system

Abstract

The system according to the embodiment aims to visualize the contributions of employees to one another and provide appropriate returns. [Solution] The system according to the embodiment includes a determination unit, an issuing unit, a transfer unit, and a providing unit. The determination unit determines the degree of contribution of others using a generation AI. The issuing unit issues gratitude points based on the degree of contribution of others determined by the determination unit. The transferring unit transfers the gratitude points issued by the issuing unit. The providing unit provides benefits based on the gratitude points transferred by the transferring unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide a system for properly evaluating and rewarding the contributions of employees to one another, and there is room for improvement.

[0005] The system according to the embodiment aims to visualize the contributions of employees to one another and provide appropriate returns. [Means for solving the problem]

[0006] The system according to the embodiment includes a determination unit, an issuing unit, a transfer unit, and a providing unit. The determination unit determines the degree of contribution of others using a generation AI. The issuing unit issues gratitude points based on the degree of contribution of others determined by the determination unit. The transferring unit transfers the gratitude points issued by the issuing unit. The providing unit provides benefits based on the gratitude points transferred by the transferring unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the contributions of employees to one another and provide appropriate returns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system for visualizing the contributions of others according to an embodiment of the present invention visualizes the contributions of employees to others and rewards them through gratitude points. In this system, a generation AI learns conversation history data from internal communication tools and emails, and then determines and scores each employee's contributions to others. Based on these scores, employees are issued unique gratitude points (tokens) that can be used in an employee-only Web 3.0 community. The gratitude points are transferable between employees, and a system is in place to award rewards based on the number of points owned. For example, in the system for visualizing the contributions of others, a generation AI learns conversation history data from internal communication tools and emails. The generation AI analyzes the content, frequency, and quality of conversations to evaluate employees' contributions to others. For example, comments supporting other employees and actions contributing to problem solving are evaluated. The generation AI then determines each employee's contributions to others and assigns a score. Based on the learned data, the generation AI quantifies each employee's contributions. For example, employees who frequently make supportive comments to other employees are awarded higher scores. Based on these points, employees are issued unique gratitude points (tokens) that can be used in an employee-only Web 3.0 community. Gratitude points are transferable between employees, and a system is in place to grant benefits based on the number of points they possess. For example, employees with a large number of gratitude points receive benefits such as the right to participate in special training sessions and events. This system stimulates contributions among employees. By recognizing employees' contributions, the system improves motivation and encourages employees to support others. Furthermore, the transferability of gratitude points also stimulates communication among employees. For example, transferring gratitude points allows employees to express gratitude to other employees, improving the workplace atmosphere. Furthermore, a system is in place to grant benefits based on the number of gratitude points they possess, encouraging employees to actively contribute to others. For example, employees with a large number of gratitude points receive benefits such as the right to participate in special training sessions and events. This promotes contributions among employees and improves productivity throughout the workplace.

[0029] The system for visualizing contributions to others according to the embodiment includes a determination unit, an issuing unit, a transfer unit, and a providing unit. The determination unit uses a generation AI to determine an employee's contribution to others. The determination unit, for example, learns conversation history data from internal communication tools and emails, and evaluates the contribution to others. For example, the determination unit evaluates comments that support other employees and actions that contribute to problem solving. The determination unit can also use the generation AI to analyze the content, frequency, and quality of conversations to quantify the employee's contribution to others. The issuing unit issues gratitude points based on the contribution to others determined by the determination unit. The issuing unit issues unique gratitude points (tokens) that can be used in an employee-only Web 3.0 community, for example, based on the contribution to others determined by the generation AI. The transferring unit enables the gratitude points issued by the issuing unit to be transferred between employees. For example, the transferring unit provides an interface for transferring gratitude points between employees. The transferring unit can also record the transfer history of gratitude points to ensure transparency. The providing unit provides benefits based on the gratitude points transferred by the transferring unit. The providing unit provides, for example, the right to participate in special training or events as a benefit depending on the number of gratitude points owned. As a result, the other people's contribution visualization system according to the embodiment visualizes the employee's contribution to others and can return the benefits through gratitude points. For example, the output unit displays benefits according to the number of gratitude points owned to the employee via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending them directly to the employee. Some or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI.

[0030] The determination unit can determine the degree of contribution to others by having the generation AI learn conversation history data from an internal communication tool or email. Internal communication tools include, but are not limited to, chat tools and email systems. Conversation history data includes, but are not limited to, text data, audio data, and video data. For example, the determination unit can use the generation AI to learn conversation history data from an internal chat tool and determine the degree of contribution to others. The determination unit can also use the generation AI to learn conversation history data from an email system and determine the degree of contribution to others. For example, the determination unit can analyze conversation history data from a chat tool and evaluate comments that support other employees and actions that contribute to problem solving. The determination unit can also analyze conversation history data from an email system and quantify the degree of contribution to others. This allows the generation AI to learn the conversation history data, enabling an accurate determination of the degree of contribution to others.

[0031] The issuing unit can issue gratitude points based on the degree of contribution to others determined by the generation AI. Gratitude points include, but are not limited to, token-type points, digital points, and physical points. For example, the issuing unit issues token-type gratitude points based on the degree of contribution to others determined by the generation AI. The issuing unit can also issue digital points. For example, the issuing unit grants digital points to an employee's account based on the degree of contribution to others determined by the generation AI. The issuing unit can also issue physical points. For example, the issuing unit issues a physical point card based on the degree of contribution to others determined by the generation AI. This enables accurate evaluation by issuing gratitude points based on the determination results of the generation AI.

[0032] The transfer unit can enable employees to transfer gratitude points to each other. Examples of transfer of gratitude points include, but are not limited to, digital transfer, physical transfer, and transfer through an online platform. For example, the transfer unit provides an interface for transferring digital gratitude points to each other. The transfer unit can also provide a procedure for transferring physical gratitude point cards to each other. For example, the transfer unit can build a system for transferring gratitude points through an online platform. The transfer unit can also record the transfer history of gratitude points to ensure transparency. For example, the transfer unit can automatically record the transfer history of gratitude points so that it can be referenced later. This enables the transfer of gratitude points, thereby promoting communication between employees.

[0033] The provision unit can provide benefits according to the number of appreciation points owned. Benefits include, but are not limited to, special training, the right to participate in an event, a gift certificate, and vacation. For example, the provision unit can provide special training as a benefit according to the number of appreciation points owned. The provision unit can also provide the right to participate in an event as a benefit. For example, the provision unit grants employees the right to participate in a special event according to the number of appreciation points owned. The provision unit can also provide gift certificates as a benefit. For example, the provision unit distributes gift certificates to employees according to the number of appreciation points owned. The provision unit can also provide vacation as a benefit. For example, the provision unit grants special vacation to employees according to the number of appreciation points owned. In this way, by providing benefits according to the number of appreciation points owned, employee motivation is improved.

[0034] The provision department can provide benefits such as the right to participate in special training or events. Examples of special training include, but are not limited to, leadership training, technical training, and management training. Examples of events include, but are not limited to, internal events, industry events, and conferences. For example, the provision department can provide leadership training as a benefit based on the number of appreciation points owned. The provision department can also provide technical training as a benefit. For example, the provision department grants employees the right to participate in technical training based on the number of appreciation points owned. The provision department can also provide management training as a benefit. For example, the provision department grants employees the right to participate in management training based on the number of appreciation points owned. The provision department can also provide benefits such as the right to participate in internal events. For example, the provision department grants employees the right to participate in special internal events based on the number of appreciation points owned. The provision department can also provide benefits such as the right to participate in industry events. For example, the provision department grants employees the right to participate in industry events based on the number of appreciation points owned. The provision department can also offer the right to participate in conferences as a benefit. For example, the provision department can grant employees the right to participate in conferences according to the number of gratitude points they have. This allows employees to improve their skills and motivation by offering the right to participate in special training sessions and events as a benefit.

