system

Abstract

The system according to this embodiment aims to improve employee motivation and increase work efficiency. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a reward unit, and a feedback unit. The analysis unit analyzes the work content of each employee. The generation unit generates daily challenges based on the work content analyzed by the analysis unit. The reward unit provides rewards for completing the daily challenges generated by the generation unit. The feedback unit provides individual feedback based on the rewards provided by the reward unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.< /

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the motivation of employees tends to decline and it is difficult to improve the efficiency of work.

[0005] The system according to the embodiment aims to improve the motivation of employees and enhance the efficiency of work.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a reward unit, and a feedback unit. The analysis unit analyzes the work content of each employee. The generation unit generates daily challenges based on the work content analyzed by the analysis unit. The reward unit provides rewards for completing the daily challenges generated by the generation unit. The feedback unit provides individual feedback based on the rewards provided by the reward unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve employee motivation and increase work efficiency. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The work environment provision system according to an embodiment of the present invention is a system in which an AI agent provides a work environment incorporating game mechanics in order to improve employee motivation. This work environment provision system analyzes the employee's work content, generates daily challenges, and provides a mechanism in which employees receive rewards for completing the daily challenges. The work environment provision system also provides individual feedback to improve employee motivation. For example, the work environment provision system analyzes the employee's work content in detail and sets daily challenges. This allows employees to find enjoyment in their daily work. Next, the work environment provision system awards points each time an employee completes a daily challenge, and pays a reward when a certain number of points are accumulated. For example, by completing a daily challenge, an employee can earn points and receive a reward. Furthermore, when an employee completes a daily challenge, the work environment provision system evaluates their performance and provides feedback. For example, by evaluating employee performance and providing feedback, the work environment provision system improves employee motivation. This mechanism improves employee engagement and makes work efficiency improve in a quantifiable way. For example, by numerically managing the completion rate of daily challenges and the status of reward collection, employees can experience an improvement in work efficiency. Furthermore, the work environment provision system aims to revitalize the company culture. When employees share daily challenges and compete with each other, internal communication becomes more active. This can lead to the growth of the entire company. For example, increased employee motivation leads to increased work efficiency and improved company performance. Thus, the work environment provision system can improve employee motivation and work efficiency.

[0029] The work environment provision system according to the embodiment comprises an analysis unit, a generation unit, a reward unit, and a feedback unit. The analysis unit analyzes the work content of each employee. The analysis unit, for example, analyzes the work content of employees in detail and collects data to prevent the routinization of work and the decline in employee motivation. The analysis unit, for example, can store the work content of employees in a database and analyze it using AI. The generation unit generates daily challenges based on the work content analyzed by the analysis unit. The generation unit, for example, sets appropriate daily challenges according to the work content of employees. The generation unit, for example, can automatically generate daily challenges based on the work content of employees using AI. The reward unit provides rewards for completing the daily challenges generated by the generation unit. The reward unit, for example, awards points each time an employee completes a daily challenge and pays a reward when a certain number of points are accumulated. The reward unit, for example, can manage the completion status of employees' daily challenges and provide rewards using AI. The feedback unit provides individual feedback based on the rewards provided by the reward unit. The feedback unit evaluates the performance of employees and provides feedback when they complete daily challenges, for example. The feedback unit can use AI, for example, to evaluate employee performance and provide feedback. As a result, the work environment provision system according to this embodiment can improve employee motivation and work efficiency.

[0030] The analytics department is responsible for analyzing the work content of each employee in detail. Specifically, it stores the content of the tasks performed by employees on a daily basis, their progress, and the time spent on those tasks in a database, and analyzes this data using AI. The AI ​​learns the work patterns of employees and collects data to prevent work from becoming routine and to prevent a decline in employee motivation. For example, by analyzing how much time employees spend on specific tasks and what tasks are performed frequently, it is possible to identify areas for work efficiency improvement and other improvements. In addition, by accumulating data on the work content of employees, it is possible to grasp long-term work trends and fluctuations in performance. As a result, the analytics department can gain a detailed understanding of employee work content and provide data that contributes to work efficiency and improved motivation. Furthermore, the analytics department can share data on employee work content with other departments to improve overall work efficiency. For example, based on the data collected by the analytics department, other departments can consider improving business processes or introducing new work methods. As a result, the analytics department can analyze employee work content in detail and provide data that contributes to work efficiency and improved motivation.

[0031] The generation unit is responsible for generating daily challenges based on the work content analyzed by the analysis unit. Specifically, it sets appropriate daily challenges considering the employee's work content and progress. Using AI, it can automatically generate daily challenges based on the employee's work content. For example, it can set challenges to improve the employee's skills to perform a specific task efficiently, or challenges to try out new work methods. The generation unit can grasp the employee's work content and progress in real time and provide daily challenges at the appropriate time. This allows employees to have new goals for their daily work and maintain motivation. In addition, the generation unit can adjust the difficulty and content of the daily challenges according to the employee's work content. For example, by customizing the challenge content according to the progress of work and the employee's skill level, it can ensure that employees can tackle the challenges without difficulty. In this way, the generation unit can generate appropriate daily challenges based on the employee's work content and improve employee motivation.

[0032] The Rewards Department is responsible for providing rewards for completing daily challenges generated by the Generation Department. Specifically, it awards points to employees each time they complete a daily challenge, and provides rewards once a certain number of points are accumulated. Using AI, it can manage employees' daily challenge completion status and provide rewards. For example, when an employee completes a daily challenge, its performance is evaluated and points are awarded. If the points reach a certain level, rewards such as monetary incentives, special leave, or training for skill development can be provided. The Rewards Department plays a crucial role in maintaining employee motivation and improving work efficiency. Furthermore, the Rewards Department can monitor employees' daily challenge completion status in real time and provide rewards at the appropriate time. This allows employees to set new goals for their daily work and maintain motivation. In addition, the Rewards Department can adjust the content of rewards according to the employee's work content and progress. For example, by customizing the content of rewards according to the progress of work and the employee's skill level, employees can take on challenges without undue burden. In this way, the Rewards Department can improve employee motivation by managing employees' daily challenge completion status and providing rewards.

