System

Abstract

To provide a system that evaluates a training ability of a superior and automatically calculates and pays an appropriate reward.SOLUTION: A system comprises a collection unit, an analysis unit, a reward calculation unit, and a payment unit. The collection unit collects history data on a combination of a subordinate and a superior, data on the superior's action evaluation, and data on the subordinate's pay rise result. The analysis unit analyzes the data collected by the collection unit, and extracts a feature of a superior who trains the subordinate well. The reward calculation unit automatically calculates a reward for a superior who has trained the subordinate well based on the feature extracted by the analysis unit. The payment unit pays the reward calculated by the reward calculation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology lacks a mechanism for properly evaluating a supervisor's training ability and reflecting it in compensation, so there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the development capabilities of supervisors and automatically calculate and pay appropriate remuneration. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a remuneration calculation unit, and a payment unit. The collection unit collects subordinate-supervisor combination history data, behavioral evaluation data of superiors, and salary increase performance data of subordinates. The analysis unit analyzes the data collected by the collection unit and extracts characteristics of superiors who are good at developing employees. The remuneration calculation unit automatically calculates remuneration for superiors who have successfully developed their subordinates based on the characteristics extracted by the analysis unit. The payment unit pays the remuneration calculated by the remuneration calculation unit. Effect of the Invention

[0007] The system according to the embodiment can evaluate the development capabilities of supervisors and automatically calculate and pay appropriate remuneration. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The training support system according to the embodiment of the present invention is a system that extracts the characteristics of a good boss using the combination history data of subordinates and superiors, behavior evaluation data of superiors, and salary increase record data of subordinates. This training support system extracts the characteristics of a good boss by collecting the combination history data of subordinates and superiors, behavior evaluation data of superiors, and salary increase record data of subordinates, and analyzing them using AI. Furthermore, it links a function that automatically calculates and pays an appropriate remuneration according to the degree of contribution to superiors who have successfully trained their subordinates. This mechanism allows superiors to obtain a common guideline on what they should do to develop their subordinates, and the priority of human resource development in work is also raised by incentives, realizing sustainable development of next-generation employees. For example, the training support system collects combination history data of subordinates and superiors, behavior evaluation data of superiors, and salary increase record data of subordinates. These data indicate the relationship between each superior and the subordinate, the behavior of the superior, and the growth of the subordinate. For example, it records in detail what actions the superior took and how the subordinate grew. Next, the collected data is analyzed using AI. AI analyzes the relationship between the behavior of a boss and the growth of a subordinate, and extracts the characteristics of a boss who is good at raising subordinates. For example, it analyzes specific actions such as what kind of feedback the boss gave to the subordinate and what kind of support the boss provided. In addition, AI automatically calculates the appropriate compensation for a boss who has successfully raised a subordinate according to the degree of contribution. For example, it evaluates the contribution of the boss based on the subordinate's salary increase record and growth degree, and calculates the appropriate compensation. Finally, the calculated compensation is automatically paid to the boss. This system allows the boss to obtain an incentive, and the priority of human resource development in the work increases. This realizes sustainable development of the next generation of employees. This system provides a common guideline of what the boss should do to develop the subordinate, so the boss can take specific actions. For example, the boss can promote the growth of the subordinate by taking actions such as providing regular feedback to the subordinate and supporting the subordinate's growth. This system also allows the boss to understand how his or her actions affect the growth of the subordinate.For example, by recording in detail what kind of feedback a supervisor gave to a subordinate and what kind of support he or she provided, and analyzing it with AI, it is possible to clarify the relationship between the supervisor's actions and the subordinate's growth. In this way, by taking specific actions, the supervisor can promote the growth of the subordinate and realize sustainable development of the next generation of employees. As a result, the development support system can extract the characteristics of a supervisor who is good at developing employees using the subordinate-supervisor combination history data, the supervisor's behavior evaluation data, and the subordinate's salary increase record data, and automatically calculate and pay appropriate remuneration.

[0029] The training support system according to the embodiment includes a collection unit, an analysis unit, a remuneration calculation unit, and a payment unit. The collection unit collects subordinate-supervisor combination history data, behavior evaluation data of the supervisor, and salary increase achievement data of the subordinate. The subordinate-supervisor combination history data includes, for example, the duration of the project, the contents of the joint work, and the like, but is not limited to such examples. The supervisor's behavior evaluation data includes, for example, the frequency of feedback, the quality of guidance, and the like, but is not limited to such examples. The subordinate's salary increase achievement data includes, for example, the frequency of salary increase, the amount of salary increase, and the like, but is not limited to such examples. The collection unit, for example, acquires the subordinate-supervisor combination history data from a database. The collection unit can also collect the supervisor's behavior evaluation data from a questionnaire or an evaluation system. Furthermore, the collection unit can also acquire the subordinate's salary increase achievement data from a personnel system. The analysis unit analyzes the data collected by the collection unit and extracts the characteristics of a supervisor who is good at raising employees. The analysis unit, for example, uses AI to analyze the relationship between the supervisor's behavior and the subordinate's growth. The AI ​​analyzes specific actions such as what kind of feedback the superior gave to the subordinate and what kind of support the superior provided. For example, the AI ​​inputs the behavioral data of the superior and the growth data of the subordinate, and performs analysis using a model that outputs the characteristics of a superior who is good at raising subordinates. The compensation calculation unit automatically calculates an appropriate compensation for the superior who has successfully raised the subordinate based on the characteristics extracted by the analysis unit. The compensation calculation unit evaluates the superior's contribution based on, for example, the subordinate's salary increase record and growth level, and calculates an appropriate compensation. The compensation calculation unit calculates the compensation using a model that inputs, for example, the amount of the subordinate's salary increase and the degree of skill improvement, and outputs the superior's contribution. The payment unit automatically pays the compensation calculated by the compensation calculation unit to the superior. The payment unit, for example, links the calculated compensation to a payroll system and reflects it in the salary of the superior. The payment unit can pay not only monetary compensation but also non-monetary compensation (for example, awards and special leave). As a result, the training support system of the embodiment can extract the characteristics of a manager who is good at training employees using subordinate-supervisor combination history data, behavioral evaluation data of the supervisor, and salary increase record data of the subordinate, and automatically calculate and pay appropriate remuneration.For example, the collection unit acquires subordinate-supervisor combination history data from a database, collects supervisor behavior evaluation data from questionnaires and evaluation systems, and acquires subordinate salary increase performance data from a personnel system. The analysis unit uses AI to analyze the relationship between the supervisor's behavior and the subordinate's growth, and extracts the characteristics of a supervisor who is good at developing employees. The compensation calculation unit evaluates the supervisor's contribution based on the subordinate's salary increase performance and growth level, and calculates an appropriate compensation. The payment unit links the calculated compensation to the salary system and reflects it in the supervisor's salary. In this way, the development support system can extract the characteristics of a supervisor who is good at developing employees, and automatically calculate and pay an appropriate compensation, using the subordinate-supervisor combination history data, the supervisor behavior evaluation data, and the subordinate salary increase performance data.

[0030] The collection unit collects subordinate-supervisor combination history data, supervisor behavior evaluation data, and subordinate pay increase performance data. The subordinate-supervisor combination history data includes, for example, the duration of a project, the contents of joint work, and the like, but is not limited to such examples. Specifically, detailed information such as the start date and end date of the project, the purpose and goal of the project, the division of roles of participating members, and the progress status is included. This allows a detailed understanding of which supervisor was involved in which subordinate and in what project. The supervisor behavior evaluation data includes, for example, the frequency of feedback, the quality of guidance, and the like, but is not limited to such examples. Specifically, the content and timing of feedback given by the supervisor to the subordinate, the specificity and constructiveness of the feedback, the subordinate's reaction and improvement points, and the like are included. This allows a detailed evaluation of the supervisor's guidance method and communication style. The subordinate pay increase performance data includes, for example, the frequency of pay increase, the amount of pay increase, and the like, but is not limited to such examples. Specifically, the timing of the subordinate's pay increase, the reason for the pay increase, the evaluation criteria, and the fluctuation of the pay increase amount are included. This allows a detailed understanding of the subordinate's growth and achievements. The collection unit, for example, acquires subordinate-supervisor combination history data from a database. The collection unit can also collect behavior evaluation data of superiors from questionnaires or evaluation systems. Furthermore, the collection unit can acquire salary increase performance data of subordinates from a personnel system. This allows the collection unit to efficiently collect necessary information from various data sources and integrate it into a database for the entire system. The collected data is updated in real time and made accessible to the analysis unit and compensation calculation unit. This allows the collection unit to ensure the consistency and reliability of data throughout the system and achieve fast and accurate data collection.