[0035] The transfer unit can enable the transfer of gratitude points within a Web 3.0 community exclusively for employees. Examples of Web 3.0 communities include, but are not limited to, platforms using blockchain technology, decentralized applications (DApps), and smart contracts. The transfer unit, for example, builds a system for transferring gratitude points on a platform using blockchain technology. The transfer unit can also transfer gratitude points using a decentralized application (DApp). For example, the transfer unit can build a system that automates the transfer of gratitude points using a smart contract. The transfer unit can also record the transfer history of gratitude points within the Web 3.0 community to ensure transparency. For example, the transfer unit can record the transfer history of gratitude points on a blockchain for later reference. This allows the transfer of gratitude points within the employee-only Web 3.0 community, thereby revitalizing the community.

[0036] The issuing unit can issue the appreciation points as tokens. Examples of tokens include, but are not limited to, ERC-20 tokens, ERC-721 tokens, and proprietary tokens. The issuing unit can issue the appreciation points as, for example, ERC-20 tokens. The issuing unit can also issue the appreciation points as ERC-721 tokens. For example, the issuing unit can issue proprietary tokens and use them as appreciation points. Issuing appreciation points as tokens makes it easier to manage and transfer points.

[0037] The determination unit can determine the degree of contribution by others by learning data from project management tools in addition to conversation history data from internal communication tools and emails. Project management tools include, but are not limited to, task management tools, progress management tools, and collaboration tools. For example, the determination unit can learn data from task management tools to determine the degree of contribution by others. The determination unit can also learn data from progress management tools to determine the degree of contribution by others. For example, the determination unit can learn data from collaboration tools to determine the degree of contribution by others. For example, the determination unit can learn task completion data from task management tools and reflect the degree of task completion and the degree of contribution in the evaluation. The determination unit can also learn comments and feedback from progress management tools and reflect the degree of support and cooperation provided to others in the evaluation. Furthermore, the determination unit can learn progress report data from project management tools and reflect the degree of contribution to the success of the project in the evaluation. This enables a more comprehensive evaluation of the degree of contribution by others by learning data from project management tools.

[0038] When determining the degree of contribution to others, the evaluation unit can customize the evaluation criteria taking into account the employee's job title and job description. Job titles include, but are not limited to, managerial, technical, and sales positions. Job descriptions include, but are not limited to, project management, technical support, and customer service. For example, for managerial employees, the evaluation unit can include the overall team's achievements and the growth of team members in the evaluation criteria. For technical employees, the evaluation unit can also include the degree of contribution to technical support and problem solving in the evaluation criteria. For example, for sales employees, the evaluation unit can also include the degree of customer service and sales contribution in the evaluation criteria. This allows for more fair evaluations by customizing the evaluation criteria according to the employee's job title and job description.

[0039] When determining the contribution of others, the determination unit can improve the accuracy of the determination by referring to the employee's past contribution history. Past contribution history includes, for example, contribution to projects, feedback, task completion rate, etc., but is not limited to these examples. The determination unit, for example, refers to the contribution to past projects and reflects it in the current contribution evaluation. The determination unit can also refer to past feedback and evaluations and reflect them in the current contribution evaluation. For example, the determination unit can refer to past task completion rates and support history and reflect them in the current contribution evaluation. In this way, by referring to the past contribution history, the accuracy of the current contribution evaluation is improved.

[0040] When determining the contribution of others, the determination unit can adjust the determination criteria by taking into account the employee's working hours and number of working days. Examples of working hours include, but are not limited to, long working hours, flextime, and remote work. Examples of number of working days include, but are not limited to, full-time work, part-time work, and shift work. For example, the determination unit evaluates the contribution of employees who work long hours based on their working hours. The determination unit can also evaluate the contribution of employees who work many days based on the number of working days. For example, the determination unit can evaluate the contribution of employees who use a flextime system, taking into account the flexibility of working hours. This allows for a fairer contribution evaluation by taking into account working hours and number of working days.

[0041] When determining the degree of contribution to others, the determination unit can also include the employee's outside activities (volunteer activities, etc.) in the evaluation. Outside activities include, but are not limited to, volunteer activities, lectures, and seminar participation. The determination unit, for example, reflects the content of volunteer activities in which the employee participated in the evaluation. The determination unit can also reflect the content of lectures and seminars given by the employee outside the company in the evaluation. For example, if the employee participates in a project outside the company, the determination unit can also reflect the degree of contribution in the evaluation. In this way, by including outside activities in the evaluation, the diverse contributions of employees can be evaluated.

[0042] The determination unit can reflect an employee's self-evaluation and evaluations from colleagues when determining the degree of contribution to others. Examples of self-evaluation include, but are not limited to, a self-evaluation sheet and a self-evaluation questionnaire. Examples of evaluations from colleagues include, but are not limited to, a feedback sheet and a 360-degree evaluation. For example, the determination unit reflects the content written by the employee in the self-evaluation in the evaluation. The determination unit can also reflect feedback from colleagues in the evaluation. For example, the determination unit can reflect an evaluation from a supervisor in the evaluation. In this way, by reflecting self-evaluation and evaluations from colleagues, a more multifaceted evaluation of contribution is possible.

[0043] When issuing appreciation points, the issuing department can issue different types of points according to the employee's level of contribution. Different types of points include, but are not limited to, basic points, bonus points, and special points. For example, the issuing department can issue special bonus points to employees who have demonstrated a high level of contribution. The issuing department can also issue regular appreciation points to employees who have demonstrated a medium level of contribution. For example, the issuing department can issue basic appreciation points to employees who have demonstrated a low level of contribution. This allows for more detailed evaluation by issuing different types of points according to the level of contribution.

[0044] When issuing appreciation points, the issuing department can issue bonus points based on the cumulative value of an employee's contribution. Examples of cumulative contribution values ​​include, but are not limited to, contributions within a certain period of time, contributions over a long period of time, and contributions in a specific project. For example, the issuing department issues bonus points based on the cumulative value to employees who have demonstrated high contributions within a certain period of time. The issuing department can also issue bonus points based on the cumulative value to employees who have demonstrated stable contributions over a long period of time. For example, the issuing department can issue bonus points based on the cumulative value to employees who have demonstrated high contributions in a specific project. This allows long-term contributions to be recognized by issuing bonus points based on the cumulative value of contributions.

[0045] When issuing appreciation points, the issuing department can give special recognition to contributions to specific projects or events. Examples of specific projects or events include, but are not limited to, internal projects, internal events, and external events. For example, the issuing department issues special appreciation points to employees who have made a high level of contribution to a specific project. The issuing department can also recognize contributions to internal events and issue special appreciation points. For example, the issuing department can recognize contributions to external events and issue special appreciation points. In this way, special recognition of contributions to specific projects or events can increase motivation for specific activities.

[0046] When issuing appreciation points, the issuing department can adjust the amount to be issued, taking into account the employee's years of service and experience. Years of service include, but are not limited to, long-term employment, short-term employment, and flextime. Experience includes, but is not limited to, technical experience, management experience, and sales experience. For example, the issuing department issues appreciation points to employees with many years of service according to their years of service. The issuing department can also issue appreciation points to experienced employees according to their experience. For example, the issuing department can issue basic appreciation points to new employees. This allows for fairer evaluation by taking into account years of service and experience.

[0047] When issuing appreciation points, the issuing department can evaluate the contribution of the entire team of employees and issue team points. Examples of the contribution of the entire team include, but are not limited to, team results, project success, and the degree of cooperation. For example, the issuing department issues team points when the entire team demonstrates a high level of contribution. The issuing department can also evaluate the success of a project as a whole team and issue team points. For example, the issuing department can evaluate the cooperation of the entire team and issue team points. This can improve teamwork by evaluating the contribution of the entire team.