[0033] The Feedback Department is responsible for providing individual feedback based on the rewards provided by the Rewards Department. Specifically, it evaluates and provides feedback on employees' performance when they complete daily challenges. Using AI, it can evaluate employee performance and provide feedback. For example, when an employee completes a daily challenge, it can analyze the performance in detail and provide specific areas for improvement and advice for the next steps. The Feedback Department can understand employees' work content and progress in real time and provide feedback at the appropriate time. This allows employees to set new goals for their daily work and maintain motivation. Furthermore, the Feedback Department can adjust the content of feedback according to the employee's work content and progress. For example, by customizing the content of feedback according to the progress of work and the employee's skill level, it can ensure that employees can tackle challenges without difficulty. In this way, the Feedback Department can improve employee motivation by evaluating employee performance and providing feedback. In addition, the Feedback Department can collect employee feedback and use it to improve the overall system. For example, by reviewing the content of daily challenges and rewards based on employee feedback, the effectiveness of the overall system can be improved. This allows the feedback department to improve employee motivation by evaluating employee performance and providing feedback.

[0034] The analysis unit can analyze work content in detail to prevent the routine nature of tasks and the decline in employee motivation. For example, the analysis unit can analyze employees' work content in detail and collect data to prevent the routine nature of tasks. For example, the analysis unit can store employee work content in a database and analyze it using AI. This allows for the maintenance of employee motivation by analyzing work content in detail to prevent the routine nature of tasks and the decline in employee motivation.

[0035] The generation unit can set daily challenges. For example, the generation unit can set appropriate daily challenges according to the employee's work content. For example, the generation unit can use AI to automatically generate daily challenges based on the employee's work content. This allows employees to find enjoyment in their daily work by setting daily challenges.

[0036] The rewards department can award points for completing daily challenges, and provide rewards once a certain number of points are accumulated. For example, the rewards department can award points to employees for completing daily challenges, and provide rewards once a certain number of points are accumulated. The rewards department can use AI, for example, to manage the completion status of employees' daily challenges and provide rewards. This can improve employee motivation by awarding points for completing daily challenges and providing rewards once a certain number of points are accumulated.

[0037] The feedback department can evaluate employees' performance and provide feedback when they complete daily challenges. For example, the feedback department can evaluate employees' performance and provide feedback when they complete daily challenges. The feedback department can use AI, for example, to evaluate employee performance and provide feedback. This allows for improved employee motivation by evaluating and providing feedback when employees complete daily challenges.

[0038] The feedback department can help employees feel a sense of accomplishment in their work and improve their motivation. For example, the feedback department can use AI to evaluate employee performance and provide feedback. This allows employees to feel a sense of accomplishment in their work and improves their motivation, thereby improving work efficiency.

[0039] The analysis department can set daily challenges for tasks that involve a lot of routine work, such as data entry and report writing. For example, the analysis department can use AI to analyze employees' work in detail and set daily challenges. This allows for the prevention of decreased employee motivation by setting daily challenges for tasks that involve a lot of routine work, such as data entry and report writing.

[0040] The rewards department can numerically manage the completion rate of daily challenges and the status of reward receipt. For example, the rewards department can use AI to manage the completion rate of employees' daily challenges and provide rewards. This allows for a tangible improvement in work efficiency by numerically managing the completion rate of daily challenges and the status of reward receipt.

[0041] The Feedback Department can revitalize internal communication by allowing employees to share daily challenges and compete with each other. For example, the Feedback Department can use AI to evaluate employee performance and provide feedback. This allows employees to share daily challenges and compete with each other, thereby revitalizing internal communication and strengthening the company culture.

[0042] The analysis unit can improve the accuracy of its analysis by referring to employees' past work history when analyzing work content. For example, the analysis unit can analyze employees' past work history to identify similar work content and improve the accuracy of the analysis. For example, the analysis unit can extract successful work patterns from employees' past work history and reflect them in the analysis. For example, the analysis unit can predict the progress of work based on employees' past work history and reflect this in the analysis results. In this way, by improving the accuracy of the analysis by referring to employees' past work history, it is possible to predict the progress of work and reflect this in the analysis results.

[0043] The analysis unit can apply different analysis algorithms to employees' work processes depending on their skill level. For example, it can apply a detailed analysis algorithm to employees with high skill levels to identify areas for improvement in their work. For employees with medium skill levels, it can apply a standard analysis algorithm to improve work efficiency. For employees with low skill levels, it can apply a simplified analysis algorithm to strengthen the basic aspects of their work. By applying different analysis algorithms according to employees' skill levels, the unit can identify areas for improvement in their work and improve work efficiency.

[0044] The analysis unit can perform analysis of work content while taking into account the geographical location information of employees. For example, if an employee is working remotely, the analysis unit will analyze work content based on their geographical location information. For example, if an employee is on a business trip, the analysis unit will analyze work content while taking their geographical location information into account. For example, if an employee is in the office, the analysis unit will analyze work content based on their geographical location information. This allows for appropriate analysis of work content while employees are working remotely or on business trips by taking their geographical location information into account.

[0045] The analysis department can analyze employees' social media activities and related work content when analyzing work processes. For example, the analysis department can analyze employees' social media activities, extract information related to work, and analyze it. For example, the analysis department can collect opinions and feedback on work from employees' social media activities and reflect them in the analysis. For example, the analysis department can grasp work trends and needs based on employees' social media activities and reflect them in the analysis. In this way, by analyzing employees' social media activities and related work content, it is possible to grasp work trends and needs and reflect them in the analysis.

[0046] The generation unit can adjust the difficulty of daily challenges based on the importance of the task when generating them. For example, it can set a high-difficulty daily challenge for high-importance tasks, a standard-difficulty daily challenge for medium-importance tasks, and an easy daily challenge for low-importance tasks. By adjusting the difficulty of challenges based on the importance of the task, the burden on employees can be reduced and areas for improvement in work processes can be identified.