[0031] The analysis unit analyzes the data collected by the collection unit and extracts the characteristics of a good boss. The analysis unit, for example, uses AI to analyze the relationship between the boss's behavior and the subordinate's growth. The AI ​​analyzes specific behaviors such as what kind of feedback the boss gave to the subordinate and what kind of support the boss provided. Specifically, it uses natural language processing technology to analyze the content of the feedback and evaluates the frequency of positive feedback and constructive criticism. In addition, it uses a machine learning algorithm to associate the boss's behavior pattern with the subordinate's growth data to identify effective teaching methods. For example, the AI ​​inputs the boss's behavior data and the subordinate's growth data and performs analysis using a model that outputs the characteristics of a good boss. This model evaluates the boss's contribution by taking into account the quality and frequency of the boss' feedback, the subordinate's salary increase record and the degree of skill improvement, etc. In addition, the analysis unit can also analyze long-term trends and patterns using past data and statistical information. For example, it extracts commonalities in the boss's teaching methods and communication styles based on the track record of a specific boss developing multiple subordinates. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to detect problems early. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The compensation calculation unit automatically calculates an appropriate compensation for a superior who has successfully trained his / her subordinates based on the characteristics extracted by the analysis unit. The compensation calculation unit evaluates the superior's contribution based on, for example, the subordinate's salary increase record and growth level, and calculates an appropriate compensation. Specifically, the compensation is calculated using a model that inputs the subordinate's salary increase amount and the degree of skill improvement, and outputs the superior's contribution. This model integrates the superior's behavior evaluation data and the subordinate's growth data, and quantitatively evaluates how much the superior's guidance contributed to the subordinate's growth. For example, if a subordinate's skill is significantly improved in a short period of time, the superior's contribution is highly evaluated. In addition, the compensation calculation unit can take into account not only the subordinate's salary increase record, but also the subordinate's project success rate and team performance. This makes it possible to evaluate the superior's contribution from multiple angles and calculate a more fair and appropriate compensation. Furthermore, the compensation calculation unit can also verify the appropriateness of the compensation by referring to past compensation data and industry standards. For example, it can check whether the superior's compensation is appropriate by comparing it with the compensation level in the same industry or in a company of the same size. In addition, the Compensation Calculation Department can make the compensation calculation process transparent and clearly explain the basis for compensation to superiors, which can increase the motivation of superiors and improve the performance of the entire organization.

[0033] The payment department automatically pays the remuneration calculated by the remuneration calculation department to the superior. For example, the payment department links the calculated remuneration to a payroll system and reflects it in the superior's salary. Specifically, the payment department inputs the remuneration data received from the remuneration calculation department into the payroll system and reflects it in the monthly salary and bonus. In addition, the payment department can pay not only monetary remuneration but also non-monetary remuneration (for example, commendation and special leave, etc.). For example, a superior who has provided excellent guidance can be provided with a special certificate of commendation, an award at an in-house event, additional vacation days, etc. This can increase the superior's motivation and encourage further growth. In addition, the payment department can automate the remuneration payment process and pay remuneration efficiently and accurately. For example, in cooperation with the payroll system, the payment schedule for remuneration can be automatically set and remuneration can be paid without delay. In addition, the payment department can monitor the remuneration payment status in real time and respond quickly when a problem occurs. As a result, the payment department can pay remuneration quickly and reliably to the superior, improving the reliability and transparency of the entire organization. In addition, the payment department can record the remuneration payment history in detail and use it for future evaluation and analysis. This enables payments to continually improve compensation payment processes and drive performance across the organisation.

[0034] The collection unit can collect behavior evaluation data of the superior. The behavior evaluation data of the superior includes, for example, the frequency of feedback, the quality of guidance, and the like, but is not limited to such examples. The collection unit, for example, collects the behavior evaluation data of the superior from a questionnaire or an evaluation system. For example, the collection unit records the frequency of feedback given by the superior to the subordinate. The collection unit can also collect data for evaluating the quality of guidance of the superior. For example, the collection unit can collect by questionnaire how the subordinate felt about the guidance of the superior. In this way, by collecting the behavior evaluation data of the superior, the relationship between the behavior of the superior and the growth of the subordinate can be analyzed. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the questionnaire data to the generation AI and cause the generation AI to execute the analysis of the behavior evaluation data of the superior.

[0035] The collection unit can collect the salary increase performance data of the subordinate. The salary increase performance data of the subordinate includes, for example, the frequency of salary increase, the amount of salary increase, etc., but is not limited to such examples. The collection unit, for example, acquires the salary increase performance data of the subordinate from a personnel system. For example, the collection unit records the amount of salary increase of the subordinate. The collection unit can also record the frequency of salary increase of the subordinate. For example, the collection unit can acquire from a database how frequently the subordinate has received a salary increase. In this way, by collecting the salary increase performance data of the subordinate, the degree of growth of the subordinate can be evaluated. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the salary increase performance data acquired from the personnel system to the generation AI and cause the generation AI to perform analysis of the data.

[0036] The analysis unit can analyze the relationship between the behavior of the boss and the growth of the subordinate. The relationship between the behavior of the boss and the growth of the subordinate includes, for example, the type of behavior and the degree of growth, but is not limited to such an example. The analysis unit, for example, uses AI to analyze the relationship between the behavior of the boss and the growth of the subordinate. For example, the analysis unit analyzes the frequency of feedback given by the boss to the subordinate and the degree of growth of the subordinate. The analysis unit can also analyze the quality of the guidance of the boss and the degree of growth of the subordinate. For example, the analysis unit inputs the behavior data of the boss and the growth data of the subordinate, and performs analysis using a model that outputs the characteristics of a boss who is good at raising children. In this way, the characteristics of a boss who is good at raising children can be extracted by analyzing the relationship between the behavior of the boss and the growth of the subordinate. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the behavior data of the boss and the growth data of the subordinate to the generation AI, and causes the generation AI to extract the characteristics of a boss who is good at raising children.

[0037] The remuneration calculation unit can evaluate the contribution of the superior based on the salary increase record and growth level of the subordinate, and calculate the remuneration. The salary increase record and growth level of the subordinate include, for example, the amount of salary increase and the improvement of skills, but are not limited to such examples. The remuneration calculation unit calculates the remuneration using a model that inputs, for example, the salary increase amount and the improvement level of skills of the subordinate, and outputs the contribution level of the superior. For example, the remuneration calculation unit evaluates the contribution level of the superior based on the salary increase amount of the subordinate, and calculates an appropriate remuneration. The remuneration calculation unit can also evaluate the contribution level of the superior based on the improvement level of the subordinate's skills, and calculate the remuneration. For example, the remuneration calculation unit evaluates the improvement level of the subordinate's skills, and calculates the contribution level of the superior based on the result. As a result, the contribution level of the superior is evaluated based on the salary increase record and growth level of the subordinate, and an appropriate remuneration is calculated, thereby making it possible to pay the remuneration fairly. A part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compensation calculation unit can input data on subordinates' actual salary increases into the generation AI and have the generation AI evaluate the supervisor's contribution and calculate compensation.

[0038] The payment unit can automatically pay the calculated remuneration to the superior. The calculated remuneration includes, for example, monetary remuneration, non-monetary remuneration, etc., but is not limited to such examples. For example, the payment unit links the calculated remuneration to a payroll system and reflects it in the salary of the superior. For example, the payment unit pays the monetary remuneration as a salary. The payment unit can also provide non-monetary remuneration (for example, an award or special leave). For example, the payment unit can grant special leave to the superior. This enables quick and accurate remuneration payment by automatically paying the calculated remuneration to the superior. A part or all of the above-mentioned processing in the payment unit may be performed, for example, using AI, or may be performed without using AI. For example, the payment unit can input the calculated remuneration data to a generation AI and cause the generation AI to execute the payment of the remuneration.

[0039] The collection unit can analyze the past data collection history and select an appropriate collection method. The past data collection history includes, for example, the collection date and time, the collection method, etc., but is not limited to such examples. The collection unit, for example, identifies the most efficient collection method from the past data collection history and applies it to future data collection. For example, the collection unit finds improvements to the collection method based on the past data collection history and optimizes the collection process. The collection unit can also analyze the past data collection history and select the optimal collection method under specific conditions. For example, the collection unit selects the optimal collection method based on the progress of a specific project. As a result, the optimal collection method is selected by analyzing the past data collection history, making it possible to collect data efficiently. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past data collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0040] The collection unit can perform filtering based on the relationship between the superior and the subordinate and the progress of the project when collecting data. The relationship between the superior and the subordinate and the progress of the project include, for example, the phase of the project and the depth of the relationship, but are not limited to such examples. For example, when the relationship between the superior and the subordinate is good, the collection unit collects detailed data. In addition, when the progress of the project is good, the collection unit can expand the range of data to be collected. For example, when the relationship between the superior and the subordinate is deteriorating, the collection unit can temporarily suspend data collection. This makes it possible to collect highly relevant data by filtering the data based on the relationship between the superior and the subordinate and the progress of the project. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the relationship data between the superior and the subordinate to the generation AI and cause the generation AI to filter the data.

[0041] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the superior and the subordinate when collecting data. The geographical location information of the superior and the subordinate includes, for example, the location of the office, the destination of a business trip, and the like, but is not limited to such examples. For example, when the superior and the subordinate are close to each other, the collection unit collects data in real time. In addition, when the superior and the subordinate are far away from each other, the collection unit can also select data to be collected based on past data. For example, the collection unit preferentially collects highly relevant data based on the geographical location information of the superior and the subordinate. As a result, highly relevant data can be preferentially collected by considering the geographical location information of the superior and the subordinate. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the superior and the subordinate to the generation AI and cause the generation AI to collect data.

