system

Abstract

The system according to this embodiment aims to resolve the lack of skills among workers and improve their motivation. [Solution] The system according to this embodiment comprises a skill demand analysis unit, a skill recommendation unit, a reward provision unit, and a monetization unit. The skill demand analysis unit analyzes skill demand. The skill recommendation unit analyzes the skill set of workers based on the skill demand analyzed by the skill demand analysis unit and recommends the most suitable training and qualifications. The reward provision unit provides token rewards to workers who complete the training and qualifications recommended by the skill recommendation unit. The monetization unit generates revenue through corporate sponsorships and educational content.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

[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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, effective means for eliminating the skill shortage of workers and improving the motivation of workers have not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to eliminate the skill shortage of workers and improve the motivation of workers. <00​​​​​The system according to this embodiment comprises a skill demand analysis unit, a skill recommendation unit, a reward provision unit, and a monetization unit. The skill demand analysis unit analyzes skill demand. The skill recommendation unit analyzes the skill set of workers based on the skill demand analyzed by the skill demand analysis unit and recommends the most suitable training and qualifications. The reward provision unit provides token rewards to workers who complete the training and qualifications recommended by the skill recommendation unit. The monetization unit generates revenue through corporate sponsorships and educational content. [Effects of the Invention]

[0007] The system according to this embodiment can resolve the lack of skills among workers and improve their motivation. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 skill-up reward token ecosystem according to an embodiment of the present invention is a system for the labor market. This system is a mechanism in which a generating AI analyzes skill demand and provides token rewards to workers each time they acquire qualifications or complete training. The skill-up reward token ecosystem aims to address skill shortages and improve worker motivation by generating revenue through corporate sponsorships and educational content. For example, the generating AI analyzes skill demand in the labor market. The generating AI collects labor market data and identifies which skills are in high demand. For example, for workers in industries that require IT skills or digital skills, the generating AI analyzes the demand for these skills. Next, the generating AI analyzes the worker's skill set and recommends the most suitable training and qualifications. The generating AI collects worker skill data and identifies which skills are lacking. For example, the generating AI recommends the training and qualifications necessary for workers to improve their IT skills. When a worker completes training or qualifications, the generating AI provides token rewards. The generating AI optimizes incentives and issues tokens according to the worker's growth. For example, when a worker acquires an IT qualification, the generating AI issues tokens and provides the worker with a reward. This ecosystem generates revenue through corporate sponsorships and educational content. Companies enter into sponsorship agreements and provide educational content to support the skill development of their workers. For example, a company provides IT training to its workers and issues tokens as compensation. This mechanism aims to address skill gaps and improve worker motivation. Workers will become more focused on self-improvement by receiving incentives for skill development and certification. Companies can also support the skill development of their workers to address skill gaps. Furthermore, generative AI utilizes worker data to design the reward system. Generative AI visualizes workers' skill growth and maintains their motivation. For example, generative AI displays workers' skill growth in a graph, allowing workers to feel a sense of their own progress. The demand for this token reward system will accelerate with the spread of Web3.0 and generative AI.Workers can build an ecosystem where they can manage their skills as assets, thereby contributing to the growth of the entire labor market and strengthening competitiveness through digital transformation. This skill-up reward token ecosystem can promote worker skill development and address skill shortages within companies.

[0029] The skill-up reward token ecosystem according to this embodiment comprises a skill demand analysis unit, a skill recommendation unit, a reward provision unit, and a monetization unit. The skill demand analysis unit analyzes skill demand. For example, the skill demand analysis unit collects labor market data and identifies which skills are in high demand. For example, the skill demand analysis unit analyzes the demand for IT skills and digital skills for workers in industries where these skills are required. The skill recommendation unit analyzes the skill set of workers based on the skill demand analyzed by the skill demand analysis unit and recommends optimal training and qualifications. For example, the skill recommendation unit collects skill data of workers and identifies which skills are lacking. For example, the skill recommendation unit recommends training and qualifications necessary for workers to improve their IT skills. The reward provision unit provides token rewards to workers who complete the training and qualifications recommended by the skill recommendation unit. For example, the reward provision unit optimizes incentives according to the worker's growth and issues tokens. For example, the reward provision unit issues tokens to workers when they acquire IT certifications and provides them with rewards. The monetization unit generates revenue through corporate sponsorships and educational content. For example, the monetization unit enters into sponsorship agreements with companies to support workers' skill development and provides educational content. For example, the monetization unit issues tokens to companies when they provide IT training to workers as compensation. This enables the skill-up reward token ecosystem according to the embodiment to analyze skill demand, recommend skills, provide token rewards, and monetize.

[0030] The Skills Demand Analysis Department analyzes skill demand. For example, it collects labor market data to identify which skills are in high demand. Specifically, the Skills Demand Analysis Department gathers information from diverse data sources such as online job postings, company recruitment data, industry reports, and economic indicators. This data is analyzed using natural language processing (NLP) techniques to extract trends and patterns in the demand for specific skills. For example, when analyzing the demand for IT and digital skills for workers in industries that require these skills, the department analyzes in detail the frequency of use of specific programming languages ​​and tools, changes in skill requirements in job advertisements, and trends in companies' technology investments. Furthermore, the Skills Demand Analysis Department can also use AI to predict future skill demand. For example, it uses AI models to predict future labor market fluctuations from historical data and identify which skills will be important in the future. This allows the Skills Demand Analysis Department to provide workers and educational institutions with information that helps them design future career paths and educational programs.

[0031] The Skill Recommendation Department analyzes workers' skill sets based on skill demand analyzed by the Skill Demand Analysis Department and recommends optimal training and qualifications. For example, the Skill Recommendation Department collects workers' skill data to identify which skills are lacking. Specifically, the Skill Recommendation Department collects data such as workers' resumes, work history, self-assessments, and skill test results, and analyzes this data using AI. The AI ​​compares the worker's current skill set with market demand to identify skill gaps. For example, when recommending training and qualifications necessary for a worker to improve their IT skills, it considers the worker's current skill level, learning style, and career goals to suggest the most suitable training program and qualification exam. Furthermore, the Skill Recommendation Department can monitor workers' progress and update recommendations as needed. For example, if a worker completes a particular training program, it evaluates the results and recommends the next skills or qualifications to learn. In this way, the Skill Recommendation Department supports workers in continuously improving their skills and promoting their career growth.

[0032] The Rewards Department provides token rewards to workers who complete training and certifications recommended by the Skill Recommendation Department. The Rewards Department optimizes incentives and issues tokens based on worker growth, for example. Specifically, it evaluates workers' achievements, such as completing training programs or passing certification exams, and provides appropriate token rewards. Tokens are issued using blockchain technology, ensuring transparency and reliability. For example, if a worker obtains an IT certification, a token is issued, rewarding the worker. This allows workers to receive concrete rewards for their skill development, boosting their motivation. Furthermore, the Rewards Department manages the supply and issuance conditions of tokens to maintain their value. For example, if demand for a particular skill or certification increases, the department increases the token rewards for that skill or certification to attract workers. The Rewards Department also diversifies the uses of tokens, allowing workers to purchase educational content and utilize career support services. This enables the Rewards Department to promote worker skill development and revitalize the entire ecosystem.