[0047] The generation unit can apply different generation algorithms depending on the category of the task when generating daily challenges. For example, the generation unit applies an efficient generation algorithm to data entry tasks. For example, the generation unit applies a detailed generation algorithm to report creation tasks. For example, the generation unit applies a flexible generation algorithm to creative tasks. This allows for increased efficiency in tasks by applying different generation algorithms depending on the category of the task.

[0048] The generation unit can determine the priority of daily challenges based on the submission deadline of the tasks when generating them. For example, the generation unit sets high-priority daily challenges for tasks with approaching deadlines. For example, the generation unit sets standard-priority daily challenges for tasks with medium-term deadlines. For example, the generation unit sets low-priority daily challenges for tasks with distant deadlines. By determining the priority of challenges based on the submission deadline of the tasks, the efficiency of the work can be improved.

[0049] The generation unit can adjust the order of daily challenges based on the relevance of the tasks when generating them. For example, the generation unit can prioritize setting highly relevant tasks as daily challenges. For example, the generation unit can set tasks of moderate relevance as daily challenges in a standard order. For example, the generation unit can postpone setting less relevant tasks as daily challenges. In this way, by adjusting the order of challenges based on the relevance of the tasks, it is possible to improve work efficiency.

[0050] The compensation department can select the type of compensation based on an employee's past performance when providing compensation. For example, the compensation department can analyze an employee's past performance and select the most appropriate compensation. For example, the compensation department can select compensation for successful tasks based on an employee's past performance. For example, the compensation department can select performance-based compensation based on an employee's past performance. By selecting the type of compensation based on an employee's past performance, employee motivation can be improved.

[0051] The compensation department can customize the form of compensation based on the employee's current living situation when providing compensation. For example, the compensation department can analyze the employee's current living situation and provide the most suitable compensation form. For example, the compensation department can select the necessary compensation form based on the employee's current living situation. For example, the compensation department can customize the compensation form based on the employee's current living situation. By customizing the form of compensation based on the employee's current living situation, employee motivation can be improved.

[0052] The compensation department can select the optimal compensation by considering the employee's geographical location when providing compensation. For example, if an employee is working remotely, the compensation department will provide the optimal compensation based on their geographical location. For example, if an employee is on a business trip, the compensation department will provide the optimal compensation by considering their geographical location. For example, if an employee is in the office, the compensation department will provide the optimal compensation based on their geographical location. By selecting the optimal compensation by considering the employee's geographical location, employee motivation can be improved.

[0053] The Compensation Department can analyze employees' social media activity and propose appropriate compensation methods when providing rewards. For example, the Compensation Department can analyze employees' social media activity and propose the most suitable compensation method. For example, the Compensation Department can collect opinions and feedback on work from employees' social media activity and reflect them in compensation methods. For example, the Compensation Department can customize compensation methods based on employees' social media activity. In this way, by analyzing employees' social media activity and proposing compensation methods, employee motivation can be improved.

[0054] The feedback department can provide optimal feedback by referring to an employee's past performance. For example, the feedback department can analyze an employee's past performance and provide feedback on successful tasks. For example, the feedback department can identify areas for improvement based on an employee's past performance and provide feedback accordingly. For example, the feedback department can provide performance-based feedback based on an employee's past performance. This allows for improved employee motivation by providing optimal feedback based on an employee's past performance.

[0055] The feedback department can customize the format of feedback based on the employee's current work situation when providing feedback. For example, the feedback department can analyze the employee's current work situation and provide the optimal feedback format. For example, the feedback department can select the necessary feedback format based on the employee's current work situation. For example, the feedback department can customize the feedback format based on the employee's current work situation. This allows for improved employee motivation by customizing the feedback format based on the employee's current work situation.

[0056] The feedback department can provide optimal feedback by considering the employee's geographical location when providing feedback. For example, if an employee is working remotely, the feedback department will provide optimal feedback based on their geographical location. For example, if an employee is on a business trip, the feedback department will provide optimal feedback based on their geographical location. For example, if an employee is in the office, the feedback department will provide optimal feedback based on their geographical location. By providing optimal feedback that takes into account the employee's geographical location, it is possible to improve employee motivation.

[0057] The feedback department can analyze employees' social media activity and propose feedback methods when providing feedback. For example, the feedback department can analyze employees' social media activity and propose the most suitable feedback method. For example, the feedback department can collect opinions and feedback on work from employees' social media activity and reflect them in the feedback method. For example, the feedback department can customize the feedback method based on employees' social media activity. In this way, by analyzing employees' social media activity and proposing feedback methods, employee motivation can be improved.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The analysis unit can improve the accuracy of its analysis by referring to employees' past work history when analyzing work content. For example, it can analyze employees' past work history to identify similar work content and improve the accuracy of the analysis. It can also extract successful work patterns from employees' past work history and reflect them in the analysis. Furthermore, it can predict the progress of work based on employees' past work history and reflect this in the analysis results. In this way, by improving the accuracy of the analysis by referring to employees' past work history, it is possible to predict the progress of work and reflect this in the analysis results.

[0060] The generation unit can adjust the difficulty of daily challenges based on the importance of the task when generating them. For example, high-importance tasks can be assigned high-difficulty daily challenges. Medium-importance tasks can be assigned standard-difficulty daily challenges. Furthermore, low-importance tasks can be assigned easy daily challenges. By adjusting the difficulty of challenges based on the importance of the task, the burden on employees can be reduced and areas for improvement in work processes can be identified.

[0061] The compensation department can select the type of compensation based on an employee's past performance when providing compensation. For example, it can analyze an employee's past performance and select the most appropriate compensation. It can also select compensation for successful tasks based on an employee's past performance. Furthermore, it can select compensation commensurate with performance based on an employee's past performance. By selecting the type of compensation based on an employee's past performance, it is possible to improve employee motivation.

[0062] The feedback department can provide optimal feedback by referencing an employee's past performance. For example, it can analyze an employee's past performance and provide feedback on successful tasks. It can also identify areas for improvement based on an employee's past performance and provide feedback accordingly. Furthermore, it can provide performance-based feedback based on an employee's past performance. By providing optimal feedback based on an employee's past performance, it is possible to improve employee motivation.