[0042] The collection unit can analyze the social media activities of the superior and the subordinate at the time of data collection and collect related data. The social media activities of the superior and the subordinate include, for example, the content of posts, the frequency of comments, and the like, but are not limited to such examples. The collection unit, for example, analyzes the social media activities of the superior and the subordinate and collects related data. For example, the collection unit selects targets for data collection based on interactions between the superior and the subordinate on social media. The collection unit can also analyze social media activities and collect data based on the relationship between the superior and the subordinate. For example, the collection unit analyzes the social media activities of the superior and the subordinate and collects related data. As a result, related data can be collected by analyzing the social media activities of the superior and the subordinate. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data of the superior and the subordinate to the generation AI and cause the generation AI to collect data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the boss's behavior during the analysis. The importance of the boss's behavior includes, but is not limited to, for example, the frequency of instruction and the quality of feedback. For example, the analysis unit performs a detailed analysis when the boss's behavior is important. In addition, the analysis unit can perform a simplified analysis when the boss's behavior is not very important. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the boss's behavior. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the boss's behavior. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the boss's behavior data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the behavior category of the boss during analysis. The behavior category of the boss includes, for example, guidance, evaluation, feedback, etc., but is not limited to such examples. For example, when the behavior of the boss is related to feedback, the analysis unit applies a feedback analysis algorithm. In addition, when the behavior of the boss is related to support, the analysis unit can also apply a support analysis algorithm. For example, the analysis unit selects an optimal analysis algorithm depending on the behavior category of the boss. As a result, highly accurate analysis is possible by applying an optimal analysis algorithm depending on the behavior category of the boss. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the behavior category data of the boss to the generation AI and cause the generation AI to select the analysis algorithm.

[0045] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The data collection time includes, for example, the start and end of a project, but is not limited to such examples. The analysis unit, for example, prioritizes the analysis of the latest data. The analysis unit can also prioritize the analysis of data of high importance based on past data. For example, the analysis unit determines the analysis priority based on the time of data collection. This enables efficient analysis by determining the analysis priority based on the time of data collection. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data collection to the generation AI and cause the generation AI to determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance between the behavior of the superior and the growth of the subordinate during analysis. The relevance between the behavior of the superior and the growth of the subordinate includes, for example, the type of behavior and the degree of growth, but is not limited to such an example. For example, the analysis unit prioritizes analysis when the behavior of the superior greatly affects the growth of the subordinate. In addition, the analysis unit can postpone the behavior of the superior when the behavior of the superior does not greatly affect the growth of the subordinate. For example, the analysis unit adjusts the order of analysis based on the relevance between the behavior of the superior and the growth of the subordinate. This allows efficient analysis by adjusting the order of analysis based on the relevance between the behavior of the superior and the growth of the subordinate. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the behavior data of the superior and the growth data of the subordinate to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The remuneration calculation unit can select the optimal remuneration calculation method by analyzing the past contribution of the superior when calculating the remuneration. The past contribution of the superior includes, for example, the success rate of the project, the growth degree of the subordinate, and the like, but is not limited to such examples. The remuneration calculation unit selects the optimal remuneration calculation method based on, for example, the past contribution of the superior. The remuneration calculation unit can also analyze the past contribution of the superior and find improvements in the remuneration calculation method. For example, the remuneration calculation unit customizes the remuneration calculation method according to the past contribution of the superior. As a result, by analyzing the past contribution of the superior, the optimal remuneration calculation method can be selected and fair remuneration payment is possible. A part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the remuneration calculation unit can input the past contribution data of the superior to the generation AI and cause the generation AI to select the optimal remuneration calculation method.

[0048] The remuneration calculation unit can customize the means of remuneration calculation based on the current work situation of the superior when calculating the remuneration. The current work situation of the superior includes, for example, the progress of the project in charge, the workload, etc., but is not limited to such examples. The remuneration calculation unit customizes the means of remuneration calculation, for example, taking into account the current work situation of the superior. In addition, the remuneration calculation unit can also apply a means of quickly calculating the remuneration when the work situation of the superior is busy. For example, the remuneration calculation unit applies a detailed remuneration calculation means when the work situation of the superior is calm. This enables quick and appropriate remuneration calculation by customizing the means of remuneration calculation based on the current work situation of the superior. Part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the remuneration calculation unit can input the current work situation data of the superior to the generation AI and cause the generation AI to execute customization of the means of remuneration calculation.

[0049] The remuneration calculation unit can select the optimal remuneration calculation method in consideration of the geographical location information of the superior when calculating the remuneration. The geographical location information of the superior includes, for example, the location of the office, the destination of the business trip, etc., but is not limited to such examples. The remuneration calculation unit selects the optimal remuneration calculation method, for example, based on the geographical location information of the superior. In addition, the remuneration calculation unit can also apply an online remuneration calculation method when the superior is in a remote location. For example, the remuneration calculation unit applies a face-to-face remuneration calculation method when the superior is nearby. This allows the optimal remuneration calculation method to be selected by considering the geographical location information of the superior, and enables efficient remuneration payment. Part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the remuneration calculation unit can input the geographical location information of the superior to the generation AI and cause the generation AI to select the optimal remuneration calculation method.

[0050] The reward calculation unit can analyze the social media activity of the superior and propose a reward calculation means when calculating the reward. The social media activity of the superior includes, but is not limited to, the posting content, the frequency of comments, and the like. The reward calculation unit can, for example, analyze the social media activity of the superior and propose an optimal reward calculation means. The reward calculation unit can also customize the reward calculation means based on the superior's activity on social media. For example, the reward calculation unit analyzes the social media activity of the superior and finds improvements to the reward calculation means. As a result, the social media activity of the superior is analyzed to propose an optimal reward calculation means, enabling efficient reward payment. A part or all of the above-mentioned processing in the reward calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reward calculation unit can input the social media activity data of the superior to the generation AI and cause the generation AI to execute the proposal of the reward calculation means.

[0051] The payment section can select the optimal payment method by referring to the supervisor's past remuneration history at the time of payment. The supervisor's past remuneration history includes, for example, past remuneration amounts, types of remuneration, etc., but is not limited to such examples. The payment section selects the optimal payment method, for example, based on the supervisor's past remuneration history. The payment section can also analyze the supervisor's past remuneration history and find improvements to the payment method. For example, the payment section customizes the payment method according to the supervisor's past remuneration history. This allows the supervisor's past remuneration history to be referred to to select the optimal payment method and make efficient payments. Part or all of the above-mentioned processing in the payment section may be performed, for example, using AI, or may be performed without using AI. For example, the payment section can input the supervisor's past remuneration history data into the generation AI and cause the generation AI to select the optimal payment method.

[0052] The payment department can customize the means of payment based on the current work situation of the supervisor at the time of payment. The current work situation of the supervisor includes, for example, the progress of the project in charge, the workload, etc., but is not limited to such examples. The payment department customizes the means of payment, for example, taking into account the current work situation of the supervisor. In addition, the payment department can also apply a means of making a payment quickly when the work situation of the supervisor is busy. For example, when the work situation of the supervisor is calm, the payment department applies a means of making a payment after detailed confirmation. This enables quick and appropriate payment by customizing the means of payment based on the current work situation of the supervisor. Part or all of the above-mentioned processing in the payment department may be performed, for example, using AI, or may be performed without using AI. For example, the payment department can input the current work situation data of the supervisor to the generation AI and cause the generation AI to execute customization of the means of payment.

[0053] The payment section can select the optimal payment method by considering the geographical location information of the superior at the time of payment. The geographical location information of the superior includes, for example, the location of the office, the destination of a business trip, etc., but is not limited to such examples. The payment section selects the optimal payment method based on, for example, the geographical location information of the superior. In addition, the payment section can also apply an online payment method when the superior is in a remote location. For example, the payment section applies a face-to-face payment method when the superior is nearby. This allows the optimal payment method to be selected by considering the geographical location information of the superior, enabling efficient payment. Part or all of the above-mentioned processing in the payment section may be performed, for example, using AI, or may be performed without using AI. For example, the payment section can input the geographical location information of the superior to the generation AI and cause the generation AI to select the optimal payment method.