[0033] The monetization unit generates revenue through corporate sponsorships and educational content. For example, the monetization unit enters into sponsorship agreements with companies to support the skill development of their workers and provides educational content. Specifically, the monetization unit partners with companies to develop training programs based on the skill sets required by the companies and provides them to workers. For example, a company provides IT training to workers and issues tokens in return. By supporting the skill development of workers, companies can secure top talent and improve their competitiveness. The monetization unit also partners with educational institutions and online learning platforms to provide high-quality educational content. This allows workers to improve their skills using a variety of learning resources. Furthermore, the monetization unit can generate revenue through token trading fees and the provision of premium services. For example, a fee is charged when workers use tokens to access premium training programs. It can also earn revenue from sponsorship fees when companies enter into sponsorship agreements. In this way, the monetization unit can establish a revenue base to support the sustainable operation of the ecosystem.

[0034] The Reward System Design Department designs the reward system. For example, the Reward System Design Department designs the types of rewards, the methods of providing them, and the evaluation criteria. This makes it possible to design the reward system. Some or all of the above processes in the Reward System Design Department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the Reward System Design Department can input worker skill growth data into a generative AI and have the generative AI execute the design of the reward system.

[0035] The Skills Demand Analysis Department collects labor market data and identifies which skills are in high demand. For example, the Skills Demand Analysis Department collects job postings and labor statistics data and analyzes skill demand. For example, the Skills Demand Analysis Department identifies the demand for IT skills and digital skills among workers in industries that require these skills. This makes it possible to identify skill demand. Some or all of the above processing in the Skills Demand Analysis Department may be performed using generative AI, or not. For example, the Skills Demand Analysis Department can input labor market data into a generative AI and have the generative AI perform the identification of skill demand.

[0036] The Skill Recommendation Unit collects worker skill data and identifies which skills are lacking. For example, the Skill Recommendation Unit collects resumes and skill evaluation results to identify skill deficiencies. For example, the Skill Recommendation Unit identifies the training and qualifications necessary for workers to improve their IT skills. This makes it possible to identify skill deficiencies. Some or all of the above processing in the Skill Recommendation Unit may be performed using or without a generative AI. For example, the Skill Recommendation Unit can input worker skill data into a generative AI and have the generative AI perform the identification of skill deficiencies.

[0037] The rewards department optimizes incentives and issues tokens according to the worker's growth. For example, the rewards department optimizes incentives and issues tokens based on the worker's growth data. For example, if a worker obtains an IT qualification, the rewards department issues a token and provides the worker with a reward. This enables the optimization of incentives and the issuance of tokens. Some or all of the above processes in the rewards department may be performed using a generative AI, or not. For example, the rewards department can input worker growth data into a generative AI and have the generative AI perform the optimization of incentives and the issuance of tokens.

[0038] The monetization unit generates revenue through corporate sponsorship agreements and the provision of educational content. For example, the monetization unit may enter into sponsorship agreements with companies to support the skill development of their workers and provide educational content. For example, the monetization unit may provide IT training to workers and issue tokens as compensation. This enables monetization. Some or all of the above processes in the monetization unit may be performed using or without a generative AI. For example, the monetization unit may input data on corporate sponsorship agreements and educational content provision into a generative AI and have the generative AI execute the monetization methods.

[0039] The Skill Demand Analysis Department collects labor market data in real time and immediately reflects changes in skill demand. For example, the Skill Demand Analysis Department uses a generative AI to collect job information in real time and immediately reflect changes in skill demand. The Skill Demand Analysis Department uses a generative AI to analyze labor market trends in real time and immediately reflect changes in skill demand. The Skill Demand Analysis Department uses a generative AI to collect corporate hiring trends in real time and immediately reflect changes in skill demand. This makes it possible to immediately reflect changes in skill demand. Some or all of the above processes in the Skill Demand Analysis Department may be performed using a generative AI or not. For example, the Skill Demand Analysis Department can input labor market data into a generative AI and have the generative AI perform the reflection of changes in skill demand.

[0040] The Skill Demand Analysis Department analyzes skill demand in detail, specifically tailored to certain industries or regions. For example, the Skill Demand Analysis Department uses a generative AI to analyze skill demand in detail specifically for the IT industry and identify the demand for IT skills. The Skill Demand Analysis Department uses a generative AI to analyze skill demand in detail specifically for the medical industry and identify the demand for medical skills. The Skill Demand Analysis Department uses a generative AI to analyze skill demand in detail specifically for certain regions and identify the skills required in those regions. This enables detailed analysis of skill demand specific to particular industries or regions. Some or all of the above-described processes in the Skill Demand Analysis Department may be performed using a generative AI, or they may be performed without a generative AI. For example, the Skill Demand Analysis Department can input data for a specific industry or region into a generative AI and have the generative AI perform a detailed analysis of skill demand.

[0041] The Skill Demand Analysis Department predicts skill demand by considering companies' growth strategies and market trends when analyzing skill demand. For example, the Skill Demand Analysis Department uses a generative AI to analyze companies' growth strategies and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze market trends and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze companies' recruitment plans and predict future skill demand. This makes it possible to predict skill demand while considering companies' growth strategies and market trends. Some or all of the above processes in the Skill Demand Analysis Department may be performed using a generative AI, or they may be performed without a generative AI. For example, the Skill Demand Analysis Department can input data on companies' growth strategies and market trends into a generative AI and have the generative AI perform skill demand predictions.

[0042] The Skill Demand Analysis Department predicts future demand by referring to past skill demand data when analyzing skill demand. For example, the Skill Demand Analysis Department uses a generative AI to analyze past skill demand data and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze past job postings and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze past labor market trends and predict future skill demand. This makes it possible to predict future demand by referring to past skill demand data. Some or all of the above processes in the Skill Demand Analysis Department may be performed using a generative AI or not. For example, the Skill Demand Analysis Department can input past skill demand data into a generative AI and have the generative AI perform a prediction of future skill demand.

[0043] The Skill Recommendation Unit considers past training and certification history when analyzing a worker's skill set. For example, the Skill Recommendation Unit uses a generative AI to analyze a worker's past training history and recommend the most suitable training. The Skill Recommendation Unit also uses a generative AI to analyze a worker's past certification history and recommend the most suitable certifications. The Skill Recommendation Unit uses a generative AI to analyze a worker's past skill set and recommend training and certifications to fill any skill gaps. This enables skill set analysis that takes past training and certification history into account. Some or all of the above processes in the Skill Recommendation Unit may be performed using a generative AI or not. For example, the Skill Recommendation Unit can input a worker's past training and certification history into a generative AI and have the generative AI perform the skill set analysis.

[0044] The Skill Recommendation Department, when making recommendations, suggests the most suitable training and qualifications based on the worker's career path and goals. For example, the Skill Recommendation Department uses a generative AI to analyze the worker's career path and suggest the most suitable training. The Skill Recommendation Department uses a generative AI to analyze the worker's goals and suggest the most suitable qualifications. The Skill Recommendation Department uses a generative AI to comprehensively analyze the worker's career path and goals and suggest the most suitable training and qualifications. This makes it possible to suggest the most suitable training and qualifications based on the worker's career path and goals. Some or all of the above processing in the Skill Recommendation Department may be performed using a generative AI, or not. For example, the Skill Recommendation Department can input data on the worker's career path and goals into a generative AI and have the generative AI suggest the most suitable training and qualifications.