[0063] The analysis unit can apply different analysis algorithms to employees based on their skill levels when analyzing work processes. For example, a detailed analysis algorithm can be applied to employees with high skill levels to identify areas for improvement in their work. A standard analysis algorithm can be applied to employees with medium skill levels to improve work efficiency. Furthermore, a simplified analysis algorithm can be applied to employees with low skill levels to strengthen the fundamental aspects of their work. In this way, by applying different analysis algorithms according to employees' skill levels, areas for improvement in work can be identified and work efficiency can be improved.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The analysis unit analyzes the work content of each employee. The analysis unit conducts a detailed analysis of employee work content and collects data to prevent work from becoming routine and to reduce employee motivation. The analysis unit stores employee work content in a database and can analyze it using AI. Step 2: The generation unit generates daily challenges based on the work content analyzed by the analysis unit. The generation unit sets appropriate daily challenges according to the employee's work content. The generation unit can use AI to automatically generate daily challenges based on the employee's work content. Step 3: The rewards department provides rewards for completing daily challenges generated by the generation department. The rewards department awards points to employees each time they complete a daily challenge, and pays out rewards once a certain number of points are accumulated. The rewards department can use AI to manage the completion status of employees' daily challenges and provide rewards accordingly. Step 4: The Feedback Department provides individual feedback based on the rewards provided by the Rewards Department. The Feedback Department evaluates and provides feedback on the performance of employees when they complete daily challenges. The Feedback Department can use AI to evaluate employee performance and provide feedback.

[0066] (Example of form 2) The work environment provision system according to an embodiment of the present invention is a system in which an AI agent provides a work environment incorporating game mechanics in order to improve employee motivation. This work environment provision system analyzes the employee's work content, generates daily challenges, and provides a mechanism in which employees receive rewards for completing the daily challenges. The work environment provision system also provides individual feedback to improve employee motivation. For example, the work environment provision system analyzes the employee's work content in detail and sets daily challenges. This allows employees to find enjoyment in their daily work. Next, the work environment provision system awards points each time an employee completes a daily challenge, and pays a reward when a certain number of points are accumulated. For example, by completing a daily challenge, an employee can earn points and receive a reward. Furthermore, when an employee completes a daily challenge, the work environment provision system evaluates their performance and provides feedback. For example, by evaluating employee performance and providing feedback, the work environment provision system improves employee motivation. This mechanism improves employee engagement and makes work efficiency improve in a quantifiable way. For example, by numerically managing the completion rate of daily challenges and the status of reward collection, employees can experience an improvement in work efficiency. Furthermore, the work environment provision system aims to revitalize the company culture. When employees share daily challenges and compete with each other, internal communication becomes more active. This can lead to the growth of the entire company. For example, increased employee motivation leads to increased work efficiency and improved company performance. Thus, the work environment provision system can improve employee motivation and work efficiency.

[0067] The work environment provision system according to the embodiment comprises an analysis unit, a generation unit, a reward unit, and a feedback unit. The analysis unit analyzes the work content of each employee. The analysis unit, for example, analyzes the work content of employees in detail and collects data to prevent the routinization of work and the decline in employee motivation. The analysis unit, for example, can store the work content of employees in a database and analyze it using AI. The generation unit generates daily challenges based on the work content analyzed by the analysis unit. The generation unit, for example, sets appropriate daily challenges according to the work content of employees. The generation unit, for example, can automatically generate daily challenges based on the work content of employees using AI. The reward unit provides rewards for completing the daily challenges generated by the generation unit. The reward unit, for example, awards points each time an employee completes a daily challenge and pays a reward when a certain number of points are accumulated. The reward unit, for example, can manage the completion status of employees' daily challenges and provide rewards using AI. The feedback unit provides individual feedback based on the rewards provided by the reward unit. The feedback unit evaluates the performance of employees and provides feedback when they complete daily challenges, for example. The feedback unit can use AI, for example, to evaluate employee performance and provide feedback. As a result, the work environment provision system according to this embodiment can improve employee motivation and work efficiency.

[0068] The analytics department is responsible for analyzing the work content of each employee in detail. Specifically, it stores the content of the tasks performed by employees on a daily basis, their progress, and the time spent on those tasks in a database, and analyzes this data using AI. The AI ​​learns the work patterns of employees and collects data to prevent work from becoming routine and to prevent a decline in employee motivation. For example, by analyzing how much time employees spend on specific tasks and what tasks are performed frequently, it is possible to identify areas for work efficiency improvement and other improvements. In addition, by accumulating data on the work content of employees, it is possible to grasp long-term work trends and fluctuations in performance. As a result, the analytics department can gain a detailed understanding of employee work content and provide data that contributes to work efficiency and improved motivation. Furthermore, the analytics department can share data on employee work content with other departments to improve overall work efficiency. For example, based on the data collected by the analytics department, other departments can consider improving business processes or introducing new work methods. As a result, the analytics department can analyze employee work content in detail and provide data that contributes to work efficiency and improved motivation.

[0069] The generation unit is responsible for generating daily challenges based on the work content analyzed by the analysis unit. Specifically, it sets appropriate daily challenges considering the employee's work content and progress. Using AI, it can automatically generate daily challenges based on the employee's work content. For example, it can set challenges to improve the employee's skills to perform a specific task efficiently, or challenges to try out new work methods. The generation unit can grasp the employee's work content and progress in real time and provide daily challenges at the appropriate time. This allows employees to have new goals for their daily work and maintain motivation. In addition, the generation unit can adjust the difficulty and content of the daily challenges according to the employee's work content. For example, by customizing the challenge content according to the progress of work and the employee's skill level, it can ensure that employees can tackle the challenges without difficulty. In this way, the generation unit can generate appropriate daily challenges based on the employee's work content and improve employee motivation.

