data collection to avoid disrupting their work. This allows for efficient data collection by adjusting the timing of data collection based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input employee emotion data into a generative AI, which can then adjust the timing of data collection.

[0068] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history and perform data collection. For example, the data collection unit can analyze past data collection history and select a method to shorten the time required for collection. For example, the data collection unit can select a method to improve the accuracy of collection based on past data collection history. In this way, the optimal collection method can be selected by analyzing past data collection history. 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 can input past data collection history into a generating AI, and the generating AI can select the optimal collection method.

[0069] The data collection unit can filter data based on employees' current job duties and positions during data collection. For example, the data collection unit can collect only highly relevant data based on employees' job duties. For example, the data collection unit can prioritize the collection of necessary data based on employees' positions. For example, the data collection unit can adjust the scope of data to be collected, taking into account employees' job duties and positions. This allows for the collection of highly relevant data by filtering based on employees' job duties and positions. 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 job duties and position data into a generating AI, which can then perform the filtering.

[0070] The data collection unit can estimate employees' emotions and prioritize the data to be collected based on those estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting high-priority data and postpone collecting low-priority data. If an employee is relaxed, the data collection unit can collect all data equally. If an employee is busy, the data collection unit can prioritize collecting data directly related to their work. This enables efficient data collection by prioritizing data based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI, which can then determine the data priority.

[0071] 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, the data collection unit can prioritize the collection of highly relevant market salary data based on the employee's work location. For example, the data collection unit can collect region-specific salary data based on the employee's place of residence. For example, the data collection unit can collect market salary data for a business trip destination based on the employee's business trip destination. This allows for the priority collection of highly relevant data by considering the geographical location information of employees. 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 employee's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0072] The data collection unit can analyze employees' social media activity and collect relevant data during data collection. For example, the data collection unit can identify industry trends from employees' social media activity and collect relevant market salary data. For example, the data collection unit can identify job types and industries of interest from employees' social media activity and collect relevant data. For example, the data collection unit can analyze employees' social media activity and collect opinions and feedback regarding salaries. In this way, relevant data can be collected by analyzing employees' social media activity. 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 employee social media activity data into a generating AI, and the generating AI can collect relevant data.

[0073] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if an employee is relaxed, the analysis unit can provide a detailed analysis result. For example, if an employee is busy, the analysis unit can provide a concise analysis result. In this way, by adjusting the presentation of the analysis based on the employee's emotions, an easy-to-understand analysis result can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the 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 can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a statistical analysis algorithm to market salary data. For example, the analysis unit can apply a machine learning algorithm to in-house salary data. The analysis unit can select and apply the most suitable analysis algorithm depending on the data category. This allows for the provision of optimal analysis results by applying different analysis algorithms depending on the data category. 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 can input the data category into a generating AI, which can then select and apply the most suitable analysis algorithm.

[0076] The analysis unit can estimate employees' emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a short, concise analysis result. For example, if an employee is relaxed, the analysis unit can provide a detailed analysis result. For example, if an employee is busy, the analysis unit can provide a brief analysis result. This allows for efficient analysis results by adjusting the length of the analysis based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input employee emotion data into the generative AI, which can then adjust the length of the analysis.

[0077] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the latest data while referring to past data. For example, the analysis unit may adjust the priority of analysis in stages according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can determine the priority of analysis.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may adjust the order of analysis step by step according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI, and the generating AI may adjust the order of analysis.

[0079] The information provision department can estimate employees' emotions and adjust the method of information provision based on the estimated emotions. For example, if an employee is stressed, the information provision department can provide simple and easy-to-understand information. For example, if an employee is relaxed, the information provision department can provide detailed information. For example, if an employee is busy, the information provision department can provide concise information. In this way, by adjusting the method of information provision based on employees' emotions, it becomes possible to provide information that is easy 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 information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input employee emotion data into a generative AI, and the generative AI can adjust the method of information provision.