[0054] The payment section can analyze the social media activity of the supervisor at the time of payment and propose a payment method. The social media activity of the supervisor includes, but is not limited to, for example, the content of posts and the frequency of comments. The payment section can, for example, analyze the social media activity of the supervisor and propose an optimal payment method. The payment section can also customize the payment method based on the supervisor's activity on social media. For example, the payment section analyzes the social media activity of the supervisor and finds improvements to the payment method. As a result, the social media activity of the supervisor is analyzed to propose an optimal payment method, enabling efficient payment. A part or all of the above-mentioned processing in the payment section can be performed, for example, using AI or can be performed without using AI. For example, the payment section can input the social media activity data of the supervisor to the generation AI and have the generation AI execute the proposal of the payment method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The training support system may further include a self-assessment collection unit that collects self-assessment data of subordinates. The self-assessment collection unit provides an interface for subordinates to input their evaluations of their own growth and their superiors' guidance. For example, subordinates may periodically perform self-assessments and input the results into the system. The self-assessment collection unit may also collect evaluations of the quality of support and usefulness of feedback felt by subordinates. In this way, collecting the self-assessment data of subordinates enables more accurate analysis in combination with the behavioral evaluation data of the superiors. Furthermore, the self-assessment data reflects the subordinates' subjective sense of growth and satisfaction, allowing the effectiveness of the superiors' guidance to be evaluated from multiple angles.

[0057] The collection unit can collect the skill update data of the subordinates. For example, when the subordinates acquire new skills, the information is collected and recorded in the database. The collection unit can also collect the contents of the training and seminars that the subordinates have received. For example, the collection unit records the type, duration, and results of the training that the subordinates have participated in. In this way, by collecting the skill update data of the subordinates, the degree of growth of the subordinates can be evaluated in more detail. Furthermore, the skill update data can also be useful in designing an effective training program to promote the growth of the subordinates.

[0058] The remuneration calculation unit can evaluate the contribution of the superior based on the self-evaluation data of the subordinate. For example, if the subordinate gives a high evaluation to the guidance of the superior in the self-evaluation, the evaluation is reflected in the remuneration calculation. In addition, if the subordinate provides specific feedback to the support of the superior in the self-evaluation, the remuneration can be calculated based on the content. For example, the remuneration calculation unit analyzes the self-evaluation data of the subordinate and calculates the remuneration using a model that evaluates the contribution of the superior. In this way, the remuneration can be paid more fairly by evaluating the contribution of the superior based on the self-evaluation data of the subordinate.

[0059] The collection unit can collect health data of subordinates and use it as data for adjusting the supervisor's instruction methods. For example, if the subordinate's health condition is not good, the collection unit can use it as data for reviewing the supervisor's instruction methods. The collection unit can also evaluate the impact of the supervisor's instruction on the subordinate's health based on the subordinate's health data. For example, the collection unit collects health data of subordinates and uses it as data for adjusting the supervisor's instruction methods. In this way, by collecting the subordinate's health data, the supervisor can review his instruction methods and provide instruction that takes the subordinate's health into consideration.

[0060] The collection department can monitor the progress of the project of its subordinates in real time and adjust the timing of data collection. For example, when the project is in a critical phase, the frequency of data collection can be increased. Also, when the project is progressing smoothly, the frequency of data collection can be reduced. For example, the collection department can monitor the progress of the project in real time and adjust the timing of data collection. This allows for efficient data collection by adjusting the timing of data collection according to the progress of the project.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection department collects subordinate-supervisor combination history data, supervisor behavior evaluation data, and subordinate salary increase performance data. The subordinate-supervisor combination history data includes the duration of the project and the content of the collaborative work. The supervisor behavior evaluation data includes the frequency of feedback and the quality of guidance. The subordinate salary increase performance data includes the frequency of salary increases and the amount of salary increases. The collection department obtains this data from databases, surveys, evaluation systems, and human resources systems. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the characteristics of a boss who is good at nurturing. The analysis unit uses AI to analyze the relationship between the boss's behavior and the subordinate's growth, and analyzes specific behavior. For example, it uses a model that inputs the boss's behavior data and the subordinate's growth data, and outputs the characteristics of a boss who is good at nurturing. Step 3: The compensation calculation unit automatically calculates the appropriate compensation for the supervisor who has successfully developed their subordinates based on the characteristics extracted by the analysis unit. The compensation calculation unit evaluates the supervisor's contribution based on the subordinate's salary increase record and level of growth, and calculates the appropriate compensation. For example, a model is used that inputs the amount of the subordinate's salary increase and the degree of skill improvement, and outputs the supervisor's contribution. Step 4: The payment department automatically pays the remuneration calculated by the remuneration calculation department to the superior. The payment department links the calculated remuneration to the payroll system and reflects it in the superior's salary. The payment department can pay not only monetary remuneration but also non-monetary remuneration (e.g., awards, special leave, etc.).

[0063] (Example 2) The training support system according to the embodiment of the present invention is a system that extracts the characteristics of a good boss using the combination history data of subordinates and superiors, behavior evaluation data of superiors, and salary increase record data of subordinates. This training support system extracts the characteristics of a good boss by collecting the combination history data of subordinates and superiors, behavior evaluation data of superiors, and salary increase record data of subordinates, and analyzing them using AI. Furthermore, it links a function that automatically calculates and pays an appropriate remuneration according to the degree of contribution to superiors who have successfully trained their subordinates. This mechanism allows superiors to obtain a common guideline on what they should do to develop their subordinates, and the priority of human resource development in work is also raised by incentives, realizing sustainable development of next-generation employees. For example, the training support system collects combination history data of subordinates and superiors, behavior evaluation data of superiors, and salary increase record data of subordinates. These data indicate the relationship between each superior and the subordinate, the behavior of the superior, and the growth of the subordinate. For example, it records in detail what actions the superior took and how the subordinate grew. Next, the collected data is analyzed using AI. AI analyzes the relationship between the behavior of a boss and the growth of a subordinate, and extracts the characteristics of a boss who is good at raising subordinates. For example, it analyzes specific actions such as what kind of feedback the boss gave to the subordinate and what kind of support the boss provided. In addition, AI automatically calculates the appropriate compensation for a boss who has successfully raised a subordinate according to the degree of contribution. For example, it evaluates the contribution of the boss based on the subordinate's salary increase record and growth degree, and calculates the appropriate compensation. Finally, the calculated compensation is automatically paid to the boss. This system allows the boss to obtain an incentive, and the priority of human resource development in the work increases. This realizes sustainable development of the next generation of employees. This system provides a common guideline of what the boss should do to develop the subordinate, so the boss can take specific actions. For example, the boss can promote the growth of the subordinate by taking actions such as providing regular feedback to the subordinate and supporting the subordinate's growth. This system also allows the boss to understand how his or her actions affect the growth of the subordinate.For example, by recording in detail what kind of feedback a supervisor gave to a subordinate and what kind of support he or she provided, and analyzing it with AI, it is possible to clarify the relationship between the supervisor's actions and the subordinate's growth. In this way, by taking specific actions, the supervisor can promote the growth of the subordinate and realize sustainable development of the next generation of employees. As a result, the development support system can extract the characteristics of a supervisor who is good at developing employees using the subordinate-supervisor combination history data, the supervisor's behavior evaluation data, and the subordinate's salary increase record data, and automatically calculate and pay appropriate remuneration.

[0064] The training support system according to the embodiment includes a collection unit, an analysis unit, a remuneration calculation unit, and a payment unit. The collection unit collects subordinate-supervisor combination history data, behavior evaluation data of the supervisor, and salary increase achievement data of the subordinate. The subordinate-supervisor combination history data includes, for example, the duration of the project, the contents of the joint work, and the like, but is not limited to such examples. The supervisor's behavior evaluation data includes, for example, the frequency of feedback, the quality of guidance, and the like, but is not limited to such examples. The subordinate's salary increase achievement data includes, for example, the frequency of salary increase, the amount of salary increase, and the like, but is not limited to such examples. The collection unit, for example, acquires the subordinate-supervisor combination history data from a database. The collection unit can also collect the supervisor's behavior evaluation data from a questionnaire or an evaluation system. Furthermore, the collection unit can also acquire the subordinate's salary increase achievement data from a personnel system. The analysis unit analyzes the data collected by the collection unit and extracts the characteristics of a supervisor who is good at raising employees. The analysis unit, for example, uses AI to analyze the relationship between the supervisor's behavior and the subordinate's growth. The AI ​​analyzes specific actions such as what kind of feedback the superior gave to the subordinate and what kind of support the superior provided. For example, the AI ​​inputs the behavioral data of the superior and the growth data of the subordinate, and performs analysis using a model that outputs the characteristics of a superior who is good at raising subordinates. The compensation calculation unit automatically calculates an appropriate compensation for the superior who has successfully raised the subordinate based on the characteristics extracted by the analysis unit. The compensation calculation unit evaluates the superior's contribution based on, for example, the subordinate's salary increase record and growth level, and calculates an appropriate compensation. The compensation calculation unit calculates the compensation using a model that inputs, for example, the amount of the subordinate's salary increase and the degree of skill improvement, and outputs the superior's contribution. The payment unit automatically pays the compensation calculated by the compensation calculation unit to the superior. The payment unit, for example, links the calculated compensation to a payroll system and reflects it in the salary of the superior. The payment unit can pay not only monetary compensation but also non-monetary compensation (for example, awards and special leave). As a result, the training support system of the embodiment can extract the characteristics of a manager who is good at training employees using subordinate-supervisor combination history data, behavioral evaluation data of the supervisor, and salary increase record data of the subordinate, and automatically calculate and pay appropriate remuneration.For example, the collection unit acquires subordinate-supervisor combination history data from a database, collects supervisor behavior evaluation data from questionnaires and evaluation systems, and acquires subordinate salary increase performance data from a personnel system. The analysis unit uses AI to analyze the relationship between the supervisor's behavior and the subordinate's growth, and extracts the characteristics of a supervisor who is good at developing employees. The compensation calculation unit evaluates the supervisor's contribution based on the subordinate's salary increase performance and growth level, and calculates an appropriate compensation. The payment unit links the calculated compensation to the salary system and reflects it in the supervisor's salary. In this way, the development support system can extract the characteristics of a supervisor who is good at developing employees, and automatically calculate and pay an appropriate compensation, using the subordinate-supervisor combination history data, the supervisor behavior evaluation data, and the subordinate salary increase performance data.