[0045] The Skill Recommendation Department, when making recommendations, takes into account the worker's geographical location information and proposes region-specific training and qualifications. For example, the Skill Recommendation Department uses a generating AI to analyze the worker's geographical location information and propose region-specific training. The Skill Recommendation Department uses a generating AI to analyze the worker's geographical location information and propose region-specific qualifications. The Skill Recommendation Department uses a generating AI to analyze the worker's geographical location information and propose training and qualifications suitable for the local labor market. This makes it possible to propose region-specific training and qualifications. Some or all of the above processing in the Skill Recommendation Department may be performed using a generating AI, or not. For example, the Skill Recommendation Department can input the worker's geographical location information into a generating AI and have the generating AI execute the proposal of region-specific training and qualifications.

[0046] The Skill Recommendation Department analyzes the worker's social media activity when making recommendations and suggests relevant training and qualifications. For example, the Skill Recommendation Department uses a generative AI to analyze the worker's social media activity and suggest relevant training. The Skill Recommendation Department uses a generative AI to analyze the worker's social media activity and suggest relevant qualifications. The Skill Recommendation Department uses a generative AI to analyze the worker's social media activity and suggest training and qualifications based on the worker's interests. This makes it possible to suggest training and qualifications that take social media activity into consideration. Some or all of the above processing in the Skill Recommendation Department may be performed using a generative AI or not. For example, the Skill Recommendation Department can input the worker's social media activity data into a generative AI and have the generative AI suggest relevant training and qualifications.

[0047] The compensation provision unit optimizes compensation by considering the worker's past performance and growth rate when providing compensation. For example, the compensation provision unit uses a generative AI to analyze the worker's past performance and optimize compensation. The compensation provision unit uses a generative AI to analyze the worker's growth rate and optimize compensation. The compensation provision unit uses a generative AI to comprehensively analyze the worker's past performance and growth rate and optimize compensation. This makes it possible to optimize compensation while considering the worker's past performance and growth rate. Some or all of the above processing in the compensation provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the compensation provision unit can input data on the worker's past performance and growth rate into a generative AI and have the generative AI perform the compensation optimization.

[0048] The reward provision unit provides special rewards for specific skills or qualifications when providing rewards. For example, the reward provision unit uses a generating AI to provide special rewards for specific skills. The reward provision unit uses a generating AI to provide special rewards for specific qualifications. The reward provision unit uses a generating AI to provide special rewards for specific skills or qualifications, thereby improving worker motivation. This makes it possible to provide special rewards for specific skills or qualifications. Some or all of the above processing in the reward provision unit may be performed using a generating AI or not. For example, the reward provision unit can input data on specific skills or qualifications into a generating AI and have the generating AI perform the provision of special rewards.

[0049] The compensation provision unit provides region-specific compensation, taking into account the worker's geographical location information when providing compensation. For example, the compensation provision unit uses a generating AI to analyze the worker's geographical location information and provide region-specific compensation. The compensation provision unit uses a generating AI to analyze the worker's geographical location information and provide compensation that is appropriate for the characteristics of the region. The compensation provision unit uses a generating AI to analyze the worker's geographical location information and provide compensation that is appropriate for the local labor market. This makes it possible to provide region-specific compensation. Some or all of the above processing in the compensation provision unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the compensation provision unit can input the worker's geographical location information into a generating AI and have the generating AI perform the provision of region-specific compensation.

[0050] The compensation provision department analyzes the worker's social media activity and adjusts the method of compensation provision when providing compensation. For example, the compensation provision department uses a generative AI to analyze the worker's social media activity and adjust the method of compensation provision. The compensation provision department uses a generative AI to analyze the worker's social media activity and provide compensation based on the worker's interests and preferences. The compensation provision department uses a generative AI to analyze the worker's social media activity and provide compensation utilizing the worker's network. This makes it possible to adjust the method of compensation provision in consideration of social media activity. Some or all of the above processing in the compensation provision department may be performed using a generative AI or not. For example, the compensation provision department can input the worker's social media activity data into a generative AI and have the generative AI perform the adjustment of the method of compensation provision.

[0051] The monetization unit optimizes its monetization strategy by considering the needs of corporate sponsors and market trends during the monetization process. For example, the monetization unit uses a generative AI to analyze the needs of corporate sponsors and optimize the monetization strategy. The monetization unit uses a generative AI to analyze market trends and optimize the monetization strategy. The monetization unit uses a generative AI to comprehensively analyze the needs of corporate sponsors and market trends and optimize the monetization strategy. This makes it possible to optimize the monetization strategy while considering the needs of corporate sponsors and market trends. Some or all of the above processes in the monetization unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the monetization unit can input data on the needs of corporate sponsors and market trends into a generative AI and have the generative AI perform the optimization of the monetization strategy.

[0052] The monetization unit provides special monetization methods for specific educational content or training programs when monetizing. For example, the monetization unit uses a generative AI to provide special monetization methods for specific educational content. The monetization unit uses a generative AI to provide special monetization methods for specific training programs. The monetization unit maximizes revenue by having the generative AI provide special monetization methods for specific educational content or training programs. This makes it possible to provide special monetization methods for specific educational content or training programs. Some or all of the above processing in the monetization unit may be performed using a generative AI or not. For example, the monetization unit can input data for specific educational content or training programs into a generative AI and have the generative AI perform the provision of special monetization methods.

[0053] The monetization unit diversifies its monetization methods when monetizing, taking into account the company's growth strategy and market trends. For example, the monetization unit uses a generative AI to analyze the company's growth strategy and diversify its monetization methods. The monetization unit uses a generative AI to analyze market trends and diversify its monetization methods. The monetization unit uses a generative AI to comprehensively analyze the company's growth strategy and market trends and diversify its monetization methods. This makes it possible to diversify monetization methods while taking into account the company's growth strategy and market trends. Some or all of the above processing in the monetization unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the monetization unit can input data on the company's growth strategy and market trends into a generative AI and have the generative AI perform the diversification of monetization methods.

[0054] The monetization unit predicts future revenue by referring to past revenue data during the monetization process. For example, the monetization unit uses a generative AI to analyze past revenue data and predict future revenue. The monetization unit uses a generative AI to refer to past revenue data and optimize the monetization strategy. The monetization unit uses a generative AI to analyze past revenue data, identify factors influencing revenue fluctuations, and predict future revenue. This makes it possible to predict future revenue by referring to past revenue data. Some or all of the above processes in the monetization unit may be performed using a generative AI or not. For example, the monetization unit can input past revenue data into a generative AI and have the generative AI perform a prediction of future revenue.

[0055] The Compensation System Design Department optimizes the compensation system by considering the worker's past performance and growth rate during the design process. For example, the Compensation System Design Department uses a generative AI to analyze the worker's past performance and optimize the compensation system. The Compensation System Design Department uses a generative AI to analyze the worker's growth rate and optimize the compensation system. The Compensation System Design Department uses a generative AI to comprehensively analyze the worker's past performance and growth rate and optimize the compensation system. This makes it possible to optimize the compensation system while considering the worker's past performance and growth rate. Some or all of the above processes in the Compensation System Design Department may be performed using a generative AI, or they may be performed without a generative AI. For example, the Compensation System Design Department can input data on the worker's past performance and growth rate into a generative AI and have the generative AI perform the optimization of the compensation system.