[0080] The information provision department can select the optimal information provision method by referring to the employee's past salary information when providing information. For example, the information provision department can select an appropriate information provision method based on the employee's past salary information. For example, the information provision department can adjust the content of the information provided by referring to the employee's past salary information. For example, the information provision department can analyze the employee's past salary information and propose the optimal information provision method. This allows the optimal information provision method to be selected by referring to the employee's past salary information. Some or all of the above processes in the information provision department may be performed using AI, for example, or without using AI. For example, the information provision department can input the employee's past salary information into a generating AI, and the generating AI can select the optimal information provision method.

[0081] The Information Provision Department can customize the means of providing information based on the employee's current job duties when providing information. For example, the Information Provision Department can select an appropriate means of providing information according to the employee's job duties. For example, the Information Provision Department can customize the content of the information provided, taking into account the employee's job duties. For example, the Information Provision Department can propose the optimal means of providing information based on the employee's job duties. This makes it possible to provide appropriate information by customizing the means of providing information based on the employee's job duties. Some or all of the above processes in the Information Provision Department may be performed using AI, for example, or without AI. For example, the Information Provision Department can input employee job content data into a generating AI, and the generating AI can customize the means of providing information.

[0082] The information provision department can estimate employees' emotions and determine the priority of information provision based on the estimated emotions. For example, if an employee is stressed, the information provision department can prioritize providing high-priority information. For example, if an employee is relaxed, the information provision department can provide all information equally. For example, if an employee is busy, the information provision department can prioritize providing information directly related to their work. This enables efficient information provision by determining the priority of information provision based on employees' 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 information provision department may be performed using AI, or not using AI. For example, the information provision department can input employee emotion data into a generative AI, and the generative AI can determine the priority of information provision.

[0083] The information provision department can select the most appropriate method of providing information by considering the geographical location of employees. For example, the information provision department can provide highly relevant information based on the employee's work location. For example, the information provision department can provide region-specific information based on the employee's place of residence. For example, the information provision department can provide relevant information about an employee's business trip destination based on their business trip destination. This allows the department to select the most appropriate method of providing information by considering the employee's geographical location. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input the employee's geographical location information into a generating AI, which can then select the most appropriate method of providing information.

[0084] The Information Provision Department can analyze employees' social media activity and propose methods for providing information when providing information. For example, the Information Provision Department can provide information of interest based on employees' social media activity. For example, the Information Provision Department can analyze employees' social media activity and propose the most suitable method for providing information. For example, the Information Provision Department can customize the content of information provided by referring to employees' social media activity. In this way, by analyzing employees' social media activity, the most suitable method for providing information can be proposed. Some or all of the above processing in the Information Provision Department may be performed using AI, for example, or without AI. For example, the Information Provision Department can input employee social media activity data into a generating AI, and the generating AI can propose methods for providing information.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The salary transparency improvement system can also evaluate the appropriateness of salaries by considering employees' career paths. For example, the analysis department analyzes an employee's past work history and skill set and compares it to the salaries of other employees with similar career paths. This allows for the evaluation of appropriate salaries in line with the employee's career growth. The information provision department can also provide salary information based on career paths, showing what kind of salary employees can expect in the future. Furthermore, the data collection department can collect industry career path data and compare it with employees' career paths to make more accurate evaluations.

[0087] The salary transparency improvement system can also collect employee performance data and evaluate the appropriateness of salaries. For example, the data collection department collects employee work results and evaluation data, and the analysis department evaluates the appropriateness of salaries based on this data. This enables fair salary evaluations based on employee performance. In addition, the information provision department provides salary information based on performance data, allowing employees to understand the appropriateness of their salary in relation to their performance. Furthermore, the data collection department can collect industry performance standard data and compare it with employee performance to make more accurate evaluations.

[0088] The salary transparency improvement system can also estimate employees' emotions and evaluate the appropriateness of their salaries based on those estimated emotions. For example, the analysis department analyzes employee emotion data and evaluates the appropriateness of salaries based on stress and satisfaction levels. This makes it possible to evaluate salaries while considering the emotional state of employees. The information provision department provides salary information based on emotion data, allowing employees to understand the appropriateness of their salaries in relation to their emotional state. Furthermore, the collection department collects employee emotion data, and the analysis department can evaluate the appropriateness of salaries based on this data.