[0070] The Rewards Department is responsible for providing rewards for completing daily challenges generated by the Generation Department. Specifically, it awards points to employees each time they complete a daily challenge, and provides rewards once a certain number of points are accumulated. Using AI, it can manage employees' daily challenge completion status and provide rewards. For example, when an employee completes a daily challenge, its performance is evaluated and points are awarded. If the points reach a certain level, rewards such as monetary incentives, special leave, or training for skill development can be provided. The Rewards Department plays a crucial role in maintaining employee motivation and improving work efficiency. Furthermore, the Rewards Department can monitor employees' daily challenge completion status in real time and provide rewards at the appropriate time. This allows employees to set new goals for their daily work and maintain motivation. In addition, the Rewards Department can adjust the content of rewards according to the employee's work content and progress. For example, by customizing the content of rewards according to the progress of work and the employee's skill level, employees can take on challenges without undue burden. In this way, the Rewards Department can improve employee motivation by managing employees' daily challenge completion status and providing rewards.

[0071] The Feedback Department is responsible for providing individual feedback based on the rewards provided by the Rewards Department. Specifically, it evaluates and provides feedback on employees' performance when they complete daily challenges. Using AI, it can evaluate employee performance and provide feedback. For example, when an employee completes a daily challenge, it can analyze the performance in detail and provide specific areas for improvement and advice for the next steps. The Feedback Department can understand employees' work content and progress in real time and provide feedback at the appropriate time. This allows employees to set new goals for their daily work and maintain motivation. Furthermore, the Feedback Department can adjust the content of feedback according to the employee's work content and progress. For example, by customizing the content of feedback according to the progress of work and the employee's skill level, it can ensure that employees can tackle challenges without difficulty. In this way, the Feedback Department can improve employee motivation by evaluating employee performance and providing feedback. In addition, the Feedback Department can collect employee feedback and use it to improve the overall system. For example, by reviewing the content of daily challenges and rewards based on employee feedback, the effectiveness of the overall system can be improved. This allows the feedback department to improve employee motivation by evaluating employee performance and providing feedback.

[0072] The analysis unit can analyze work content in detail to prevent the routine nature of tasks and the decline in employee motivation. For example, the analysis unit can analyze employees' work content in detail and collect data to prevent the routine nature of tasks. For example, the analysis unit can store employee work content in a database and analyze it using AI. This allows for the maintenance of employee motivation by analyzing work content in detail to prevent the routine nature of tasks and the decline in employee motivation.

[0073] The generation unit can set daily challenges. For example, the generation unit can set appropriate daily challenges according to the employee's work content. For example, the generation unit can use AI to automatically generate daily challenges based on the employee's work content. This allows employees to find enjoyment in their daily work by setting daily challenges.

[0074] The rewards department can award points for completing daily challenges, and provide rewards once a certain number of points are accumulated. For example, the rewards department can award points to employees for completing daily challenges, and provide rewards once a certain number of points are accumulated. The rewards department can use AI, for example, to manage the completion status of employees' daily challenges and provide rewards. This can improve employee motivation by awarding points for completing daily challenges and providing rewards once a certain number of points are accumulated.

[0075] The feedback department can evaluate employees' performance and provide feedback when they complete daily challenges. For example, the feedback department can evaluate employees' performance and provide feedback when they complete daily challenges. The feedback department can use AI, for example, to evaluate employee performance and provide feedback. This allows for improved employee motivation by evaluating and providing feedback when employees complete daily challenges.

[0076] The feedback department can help employees feel a sense of accomplishment in their work and improve their motivation. For example, the feedback department can use AI to evaluate employee performance and provide feedback. This allows employees to feel a sense of accomplishment in their work and improves their motivation, thereby improving work efficiency.

[0077] The analysis department can set daily challenges for tasks that involve a lot of routine work, such as data entry and report writing. For example, the analysis department can use AI to analyze employees' work in detail and set daily challenges. This allows for the prevention of decreased employee motivation by setting daily challenges for tasks that involve a lot of routine work, such as data entry and report writing.

[0078] The rewards department can numerically manage the completion rate of daily challenges and the status of reward receipt. For example, the rewards department can use AI to manage the completion rate of employees' daily challenges and provide rewards. This allows for a tangible improvement in work efficiency by numerically managing the completion rate of daily challenges and the status of reward receipt.

[0079] The Feedback Department can revitalize internal communication by allowing employees to share daily challenges and compete with each other. For example, the Feedback Department can use AI to evaluate employee performance and provide feedback. This allows employees to share daily challenges and compete with each other, thereby revitalizing internal communication and strengthening the company culture.

[0080] The analysis unit can estimate employees' emotions and adjust the method of analyzing work content based on the estimated emotions. For example, the analysis unit estimates employees' emotions and adjusts the method of analyzing work content based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if an employee is stressed, the analysis unit simplifies the analysis of work content to reduce the burden. For example, if an employee is relaxed, the analysis unit performs a detailed analysis to find areas for improvement in work. For example, if an employee is highly motivated, the analysis unit analyzes challenging work content to encourage further growth. In this way, by adjusting the method of analyzing work content based on employees' emotions, the burden on employees can be reduced and areas for improvement in work can be found.

[0081] The analysis unit can improve the accuracy of its analysis by referring to employees' past work history when analyzing work content. For example, the analysis unit can analyze employees' past work history to identify similar work content and improve the accuracy of the analysis. For example, the analysis unit can extract successful work patterns from employees' past work history and reflect them in the analysis. For example, the analysis unit can predict the progress of work based on employees' past work history and reflect this in the analysis results. In this way, by improving the accuracy of the analysis by referring to employees' past work history, it is possible to predict the progress of work and reflect this in the analysis results.

[0082] The analysis unit can apply different analysis algorithms to employees' work processes depending on their skill level. For example, it can apply a detailed analysis algorithm to employees with high skill levels to identify areas for improvement in their work. For employees with medium skill levels, it can apply a standard analysis algorithm to improve work efficiency. For employees with low skill levels, it can apply a simplified analysis algorithm to strengthen the basic aspects of their work. By applying different analysis algorithms according to employees' skill levels, the unit can identify areas for improvement in their work and improve work efficiency.