[0065] The collection unit collects subordinate-supervisor combination history data, supervisor behavior evaluation data, and subordinate pay increase performance data. The subordinate-supervisor combination history data includes, for example, the duration of a project, the contents of joint work, and the like, but is not limited to such examples. Specifically, detailed information such as the start date and end date of the project, the purpose and goal of the project, the division of roles of participating members, and the progress status is included. This allows a detailed understanding of which supervisor was involved in which subordinate and in what project. The supervisor behavior evaluation data includes, for example, the frequency of feedback, the quality of guidance, and the like, but is not limited to such examples. Specifically, the content and timing of feedback given by the supervisor to the subordinate, the specificity and constructiveness of the feedback, the subordinate's reaction and improvement points, and the like are included. This allows a detailed evaluation of the supervisor's guidance method and communication style. The subordinate pay increase performance data includes, for example, the frequency of pay increase, the amount of pay increase, and the like, but is not limited to such examples. Specifically, the timing of the subordinate's pay increase, the reason for the pay increase, the evaluation criteria, and the fluctuation of the pay increase amount are included. This allows a detailed understanding of the subordinate's growth and achievements. The collection unit, for example, acquires subordinate-supervisor combination history data from a database. The collection unit can also collect behavior evaluation data of superiors from questionnaires or evaluation systems. Furthermore, the collection unit can acquire salary increase performance data of subordinates from a personnel system. This allows the collection unit to efficiently collect necessary information from various data sources and integrate it into a database for the entire system. The collected data is updated in real time and made accessible to the analysis unit and compensation calculation unit. This allows the collection unit to ensure the consistency and reliability of data throughout the system and achieve fast and accurate data collection.

[0066] The analysis unit analyzes the data collected by the collection unit and extracts the characteristics of a good boss. The analysis unit, for example, uses AI to analyze the relationship between the boss's behavior and the subordinate's growth. The AI ​​analyzes specific behaviors such as what kind of feedback the boss gave to the subordinate and what kind of support the boss provided. Specifically, it uses natural language processing technology to analyze the content of the feedback and evaluates the frequency of positive feedback and constructive criticism. In addition, it uses a machine learning algorithm to associate the boss's behavior pattern with the subordinate's growth data to identify effective teaching methods. For example, the AI ​​inputs the boss's behavior data and the subordinate's growth data and performs analysis using a model that outputs the characteristics of a good boss. This model evaluates the boss's contribution by taking into account the quality and frequency of the boss' feedback, the subordinate's salary increase record and the degree of skill improvement, etc. In addition, the analysis unit can also analyze long-term trends and patterns using past data and statistical information. For example, it extracts commonalities in the boss's teaching methods and communication styles based on the track record of a specific boss developing multiple subordinates. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to detect problems early. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0067] The compensation calculation unit automatically calculates an appropriate compensation for a superior who has successfully trained his / her subordinates based on the characteristics extracted by the analysis unit. The compensation calculation unit evaluates the superior's contribution based on, for example, the subordinate's salary increase record and growth level, and calculates an appropriate compensation. Specifically, the compensation is calculated using a model that inputs the subordinate's salary increase amount and the degree of skill improvement, and outputs the superior's contribution. This model integrates the superior's behavior evaluation data and the subordinate's growth data, and quantitatively evaluates how much the superior's guidance contributed to the subordinate's growth. For example, if a subordinate's skill is significantly improved in a short period of time, the superior's contribution is highly evaluated. In addition, the compensation calculation unit can take into account not only the subordinate's salary increase record, but also the subordinate's project success rate and team performance. This makes it possible to evaluate the superior's contribution from multiple angles and calculate a more fair and appropriate compensation. Furthermore, the compensation calculation unit can also verify the appropriateness of the compensation by referring to past compensation data and industry standards. For example, it can check whether the superior's compensation is appropriate by comparing it with the compensation level in the same industry or in a company of the same size. In addition, the Compensation Calculation Department can make the compensation calculation process transparent and clearly explain the basis for compensation to superiors, which can increase the motivation of superiors and improve the performance of the entire organization.

[0068] The payment department automatically pays the remuneration calculated by the remuneration calculation department to the superior. For example, the payment department links the calculated remuneration to a payroll system and reflects it in the superior's salary. Specifically, the payment department inputs the remuneration data received from the remuneration calculation department into the payroll system and reflects it in the monthly salary and bonus. In addition, the payment department can pay not only monetary remuneration but also non-monetary remuneration (for example, commendation and special leave, etc.). For example, a superior who has provided excellent guidance can be provided with a special certificate of commendation, an award at an in-house event, additional vacation days, etc. This can increase the superior's motivation and encourage further growth. In addition, the payment department can automate the remuneration payment process and pay remuneration efficiently and accurately. For example, in cooperation with the payroll system, the payment schedule for remuneration can be automatically set and remuneration can be paid without delay. In addition, the payment department can monitor the remuneration payment status in real time and respond quickly when a problem occurs. As a result, the payment department can pay remuneration quickly and reliably to the superior, improving the reliability and transparency of the entire organization. In addition, the payment department can record the remuneration payment history in detail and use it for future evaluation and analysis. This enables payments to continually improve compensation payment processes and drive performance across the organisation.

[0069] The collection unit can collect behavior evaluation data of the superior. The behavior evaluation data of the superior includes, for example, the frequency of feedback, the quality of guidance, and the like, but is not limited to such examples. The collection unit, for example, collects the behavior evaluation data of the superior from a questionnaire or an evaluation system. For example, the collection unit records the frequency of feedback given by the superior to the subordinate. The collection unit can also collect data for evaluating the quality of guidance of the superior. For example, the collection unit can collect by questionnaire how the subordinate felt about the guidance of the superior. In this way, by collecting the behavior evaluation data of the superior, the relationship between the behavior of the superior and the growth of the subordinate can be analyzed. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the questionnaire data to the generation AI and cause the generation AI to execute the analysis of the behavior evaluation data of the superior.

[0070] The collection unit can collect the salary increase performance data of the subordinate. The salary increase performance data of the subordinate includes, for example, the frequency of salary increase, the amount of salary increase, etc., but is not limited to such examples. The collection unit, for example, acquires the salary increase performance data of the subordinate from a personnel system. For example, the collection unit records the amount of salary increase of the subordinate. The collection unit can also record the frequency of salary increase of the subordinate. For example, the collection unit can acquire from a database how frequently the subordinate has received a salary increase. In this way, by collecting the salary increase performance data of the subordinate, the degree of growth of the subordinate can be evaluated. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the salary increase performance data acquired from the personnel system to the generation AI and cause the generation AI to perform analysis of the data.

[0071] The analysis unit can analyze the relationship between the behavior of the boss and the growth of the subordinate. The relationship between the behavior of the boss and the growth of the subordinate includes, for example, the type of behavior and the degree of growth, but is not limited to such an example. The analysis unit, for example, uses AI to analyze the relationship between the behavior of the boss and the growth of the subordinate. For example, the analysis unit analyzes the frequency of feedback given by the boss to the subordinate and the degree of growth of the subordinate. The analysis unit can also analyze the quality of the guidance of the boss and the degree of growth of the subordinate. For example, the analysis unit inputs the behavior data of the boss and the growth data of the subordinate, and performs analysis using a model that outputs the characteristics of a boss who is good at raising children. In this way, the characteristics of a boss who is good at raising children can be extracted by analyzing the relationship between the behavior of the boss and the growth of the subordinate. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the behavior data of the boss and the growth data of the subordinate to the generation AI, and causes the generation AI to extract the characteristics of a boss who is good at raising children.

[0072] The remuneration calculation unit can evaluate the contribution of the superior based on the salary increase record and growth level of the subordinate, and calculate the remuneration. The salary increase record and growth level of the subordinate include, for example, the amount of salary increase and the improvement of skills, but are not limited to such examples. The remuneration calculation unit calculates the remuneration using a model that inputs, for example, the salary increase amount and the improvement level of skills of the subordinate, and outputs the contribution level of the superior. For example, the remuneration calculation unit evaluates the contribution level of the superior based on the salary increase amount of the subordinate, and calculates an appropriate remuneration. The remuneration calculation unit can also evaluate the contribution level of the superior based on the improvement level of the subordinate's skills, and calculate the remuneration. For example, the remuneration calculation unit evaluates the improvement level of the subordinate's skills, and calculates the contribution level of the superior based on the result. As a result, the contribution level of the superior is evaluated based on the salary increase record and growth level of the subordinate, and an appropriate remuneration is calculated, thereby making it possible to pay the remuneration fairly. A part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compensation calculation unit can input data on subordinates' actual salary increases into the generation AI and have the generation AI evaluate the supervisor's contribution and calculate compensation.