[0089] The salary transparency improvement system can also evaluate the appropriateness of salaries by considering employees' life events. For example, the analysis department analyzes employee life event data such as marriage, childbirth, and relocation, and evaluates the appropriateness of salaries based on these events. This enables appropriate salary evaluation according to the employee's life stage. In addition, the information provision department provides salary information based on life events, allowing employees to understand the appropriateness of their salary in relation to their life stage. Furthermore, the collection department collects employee life event data, and the analysis department can evaluate the appropriateness of salaries based on this data.

[0090] The salary transparency improvement system can also collect employee health data and evaluate the appropriateness of their salaries. For example, the data collection unit collects employee health checkup results and fitness data, and the analysis unit evaluates the appropriateness of their salaries based on this data. This makes it possible to evaluate salaries while taking into account the health status of employees. In addition, the information provision unit provides salary information based on health data, allowing employees to understand the appropriateness of their salary in relation to their health status. Furthermore, the data collection unit can collect industry health standard data and compare it with employees' health status to make more accurate evaluations.

[0091] The salary transparency improvement system can also estimate employees' emotions and adjust the timing of information delivery based on those estimates. For example, if an employee is feeling stressed, the information delivery department can delay the timing of information delivery, providing it when the employee is relaxed. This ensures that information is delivered at a time when employees are more receptive to receiving it. Conversely, if an employee is relaxed, the information delivery department can deliver information earlier, enabling more efficient information delivery. Furthermore, the data collection department can collect employee emotion data, allowing the information delivery department to adjust the timing of information delivery based on this data.

[0092] The salary transparency improvement system can also estimate employee emotions and customize the content of information provided based on those estimates. For example, if an employee is feeling stressed, the information department can provide simple and easy-to-understand information, making it easier for the employee to comprehend. Conversely, if an employee is relaxed, the information department can provide more detailed information. Furthermore, the data collection department can collect employee emotion data, which the information department can then use to customize the content of information provided.

[0093] The salary transparency improvement system can also estimate employee emotions and adjust the format of information delivery based on those estimates. For example, if an employee is feeling stressed, the information delivery department can provide information using visually easy-to-understand graphs and charts, making it easier for employees to intuitively understand the information. Conversely, if an employee is relaxed, the information delivery department can provide detailed text information. Furthermore, the data collection department can collect employee emotion data, allowing the information delivery department to adjust the format of information delivery based on this data.

[0094] The salary transparency improvement system can also estimate employee emotions and adjust the frequency of information provision based on those estimates. For example, if an employee is feeling stressed, the information provision department can reduce the frequency of information provision and provide only the minimum necessary information. This reduces the burden on the employee. Conversely, if an employee is relaxed, the information provision department can increase the frequency of information provision and provide more detailed information. Furthermore, the data collection department can collect employee emotion data, and the information provision department can adjust the frequency of information provision based on this data.