[0048] When issuing appreciation points, the issuing department can include an employee's self-development activities (such as participation in training) in the evaluation. Self-development activities include, but are not limited to, participation in training, obtaining qualifications, and acquiring new skills. For example, the issuing department evaluates the content of the training that the employee participated in and issues appreciation points. Furthermore, when an employee engages in self-development activities, the issuing department can evaluate the content and issue appreciation points. For example, when an employee acquires a new skill, the issuing department can evaluate the content and issue appreciation points. In this way, by including self-development activities in the evaluation, it is possible to promote employee growth.

[0049] The transfer department can record the transfer history when transferring gratitude points to ensure transparency. The transfer history includes, for example, but is not limited to, the transfer date and time, the transfer amount, and the transfer recipient. For example, the transfer department can automatically record the transfer history of gratitude points so that it can be referenced later. The transfer department can also publish the transfer history in a Web 3.0 community for employees to ensure transparency. For example, the transfer department can analyze the transfer history to check for any fraudulent transfers. In this way, transparency is ensured by recording the transfer history.

[0050] When transferring appreciation points, the transfer unit can automatically adjust the transfer amount according to the contribution level of the employee receiving the points. The contribution level of the employee receiving the points includes, but is not limited to, past contribution history, current contribution level, and the contribution level of the entire team. For example, the transfer unit increases the transfer amount if the contribution level of the employee receiving the points is high. The transfer unit can also decrease the transfer amount if the contribution level of the employee receiving the points is low. For example, the transfer unit can automatically adjust the transfer amount according to the contribution level of the employee receiving the points. This allows for a more fair transfer by adjusting the transfer amount according to the contribution level of the employee receiving the points.

[0051] The transfer department can record the reason for the transfer when the gratitude points are transferred so that it can be referenced later. Examples of the reason for the transfer include, but are not limited to, gratitude, a reward for a specific contribution, team cooperation, etc. For example, the transfer department can provide a field for inputting the reason for the transfer when the gratitude points are transferred. The transfer department can also automatically record the reason for the transfer so that it can be referenced later. For example, the transfer department can analyze the reason for the transfer and improve communication between employees. Thus, by recording the reason for the transfer, the transparency of the transfer is improved.

[0052] When transferring gratitude points, the transfer department can adjust the transfer amount taking into account the position and job content of the employee receiving the points. Examples of job titles include, but are not limited to, managerial positions, technical positions, and sales positions. Examples of job content include, but are not limited to, project management, technical support, and customer support. For example, the transfer department increases the transfer amount if the employee receiving the points is a manager. Furthermore, the transfer department can also keep the transfer amount normal if the employee receiving the points is a technical position. For example, the transfer department can decrease the transfer amount if the employee receiving the points is a sales position. This allows for more fair transfers by adjusting the transfer amount according to the position and job content.

[0053] When transferring gratitude points, the transfer unit can determine the transfer amount by referring to the past contribution history of the employee to whom the points are to be transferred. Past contribution history includes, but is not limited to, project contribution, feedback, task completion rate, etc. For example, the transfer unit can increase the transfer amount if the employee to whom the points are to be transferred has a high past contribution history. The transfer unit can also decrease the transfer amount if the employee to whom the points are to be transferred has a low past contribution history. For example, the transfer unit can automatically adjust the transfer amount based on the employee to whom the points are to be transferred. This allows a more appropriate transfer amount to be determined by referring to the past contribution history.

[0054] When transferring gratitude points, the transferor can evaluate the transferee employee's contribution to the entire team and transfer them as team points. Examples of the team's contribution include, but are not limited to, team results, project success, and the degree of cooperation. For example, the transferor can transfer the points as team points if the entire team demonstrates a high level of contribution. The transferor can also evaluate the project success of the entire team and transfer them as team points. For example, the transferor can evaluate the cooperation of the entire team and transfer them as team points. This can improve teamwork by evaluating the contribution of the entire team.

[0055] When providing benefits, the provision department can provide different types of benefits depending on the employee's level of contribution. Different types of benefits include, but are not limited to, special training, the right to participate in events, gift certificates, and vacations. For example, the provision department can provide special training or the right to participate in events to employees who have demonstrated a high level of contribution. The provision department can also provide regular benefits to employees who have demonstrated a medium level of contribution. For example, the provision department can provide basic benefits to employees who have demonstrated a low level of contribution. This allows for more detailed evaluation by providing different types of benefits depending on the level of contribution.

[0056] When providing benefits, the provision unit can provide a bonus benefit based on the cumulative value of an employee's contribution. Examples of cumulative contribution values ​​include, but are not limited to, contributions within a certain period of time, contributions over a long period of time, and contributions in a specific project. For example, the provision unit can provide a bonus benefit based on the cumulative value to an employee who has demonstrated a high level of contribution within a certain period of time. The provision unit can also provide a bonus benefit based on the cumulative value to an employee who has demonstrated stable contributions over a long period of time. For example, the provision unit can provide a bonus benefit based on the cumulative value to an employee who has demonstrated a high level of contribution in a specific project. This allows long-term contributions to be evaluated by providing a bonus benefit based on the cumulative value of contributions.

[0057] When providing rewards, the provision department can provide rewards by specially recognizing contributions to specific projects or events. Specific projects and events include, but are not limited to, internal projects, internal events, and external events. For example, the provision department can provide special rewards to employees who have made a high level of contribution to a specific project. The provision department can also provide special rewards by recognizing contributions at internal events. For example, the provision department can also provide special rewards by recognizing contributions at external events. In this way, special recognition of contributions to specific projects or events can increase motivation for specific activities.

[0058] When providing benefits, the provision department can adjust the content of benefits by taking into account the employee's years of service and experience. Years of service include, but are not limited to, long-term employment, short-term employment, and flextime. Experience includes, but is not limited to, technical experience, management experience, and sales experience. For example, the provision department provides benefits to employees with many years of service according to their years of service. The provision department can also provide benefits to experienced employees according to their experience. For example, the provision department can provide basic benefits to new employees. This makes it possible to provide benefits more fairly by taking into account years of service and experience.

[0059] When providing rewards, the provision department can evaluate the employee's overall team contribution and provide team rewards. Examples of the team's overall contribution include, but are not limited to, team results, project success, and degree of cooperation. For example, the provision department can provide team rewards when the team as a whole demonstrates a high level of contribution. The provision department can also evaluate the project success of the team as a whole and provide team rewards. For example, the provision department can evaluate the cooperation of the team as a whole and provide team rewards. This can improve teamwork by evaluating the team's overall contribution.

[0060] When providing benefits, the provision department can include an employee's self-development activities (such as participation in training) in the evaluation. Self-development activities include, but are not limited to, participation in training, obtaining qualifications, and acquiring new skills. For example, the provision department evaluates the content of the training that the employee participated in and provides the benefit. Furthermore, when an employee engages in self-development activities, the provision department can evaluate the content and provide the benefit. For example, when an employee acquires a new skill, the provision department can evaluate the content and provide the benefit. In this way, by including self-development activities in the evaluation, it is possible to promote employee growth.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The assessment unit can learn data from project management tools in addition to conversation history data from internal communication tools and emails to assess the degree of contribution of others. For example, it can learn task completion data from a task management tool and reflect the degree of task completion and contribution in the evaluation. It can also learn comments and feedback from a progress management tool and reflect the degree of support and cooperation provided to others in the evaluation. It can also learn progress report data from a project management tool and reflect the degree of contribution to the success of the project in the evaluation. In this way, by learning data from project management tools as well, it becomes possible to assess the degree of contribution of others more comprehensively.

[0063] When issuing appreciation points, the issuing department can issue different types of points according to the employee's level of contribution. For example, special bonus points can be issued to employees who have made a high level of contribution. Normal appreciation points can also be issued to employees who have made a medium level of contribution. Furthermore, basic appreciation points can also be issued to employees who have made a low level of contribution. This allows for more detailed evaluation by issuing different types of points according to the level of contribution.