[0083] The analysis unit can estimate employees' emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if an employee is stressed, the analysis unit provides a simple and easy-to-understand display. For example, if an employee is relaxed, the analysis unit provides a display that includes detailed information. For example, if an employee is highly motivated, the analysis unit displays challenging analysis results to encourage further growth. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for adjustment of how analysis results are displayed based on employee emotions, thereby reducing employee burden and identifying areas for improvement in work processes.

[0084] The analysis unit can perform analysis of work content while taking into account the geographical location information of employees. For example, if an employee is working remotely, the analysis unit will analyze work content based on their geographical location information. For example, if an employee is on a business trip, the analysis unit will analyze work content while taking their geographical location information into account. For example, if an employee is in the office, the analysis unit will analyze work content based on their geographical location information. This allows for appropriate analysis of work content while employees are working remotely or on business trips by taking their geographical location information into account.

[0085] The analysis department can analyze employees' social media activities and related work content when analyzing work processes. For example, the analysis department can analyze employees' social media activities, extract information related to work, and analyze it. For example, the analysis department can collect opinions and feedback on work from employees' social media activities and reflect them in the analysis. For example, the analysis department can grasp work trends and needs based on employees' social media activities and reflect them in the analysis. In this way, by analyzing employees' social media activities and related work content, it is possible to grasp work trends and needs and reflect them in the analysis.

[0086] The generation unit can estimate employees' emotions and adjust the content of daily challenges based on those emotions. For example, if an employee is stressed, the generation unit can provide an easy daily challenge. If an employee is relaxed, the generation unit can provide a challenging daily challenge. If an employee is highly motivated, the generation unit can provide a difficult daily challenge. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. By adjusting the content of daily challenges based on employee emotions, it is possible to reduce employee burden and identify areas for improvement in work processes.

[0087] The generation unit can adjust the difficulty of daily challenges based on the importance of the task when generating them. For example, it can set a high-difficulty daily challenge for high-importance tasks, a standard-difficulty daily challenge for medium-importance tasks, and an easy daily challenge for low-importance tasks. By adjusting the difficulty of challenges based on the importance of the task, the burden on employees can be reduced and areas for improvement in work processes can be identified.

[0088] The generation unit can apply different generation algorithms depending on the category of the task when generating daily challenges. For example, the generation unit applies an efficient generation algorithm to data entry tasks. For example, the generation unit applies a detailed generation algorithm to report creation tasks. For example, the generation unit applies a flexible generation algorithm to creative tasks. This allows for increased efficiency in tasks by applying different generation algorithms depending on the category of the task.

[0089] The generation unit can estimate an employee's emotions and adjust the length of the daily challenge based on the estimated emotions. For example, if an employee is stressed, the generation unit will provide a short daily challenge. If an employee is relaxed, the generation unit will provide a daily challenge of standard length. If an employee is highly motivated, the generation unit will provide a longer daily challenge. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. By adjusting the length of the daily challenge based on employee emotions, it is possible to reduce employee burden and identify areas for improvement in work processes.

[0090] The generation unit can determine the priority of daily challenges based on the submission deadline of the tasks when generating them. For example, the generation unit sets high-priority daily challenges for tasks with approaching deadlines. For example, the generation unit sets standard-priority daily challenges for tasks with medium-term deadlines. For example, the generation unit sets low-priority daily challenges for tasks with distant deadlines. By determining the priority of challenges based on the submission deadline of the tasks, the efficiency of the work can be improved.

[0091] The generation unit can adjust the order of daily challenges based on the relevance of the tasks when generating them. For example, the generation unit can prioritize setting highly relevant tasks as daily challenges. For example, the generation unit can set tasks of moderate relevance as daily challenges in a standard order. For example, the generation unit can postpone setting less relevant tasks as daily challenges. In this way, by adjusting the order of challenges based on the relevance of the tasks, it is possible to improve work efficiency.

[0092] The rewards department can estimate employees' emotions and adjust the content of rewards based on those emotions. For example, if an employee is stressed, the rewards department can provide relaxing rewards. For example, if an employee is relaxed, the rewards department can provide challenging rewards. For example, if an employee is highly motivated, the rewards department can provide challenging rewards. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for improved employee motivation by adjusting rewards based on employee emotions.

[0093] The compensation department can select the type of compensation based on an employee's past performance when providing compensation. For example, the compensation department can analyze an employee's past performance and select the most appropriate compensation. For example, the compensation department can select compensation for successful tasks based on an employee's past performance. For example, the compensation department can select performance-based compensation based on an employee's past performance. By selecting the type of compensation based on an employee's past performance, employee motivation can be improved.

[0094] The compensation department can customize the form of compensation based on the employee's current living situation when providing compensation. For example, the compensation department can analyze the employee's current living situation and provide the most suitable compensation form. For example, the compensation department can select the necessary compensation form based on the employee's current living situation. For example, the compensation department can customize the compensation form based on the employee's current living situation. By customizing the form of compensation based on the employee's current living situation, employee motivation can be improved.

[0095] The rewards department can estimate employee emotions and prioritize rewards based on those emotions. For example, if an employee is stressed, the rewards department will prioritize relaxing rewards. If an employee is relaxed, the rewards department will prioritize challenging rewards. If an employee is highly motivated, the rewards department will prioritize challenging rewards. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for improved employee motivation by prioritizing rewards based on employee emotions.

[0096] The compensation department can select the optimal compensation by considering the employee's geographical location when providing compensation. For example, if an employee is working remotely, the compensation department will provide the optimal compensation based on their geographical location. For example, if an employee is on a business trip, the compensation department will provide the optimal compensation by considering their geographical location. For example, if an employee is in the office, the compensation department will provide the optimal compensation based on their geographical location. By selecting the optimal compensation by considering the employee's geographical location, employee motivation can be improved.

[0097] The Compensation Department can analyze employees' social media activity and propose appropriate compensation methods when providing rewards. For example, the Compensation Department can analyze employees' social media activity and propose the most suitable compensation method. For example, the Compensation Department can collect opinions and feedback on work from employees' social media activity and reflect them in compensation methods. For example, the Compensation Department can customize compensation methods based on employees' social media activity. In this way, by analyzing employees' social media activity and proposing compensation methods, employee motivation can be improved.