[0073] The payment unit can automatically pay the calculated remuneration to the superior. The calculated remuneration includes, for example, monetary remuneration, non-monetary remuneration, etc., but is not limited to such examples. For example, the payment unit links the calculated remuneration to a payroll system and reflects it in the salary of the superior. For example, the payment unit pays the monetary remuneration as a salary. The payment unit can also provide non-monetary remuneration (for example, an award or special leave). For example, the payment unit can grant special leave to the superior. This enables quick and accurate remuneration payment by automatically paying the calculated remuneration to the superior. A part or all of the above-mentioned processing in the payment unit may be performed, for example, using AI, or may be performed without using AI. For example, the payment unit can input the calculated remuneration data to a generation AI and cause the generation AI to execute the payment of the remuneration.

[0074] The collection unit can estimate the emotion of a user (e.g., at least one of a superior and a subordinate; the same applies below) and adjust the timing of data collection based on the estimated emotion of the user. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can increase the frequency of collection to collect detailed data. For example, when the user is busy, the collection unit can postpone data collection and adjust it according to the user's schedule. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the emotion data of the user to the generation AI and cause the generation AI to adjust the timing of data collection.

[0075] The collection unit can analyze the past data collection history and select an appropriate collection method. The past data collection history includes, for example, the collection date and time, the collection method, etc., but is not limited to such examples. The collection unit, for example, identifies the most efficient collection method from the past data collection history and applies it to future data collection. For example, the collection unit finds improvements to the collection method based on the past data collection history and optimizes the collection process. The collection unit can also analyze the past data collection history and select the optimal collection method under specific conditions. For example, the collection unit selects the optimal collection method based on the progress of a specific project. As a result, the optimal collection method is selected by analyzing the past data collection history, making it possible to collect data efficiently. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past data collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0076] The collection unit can perform filtering based on the relationship between the superior and the subordinate and the progress of the project when collecting data. The relationship between the superior and the subordinate and the progress of the project include, for example, the phase of the project and the depth of the relationship, but are not limited to such examples. For example, when the relationship between the superior and the subordinate is good, the collection unit collects detailed data. In addition, when the progress of the project is good, the collection unit can expand the range of data to be collected. For example, when the relationship between the superior and the subordinate is deteriorating, the collection unit can temporarily suspend data collection. This makes it possible to collect highly relevant data by filtering the data based on the relationship between the superior and the subordinate and the progress of the project. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the relationship data between the superior and the subordinate to the generation AI and cause the generation AI to filter the data.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit preferentially collects data with high importance. In addition, when the user is relaxed, the collection unit can also preferentially collect detailed data. For example, when the user is busy, the collection unit can reduce the amount of data to be collected and collect only important data. In this way, important data can be preferentially collected by determining the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the data.

[0078] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the superior and the subordinate when collecting data. The geographical location information of the superior and the subordinate includes, for example, the location of the office, the destination of a business trip, and the like, but is not limited to such examples. For example, when the superior and the subordinate are close to each other, the collection unit collects data in real time. In addition, when the superior and the subordinate are far away from each other, the collection unit can also select data to be collected based on past data. For example, the collection unit preferentially collects highly relevant data based on the geographical location information of the superior and the subordinate. As a result, highly relevant data can be preferentially collected by considering the geographical location information of the superior and the subordinate. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the superior and the subordinate to the generation AI and cause the generation AI to collect data.

[0079] The collection unit can analyze the social media activities of the superior and the subordinate at the time of data collection and collect related data. The social media activities of the superior and the subordinate include, for example, the content of posts, the frequency of comments, and the like, but are not limited to such examples. The collection unit, for example, analyzes the social media activities of the superior and the subordinate and collects related data. For example, the collection unit selects targets for data collection based on interactions between the superior and the subordinate on social media. The collection unit can also analyze social media activities and collect data based on the relationship between the superior and the subordinate. For example, the collection unit analyzes the social media activities of the superior and the subordinate and collects related data. As a result, related data can be collected by analyzing the social media activities of the superior and the subordinate. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data of the superior and the subordinate to the generation AI and cause the generation AI to collect data.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, when the user is in a hurry, the analysis unit provides an analysis result that covers the main points. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the boss's behavior during the analysis. The importance of the boss's behavior includes, but is not limited to, for example, the frequency of instruction and the quality of feedback. For example, the analysis unit performs a detailed analysis when the boss's behavior is important. In addition, the analysis unit can perform a simplified analysis when the boss's behavior is not very important. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the boss's behavior. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the boss's behavior. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the boss's behavior data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the behavior category of the boss during analysis. The behavior category of the boss includes, for example, guidance, evaluation, feedback, etc., but is not limited to such examples. For example, when the behavior of the boss is related to feedback, the analysis unit applies a feedback analysis algorithm. In addition, when the behavior of the boss is related to support, the analysis unit can also apply a support analysis algorithm. For example, the analysis unit selects an optimal analysis algorithm depending on the behavior category of the boss. As a result, highly accurate analysis is possible by applying an optimal analysis algorithm depending on the behavior category of the boss. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the behavior category data of the boss to the generation AI and cause the generation AI to select the analysis algorithm.

[0083] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. In addition, when the user is relaxed, the analysis unit can also provide a display method including detailed information. For example, when the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows the display to be easily understood by the user by adjusting the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0084] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The data collection time includes, for example, the start and end of a project, but is not limited to such examples. The analysis unit, for example, prioritizes the analysis of the latest data. The analysis unit can also prioritize the analysis of data of high importance based on past data. For example, the analysis unit determines the analysis priority based on the time of data collection. This enables efficient analysis by determining the analysis priority based on the time of data collection. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data collection to the generation AI and cause the generation AI to determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance between the behavior of the superior and the growth of the subordinate during analysis. The relevance between the behavior of the superior and the growth of the subordinate includes, for example, the type of behavior and the degree of growth, but is not limited to such an example. For example, the analysis unit prioritizes analysis when the behavior of the superior greatly affects the growth of the subordinate. In addition, the analysis unit can postpone the behavior of the superior when the behavior of the superior does not greatly affect the growth of the subordinate. For example, the analysis unit adjusts the order of analysis based on the relevance between the behavior of the superior and the growth of the subordinate. This allows efficient analysis by adjusting the order of analysis based on the relevance between the behavior of the superior and the growth of the subordinate. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the behavior data of the superior and the growth data of the subordinate to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] The reward calculation unit can estimate the user's emotion and adjust the reward calculation method based on the estimated user's emotion. For example, when the user is stressed, the reward calculation unit applies a simple reward calculation method. In addition, when the user is relaxed, the reward calculation unit can also apply a detailed reward calculation method. For example, when the user is in a hurry, the reward calculation unit applies a method for quickly calculating the reward. This allows for appropriate reward calculation for the user by adjusting the reward calculation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reward calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reward calculation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the reward calculation method.

[0087] The remuneration calculation unit can select the optimal remuneration calculation method by analyzing the past contribution of the superior when calculating the remuneration. The past contribution of the superior includes, for example, the success rate of the project, the growth degree of the subordinate, and the like, but is not limited to such examples. The remuneration calculation unit selects the optimal remuneration calculation method based on, for example, the past contribution of the superior. The remuneration calculation unit can also analyze the past contribution of the superior and find improvements in the remuneration calculation method. For example, the remuneration calculation unit customizes the remuneration calculation method according to the past contribution of the superior. As a result, by analyzing the past contribution of the superior, the optimal remuneration calculation method can be selected and fair remuneration payment is possible. A part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the remuneration calculation unit can input the past contribution data of the superior to the generation AI and cause the generation AI to select the optimal remuneration calculation method.

[0088] The remuneration calculation unit can customize the means of remuneration calculation based on the current work situation of the superior when calculating the remuneration. The current work situation of the superior includes, for example, the progress of the project in charge, the workload, etc., but is not limited to such examples. The remuneration calculation unit customizes the means of remuneration calculation, for example, taking into account the current work situation of the superior. In addition, the remuneration calculation unit can also apply a means of quickly calculating the remuneration when the work situation of the superior is busy. For example, the remuneration calculation unit applies a detailed remuneration calculation means when the work situation of the superior is calm. This enables quick and appropriate remuneration calculation by customizing the means of remuneration calculation based on the current work situation of the superior. Part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the remuneration calculation unit can input the current work situation data of the superior to the generation AI and cause the generation AI to execute customization of the means of remuneration calculation.

[0089] The reward calculation unit can estimate the user's emotion and determine the priority of reward calculation based on the estimated user's emotion. For example, when the user is stressed, the reward calculation unit prioritizes reward calculation with high importance. In addition, when the user is relaxed, the reward calculation unit can also prioritize detailed reward calculation. For example, when the user is in a hurry, the reward calculation unit quickly performs reward calculation. As a result, important reward calculation can be prioritized by determining the priority of reward calculation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reward calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reward calculation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of reward calculation.