[0064] When transferring gratitude points, the transfer unit can automatically adjust the transfer amount according to the contribution level of the employee receiving the points. For example, if the employee receiving the points has a high level of contribution, the transfer amount can be increased. Conversely, if the employee receiving the points has a low level of contribution, the transfer amount can be decreased. Furthermore, the transfer amount can be automatically adjusted based on the employee receiving the points' contribution level. This allows for a fairer transfer by adjusting the transfer amount according to the employee's level of contribution.

[0065] The provision unit can provide bonus benefits based on the cumulative value of an employee's contribution when providing benefits. For example, an employee who has demonstrated a high level of contribution within a certain period of time can be provided with a bonus benefit based on the cumulative value. An employee who has demonstrated stable contribution over a long period of time can also be provided with a bonus benefit based on the cumulative value. Furthermore, an employee who has demonstrated a high level of contribution in a specific project can be provided with a bonus benefit based on the cumulative value. In this way, by providing bonus benefits based on the cumulative value of contribution, long-term contribution can be evaluated.

[0066] When providing benefits, the provision department can include employees' self-development activities (such as participation in training) in the evaluation. For example, the content of the training an employee has participated in can be evaluated and a benefit can be provided. Also, when an employee engages in self-development activities, the content can be evaluated and a benefit can be provided. Furthermore, when an employee acquires a new skill, the content can be evaluated and a benefit can be provided. In this way, by including self-development activities in the evaluation, it is possible to promote employee growth.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The judgment unit uses generative AI to determine an employee's level of contribution to others. The judgment unit learns conversation history data from internal communication tools and emails, etc., and evaluates the level of contribution to others. For example, it evaluates comments that support other employees and actions that contribute to problem-solving. It can also analyze the content, frequency, and quality of conversations to quantify an employee's level of contribution to others. Step 2: The issuing unit issues gratitude points based on the degree of contribution by others determined by the determining unit. For example, based on the degree of contribution by others determined by the generating AI, it issues unique gratitude points (tokens) that can be used in a Web 3.0 community exclusively for employees. Step 3: The transfer department makes the gratitude points issued by the issuing department transferable between employees. The transfer department provides an interface for transferring gratitude points between employees, records the transfer history of gratitude points, and ensures transparency. Step 4: The providing unit provides benefits based on the gratitude points transferred by the transferring unit. For example, the providing unit provides benefits such as the right to participate in special training or events according to the number of gratitude points owned.

[0069] (Example 2) The system for visualizing the contributions of others according to an embodiment of the present invention visualizes the contributions of employees to others and rewards them through gratitude points. In this system, a generation AI learns conversation history data from internal communication tools and emails, and then determines and scores each employee's contributions to others. Based on these scores, employees are issued unique gratitude points (tokens) that can be used in an employee-only Web 3.0 community. The gratitude points are transferable between employees, and a system is in place to award rewards based on the number of points owned. For example, in the system for visualizing the contributions of others, a generation AI learns conversation history data from internal communication tools and emails. The generation AI analyzes the content, frequency, and quality of conversations to evaluate employees' contributions to others. For example, comments supporting other employees and actions contributing to problem solving are evaluated. The generation AI then determines each employee's contributions to others and assigns a score. Based on the learned data, the generation AI quantifies each employee's contributions. For example, employees who frequently make supportive comments to other employees are awarded higher scores. Based on these points, employees are issued unique gratitude points (tokens) that can be used in an employee-only Web 3.0 community. Gratitude points are transferable between employees, and a system is in place to grant benefits based on the number of points they possess. For example, employees with a large number of gratitude points receive benefits such as the right to participate in special training sessions and events. This system stimulates contributions among employees. By recognizing employees' contributions, the system improves motivation and encourages employees to support others. Furthermore, the transferability of gratitude points also stimulates communication among employees. For example, transferring gratitude points allows employees to express gratitude to other employees, improving the workplace atmosphere. Furthermore, a system is in place to grant benefits based on the number of gratitude points they possess, encouraging employees to actively contribute to others. For example, employees with a large number of gratitude points receive benefits such as the right to participate in special training sessions and events. This promotes contributions among employees and improves productivity throughout the workplace.

[0070] The system for visualizing contributions to others according to the embodiment includes a determination unit, an issuing unit, a transfer unit, and a providing unit. The determination unit uses a generation AI to determine an employee's contribution to others. The determination unit, for example, learns conversation history data from internal communication tools and emails, and evaluates the contribution to others. For example, the determination unit evaluates comments that support other employees and actions that contribute to problem solving. The determination unit can also use the generation AI to analyze the content, frequency, and quality of conversations to quantify the employee's contribution to others. The issuing unit issues gratitude points based on the contribution to others determined by the determination unit. The issuing unit issues unique gratitude points (tokens) that can be used in an employee-only Web 3.0 community, for example, based on the contribution to others determined by the generation AI. The transferring unit enables the gratitude points issued by the issuing unit to be transferred between employees. For example, the transferring unit provides an interface for transferring gratitude points between employees. The transferring unit can also record the transfer history of gratitude points to ensure transparency. The providing unit provides benefits based on the gratitude points transferred by the transferring unit. The providing unit provides, for example, the right to participate in special training or events as a benefit depending on the number of gratitude points owned. As a result, the other people's contribution visualization system according to the embodiment visualizes the employee's contribution to others and can return the benefits through gratitude points. For example, the output unit displays benefits according to the number of gratitude points owned to the employee via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending them directly to the employee. Some or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI.

[0071] The determination unit can determine the degree of contribution to others by having the generation AI learn conversation history data from an internal communication tool or email. Internal communication tools include, but are not limited to, chat tools and email systems. Conversation history data includes, but are not limited to, text data, audio data, and video data. For example, the determination unit can use the generation AI to learn conversation history data from an internal chat tool and determine the degree of contribution to others. The determination unit can also use the generation AI to learn conversation history data from an email system and determine the degree of contribution to others. For example, the determination unit can analyze conversation history data from a chat tool and evaluate comments that support other employees and actions that contribute to problem solving. The determination unit can also analyze conversation history data from an email system and quantify the degree of contribution to others. This allows the generation AI to learn the conversation history data, enabling an accurate determination of the degree of contribution to others.

[0072] The issuing unit can issue gratitude points based on the degree of contribution to others determined by the generation AI. Gratitude points include, but are not limited to, token-type points, digital points, and physical points. For example, the issuing unit issues token-type gratitude points based on the degree of contribution to others determined by the generation AI. The issuing unit can also issue digital points. For example, the issuing unit grants digital points to an employee's account based on the degree of contribution to others determined by the generation AI. The issuing unit can also issue physical points. For example, the issuing unit issues a physical point card based on the degree of contribution to others determined by the generation AI. This enables accurate evaluation by issuing gratitude points based on the determination results of the generation AI.

[0073] The transfer unit can enable employees to transfer gratitude points to each other. Examples of transfer of gratitude points include, but are not limited to, digital transfer, physical transfer, and transfer through an online platform. For example, the transfer unit provides an interface for transferring digital gratitude points to each other. The transfer unit can also provide a procedure for transferring physical gratitude point cards to each other. For example, the transfer unit can build a system for transferring gratitude points through an online platform. The transfer unit can also record the transfer history of gratitude points to ensure transparency. For example, the transfer unit can automatically record the transfer history of gratitude points so that it can be referenced later. This enables the transfer of gratitude points, thereby promoting communication between employees.