[0056] The Compensation System Design Department diversifies the compensation system when designing it, taking into account the company's growth strategy and market trends. For example, the Compensation System Design Department uses a generative AI to analyze the company's growth strategy and diversify the compensation system. The Compensation System Design Department uses a generative AI to analyze market trends and diversify the compensation system. The Compensation System Design Department uses a generative AI to comprehensively analyze the company's growth strategy and market trends and diversify the compensation system. This makes it possible to diversify the compensation system while taking into account the company's growth strategy and market trends. Some or all of the above processes in the Compensation System Design Department may be performed using a generative AI, or they may be performed without using a generative AI. For example, the Compensation System Design Department can input data on the company's growth strategy and market trends into a generative AI and have the generative AI perform the diversification of the compensation system.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The skill-up reward token ecosystem can also include a community feedback section. This section collects and shares feedback from workers regarding the training and qualifications they receive. For example, after a worker completes a specific IT training course, they can provide feedback on its usefulness and difficulty. The community feedback section can aggregate this feedback and provide it as reference information for other workers when selecting training. Furthermore, the community feedback section can provide feedback to corporate sponsors to help improve training programs. This promotes information sharing among workers and improves the quality of training.

[0059] The skill-based reward token ecosystem can also include a performance monitoring unit. This unit monitors workers' training and post-certification work performance, helping to optimize token rewards. For example, after a worker obtains an IT certification, the unit monitors their work efficiency and results, adjusting their token rewards accordingly. The performance monitoring unit can continuously track worker growth and provide feedback to the rewards provider. Furthermore, the performance monitoring unit can provide worker performance data to corporate sponsors, which can be used to evaluate the effectiveness of training programs. This enables the optimization of both worker growth and rewards.

[0060] The skill-up reward token ecosystem can also include a career advice department. This department would suggest optimal training and qualifications based on the worker's career path and goals. For example, if a worker aspires to a management position in the future, the department would suggest training and qualifications to improve their management skills. The department would provide advice tailored to the worker's career goals, supporting them in efficiently developing their skills towards those goals. Furthermore, the department could propose training programs based on the worker's career path to corporate sponsors, contributing to the company's growth strategy. This would ensure that both the worker's career growth and the company's growth are achieved simultaneously.

[0061] The skill-up reward token ecosystem can also include a skill matching function. This function matches workers' skill sets with company job postings, suggesting the most suitable employment opportunities. For example, if a worker possesses IT skills, the skill matching function provides job postings from companies seeking IT skills, helping the worker find a suitable position. The skill matching function analyzes labor market data and matches workers' skills with company needs, supporting workers' job-seeking activities. Furthermore, the skill matching function provides worker skill data to corporate sponsors, streamlining their recruitment processes. This facilitates smoother job-seeking for workers and efficient recruitment for companies.

[0062] The skill-up reward token ecosystem can also include a skills assessment unit. This unit objectively evaluates workers' skills and recommends training and certifications based on the evaluation results. For example, if a worker possesses IT skills, the skills assessment unit evaluates their IT skills and identifies areas where skill improvement is needed. Based on the worker's skill assessment results, the unit can propose optimal training and certifications. Furthermore, the skills assessment unit can provide worker skill assessment data to corporate sponsors, which can be used to design corporate training programs. This enables both worker skill development and optimization of corporate training programs.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The Skills Demand Analysis Department analyzes skills demand. Specifically, it collects labor market data and identifies which skills are in high demand. For example, it analyzes the demand for IT skills and digital skills among workers in industries where these skills are required. Step 2: The Skill Recommendation Department analyzes the skill sets of workers based on the skill demands analyzed by the Skill Demand Analysis Department and recommends the most suitable training and certifications. Specifically, it collects worker skill data and identifies which skills are lacking. For example, it recommends the training and certifications necessary for workers to improve their IT skills. Step 3: The rewards department provides token rewards to workers who complete training or certifications recommended by the skills recommendation department. Specifically, it optimizes incentives and issues tokens according to the worker's progress. For example, if a worker obtains an IT certification, a token is issued and the worker is rewarded. Step 4: The monetization unit generates revenue through corporate sponsorships and educational content. Specifically, companies enter into sponsorship agreements to support the skill development of workers and provide educational content. For example, a company provides IT training to workers and issues tokens as compensation.

[0065] (Example of form 2) The skill-up reward token ecosystem according to an embodiment of the present invention is a system for the labor market. This system is a mechanism in which a generating AI analyzes skill demand and provides token rewards to workers each time they acquire qualifications or complete training. The skill-up reward token ecosystem aims to address skill shortages and improve worker motivation by generating revenue through corporate sponsorships and educational content. For example, the generating AI analyzes skill demand in the labor market. The generating AI collects labor market data and identifies which skills are in high demand. For example, for workers in industries that require IT skills or digital skills, the generating AI analyzes the demand for these skills. Next, the generating AI analyzes the worker's skill set and recommends the most suitable training and qualifications. The generating AI collects worker skill data and identifies which skills are lacking. For example, the generating AI recommends the training and qualifications necessary for workers to improve their IT skills. When a worker completes training or qualifications, the generating AI provides token rewards. The generating AI optimizes incentives and issues tokens according to the worker's growth. For example, when a worker acquires an IT qualification, the generating AI issues tokens and provides the worker with a reward. This ecosystem generates revenue through corporate sponsorships and educational content. Companies enter into sponsorship agreements and provide educational content to support the skill development of their workers. For example, a company provides IT training to its workers and issues tokens as compensation. This mechanism aims to address skill gaps and improve worker motivation. Workers will become more focused on self-improvement by receiving incentives for skill development and certification. Companies can also support the skill development of their workers to address skill gaps. Furthermore, generative AI utilizes worker data to design the reward system. Generative AI visualizes workers' skill growth and maintains their motivation. For example, generative AI displays workers' skill growth in a graph, allowing workers to feel a sense of their own progress. The demand for this token reward system will accelerate with the spread of Web3.0 and generative AI.Workers can build an ecosystem where they can manage their skills as assets, thereby contributing to the growth of the entire labor market and strengthening competitiveness through digital transformation. This skill-up reward token ecosystem can promote worker skill development and address skill shortages within companies.

[0066] The skill-up reward token ecosystem according to this embodiment comprises a skill demand analysis unit, a skill recommendation unit, a reward provision unit, and a monetization unit. The skill demand analysis unit analyzes skill demand. For example, the skill demand analysis unit collects labor market data and identifies which skills are in high demand. For example, the skill demand analysis unit analyzes the demand for IT skills and digital skills for workers in industries where these skills are required. The skill recommendation unit analyzes the skill set of workers based on the skill demand analyzed by the skill demand analysis unit and recommends optimal training and qualifications. For example, the skill recommendation unit collects skill data of workers and identifies which skills are lacking. For example, the skill recommendation unit recommends training and qualifications necessary for workers to improve their IT skills. The reward provision unit provides token rewards to workers who complete the training and qualifications recommended by the skill recommendation unit. For example, the reward provision unit optimizes incentives according to the worker's growth and issues tokens. For example, the reward provision unit issues tokens to workers when they acquire IT certifications and provides them with rewards. The monetization unit generates revenue through corporate sponsorships and educational content. For example, the monetization unit enters into sponsorship agreements with companies to support workers' skill development and provides educational content. For example, the monetization unit issues tokens to companies when they provide IT training to workers as compensation. This enables the skill-up reward token ecosystem according to the embodiment to analyze skill demand, recommend skills, provide token rewards, and monetize.