[0098] The feedback system can estimate an employee's emotions and adjust the content of the feedback based on those emotions. For example, if an employee is stressed, the feedback system will provide positive feedback. If an employee is relaxed, the feedback system will provide detailed feedback. If an employee is highly motivated, the feedback system will provide challenging feedback. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for improved employee motivation by adjusting the content of feedback based on the employee's emotions.

[0099] The feedback department can provide optimal feedback by referring to an employee's past performance. For example, the feedback department can analyze an employee's past performance and provide feedback on successful tasks. For example, the feedback department can identify areas for improvement based on an employee's past performance and provide feedback accordingly. For example, the feedback department can provide performance-based feedback based on an employee's past performance. This allows for improved employee motivation by providing optimal feedback based on an employee's past performance.

[0100] The feedback department can customize the format of feedback based on the employee's current work situation when providing feedback. For example, the feedback department can analyze the employee's current work situation and provide the optimal feedback format. For example, the feedback department can select the necessary feedback format based on the employee's current work situation. For example, the feedback department can customize the feedback format based on the employee's current work situation. This allows for improved employee motivation by customizing the feedback format based on the employee's current work situation.

[0101] The feedback system can estimate an employee's emotions and prioritize feedback based on those emotions. For example, if an employee is stressed, the system will prioritize positive feedback. If an employee is relaxed, the system will prioritize detailed feedback. If an employee is highly motivated, the system will prioritize challenging feedback. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for improved employee motivation by prioritizing feedback based on employee emotions.

[0102] The feedback department can provide optimal feedback by considering the employee's geographical location when providing feedback. For example, if an employee is working remotely, the feedback department will provide optimal feedback based on their geographical location. For example, if an employee is on a business trip, the feedback department will provide optimal feedback based on their geographical location. For example, if an employee is in the office, the feedback department will provide optimal feedback based on their geographical location. By providing optimal feedback that takes into account the employee's geographical location, it is possible to improve employee motivation.

[0103] The feedback department can analyze employees' social media activity and propose feedback methods when providing feedback. For example, the feedback department can analyze employees' social media activity and propose the most suitable feedback method. For example, the feedback department can collect opinions and feedback on work from employees' social media activity and reflect them in the feedback method. For example, the feedback department can customize the feedback method based on employees' social media activity. In this way, by analyzing employees' social media activity and proposing feedback methods, employee motivation can be improved.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis department can estimate employees' emotions and adjust the analysis method of work content based on those estimated emotions. For example, if an employee is stressed, the analysis department can simplify the analysis of work content to reduce their burden. Conversely, if an employee is relaxed, the analysis department can perform a detailed analysis to identify areas for improvement in work. Furthermore, if an employee is highly motivated, the analysis department can analyze challenging work content to encourage further growth. In this way, by adjusting the analysis method of work content based on employees' emotions, the burden on employees can be reduced and areas for improvement in work can be identified.

[0106] The generation unit can estimate employees' emotions and adjust the content of daily challenges based on those estimates. For example, if an employee is stressed, the generation unit can provide an easy daily challenge. If an employee is relaxed, the generation unit can provide a challenging daily challenge. Furthermore, if an employee is highly motivated, the generation unit can provide a challenging daily challenge. By adjusting the content of daily challenges based on employees' emotions, the burden on employees can be reduced and areas for improvement in work can be identified.

[0107] The compensation department can estimate employees' emotions and adjust the content of rewards based on those estimates. For example, if an employee is stressed, the compensation department can offer relaxing rewards. If an employee is relaxed, the compensation department can offer challenging rewards. Furthermore, if an employee is highly motivated, the compensation department can offer challenging rewards. In this way, adjusting the content of rewards based on employees' emotions can improve employee motivation.

[0108] The feedback department can estimate an employee's emotions and adjust the content of the feedback based on those emotions. For example, if an employee is stressed, the feedback department can provide positive feedback. If an employee is relaxed, the feedback department can provide detailed feedback. Furthermore, if an employee is highly motivated, the feedback department can provide challenging feedback. In this way, by adjusting the content of feedback based on an employee's emotions, employee motivation can be improved.

[0109] The analysis unit can estimate employees' emotions and adjust how the analysis results are displayed based on those estimates. For example, if an employee is stressed, the analysis unit can provide a simple and easy-to-understand display. If an employee is relaxed, the analysis unit can provide a display that includes more detailed information. Furthermore, if an employee is highly motivated, the analysis unit can display challenging analysis results to encourage further growth. By adjusting how analysis results are displayed based on employees' emotions, the burden on employees can be reduced and areas for improvement in work can be identified.

[0110] The analysis unit can improve the accuracy of its analysis by referring to employees' past work history when analyzing work content. For example, it can analyze employees' past work history to identify similar work content and improve the accuracy of the analysis. It can also extract successful work patterns from employees' past work history and reflect them in the analysis. Furthermore, it can predict the progress of work based on employees' past work history and reflect this in the analysis results. In this way, by improving the accuracy of the analysis by referring to employees' past work history, it is possible to predict the progress of work and reflect this in the analysis results.

[0111] The generation unit can adjust the difficulty of daily challenges based on the importance of the task when generating them. For example, high-importance tasks can be assigned high-difficulty daily challenges. Medium-importance tasks can be assigned standard-difficulty daily challenges. Furthermore, low-importance tasks can be assigned easy daily challenges. By adjusting the difficulty of challenges based on the importance of the task, the burden on employees can be reduced and areas for improvement in work processes can be identified.

[0112] The compensation department can select the type of compensation based on an employee's past performance when providing compensation. For example, it can analyze an employee's past performance and select the most appropriate compensation. It can also select compensation for successful tasks based on an employee's past performance. Furthermore, it can select compensation commensurate with performance based on an employee's past performance. By selecting the type of compensation based on an employee's past performance, it is possible to improve employee motivation.

[0113] The feedback department can provide optimal feedback by referencing an employee's past performance. For example, it can analyze an employee's past performance and provide feedback on successful tasks. It can also identify areas for improvement based on an employee's past performance and provide feedback accordingly. Furthermore, it can provide performance-based feedback based on an employee's past performance. By providing optimal feedback based on an employee's past performance, it is possible to improve employee motivation.