[0074] The provision unit can provide benefits according to the number of appreciation points owned. Benefits include, but are not limited to, special training, the right to participate in an event, a gift certificate, and vacation. For example, the provision unit can provide special training as a benefit according to the number of appreciation points owned. The provision unit can also provide the right to participate in an event as a benefit. For example, the provision unit grants employees the right to participate in a special event according to the number of appreciation points owned. The provision unit can also provide gift certificates as a benefit. For example, the provision unit distributes gift certificates to employees according to the number of appreciation points owned. The provision unit can also provide vacation as a benefit. For example, the provision unit grants special vacation to employees according to the number of appreciation points owned. In this way, by providing benefits according to the number of appreciation points owned, employee motivation is improved.

[0075] The provision department can provide benefits such as the right to participate in special training or events. Examples of special training include, but are not limited to, leadership training, technical training, and management training. Examples of events include, but are not limited to, internal events, industry events, and conferences. For example, the provision department can provide leadership training as a benefit based on the number of appreciation points owned. The provision department can also provide technical training as a benefit. For example, the provision department grants employees the right to participate in technical training based on the number of appreciation points owned. The provision department can also provide management training as a benefit. For example, the provision department grants employees the right to participate in management training based on the number of appreciation points owned. The provision department can also provide benefits such as the right to participate in internal events. For example, the provision department grants employees the right to participate in special internal events based on the number of appreciation points owned. The provision department can also provide benefits such as the right to participate in industry events. For example, the provision department grants employees the right to participate in industry events based on the number of appreciation points owned. The provision department can also offer the right to participate in conferences as a benefit. For example, the provision department can grant employees the right to participate in conferences according to the number of gratitude points they have. This allows employees to improve their skills and motivation by offering the right to participate in special training sessions and events as a benefit.

[0076] The transfer unit can enable the transfer of gratitude points within a Web 3.0 community exclusively for employees. Examples of Web 3.0 communities include, but are not limited to, platforms using blockchain technology, decentralized applications (DApps), and smart contracts. The transfer unit, for example, builds a system for transferring gratitude points on a platform using blockchain technology. The transfer unit can also transfer gratitude points using a decentralized application (DApp). For example, the transfer unit can build a system that automates the transfer of gratitude points using a smart contract. The transfer unit can also record the transfer history of gratitude points within the Web 3.0 community to ensure transparency. For example, the transfer unit can record the transfer history of gratitude points on a blockchain for later reference. This allows the transfer of gratitude points within the employee-only Web 3.0 community, thereby revitalizing the community.

[0077] The issuing unit can issue the appreciation points as tokens. Examples of tokens include, but are not limited to, ERC-20 tokens, ERC-721 tokens, and proprietary tokens. The issuing unit can issue the appreciation points as, for example, ERC-20 tokens. The issuing unit can also issue the appreciation points as ERC-721 tokens. For example, the issuing unit can issue proprietary tokens and use them as appreciation points. Issuing appreciation points as tokens makes it easier to manage and transfer points.

[0078] The determination unit can estimate the employee's emotions and adjust the criteria for determining the contribution to others based on the estimated employee's emotions. Examples of employee emotions include, but are not limited to, stress, relaxation, and motivation. The determination unit can estimate the employee's emotions using, for example, emotion recognition technology. The determination unit can also estimate the employee's emotions using generative AI. For example, the determination unit can analyze the employee's facial expressions and voice data to estimate the employee's emotions. The determination unit can also adjust the criteria for determining the contribution to others based on the employee's emotions. For example, if the employee is stressed, the criteria for determining the contribution to others can be relaxed, and positive behavior can be evaluated more highly. If the employee is relaxed, the normal criteria can be applied, resulting in a balanced evaluation. Furthermore, if the employee is highly motivated, the criteria can be tightened, requiring a higher contribution. This allows the criteria to be adjusted according to the employee's emotions, enabling a more appropriate evaluation of the contribution to others.

[0079] The determination unit can determine the degree of contribution by others by learning data from project management tools in addition to conversation history data from internal communication tools and emails. Project management tools include, but are not limited to, task management tools, progress management tools, and collaboration tools. For example, the determination unit can learn data from task management tools to determine the degree of contribution by others. The determination unit can also learn data from progress management tools to determine the degree of contribution by others. For example, the determination unit can learn data from collaboration tools to determine the degree of contribution by others. For example, the determination unit can learn task completion data from task management tools and reflect the degree of task completion and the degree of contribution in the evaluation. The determination unit can also learn comments and feedback from progress management tools and reflect the degree of support and cooperation provided to others in the evaluation. Furthermore, the determination unit can learn progress report data from project management tools and reflect the degree of contribution to the success of the project in the evaluation. This enables a more comprehensive evaluation of the degree of contribution by others by learning data from project management tools.

[0080] When determining the degree of contribution to others, the evaluation unit can customize the evaluation criteria taking into account the employee's job title and job description. Job titles include, but are not limited to, managerial, technical, and sales positions. Job descriptions include, but are not limited to, project management, technical support, and customer service. For example, for managerial employees, the evaluation unit can include the overall team's achievements and the growth of team members in the evaluation criteria. For technical employees, the evaluation unit can also include the degree of contribution to technical support and problem solving in the evaluation criteria. For example, for sales employees, the evaluation unit can also include the degree of customer service and sales contribution in the evaluation criteria. This allows for more fair evaluations by customizing the evaluation criteria according to the employee's job title and job description.

[0081] When determining the contribution of others, the determination unit can improve the accuracy of the determination by referring to the employee's past contribution history. Past contribution history includes, for example, contribution to projects, feedback, task completion rate, etc., but is not limited to these examples. The determination unit, for example, refers to the contribution to past projects and reflects it in the current contribution evaluation. The determination unit can also refer to past feedback and evaluations and reflect them in the current contribution evaluation. For example, the determination unit can refer to past task completion rates and support history and reflect them in the current contribution evaluation. In this way, by referring to the past contribution history, the accuracy of the current contribution evaluation is improved.

[0082] The determination unit can estimate the employee's emotions and adjust the display method of the assessment result of the contribution to others based on the estimated employee's emotions. Examples of the employee's emotions include, but are not limited to, stress, relaxation, and motivation. The determination unit, for example, estimates the employee's emotions using emotion recognition technology. The determination unit can also estimate the employee's emotions using generative AI. For example, the determination unit can analyze the employee's facial expressions and voice data to estimate the employee's emotions. The determination unit can also adjust the display method of the assessment result of the contribution to others based on the employee's emotions. For example, if the employee is feeling stressed, positive feedback can be emphasized. If the employee is relaxed, detailed feedback can be displayed, including areas for improvement. Furthermore, if the employee is highly motivated, challenging goals can be presented to encourage further growth. This allows for more appropriate feedback to be provided by adjusting the display method of the assessment result according to the employee's emotions.

[0083] When determining the contribution of others, the determination unit can adjust the determination criteria by taking into account the employee's working hours and number of working days. Examples of working hours include, but are not limited to, long working hours, flextime, and remote work. Examples of number of working days include, but are not limited to, full-time work, part-time work, and shift work. For example, the determination unit evaluates the contribution of employees who work long hours based on their working hours. The determination unit can also evaluate the contribution of employees who work many days based on the number of working days. For example, the determination unit can evaluate the contribution of employees who use a flextime system, taking into account the flexibility of working hours. This allows for a fairer contribution evaluation by taking into account working hours and number of working days.

[0084] When determining the degree of contribution to others, the determination unit can also include the employee's outside activities (volunteer activities, etc.) in the evaluation. Outside activities include, but are not limited to, volunteer activities, lectures, and seminar participation. The determination unit, for example, reflects the content of volunteer activities in which the employee participated in the evaluation. The determination unit can also reflect the content of lectures and seminars given by the employee outside the company in the evaluation. For example, if the employee participates in a project outside the company, the determination unit can also reflect the degree of contribution in the evaluation. In this way, by including outside activities in the evaluation, the diverse contributions of employees can be evaluated.