[0067] The Skills Demand Analysis Department analyzes skill demand. For example, it collects labor market data to identify which skills are in high demand. Specifically, the Skills Demand Analysis Department gathers information from diverse data sources such as online job postings, company recruitment data, industry reports, and economic indicators. This data is analyzed using natural language processing (NLP) techniques to extract trends and patterns in the demand for specific skills. For example, when analyzing the demand for IT and digital skills for workers in industries that require these skills, the department analyzes in detail the frequency of use of specific programming languages ​​and tools, changes in skill requirements in job advertisements, and trends in companies' technology investments. Furthermore, the Skills Demand Analysis Department can also use AI to predict future skill demand. For example, it uses AI models to predict future labor market fluctuations from historical data and identify which skills will be important in the future. This allows the Skills Demand Analysis Department to provide workers and educational institutions with information that helps them design future career paths and educational programs.

[0068] The Skill Recommendation Department analyzes workers' skill sets based on skill demand analyzed by the Skill Demand Analysis Department and recommends optimal training and qualifications. For example, the Skill Recommendation Department collects workers' skill data to identify which skills are lacking. Specifically, the Skill Recommendation Department collects data such as workers' resumes, work history, self-assessments, and skill test results, and analyzes this data using AI. The AI ​​compares the worker's current skill set with market demand to identify skill gaps. For example, when recommending training and qualifications necessary for a worker to improve their IT skills, it considers the worker's current skill level, learning style, and career goals to suggest the most suitable training program and qualification exam. Furthermore, the Skill Recommendation Department can monitor workers' progress and update recommendations as needed. For example, if a worker completes a particular training program, it evaluates the results and recommends the next skills or qualifications to learn. In this way, the Skill Recommendation Department supports workers in continuously improving their skills and promoting their career growth.

[0069] The Rewards Department provides token rewards to workers who complete training and certifications recommended by the Skill Recommendation Department. The Rewards Department optimizes incentives and issues tokens based on worker growth, for example. Specifically, it evaluates workers' achievements, such as completing training programs or passing certification exams, and provides appropriate token rewards. Tokens are issued using blockchain technology, ensuring transparency and reliability. For example, if a worker obtains an IT certification, a token is issued, rewarding the worker. This allows workers to receive concrete rewards for their skill development, boosting their motivation. Furthermore, the Rewards Department manages the supply and issuance conditions of tokens to maintain their value. For example, if demand for a particular skill or certification increases, the department increases the token rewards for that skill or certification to attract workers. The Rewards Department also diversifies the uses of tokens, allowing workers to purchase educational content and utilize career support services. This enables the Rewards Department to promote worker skill development and revitalize the entire ecosystem.

[0070] The monetization unit generates revenue through corporate sponsorships and educational content. For example, the monetization unit enters into sponsorship agreements with companies to support the skill development of their workers and provides educational content. Specifically, the monetization unit partners with companies to develop training programs based on the skill sets required by the companies and provides them to workers. For example, a company provides IT training to workers and issues tokens in return. By supporting the skill development of workers, companies can secure top talent and improve their competitiveness. The monetization unit also partners with educational institutions and online learning platforms to provide high-quality educational content. This allows workers to improve their skills using a variety of learning resources. Furthermore, the monetization unit can generate revenue through token trading fees and the provision of premium services. For example, a fee is charged when workers use tokens to access premium training programs. It can also earn revenue from sponsorship fees when companies enter into sponsorship agreements. In this way, the monetization unit can establish a revenue base to support the sustainable operation of the ecosystem.

[0071] The Reward System Design Department designs the reward system. For example, the Reward System Design Department designs the types of rewards, the methods of providing them, and the evaluation criteria. This makes it possible to design the reward system. Some or all of the above processes in the Reward System Design Department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the Reward System Design Department can input worker skill growth data into a generative AI and have the generative AI execute the design of the reward system.

[0072] The Skills Demand Analysis Department collects labor market data and identifies which skills are in high demand. For example, the Skills Demand Analysis Department collects job postings and labor statistics data and analyzes skill demand. For example, the Skills Demand Analysis Department identifies the demand for IT skills and digital skills among workers in industries that require these skills. This makes it possible to identify skill demand. Some or all of the above processing in the Skills Demand Analysis Department may be performed using generative AI, or not. For example, the Skills Demand Analysis Department can input labor market data into a generative AI and have the generative AI perform the identification of skill demand.

[0073] The Skill Recommendation Unit collects worker skill data and identifies which skills are lacking. For example, the Skill Recommendation Unit collects resumes and skill evaluation results to identify skill deficiencies. For example, the Skill Recommendation Unit identifies the training and qualifications necessary for workers to improve their IT skills. This makes it possible to identify skill deficiencies. Some or all of the above processing in the Skill Recommendation Unit may be performed using or without a generative AI. For example, the Skill Recommendation Unit can input worker skill data into a generative AI and have the generative AI perform the identification of skill deficiencies.

[0074] The rewards department optimizes incentives and issues tokens according to the worker's growth. For example, the rewards department optimizes incentives and issues tokens based on the worker's growth data. For example, if a worker obtains an IT qualification, the rewards department issues a token and provides the worker with a reward. This enables the optimization of incentives and the issuance of tokens. Some or all of the above processes in the rewards department may be performed using a generative AI, or not. For example, the rewards department can input worker growth data into a generative AI and have the generative AI perform the optimization of incentives and the issuance of tokens.

[0075] The monetization unit generates revenue through corporate sponsorship agreements and the provision of educational content. For example, the monetization unit may enter into sponsorship agreements with companies to support the skill development of their workers and provide educational content. For example, the monetization unit may provide IT training to workers and issue tokens as compensation. This enables monetization. Some or all of the above processes in the monetization unit may be performed using or without a generative AI. For example, the monetization unit may input data on corporate sponsorship agreements and educational content provision into a generative AI and have the generative AI execute the monetization methods.

[0076] The Skill Demand Analysis Unit estimates the worker's emotions and adjusts the skill demand analysis results based on the estimated worker's emotions. For example, if a worker is stressed, the Skill Demand Analysis Unit uses a generative AI to simplify the skill demand analysis results and provide them in an easy-to-understand format. For example, if a worker is relaxed, the Skill Demand Analysis Unit uses a generative AI to provide detailed skill demand analysis results so that the worker can understand them more deeply. If a worker is in a hurry, the Skill Demand Analysis Unit uses a generative AI to quickly provide the skill demand analysis results so that immediate action can be taken. This makes it possible to adjust the skill demand analysis results according to the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the Skill Demand Analysis Unit may be performed using or without a generative AI. For example, the Skill Demand Analysis Unit can input worker emotion data into a generative AI and have the generative AI perform the adjustment of the skill demand analysis results.

[0077] The Skill Demand Analysis Department collects labor market data in real time and immediately reflects changes in skill demand. For example, the Skill Demand Analysis Department uses a generative AI to collect job information in real time and immediately reflect changes in skill demand. The Skill Demand Analysis Department uses a generative AI to analyze labor market trends in real time and immediately reflect changes in skill demand. The Skill Demand Analysis Department uses a generative AI to collect corporate hiring trends in real time and immediately reflect changes in skill demand. This makes it possible to immediately reflect changes in skill demand. Some or all of the above processes in the Skill Demand Analysis Department may be performed using a generative AI or not. For example, the Skill Demand Analysis Department can input labor market data into a generative AI and have the generative AI perform the reflection of changes in skill demand.