[0090] The remuneration calculation unit can select the optimal remuneration calculation method in consideration of the geographical location information of the superior when calculating the remuneration. The geographical location information of the superior includes, for example, the location of the office, the destination of the business trip, etc., but is not limited to such examples. The remuneration calculation unit selects the optimal remuneration calculation method, for example, based on the geographical location information of the superior. In addition, the remuneration calculation unit can also apply an online remuneration calculation method when the superior is in a remote location. For example, the remuneration calculation unit applies a face-to-face remuneration calculation method when the superior is nearby. This allows the optimal remuneration calculation method to be selected by considering the geographical location information of the superior, and enables efficient remuneration payment. Part or all of the above-mentioned processing in the remuneration calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the remuneration calculation unit can input the geographical location information of the superior to the generation AI and cause the generation AI to select the optimal remuneration calculation method.

[0091] The reward calculation unit can analyze the social media activity of the superior and propose a reward calculation means when calculating the reward. The social media activity of the superior includes, but is not limited to, the posting content, the frequency of comments, and the like. The reward calculation unit can, for example, analyze the social media activity of the superior and propose an optimal reward calculation means. The reward calculation unit can also customize the reward calculation means based on the superior's activity on social media. For example, the reward calculation unit analyzes the social media activity of the superior and finds improvements to the reward calculation means. As a result, the social media activity of the superior is analyzed to propose an optimal reward calculation means, enabling efficient reward payment. A part or all of the above-mentioned processing in the reward calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reward calculation unit can input the social media activity data of the superior to the generation AI and cause the generation AI to execute the proposal of the reward calculation means.

[0092] The payment unit can estimate the user's emotion and adjust the timing of payment based on the estimated user's emotion. For example, the payment unit makes a payment quickly when the user is stressed. Also, the payment unit can make a payment after detailed confirmation when the user is relaxed. For example, the payment unit prioritizes immediate payment when the user is in a hurry. This allows for a quick and appropriate payment by adjusting the timing of payment according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the payment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the payment unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of payment.

[0093] The payment section can select the optimal payment method by referring to the supervisor's past remuneration history at the time of payment. The supervisor's past remuneration history includes, for example, past remuneration amounts, types of remuneration, etc., but is not limited to such examples. The payment section selects the optimal payment method, for example, based on the supervisor's past remuneration history. The payment section can also analyze the supervisor's past remuneration history and find improvements to the payment method. For example, the payment section customizes the payment method according to the supervisor's past remuneration history. This allows the supervisor's past remuneration history to be referred to to select the optimal payment method and make efficient payments. Part or all of the above-mentioned processing in the payment section may be performed, for example, using AI, or may be performed without using AI. For example, the payment section can input the supervisor's past remuneration history data into the generation AI and cause the generation AI to select the optimal payment method.

[0094] The payment department can customize the means of payment based on the current work situation of the supervisor at the time of payment. The current work situation of the supervisor includes, for example, the progress of the project in charge, the workload, etc., but is not limited to such examples. The payment department customizes the means of payment, for example, taking into account the current work situation of the supervisor. In addition, the payment department can also apply a means of making a payment quickly when the work situation of the supervisor is busy. For example, when the work situation of the supervisor is calm, the payment department applies a means of making a payment after detailed confirmation. This enables quick and appropriate payment by customizing the means of payment based on the current work situation of the supervisor. Part or all of the above-mentioned processing in the payment department may be performed, for example, using AI, or may be performed without using AI. For example, the payment department can input the current work situation data of the supervisor to the generation AI and cause the generation AI to execute customization of the means of payment.

[0095] The payment unit can estimate the user's emotion and determine the priority of the payment based on the estimated user's emotion. For example, when the user is stressed, the payment unit prioritizes a payment with a high importance. In addition, when the user is relaxed, the payment unit can also make a payment after detailed confirmation. For example, when the user is in a hurry, the payment unit prioritizes immediate payment. In this way, important payments can be prioritized by determining the priority of payments according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the payment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the payment unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the payment.

[0096] The payment section can select the optimal payment method by considering the geographical location information of the superior at the time of payment. The geographical location information of the superior includes, for example, the location of the office, the destination of a business trip, etc., but is not limited to such examples. The payment section selects the optimal payment method based on, for example, the geographical location information of the superior. In addition, the payment section can also apply an online payment method when the superior is in a remote location. For example, the payment section applies a face-to-face payment method when the superior is nearby. This allows the optimal payment method to be selected by considering the geographical location information of the superior, enabling efficient payment. Part or all of the above-mentioned processing in the payment section may be performed, for example, using AI, or may be performed without using AI. For example, the payment section can input the geographical location information of the superior to the generation AI and cause the generation AI to select the optimal payment method.

[0097] The payment section can analyze the social media activity of the supervisor at the time of payment and propose a payment method. The social media activity of the supervisor includes, but is not limited to, for example, the content of posts and the frequency of comments. The payment section can, for example, analyze the social media activity of the supervisor and propose an optimal payment method. The payment section can also customize the payment method based on the supervisor's activity on social media. For example, the payment section analyzes the social media activity of the supervisor and finds improvements to the payment method. As a result, the social media activity of the supervisor is analyzed to propose an optimal payment method, enabling efficient payment. A part or all of the above-mentioned processing in the payment section can be performed, for example, using AI or can be performed without using AI. For example, the payment section can input the social media activity data of the supervisor to the generation AI and have the generation AI execute the proposal of the payment method.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The training support system may further include a self-assessment collection unit that collects self-assessment data of subordinates. The self-assessment collection unit provides an interface for subordinates to input their evaluations of their own growth and their superiors' guidance. For example, subordinates may periodically perform self-assessments and input the results into the system. The self-assessment collection unit may also collect evaluations of the quality of support and usefulness of feedback felt by subordinates. In this way, collecting the self-assessment data of subordinates enables more accurate analysis in combination with the behavioral evaluation data of the superiors. Furthermore, the self-assessment data reflects the subordinates' subjective sense of growth and satisfaction, allowing the effectiveness of the superiors' guidance to be evaluated from multiple angles.

[0100] The collection unit can have a function of monitoring the stress level of the subordinate. For example, when the subordinate feels stressed, the collection unit reduces the frequency of data collection to reduce the burden on the subordinate. Also, when the subordinate feels relaxed, the collection unit can increase the frequency of collection to collect detailed data. For example, the collection unit can monitor the stress level of the subordinate and temporarily suspend data collection when the stress level is high. This allows the timing of data collection to be adjusted according to the stress level of the subordinate, thereby reducing the burden on the subordinate and collecting more accurate data.

[0101] The collection unit can collect the skill update data of the subordinates. For example, when the subordinates acquire new skills, the information is collected and recorded in the database. The collection unit can also collect the contents of the training and seminars that the subordinates have received. For example, the collection unit records the type, duration, and results of the training that the subordinates have participated in. In this way, by collecting the skill update data of the subordinates, the degree of growth of the subordinates can be evaluated in more detail. Furthermore, the skill update data can also be useful in designing an effective training program to promote the growth of the subordinates.

[0102] The analysis unit can estimate the emotions of the subordinate and determine the priority of the analysis based on the estimated emotions. For example, if the subordinate is feeling stressed, the analysis unit prioritizes the analysis of the boss's behavior that causes the stress. Also, if the subordinate is relaxed, the analysis unit can analyze in detail the boss's behavior that promotes growth. For example, the analysis unit analyzes the relationship between the boss's behavior and the subordinate's emotions based on the subordinate's emotion data, and determines the priority of the analysis based on the emotions. This makes it possible to provide more effective feedback by adjusting the priority of the analysis according to the subordinate's emotions.

[0103] The remuneration calculation unit can evaluate the contribution of the superior based on the self-evaluation data of the subordinate. For example, if the subordinate gives a high evaluation to the guidance of the superior in the self-evaluation, the evaluation is reflected in the remuneration calculation. In addition, if the subordinate provides specific feedback to the support of the superior in the self-evaluation, the remuneration can be calculated based on the content. For example, the remuneration calculation unit analyzes the self-evaluation data of the subordinate and calculates the remuneration using a model that evaluates the contribution of the superior. In this way, the remuneration can be paid more fairly by evaluating the contribution of the superior based on the self-evaluation data of the subordinate.

[0104] The payment unit can estimate the subordinate's emotions and adjust the timing of payment based on the estimated emotions. For example, if the subordinate is stressed, the payment unit can make a quick payment. Also, if the subordinate is relaxed, the payment unit can make a payment after detailed confirmation. For example, if the subordinate is in a hurry, the payment unit prioritizes immediate payment. This allows for quick and appropriate payment by adjusting the timing of payment according to the subordinate's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] The collection unit can collect health data of subordinates and use it as data for adjusting the supervisor's instruction methods. For example, if the subordinate's health condition is not good, the collection unit can use it as data for reviewing the supervisor's instruction methods. The collection unit can also evaluate the impact of the supervisor's instruction on the subordinate's health based on the subordinate's health data. For example, the collection unit collects health data of subordinates and uses it as data for adjusting the supervisor's instruction methods. In this way, by collecting the subordinate's health data, the supervisor can review his instruction methods and provide instruction that takes the subordinate's health into consideration.