[0085] The determination unit can reflect an employee's self-evaluation and evaluations from colleagues when determining the degree of contribution to others. Examples of self-evaluation include, but are not limited to, a self-evaluation sheet and a self-evaluation questionnaire. Examples of evaluations from colleagues include, but are not limited to, a feedback sheet and a 360-degree evaluation. For example, the determination unit reflects the content written by the employee in the self-evaluation in the evaluation. The determination unit can also reflect feedback from colleagues in the evaluation. For example, the determination unit can reflect an evaluation from a supervisor in the evaluation. In this way, by reflecting self-evaluation and evaluations from colleagues, a more multifaceted evaluation of contribution is possible.

[0086] The issuing department can estimate an employee's emotions and adjust the frequency of issuing gratitude points based on the estimated employee emotions. Employee emotions include, but are not limited to, stress, relaxation, and motivation. The issuing department can estimate an employee's emotions using, for example, emotion recognition technology. The issuing department can also estimate an employee's emotions using generative AI. For example, the issuing department can analyze an employee's facial expressions and voice data to estimate emotions. The issuing department can also adjust the frequency of issuing gratitude points based on the employee's emotions. For example, if an employee is stressed, the issuing department can increase the frequency of issuing gratitude points to increase motivation. If an employee is relaxed, the issuing department can maintain the normal frequency. Furthermore, if an employee is highly motivated, the issuing department can decrease the frequency of issuing gratitude points to encourage further contributions. This allows for improving motivation by adjusting the issuing frequency according to an employee's emotions.

[0087] When issuing appreciation points, the issuing department can issue different types of points according to the employee's level of contribution. Different types of points include, but are not limited to, basic points, bonus points, and special points. For example, the issuing department can issue special bonus points to employees who have demonstrated a high level of contribution. The issuing department can also issue regular appreciation points to employees who have demonstrated a medium level of contribution. For example, the issuing department can issue basic appreciation points to employees who have demonstrated a low level of contribution. This allows for more detailed evaluation by issuing different types of points according to the level of contribution.

[0088] When issuing appreciation points, the issuing department can issue bonus points based on the cumulative value of an employee's contribution. Examples of cumulative contribution values ​​include, but are not limited to, contributions within a certain period of time, contributions over a long period of time, and contributions in a specific project. For example, the issuing department issues bonus points based on the cumulative value to employees who have demonstrated high contributions within a certain period of time. The issuing department can also issue bonus points based on the cumulative value to employees who have demonstrated stable contributions over a long period of time. For example, the issuing department can issue bonus points based on the cumulative value to employees who have demonstrated high contributions in a specific project. This allows long-term contributions to be recognized by issuing bonus points based on the cumulative value of contributions.

[0089] When issuing appreciation points, the issuing department can give special recognition to contributions to specific projects or events. Examples of specific projects or events include, but are not limited to, internal projects, internal events, and external events. For example, the issuing department issues special appreciation points to employees who have made a high level of contribution to a specific project. The issuing department can also recognize contributions to internal events and issue special appreciation points. For example, the issuing department can recognize contributions to external events and issue special appreciation points. In this way, special recognition of contributions to specific projects or events can increase motivation for specific activities.

[0090] The issuing unit can estimate an employee's emotions and adjust the amount of gratitude points issued based on the estimated employee emotions. Employee emotions include, but are not limited to, stress, relaxation, and motivation. The issuing unit can estimate an employee's emotions using, for example, emotion recognition technology. The issuing unit can also estimate an employee's emotions using generative AI. For example, the issuing unit can analyze an employee's facial expressions and voice data to estimate emotions. The issuing unit can also adjust the amount of gratitude points issued based on the employee's emotions. For example, if an employee is stressed, the issuing unit can increase the amount of gratitude points issued to increase motivation. If an employee is relaxed, the issuing unit can maintain the normal amount. Furthermore, if an employee is highly motivated, the issuing unit can decrease the amount of gratitude points issued to encourage further contributions. This allows for improving motivation by adjusting the amount issued according to an employee's emotions.

[0091] When issuing appreciation points, the issuing department can adjust the amount to be issued, taking into account the employee's years of service and experience. Years of service include, but are not limited to, long-term employment, short-term employment, and flextime. Experience includes, but is not limited to, technical experience, management experience, and sales experience. For example, the issuing department issues appreciation points to employees with many years of service according to their years of service. The issuing department can also issue appreciation points to experienced employees according to their experience. For example, the issuing department can issue basic appreciation points to new employees. This allows for fairer evaluation by taking into account years of service and experience.

[0092] When issuing appreciation points, the issuing department can evaluate the contribution of the entire team of employees and issue team points. Examples of the contribution of the entire team include, but are not limited to, team results, project success, and the degree of cooperation. For example, the issuing department issues team points when the entire team demonstrates a high level of contribution. The issuing department can also evaluate the success of a project as a whole team and issue team points. For example, the issuing department can evaluate the cooperation of the entire team and issue team points. This can improve teamwork by evaluating the contribution of the entire team.

[0093] When issuing appreciation points, the issuing department can include an employee's self-development activities (such as participation in training) in the evaluation. Self-development activities include, but are not limited to, participation in training, obtaining qualifications, and acquiring new skills. For example, the issuing department evaluates the content of the training that the employee participated in and issues appreciation points. Furthermore, when an employee engages in self-development activities, the issuing department can evaluate the content and issue appreciation points. For example, when an employee acquires a new skill, the issuing department can evaluate the content and issue appreciation points. In this way, by including self-development activities in the evaluation, it is possible to promote employee growth.

[0094] The transfer unit can estimate an employee's emotions and simplify the gratitude point transfer procedure based on the estimated employee emotions. Employee emotions include, but are not limited to, stress, relaxation, and motivation. The transfer unit can estimate an employee's emotions using, for example, emotion recognition technology. The transfer unit can also estimate an employee's emotions using generative AI. For example, the transfer unit can analyze an employee's facial expressions and voice data to estimate emotions. The transfer unit can also simplify the gratitude point transfer procedure based on the employee's emotions. For example, if an employee is stressed, the gratitude point transfer procedure can be simplified and made faster. Alternatively, if an employee is relaxed, the normal transfer procedure can be maintained. Furthermore, if an employee is highly motivated, the transfer procedure can be made more detailed and more transparent. This allows for faster transfer by simplifying the transfer procedure according to the employee's emotions.

[0095] The transfer department can record the transfer history when transferring gratitude points to ensure transparency. The transfer history includes, for example, but is not limited to, the transfer date and time, the transfer amount, and the transfer recipient. For example, the transfer department can automatically record the transfer history of gratitude points so that it can be referenced later. The transfer department can also publish the transfer history in a Web 3.0 community for employees to ensure transparency. For example, the transfer department can analyze the transfer history to check for any fraudulent transfers. In this way, transparency is ensured by recording the transfer history.

[0096] When transferring appreciation points, the transfer unit can automatically adjust the transfer amount according to the contribution level of the employee receiving the points. The contribution level of the employee receiving the points includes, but is not limited to, past contribution history, current contribution level, and the contribution level of the entire team. For example, the transfer unit increases the transfer amount if the contribution level of the employee receiving the points is high. The transfer unit can also decrease the transfer amount if the contribution level of the employee receiving the points is low. For example, the transfer unit can automatically adjust the transfer amount according to the contribution level of the employee receiving the points. This allows for a more fair transfer by adjusting the transfer amount according to the contribution level of the employee receiving the points.

[0097] The transfer department can record the reason for the transfer when the gratitude points are transferred so that it can be referenced later. Examples of the reason for the transfer include, but are not limited to, gratitude, a reward for a specific contribution, team cooperation, etc. For example, the transfer department can provide a field for inputting the reason for the transfer when the gratitude points are transferred. The transfer department can also automatically record the reason for the transfer so that it can be referenced later. For example, the transfer department can analyze the reason for the transfer and improve communication between employees. Thus, by recording the reason for the transfer, the transparency of the transfer is improved.