[0078] The Skill Demand Analysis Department analyzes skill demand in detail, specifically tailored to certain industries or regions. For example, the Skill Demand Analysis Department uses a generative AI to analyze skill demand in detail specifically for the IT industry and identify the demand for IT skills. The Skill Demand Analysis Department uses a generative AI to analyze skill demand in detail specifically for the medical industry and identify the demand for medical skills. The Skill Demand Analysis Department uses a generative AI to analyze skill demand in detail specifically for certain regions and identify the skills required in those regions. This enables detailed analysis of skill demand specific to particular industries or regions. Some or all of the above-described processes in the Skill Demand Analysis Department may be performed using a generative AI, or they may be performed without a generative AI. For example, the Skill Demand Analysis Department can input data for a specific industry or region into a generative AI and have the generative AI perform a detailed analysis of skill demand.

[0079] The Skill Demand Analysis Unit estimates the worker's emotions and determines the priority of skill demands based on the estimated emotions. For example, if a worker is stressed, the Skill Demand Analysis Unit uses a generative AI to adjust the priority of skill demands, prioritizing skills that are easier for the worker to tackle. If a worker is relaxed, the Skill Demand Analysis Unit uses a generative AI to adjust the priority of skill demands, allowing the worker to tackle challenging skills. If a worker is in a hurry, the Skill Demand Analysis Unit uses a generative AI to adjust the priority of skill demands, prioritizing skills that can be tackled immediately. This makes it possible to determine the priority of skill demands in accordance with the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the Skill Demand Analysis Unit may be performed using or without a generative AI. For example, the Skill Demand Analysis Unit can input worker emotion data into a generative AI and have the generative AI perform the determination of skill demand priorities.

[0080] The Skill Demand Analysis Department predicts skill demand by considering companies' growth strategies and market trends when analyzing skill demand. For example, the Skill Demand Analysis Department uses a generative AI to analyze companies' growth strategies and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze market trends and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze companies' recruitment plans and predict future skill demand. This makes it possible to predict skill demand while considering companies' growth strategies and market trends. Some or all of the above processes in the Skill Demand Analysis Department may be performed using a generative AI, or they may be performed without a generative AI. For example, the Skill Demand Analysis Department can input data on companies' growth strategies and market trends into a generative AI and have the generative AI perform skill demand predictions.

[0081] The Skill Demand Analysis Department predicts future demand by referring to past skill demand data when analyzing skill demand. For example, the Skill Demand Analysis Department uses a generative AI to analyze past skill demand data and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze past job postings and predict future skill demand. The Skill Demand Analysis Department uses a generative AI to analyze past labor market trends and predict future skill demand. This makes it possible to predict future demand by referring to past skill demand data. Some or all of the above processes in the Skill Demand Analysis Department may be performed using a generative AI or not. For example, the Skill Demand Analysis Department can input past skill demand data into a generative AI and have the generative AI perform a prediction of future skill demand.

[0082] The skill recommendation unit estimates the worker's emotions and adjusts the recommendations based on the estimated emotions. For example, if a worker is stressed, the skill recommendation unit's generative AI will recommend easy training or qualifications. If a worker is relaxed, the skill recommendation unit's generative AI will recommend challenging training or qualifications. If a worker is in a hurry, the skill recommendation unit's generative AI will recommend training or qualifications that can be completed in a short period of time. This allows for the recommendation content to be adjusted according to the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skill recommendation unit may be performed using or without generative AI. For example, the skill recommendation unit can input worker emotional data into a generating AI and have the AI ​​adjust the recommendation content.

[0083] The Skill Recommendation Unit considers past training and certification history when analyzing a worker's skill set. For example, the Skill Recommendation Unit uses a generative AI to analyze a worker's past training history and recommend the most suitable training. The Skill Recommendation Unit also uses a generative AI to analyze a worker's past certification history and recommend the most suitable certifications. The Skill Recommendation Unit uses a generative AI to analyze a worker's past skill set and recommend training and certifications to fill any skill gaps. This enables skill set analysis that takes past training and certification history into account. Some or all of the above processes in the Skill Recommendation Unit may be performed using a generative AI or not. For example, the Skill Recommendation Unit can input a worker's past training and certification history into a generative AI and have the generative AI perform the skill set analysis.

[0084] The Skill Recommendation Department, when making recommendations, suggests the most suitable training and qualifications based on the worker's career path and goals. For example, the Skill Recommendation Department uses a generative AI to analyze the worker's career path and suggest the most suitable training. The Skill Recommendation Department uses a generative AI to analyze the worker's goals and suggest the most suitable qualifications. The Skill Recommendation Department uses a generative AI to comprehensively analyze the worker's career path and goals and suggest the most suitable training and qualifications. This makes it possible to suggest the most suitable training and qualifications based on the worker's career path and goals. Some or all of the above processing in the Skill Recommendation Department may be performed using a generative AI, or not. For example, the Skill Recommendation Department can input data on the worker's career path and goals into a generative AI and have the generative AI suggest the most suitable training and qualifications.

[0085] The skill recommendation unit estimates the worker's emotions and determines the recommendation priority based on the estimated emotions. For example, if a worker is stressed, the skill recommendation unit's generative AI will prioritize recommending easy training or qualifications. If a worker is relaxed, the skill recommendation unit's generative AI will prioritize recommending challenging training or qualifications. If a worker is in a hurry, the skill recommendation unit's generative AI will prioritize recommending training or qualifications that can be completed in a short period of time. This makes it possible to determine the recommendation priority according to the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skill recommendation unit may be performed using or without generative AI. For example, the skill recommendation unit can input worker emotional data into a generating AI and have the AI ​​determine the priority of recommendations.

[0086] The Skill Recommendation Department, when making recommendations, takes into account the worker's geographical location information and proposes region-specific training and qualifications. For example, the Skill Recommendation Department uses a generating AI to analyze the worker's geographical location information and propose region-specific training. The Skill Recommendation Department uses a generating AI to analyze the worker's geographical location information and propose region-specific qualifications. The Skill Recommendation Department uses a generating AI to analyze the worker's geographical location information and propose training and qualifications suitable for the local labor market. This makes it possible to propose region-specific training and qualifications. Some or all of the above processing in the Skill Recommendation Department may be performed using a generating AI, or not. For example, the Skill Recommendation Department can input the worker's geographical location information into a generating AI and have the generating AI execute the proposal of region-specific training and qualifications.

[0087] The Skill Recommendation Department analyzes the worker's social media activity when making recommendations and suggests relevant training and qualifications. For example, the Skill Recommendation Department uses a generative AI to analyze the worker's social media activity and suggest relevant training. The Skill Recommendation Department uses a generative AI to analyze the worker's social media activity and suggest relevant qualifications. The Skill Recommendation Department uses a generative AI to analyze the worker's social media activity and suggest training and qualifications based on the worker's interests. This makes it possible to suggest training and qualifications that take social media activity into consideration. Some or all of the above processing in the Skill Recommendation Department may be performed using a generative AI or not. For example, the Skill Recommendation Department can input the worker's social media activity data into a generative AI and have the generative AI suggest relevant training and qualifications.