[0106] The collection unit can estimate the emotions of the subordinate and determine the priority of data to be collected based on the estimated emotions. For example, if the subordinate is feeling stressed, data of high importance is collected with priority. Also, if the subordinate is relaxed, detailed data can be collected with priority. For example, if the subordinate is busy, the collection unit can reduce the amount of data to be collected and collect only important data. In this way, important data can be collected with priority by determining the priority of data to be collected according to the emotions of the subordinate.

[0107] The collection department can monitor the progress of the project of its subordinates in real time and adjust the timing of data collection. For example, when the project is in a critical phase, the frequency of data collection can be increased. Also, when the project is progressing smoothly, the frequency of data collection can be reduced. For example, the collection department can monitor the progress of the project in real time and adjust the timing of data collection. This allows for efficient data collection by adjusting the timing of data collection according to the progress of the project.

[0108] The analysis unit can estimate the emotions of the subordinate and adjust the display method of the analysis results based on the estimated emotions. For example, if the subordinate is nervous, a simple and highly visible display method is provided. Also, if the subordinate is relaxed, a display method including detailed information can be provided. For example, if the subordinate is in a hurry, the analysis unit provides a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the emotions of the subordinate, it is possible to display the results in a way that is easy for the subordinate to understand.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection department collects subordinate-supervisor combination history data, supervisor behavior evaluation data, and subordinate salary increase performance data. The subordinate-supervisor combination history data includes the duration of the project and the content of the collaborative work. The supervisor behavior evaluation data includes the frequency of feedback and the quality of guidance. The subordinate salary increase performance data includes the frequency of salary increases and the amount of salary increases. The collection department obtains this data from databases, surveys, evaluation systems, and human resources systems. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the characteristics of a boss who is good at nurturing. The analysis unit uses AI to analyze the relationship between the boss's behavior and the subordinate's growth, and analyzes specific behavior. For example, it uses a model that inputs the boss's behavior data and the subordinate's growth data, and outputs the characteristics of a boss who is good at nurturing. Step 3: The compensation calculation unit automatically calculates the appropriate compensation for the supervisor who has successfully developed their subordinates based on the characteristics extracted by the analysis unit. The compensation calculation unit evaluates the supervisor's contribution based on the subordinate's salary increase record and level of growth, and calculates the appropriate compensation. For example, a model is used that inputs the amount of the subordinate's salary increase and the degree of skill improvement, and outputs the supervisor's contribution. Step 4: The payment department automatically pays the remuneration calculated by the remuneration calculation department to the superior. The payment department links the calculated remuneration to the payroll system and reflects it in the superior's salary. The payment department can pay not only monetary remuneration but also non-monetary remuneration (e.g., awards, special leave, etc.).

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, remuneration calculation unit, and payment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and collects subordinate-supervisor combination history data, behavior evaluation data of the superior, and salary increase performance data of the subordinate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to extract characteristics of a superior who is good at raising employees. The remuneration calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an appropriate remuneration based on the subordinate's salary increase performance and growth degree. The payment unit is realized, for example, by the control unit 46A of the smart device 14, and automatically pays the calculated remuneration to the superior. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, remuneration calculation unit, and payment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and collects subordinate-supervisor combination history data, behavior evaluation data of the supervisor, and salary increase performance data of the subordinate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to extract characteristics of a supervisor who is good at raising employees. The remuneration calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an appropriate remuneration based on the subordinate's salary increase performance and growth degree. The payment unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically pays the calculated remuneration to the supervisor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, remuneration calculation unit, and payment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and collects subordinate-supervisor combination history data, behavior evaluation data of the supervisor, and salary increase performance data of the subordinate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to extract characteristics of a supervisor who is good at raising employees. The remuneration calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an appropriate remuneration based on the salary increase performance and growth degree of the subordinate. The payment unit is realized, for example, by the control unit 46A of the headset type terminal 314, and automatically pays the calculated remuneration to the supervisor. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, reward calculation unit, and payment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and collects subordinate-supervisor combination history data, behavior evaluation data of the supervisor, and salary increase performance data of the subordinate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to extract characteristics of a supervisor who is good at raising employees. The reward calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an appropriate reward based on the subordinate's salary increase performance and growth degree. The payment unit is realized, for example, by the control unit 46A of the robot 414, and automatically pays the calculated reward to the supervisor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) a collection unit for collecting data on the combination history of subordinates and superiors, data on the behavior evaluation of superiors, and data on salary increase records of subordinates; an analysis unit that analyzes the data collected by the collection unit and extracts characteristics of a superior who is good at raising employees; a reward calculation unit that automatically calculates a reward for a superior who has successfully trained his / her subordinates based on the characteristics extracted by the analysis unit; A payment unit that pays the remuneration calculated by the remuneration calculation unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect behavioral evaluation data for your managers 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect data on salary increases for subordinates 2. The system of claim 1, (Appendix 4) The analysis unit is Analyzing the relationship between a manager's behavior and subordinates' growth 2. The system of claim 1, (Appendix 5) The system described in Appendix 1, characterized in that the reward calculation unit evaluates the supervisor's contribution and calculates reward based on the subordinate's salary increase history and degree of growth. (Appendix 6) The payment unit, Automatically pay the calculated reward to your boss 2. The system of claim 1, (Appendix 7) The system described in Appendix 1, wherein the collection unit analyzes a user's emotions and adjusts the timing of data collection based on the analyzed user's emotions. (Appendix 8) The system described in Appendix 1, wherein the collection unit analyzes past data collection history and selects an appropriate collection method. (Appendix 9) The collecting unit includes: When collecting data, filter it based on the manager-subordinate relationship and project progress 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collecting the most relevant data by considering the geographic location of the manager and subordinates. 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, analyze the social media activity of superiors and subordinates and collect relevant data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the boss' actions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the boss's behavior category. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the correlation between the boss's actions and the subordinate's growth 2. The system of claim 1, (Appendix 19) The remuneration calculation unit is Infer user emotions and adjust reward calculation methods based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The remuneration calculation unit is When calculating compensation, analyze the supervisor's past contributions to select the optimal compensation calculation method 2. The system of claim 1, (Appendix 21) The remuneration calculation unit is Customize compensation calculation methods based on the manager's current work situation during compensation calculation 2. The system of claim 1, (Appendix 22) The remuneration calculation unit is Estimate the user's emotions and determine the priority of reward calculation based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The remuneration calculation unit is When calculating compensation, the geographic location information of the superior is taken into consideration to select the optimal compensation calculation method. 2. The system of claim 1, (Appendix 24) The remuneration calculation unit is When calculating compensation, analyze the social media activity of the superior and suggest compensation calculation methods 2. The system of claim 1, (Appendix 25) The payment unit, Infer user sentiment and adjust payment timing based on the inferred sentiment 2. The system of claim 1, (Appendix 26) The payment unit, When making payments, the system will select the most appropriate payment method by looking at the supervisor's past compensation history. 2. The system of claim 1, (Appendix 27) The payment unit, When making payments, customize the payment method based on the manager's current business situation 2. The system of claim 1, (Appendix 28) The payment unit, Infer user sentiment and prioritize payments based on the inferred sentiment 2. The system of claim 1, (Appendix 29) The payment unit, When making a payment, the geographic location of the manager is taken into account to select the most appropriate payment method 2. The system of claim 1, (Appendix 30) The payment unit, When it's time to make a payment, analyze your boss' social media activity to suggest payment methods 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting data on the combination history of subordinates and superiors, data on the behavior evaluation of superiors, and data on salary increase records of subordinates; an analysis unit that analyzes the data collected by the collection unit and extracts characteristics of a superior who is good at raising employees; a reward calculation unit that automatically calculates a reward for a superior who has successfully trained his / her subordinates based on the characteristics extracted by the analysis unit; A payment unit that pays the remuneration calculated by the remuneration calculation unit. A system characterized by:

2. The remuneration calculation unit is Evaluate the contribution of the manager and calculate compensation based on the salary increase record and growth rate of the subordinates 2. The system of claim 1 .

3. The payment unit, When making payments, customize the payment method based on the manager's current business situation 2. The system of claim 1 .

4. The payment unit, When it's time to make a payment, analyze your boss's social media activity to suggest payment methods 2. The system of claim 1 .

5. The remuneration calculation unit is When calculating compensation, the geographic location information of the superior is taken into consideration to select the optimal compensation calculation method.

2. The system of claim 1 .

6. The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the boss' actions 2. The system of claim 1 .

7. The collecting unit includes: Analyze user sentiment and adjust the timing of data collection based on the analyzed user sentiment.

2. The system of claim 1 .

8. The collecting unit includes: Analyze past data collection history and select appropriate collection methods 2. The system of claim 1 .

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