[0098] The transfer unit can estimate an employee's emotions and set a transfer limit for gratitude points based on the estimated employee emotions. Employee emotions include, but are not limited to, stress, relaxation, and motivation. The transfer unit can estimate an employee's emotions using, for example, emotion recognition technology. The transfer unit can also estimate an employee's emotions using generative AI. For example, the transfer unit can analyze an employee's facial expressions and voice data to estimate emotions. The transfer unit can also set a transfer limit for gratitude points based on the employee's emotions. For example, if an employee is stressed, the transfer limit for gratitude points can be relaxed, allowing them to be freely transferred. Alternatively, if an employee is relaxed, the normal transfer limit can be applied. Furthermore, if an employee is highly motivated, the transfer limit can be tightened, increasing the value of the points. Thus, by setting a transfer limit according to an employee's emotions, the value of the points can be increased.

[0099] When transferring gratitude points, the transfer department can adjust the transfer amount taking into account the position and job content of the employee receiving the points. Examples of job titles include, but are not limited to, managerial positions, technical positions, and sales positions. Examples of job content include, but are not limited to, project management, technical support, and customer support. For example, the transfer department increases the transfer amount if the employee receiving the points is a manager. Furthermore, the transfer department can also keep the transfer amount normal if the employee receiving the points is a technical position. For example, the transfer department can decrease the transfer amount if the employee receiving the points is a sales position. This allows for more fair transfers by adjusting the transfer amount according to the position and job content.

[0100] When transferring gratitude points, the transfer unit can determine the transfer amount by referring to the past contribution history of the employee to whom the points are to be transferred. Past contribution history includes, but is not limited to, project contribution, feedback, task completion rate, etc. For example, the transfer unit can increase the transfer amount if the employee to whom the points are to be transferred has a high past contribution history. The transfer unit can also decrease the transfer amount if the employee to whom the points are to be transferred has a low past contribution history. For example, the transfer unit can automatically adjust the transfer amount based on the employee to whom the points are to be transferred. This allows a more appropriate transfer amount to be determined by referring to the past contribution history.

[0101] When transferring gratitude points, the transferor can evaluate the transferee employee's contribution to the entire team and transfer them as team points. Examples of the team's contribution include, but are not limited to, team results, project success, and the degree of cooperation. For example, the transferor can transfer the points as team points if the entire team demonstrates a high level of contribution. The transferor can also evaluate the project success of the entire team and transfer them as team points. For example, the transferor can evaluate the cooperation of the entire team and transfer them as team points. This can improve teamwork by evaluating the contribution of the entire team.

[0102] The provision unit can estimate the employee's emotions and adjust the method of providing rewards based on the estimated employee emotions. Employee emotions include, but are not limited to, stress, relaxation, and motivation. The provision unit can estimate the employee's emotions using, for example, emotion recognition technology. The provision unit can also estimate the employee's emotions using generative AI. For example, the provision unit can analyze the employee's facial expressions and voice data to estimate the employee's emotions. The provision unit can also adjust the method of providing rewards based on the employee's emotions. For example, if the employee is feeling stressed, a reward that allows them to relax can be provided. If the employee is relaxed, a regular reward can be provided. Furthermore, if the employee is highly motivated, a challenging reward can be provided. This allows the provision of more appropriate rewards by adjusting the method of providing rewards according to the employee's emotions.

[0103] When providing benefits, the provision department can provide different types of benefits depending on the employee's level of contribution. Different types of benefits include, but are not limited to, special training, the right to participate in events, gift certificates, and vacations. For example, the provision department can provide special training or the right to participate in events to employees who have demonstrated a high level of contribution. The provision department can also provide regular benefits to employees who have demonstrated a medium level of contribution. For example, the provision department can provide basic benefits to employees who have demonstrated a low level of contribution. This allows for more detailed evaluation by providing different types of benefits depending on the level of contribution.

[0104] When providing benefits, the provision unit can provide a bonus benefit based on the cumulative value of an employee's contribution. Examples of cumulative contribution values ​​include, but are not limited to, contributions within a certain period of time, contributions over a long period of time, and contributions in a specific project. For example, the provision unit can provide a bonus benefit based on the cumulative value to an employee who has demonstrated a high level of contribution within a certain period of time. The provision unit can also provide a bonus benefit based on the cumulative value to an employee who has demonstrated stable contributions over a long period of time. For example, the provision unit can provide a bonus benefit based on the cumulative value to an employee who has demonstrated a high level of contribution in a specific project. This allows long-term contributions to be evaluated by providing a bonus benefit based on the cumulative value of contributions.

[0105] When providing rewards, the provision department can provide rewards by specially recognizing contributions to specific projects or events. Specific projects and events include, but are not limited to, internal projects, internal events, and external events. For example, the provision department can provide special rewards to employees who have made a high level of contribution to a specific project. The provision department can also provide special rewards by recognizing contributions at internal events. For example, the provision department can also provide special rewards by recognizing contributions at external events. In this way, special recognition of contributions to specific projects or events can increase motivation for specific activities.

[0106] The provision unit can estimate the employee's emotions and adjust the frequency of providing rewards based on the estimated employee emotions. Employee emotions include, but are not limited to, stress, relaxation, and motivation. The provision unit can estimate the employee's emotions using, for example, emotion recognition technology. The provision unit can also estimate the employee's emotions using generative AI. For example, the provision unit can analyze the employee's facial expressions and voice data to estimate the employee's emotions. The provision unit can also adjust the frequency of providing rewards based on the employee's emotions. For example, if the employee is stressed, the frequency of providing rewards can be increased to increase motivation. If the employee is relaxed, the normal frequency of providing rewards can be maintained. Furthermore, if the employee is highly motivated, the frequency of providing rewards can be reduced to encourage further contributions. In this way, motivation can be improved by adjusting the frequency of providing rewards according to the employee's emotions.

[0107] When providing benefits, the provision department can adjust the content of benefits by taking into account the employee's years of service and experience. Years of service include, but are not limited to, long-term employment, short-term employment, and flextime. Experience includes, but is not limited to, technical experience, management experience, and sales experience. For example, the provision department provides benefits to employees with many years of service according to their years of service. The provision department can also provide benefits to experienced employees according to their experience. For example, the provision department can provide basic benefits to new employees. This makes it possible to provide benefits more fairly by taking into account years of service and experience.

[0108] When providing rewards, the provision department can evaluate the employee's overall team contribution and provide team rewards. Examples of the team's overall contribution include, but are not limited to, team results, project success, and degree of cooperation. For example, the provision department can provide team rewards when the team as a whole demonstrates a high level of contribution. The provision department can also evaluate the project success of the team as a whole and provide team rewards. For example, the provision department can evaluate the cooperation of the team as a whole and provide team rewards. This can improve teamwork by evaluating the team's overall contribution.