[0095] The salary transparency improvement system can also estimate employee emotions and prioritize information delivery based on those emotions. For example, the information delivery department can prioritize providing high-priority information when an employee is stressed, allowing employees to quickly obtain the information they need. Conversely, when an employee is relaxed, the information delivery department can provide all information equally. Furthermore, the data collection department can collect employee emotion data, which the information delivery department can then use to prioritize information delivery.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects market salary data and internal salary data. For example, it collects market salary data from online job postings and salary survey databases, and internal salary data from the company's internal payroll system. The data collection unit can obtain market salary data from specific websites and internal salary data from internal databases. Furthermore, the data collection unit can use AI to improve the efficiency of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the appropriateness of salaries. For example, it compares the collected market salary data with the company's internal salary data to evaluate whether each employee's salary is appropriate compared to market levels. The analysis unit determines whether each employee's salary is appropriate by comparing it with market salary data for the same job type or industry. The analysis unit can also use AI to improve the accuracy of data analysis. Step 3: The Information Provision Department provides information to employees based on the evaluation results obtained by the Analysis Department. For example, it summarizes the evaluation results in an easy-to-understand format and provides the information individually to each employee. The Information Provision Department provides each employee with information showing how their salary compares to market standards. This helps employees understand whether their salary is fair and improves their motivation. Some or all of the processing in the Information Provision Department may be performed using AI or not.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the collection unit, analysis unit, and information provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the communication I / F 44 of the smart device 14 to collect market salary data from job postings and salary survey databases on the internet, and to collect in-house salary data from the company's salary system. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to compare the collected market salary data with the in-house salary data and evaluate whether each employee's salary is reasonable in comparison to market levels. The information provision unit is implemented in the control unit 46A of the smart device 14, for example, to summarize the evaluation results in an easy-to-understand manner and provide information to each employee individually. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, and information provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the communication I / F 44 of the smart glasses 214 to collect market salary data from job postings and salary survey databases on the internet, and to collect in-house salary data from the company's salary system. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which compares the collected market salary data with the in-house salary data and evaluates whether each employee's salary is reasonable in comparison to market levels. The information provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, which summarizes the evaluation results in an easy-to-understand manner and provides information to each employee individually. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, and information provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the communication I / F 44 of the headset terminal 314 to collect market salary data from job postings and salary survey databases on the internet, and to collect internal salary data from the company's internal salary system. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which compares the collected market salary data with the internal salary data and evaluates whether each employee's salary is reasonable in comparison to market levels. The information provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which summarizes the evaluation results in an easy-to-understand manner and provides information to each employee individually. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, and information provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the robot 414's communication I / F 44 to collect market salary data from job postings and salary survey databases on the internet, and to collect in-house salary data from the company's salary system. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which compares the collected market salary data with the in-house salary data and evaluates whether each employee's salary is reasonable in comparison to market levels. The information provision unit is implemented, for example, by the control unit 46A of the robot 414, which summarizes the evaluation results in an easy-to-understand manner and provides information to each employee individually. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) The data collection department collects market salary data and internal salary data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the appropriateness of the salary, The system includes an information provision unit that provides information to employees based on the evaluation results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect market salary data and internal salary data from the internet and internal systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We compare collected market salary data with internal salary data to assess whether each employee's salary is reasonable compared to market standards. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information provision unit, The evaluation results will be summarized in an easy-to-understand format, and the information will be provided individually to each employee. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information provision unit, Provide each employee with information showing how their salary compares to market rates. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We determine whether each employee's salary is appropriate by comparing it to market salary data for the same job type and industry. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate employees' 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 past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the employee's current job responsibilities and position. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate employees' emotions and prioritize 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, We estimate the emotions of our employees and adjust the representation of the analysis based on those 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 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 data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of employees and adjusts the length of the analysis based on the estimated 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 when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information provision unit, We estimate employees' emotions and adjust the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, When providing information, the most suitable method of information provision is selected by referring to the employee's past salary information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information provision unit, When providing information, customize the method of information provision based on the employee's current job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information provision unit, The system estimates employees' emotions and determines the priority of information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information provision unit, When providing information, the most suitable method of information delivery will be selected, taking into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, When providing information, we analyze employees' social media activity and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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 market salary data and internal salary data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the appropriateness of the salary, The system includes an information provision unit that provides information to employees based on the evaluation results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect market salary data and internal salary data from the internet and internal systems. The system according to feature 1.

3. The aforementioned analysis unit, We compare collected market salary data with internal salary data to assess whether each employee's salary is reasonable compared to market standards. The system according to feature 1.

4. The aforementioned information provision unit, The evaluation results will be summarized in an easy-to-understand format, and the information will be provided individually to each employee. The system according to feature 1.

5. The aforementioned information provision unit, Provide each employee with information showing how their salary compares to market rates. The system according to feature 1.

6. The aforementioned analysis unit, We determine whether each employee's salary is appropriate by comparing it to market salary data for the same job type and industry. The system according to feature 1.

7. The aforementioned collection unit is We estimate employees' 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 past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the employee's current job responsibilities and position. The system according to feature 1.

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