[0088] The reward system estimates the worker's emotions and adjusts the reward delivery method based on the estimated emotions. For example, if a worker is stressed, the reward system uses a generative AI to simplify the reward delivery method and deliver the reward quickly. If a worker is relaxed, the reward system uses a generative AI to explain the reward delivery method in detail, making it easier for the worker to understand. If a worker is in a hurry, the reward system uses a generative AI to quickly deliver the reward and deliver it immediately. This allows for adjustments to the reward delivery method according to the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reward system may be performed using or without a generative AI. For example, the reward system can input worker emotion data into a generative AI and have the generative AI perform the adjustment of the reward delivery method.

[0089] The compensation provision unit optimizes compensation by considering the worker's past performance and growth rate when providing compensation. For example, the compensation provision unit uses a generative AI to analyze the worker's past performance and optimize compensation. The compensation provision unit uses a generative AI to analyze the worker's growth rate and optimize compensation. The compensation provision unit uses a generative AI to comprehensively analyze the worker's past performance and growth rate and optimize compensation. This makes it possible to optimize compensation while considering the worker's past performance and growth rate. Some or all of the above processing in the compensation provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the compensation provision unit can input data on the worker's past performance and growth rate into a generative AI and have the generative AI perform the compensation optimization.

[0090] The reward provision unit provides special rewards for specific skills or qualifications when providing rewards. For example, the reward provision unit uses a generating AI to provide special rewards for specific skills. The reward provision unit uses a generating AI to provide special rewards for specific qualifications. The reward provision unit uses a generating AI to provide special rewards for specific skills or qualifications, thereby improving worker motivation. This makes it possible to provide special rewards for specific skills or qualifications. Some or all of the above processing in the reward provision unit may be performed using a generating AI or not. For example, the reward provision unit can input data on specific skills or qualifications into a generating AI and have the generating AI perform the provision of special rewards.

[0091] The reward system estimates the worker's emotions and determines reward priorities based on the estimated emotions. For example, if a worker is stressed, the reward system uses a generative AI to adjust reward priorities, prioritizing rewards that are easier for the worker to undertake. If a worker is relaxed, the reward system uses a generative AI to adjust reward priorities, encouraging the worker to take on more challenging rewards. If a worker is in a hurry, the reward system uses a generative AI to adjust reward priorities, prioritizing rewards that can be tackled immediately. This enables the determination of reward priorities in accordance with the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the reward system may be performed using or without a generative AI. For example, the reward system can input worker emotion data into a generative AI and have the generative AI determine reward priorities.

[0092] The compensation provision unit provides region-specific compensation, taking into account the worker's geographical location information when providing compensation. For example, the compensation provision unit uses a generating AI to analyze the worker's geographical location information and provide region-specific compensation. The compensation provision unit uses a generating AI to analyze the worker's geographical location information and provide compensation that is appropriate for the characteristics of the region. The compensation provision unit uses a generating AI to analyze the worker's geographical location information and provide compensation that is appropriate for the local labor market. This makes it possible to provide region-specific compensation. Some or all of the above processing in the compensation provision unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the compensation provision unit can input the worker's geographical location information into a generating AI and have the generating AI perform the provision of region-specific compensation.

[0093] The compensation provision department analyzes the worker's social media activity and adjusts the method of compensation provision when providing compensation. For example, the compensation provision department uses a generative AI to analyze the worker's social media activity and adjust the method of compensation provision. The compensation provision department uses a generative AI to analyze the worker's social media activity and provide compensation based on the worker's interests and preferences. The compensation provision department uses a generative AI to analyze the worker's social media activity and provide compensation utilizing the worker's network. This makes it possible to adjust the method of compensation provision in consideration of social media activity. Some or all of the above processing in the compensation provision department may be performed using a generative AI or not. For example, the compensation provision department can input the worker's social media activity data into a generative AI and have the generative AI perform the adjustment of the method of compensation provision.

[0094] The monetization unit estimates the worker's emotions and adjusts the monetization method based on the estimated emotions. For example, if the worker is stressed, the monetization unit uses a generative AI to simplify the monetization method and provide it in a format that is easy for the worker to understand. If the worker is relaxed, the monetization unit uses a generative AI to provide a detailed monetization method that the worker can understand more deeply. If the worker is in a hurry, the monetization unit uses a generative AI to quickly provide a monetization method so that it can act immediately. This allows for adjustment of the monetization method according to the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monetization unit may be performed using a generative AI or not. For example, the monetization unit can input worker emotion data into a generative AI and have the generative AI perform the adjustment of the monetization method.

[0095] The monetization unit optimizes its monetization strategy by considering the needs of corporate sponsors and market trends during the monetization process. For example, the monetization unit uses a generative AI to analyze the needs of corporate sponsors and optimize the monetization strategy. The monetization unit uses a generative AI to analyze market trends and optimize the monetization strategy. The monetization unit uses a generative AI to comprehensively analyze the needs of corporate sponsors and market trends and optimize the monetization strategy. This makes it possible to optimize the monetization strategy while considering the needs of corporate sponsors and market trends. Some or all of the above processes in the monetization unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the monetization unit can input data on the needs of corporate sponsors and market trends into a generative AI and have the generative AI perform the optimization of the monetization strategy.

[0096] The monetization unit provides special monetization methods for specific educational content or training programs when monetizing. For example, the monetization unit uses a generative AI to provide special monetization methods for specific educational content. The monetization unit uses a generative AI to provide special monetization methods for specific training programs. The monetization unit maximizes revenue by having the generative AI provide special monetization methods for specific educational content or training programs. This makes it possible to provide special monetization methods for specific educational content or training programs. Some or all of the above processing in the monetization unit may be performed using a generative AI or not. For example, the monetization unit can input data for specific educational content or training programs into a generative AI and have the generative AI perform the provision of special monetization methods.

[0097] The monetization unit estimates the worker's emotions and determines monetization priorities based on the estimated emotions. For example, if a worker is stressed, the monetization unit uses a generative AI to adjust monetization priorities, prioritizing monetization methods that are easier for the worker to engage with. If a worker is relaxed, the monetization unit uses a generative AI to adjust monetization priorities, allowing the worker to engage with challenging monetization methods. If a worker is in a hurry, the monetization unit uses a generative AI to adjust monetization priorities, prioritizing monetization methods that can be tackled immediately. This makes it possible to determine monetization priorities in accordance with the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monetization unit may be performed using or without a generative AI. For example, the monetization unit can input worker emotion data into a generative AI and have the generative AI perform the determination of monetization priorities.

[0098] The monetization unit diversifies its monetization methods when monetizing, taking into account the company's growth strategy and market trends. For example, the monetization unit uses a generative AI to analyze the company's growth strategy and diversify its monetization methods. The monetization unit uses a generative AI to analyze market trends and diversify its monetization methods. The monetization unit uses a generative AI to comprehensively analyze the company's growth strategy and market trends and diversify its monetization methods. This makes it possible to diversify monetization methods while taking into account the company's growth strategy and market trends. Some or all of the above processing in the monetization unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the monetization unit can input data on the company's growth strategy and market trends into a generative AI and have the generative AI perform the diversification of monetization methods.