[0109] When providing benefits, the provision department can include an employee's self-development activities (such as participation in training) in the evaluation. Self-development activities include, but are not limited to, participation in training, obtaining qualifications, and acquiring new skills. For example, the provision department evaluates the content of the training that the employee participated in and provides the benefit. Furthermore, when an employee engages in self-development activities, the provision department can evaluate the content and provide the benefit. For example, when an employee acquires a new skill, the provision department can evaluate the content and provide the benefit. In this way, by including self-development activities in the evaluation, it is possible to promote employee growth. === Hard Collateral 1-1 === Each of the multiple elements, including the determination unit, issuance unit, transfer unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the determination unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and learns conversation history data such as internal communication tools and emails, and evaluates the contribution of others. The issuance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and issues gratitude points based on the contribution of others determined by the determination unit. The transfer unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and enables the transfer of gratitude points between employees. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides benefits based on the transferred gratitude points. === Hard Collateral 1-2 === Each of the multiple elements, including the determination unit, issuance unit, transfer unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the determination unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and learns conversation history data such as internal communication tools and emails, and evaluates the degree of contribution of others. The issuance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues gratitude points based on the degree of contribution of others determined by the determination unit. The transfer unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and enables the transfer of gratitude points between employees. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides benefits based on the transferred gratitude points. === Hard Collateral 1-3 === Each of the multiple elements, including the determination unit, issuance unit, transfer unit, and provision unit, described above, is implemented, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the determination unit is implemented by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and learns conversation history data such as internal communication tools and emails, and evaluates the contribution of others. The issuance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and issues gratitude points based on the contribution of others determined by the determination unit. The transfer unit is implemented, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and enables the transfer of gratitude points between employees. The provision unit is implemented, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides benefits based on the transferred gratitude points. === Hard Collateral 1-4 === Each of the multiple elements, including the determination unit, issuance unit, transfer unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the determination unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and learns conversation history data from internal communication tools, emails, etc., and evaluates the degree of contribution of others. The issuance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues gratitude points based on the degree of contribution of others determined by the determination unit. The transfer unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and enables the transfer of gratitude points between employees. The provision unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides benefits based on the transferred gratitude points.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The judgment unit can estimate the employee's emotions and adjust the criteria for judging contribution to others based on the estimated emotions. For example, if an employee is feeling stressed, the judgment criteria for contribution to others can be relaxed and positive behavior can be evaluated more highly. Alternatively, if an employee is relaxed, the normal judgment criteria can be applied to provide a balanced evaluation. Furthermore, if an employee is highly motivated, the judgment criteria can be tightened and a higher contribution can be required. In this way, by adjusting the judgment criteria according to the employee's emotions, a more appropriate evaluation of contribution to others can be achieved.

[0112] The assessment unit can learn data from project management tools in addition to conversation history data from internal communication tools and emails to assess the degree of contribution of others. For example, it can learn task completion data from a task management tool and reflect the degree of task completion and contribution in the evaluation. It can also learn comments and feedback from a progress management tool and reflect the degree of support and cooperation provided to others in the evaluation. It can also learn progress report data from a project management tool and reflect the degree of contribution to the success of the project in the evaluation. In this way, by learning data from project management tools as well, it becomes possible to assess the degree of contribution of others more comprehensively.

[0113] When issuing appreciation points, the issuing department can issue different types of points according to the employee's level of contribution. For example, special bonus points can be issued to employees who have made a high level of contribution. Normal appreciation points can also be issued to employees who have made a medium level of contribution. Furthermore, basic appreciation points can also be issued to employees who have made a low level of contribution. This allows for more detailed evaluation by issuing different types of points according to the level of contribution.

[0114] When transferring gratitude points, the transfer unit can automatically adjust the transfer amount according to the contribution level of the employee receiving the points. For example, if the employee receiving the points has a high level of contribution, the transfer amount can be increased. Conversely, if the employee receiving the points has a low level of contribution, the transfer amount can be decreased. Furthermore, the transfer amount can be automatically adjusted based on the employee receiving the points' contribution level. This allows for a fairer transfer by adjusting the transfer amount according to the employee's level of contribution.

[0115] The provision unit can provide bonus benefits based on the cumulative value of an employee's contribution when providing benefits. For example, an employee who has demonstrated a high level of contribution within a certain period of time can be provided with a bonus benefit based on the cumulative value. An employee who has demonstrated stable contribution over a long period of time can also be provided with a bonus benefit based on the cumulative value. Furthermore, an employee who has demonstrated a high level of contribution in a specific project can be provided with a bonus benefit based on the cumulative value. In this way, by providing bonus benefits based on the cumulative value of contribution, long-term contribution can be evaluated.

[0116] The assessment unit can estimate the employee's emotions and adjust the way in which the assessment results of the employee's contribution to others are displayed based on the estimated employee's emotions. For example, if an employee is feeling stressed, positive feedback can be emphasized. If an employee is relaxed, detailed feedback can be displayed, including areas for improvement. Furthermore, if an employee is highly motivated, challenging goals can be presented to encourage further growth. This makes it possible to provide more appropriate feedback by adjusting the way in which the assessment results are displayed according to the employee's emotions.

[0117] The issuing department can estimate the emotions of employees and adjust the frequency of issuing gratitude points based on the estimated emotions of employees. For example, if an employee is feeling stressed, the frequency of issuing gratitude points can be increased to increase motivation. If an employee is relaxed, the normal frequency of issuing gratitude points can be maintained. Furthermore, if an employee is highly motivated, the frequency of issuing gratitude points can be reduced to encourage further contributions. In this way, motivation can be improved by adjusting the frequency of issuing gratitude points according to the employee's emotions.

[0118] The transfer unit can estimate the employee's emotions and simplify the gratitude point transfer procedure based on the estimated employee's emotions. For example, if an employee is feeling stressed, the gratitude point transfer procedure can be simplified and made quicker. If an employee is relaxed, the normal transfer procedure can be maintained. Furthermore, if an employee is highly motivated, the transfer procedure can be made more detailed and more transparent. This allows for quicker transfer by simplifying the transfer procedure according to the employee's emotions.

[0119] The provision unit can estimate the employee's emotions and adjust the method of providing rewards based on the estimated employee emotions. For example, if an employee is feeling stressed, a reward that allows them to relax can be provided. If the employee is relaxed, a regular reward can be provided. Furthermore, if the employee is highly motivated, a challenging reward can be provided. In this way, by adjusting the method of providing rewards according to the employee's emotions, more appropriate rewards can be provided.

[0120] When providing benefits, the provision department can include employees' self-development activities (such as participation in training) in the evaluation. For example, the content of the training an employee has participated in can be evaluated and a benefit can be provided. Also, when an employee engages in self-development activities, the content can be evaluated and a benefit can be provided. Furthermore, when an employee acquires a new skill, the content can be evaluated and a benefit can be provided. In this way, by including self-development activities in the evaluation, it is possible to promote employee growth.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The judgment unit uses generative AI to determine an employee's level of contribution to others. The judgment unit learns conversation history data from internal communication tools and emails, etc., and evaluates the level of contribution to others. For example, it evaluates comments that support other employees and actions that contribute to problem-solving. It can also analyze the content, frequency, and quality of conversations to quantify an employee's level of contribution to others. Step 2: The issuing unit issues gratitude points based on the degree of contribution by others determined by the determining unit. For example, based on the degree of contribution by others determined by the generating AI, it issues unique gratitude points (tokens) that can be used in a Web 3.0 community exclusively for employees. Step 3: The transfer department makes the gratitude points issued by the issuing department transferable between employees. The transfer department provides an interface for transferring gratitude points between employees, records the transfer history of gratitude points, and ensures transparency. Step 4: The providing unit provides benefits based on the gratitude points transferred by the transferring unit. For example, the providing unit provides benefits such as the right to participate in special training or events according to the number of gratitude points owned.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] 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.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] 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.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] 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.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 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 determination unit that determines the contribution of others using a generation AI; an issuing unit that issues thank-you points based on the degree of contribution to others determined by the determining unit; a transfer unit that transfers the appreciation points issued by the issuing unit; a providing unit that provides a benefit based on the gratitude points transferred by the transferring unit; A system characterized by:

2. The determination unit Generate conversation history data from internal communication tools or emails, and AI learns from it to determine the contribution of others 2. The system of claim 1.

3. The issuing department: Generated AI issues gratitude points based on the contribution of others.

2. The system of claim 1.

4. The transfer unit is Make gratitude points transferable between employees 2. The system of claim 1.

5. The providing unit Offer rewards according to the number of gratitude points you have 2. The system of claim 1.

6. The providing unit Offer special training and event access as a perk 2. The system of claim 1.

7. The transfer unit is Allowing the transfer of gratitude points to be done within the employee-only Web 3.0 community 2. The system of claim 1.

8. The issuing department: Issue gratitude points as tokens 2. The system of claim 1.

Images