[0114] The analysis unit can apply different analysis algorithms to employees based on their skill levels when analyzing work processes. For example, a detailed analysis algorithm can be applied to employees with high skill levels to identify areas for improvement in their work. A standard analysis algorithm can be applied to employees with medium skill levels to improve work efficiency. Furthermore, a simplified analysis algorithm can be applied to employees with low skill levels to strengthen the fundamental aspects of their work. In this way, by applying different analysis algorithms according to employees' skill levels, areas for improvement in work can be identified and work efficiency can be improved.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The analysis unit analyzes the work content of each employee. The analysis unit conducts a detailed analysis of employee work content and collects data to prevent work from becoming routine and to reduce employee motivation. The analysis unit stores employee work content in a database and can analyze it using AI. Step 2: The generation unit generates daily challenges based on the work content analyzed by the analysis unit. The generation unit sets appropriate daily challenges according to the employee's work content. The generation unit can use AI to automatically generate daily challenges based on the employee's work content. Step 3: The rewards department provides rewards for completing daily challenges generated by the generation department. The rewards department awards points to employees each time they complete a daily challenge, and pays out rewards once a certain number of points are accumulated. The rewards department can use AI to manage the completion status of employees' daily challenges and provide rewards accordingly. Step 4: The Feedback Department provides individual feedback based on the rewards provided by the Rewards Department. The Feedback Department evaluates and provides feedback on the performance of employees when they complete daily challenges. The Feedback Department can use AI to evaluate employee performance and provide feedback.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the analysis unit, generation unit, reward unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and analyzes the employee's work content in detail. The generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and generates a daily challenge based on the analyzed work content. The reward unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and provides a reward for completing the daily challenge. The feedback unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and provides individual feedback based on the reward. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the analysis unit, generation unit, reward unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and analyzes the employee's work content in detail. The generation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and generates a daily challenge based on the analyzed work content. The reward unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and provides a reward for completing the daily challenge. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and provides individual feedback based on the reward. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the analysis unit, generation unit, reward unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and analyzes the employee's work content in detail. The generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and generates a daily challenge based on the analyzed work content. The reward unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and provides a reward for completing the daily challenge. The feedback unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and provides individual feedback based on the reward. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the analysis unit, generation unit, reward unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and analyzes the employee's work content in detail. The generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and generates a daily challenge based on the analyzed work content. The reward unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and provides a reward for completing the daily challenge. The feedback unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and provides individual feedback based on the reward. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) The analysis department analyzes the work content of each employee, A generation unit generates a daily challenge based on the business content analyzed by the aforementioned analysis unit, A reward unit provides rewards for completing the daily challenges generated by the generation unit, The system includes a feedback unit that provides individual feedback based on the rewards provided by the reward unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, To prevent the routine nature of tasks and a decline in employee motivation, we conduct a detailed analysis of work processes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Set a daily challenge The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned compensation unit is, Points are awarded for completing daily challenges, and rewards are given once a certain number of points are accumulated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is When employees complete daily challenges, their performance is evaluated and feedback is provided. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is To allow employees to feel the results of their work and to improve their motivation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Set a daily challenge for tasks that involve a lot of routine work, such as data entry and report writing. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned compensation unit is, The daily challenge completion rate and reward collection status are managed numerically. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned feedback unit is By having employees share daily challenges and compete with each other, we can revitalize internal communication. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate employees' emotions and adjust the analysis method of work content based on the estimated employee emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing work content, we improve the accuracy of the analysis by referring to the employee's past work history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing job descriptions, different analysis algorithms are applied depending on the employee's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates employee emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing work content, the analysis will take into account the geographical location information of employees. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing work content, we analyze employees' social media activity and then analyze the relevant work content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate employee sentiment and adjust the content of daily challenges based on that sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating daily challenges, adjust the difficulty of the challenges based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating daily challenges, different generation algorithms are applied depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates employee sentiment and adjusts the length of daily challenges based on that sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating daily challenges, prioritize challenges based on the submission deadline for the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating daily challenges, adjust the order of challenges based on their relevance to the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned compensation unit is, Estimate employee sentiment and adjust compensation based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned compensation unit is, When providing compensation, the type of compensation is selected based on the employee's past performance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned compensation unit is, When providing compensation, customize the format of the compensation based on the employee's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned compensation unit is, The system estimates employee sentiment and determines reward priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned compensation unit is, When providing compensation, the company selects the most appropriate compensation by taking into account the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned compensation unit is, When providing rewards, we analyze employees' social media activity and suggest reward methods. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is The system estimates employee emotions and adjusts the content of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, refer to the employee's past performance to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, customize the feedback format based on the employee's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is Estimate employee emotions and prioritize feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, we take into account the employee's geographical location to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, we analyze employees' social media activity and suggest methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the work content of each employee, A generation unit generates a daily challenge based on the business content analyzed by the aforementioned analysis unit, A reward unit provides rewards for completing the daily challenges generated by the generation unit, The system includes a feedback unit that provides individual feedback based on the rewards provided by the reward unit. A system characterized by the following features.

2. The aforementioned analysis unit, To prevent the routine nature of tasks and a decline in employee motivation, we conduct a detailed analysis of work processes. The system according to feature 1.

3. The generating unit is Set a daily challenge The system according to feature 1.

4. The aforementioned compensation unit is, Points are awarded for completing daily challenges, and rewards are given once a certain number of points are accumulated. The system according to feature 1.

5. The aforementioned feedback unit is When employees complete daily challenges, their performance is evaluated and feedback is provided. The system according to feature 1.

6. The aforementioned feedback unit is To allow employees to feel the results of their work and to improve their motivation. The system according to feature 1.

7. The aforementioned analysis unit, Set a daily challenge for tasks that involve a lot of routine work, such as data entry and report writing. The system according to feature 1.

8. The aforementioned compensation unit is, The daily challenge completion rate and reward collection status are managed numerically. The system according to feature 1.

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