[0099] The monetization unit predicts future revenue by referring to past revenue data during the monetization process. For example, the monetization unit uses a generative AI to analyze past revenue data and predict future revenue. The monetization unit uses a generative AI to refer to past revenue data and optimize the monetization strategy. The monetization unit uses a generative AI to analyze past revenue data, identify factors influencing revenue fluctuations, and predict future revenue. This makes it possible to predict future revenue by referring to past revenue data. Some or all of the above processes in the monetization unit may be performed using a generative AI or not. For example, the monetization unit can input past revenue data into a generative AI and have the generative AI perform a prediction of future revenue.

[0100] The reward system design department estimates the worker's emotions and adjusts the reward system design based on the estimated emotions. For example, if a worker is stressed, the reward system design department uses a generative AI to simplify the reward system design and provide it in a format that is easy for the worker to understand. If a worker is relaxed, the reward system design department uses a generative AI to provide a detailed reward system design that the worker can understand more deeply. If a worker is in a hurry, the reward system design department uses a generative AI to quickly provide a reward system design that can be acted on immediately. This makes it possible to adjust the reward system design according to the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reward system design department may be performed using or without a generative AI. For example, the reward system design department can input worker emotion data into a generative AI and have the generative AI perform adjustments to the reward system design.

[0101] The Compensation System Design Department optimizes the compensation system by considering the worker's past performance and growth rate during the design process. For example, the Compensation System Design Department uses a generative AI to analyze the worker's past performance and optimize the compensation system. The Compensation System Design Department uses a generative AI to analyze the worker's growth rate and optimize the compensation system. The Compensation System Design Department uses a generative AI to comprehensively analyze the worker's past performance and growth rate and optimize the compensation system. This makes it possible to optimize the compensation system while considering the worker's past performance and growth rate. Some or all of the above processes in the Compensation System Design Department may be performed using a generative AI, or they may be performed without a generative AI. For example, the Compensation System Design Department can input data on the worker's past performance and growth rate into a generative AI and have the generative AI perform the optimization of the compensation system.

[0102] The reward system design department estimates the worker's emotions and determines the priority of the reward system based on the estimated emotions. For example, if a worker is stressed, the reward system design department uses a generative AI to adjust the priority of the reward system, prioritizing rewards that are easier for the worker to undertake. If a worker is relaxed, the reward system design department uses a generative AI to adjust the priority of the reward system, encouraging the worker to take on challenging rewards. If a worker is in a hurry, the reward system design department uses a generative AI to adjust the priority of the reward system, prioritizing rewards that can be tackled immediately. This makes it possible to determine the priority of the reward system in accordance with the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reward system design department may be performed using a generative AI or not. For example, the reward system design department can input worker emotion data into a generative AI and have the generative AI perform the determination of the reward system priority.

[0103] The Compensation System Design Department diversifies the compensation system when designing it, taking into account the company's growth strategy and market trends. For example, the Compensation System Design Department uses a generative AI to analyze the company's growth strategy and diversify the compensation system. The Compensation System Design Department uses a generative AI to analyze market trends and diversify the compensation system. The Compensation System Design Department uses a generative AI to comprehensively analyze the company's growth strategy and market trends and diversify the compensation system. This makes it possible to diversify the compensation system while taking into account the company's growth strategy and market trends. Some or all of the above processes in the Compensation System Design Department may be performed using a generative AI, or they may be performed without using a generative AI. For example, the Compensation System Design Department can input data on the company's growth strategy and market trends into a generative AI and have the generative AI perform the diversification of the compensation system.

[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 skill-up reward token ecosystem can also include a community feedback section. This section collects and shares feedback from workers regarding the training and qualifications they receive. For example, after a worker completes a specific IT training course, they can provide feedback on its usefulness and difficulty. The community feedback section can aggregate this feedback and provide it as reference information for other workers when selecting training. Furthermore, the community feedback section can provide feedback to corporate sponsors to help improve training programs. This promotes information sharing among workers and improves the quality of training.

[0106] The skill-based reward token ecosystem can also include a performance monitoring unit. This unit monitors workers' training and post-certification work performance, helping to optimize token rewards. For example, after a worker obtains an IT certification, the unit monitors their work efficiency and results, adjusting their token rewards accordingly. The performance monitoring unit can continuously track worker growth and provide feedback to the rewards provider. Furthermore, the performance monitoring unit can provide worker performance data to corporate sponsors, which can be used to evaluate the effectiveness of training programs. This enables the optimization of both worker growth and rewards.

[0107] The skill-up reward token ecosystem can also include a career advice department. This department would suggest optimal training and qualifications based on the worker's career path and goals. For example, if a worker aspires to a management position in the future, the department would suggest training and qualifications to improve their management skills. The department would provide advice tailored to the worker's career goals, supporting them in efficiently developing their skills towards those goals. Furthermore, the department could propose training programs based on the worker's career path to corporate sponsors, contributing to the company's growth strategy. This would ensure that both the worker's career growth and the company's growth are achieved simultaneously.

[0108] The skill-up reward token ecosystem can also include a skill matching function. This function matches workers' skill sets with company job postings, suggesting the most suitable employment opportunities. For example, if a worker possesses IT skills, the skill matching function provides job postings from companies seeking IT skills, helping the worker find a suitable position. The skill matching function analyzes labor market data and matches workers' skills with company needs, supporting workers' job-seeking activities. Furthermore, the skill matching function provides worker skill data to corporate sponsors, streamlining their recruitment processes. This facilitates smoother job-seeking for workers and efficient recruitment for companies.

[0109] The skill-up reward token ecosystem can also include a skills assessment unit. This unit objectively evaluates workers' skills and recommends training and certifications based on the evaluation results. For example, if a worker possesses IT skills, the skills assessment unit evaluates their IT skills and identifies areas where skill improvement is needed. Based on the worker's skill assessment results, the unit can propose optimal training and certifications. Furthermore, the skills assessment unit can provide worker skill assessment data to corporate sponsors, which can be used to design corporate training programs. This enables both worker skill development and optimization of corporate training programs.

[0110] The skill-up reward token ecosystem can also incorporate an emotional feedback component. This component estimates the worker's emotions and adjusts training and qualification recommendations based on those estimates. For example, if a worker is stressed, the emotional feedback component recommends relaxing training or qualifications. If a worker is relaxed, it recommends challenging training or qualifications. The emotional feedback component can collect worker emotional data and optimize training and qualification recommendations. Furthermore, the emotional feedback component can provide worker emotional data to corporate sponsors to aid in training program design. This enables training and qualification recommendations tailored to the worker's emotions.

[0111] The skill-up reward token ecosystem can also include an emotion monitoring unit. This unit continuously monitors workers' emotions and provides feedback based on their training and qualification progress. For example, if a worker is experiencing stress during training, the emotion monitoring unit suggests relaxing activities. If a worker is progressing well in training, the emotion monitoring unit suggests further challenges. The emotion monitoring unit collects workers' emotional data and provides feedback tailored to their training and qualification progress. Furthermore, the emotion monitoring unit can provide workers' emotional data to corporate sponsors to help improve training programs. This results in feedback tailored to workers' emotions, improving the effectiveness of training.

[0112] The skill-up reward token ecosystem can also incorporate an emotional analytics unit. This unit analyzes workers' emotional data to evaluate the effectiveness of training and certifications. For example, it can analyze workers' emotional data after training to assess the impact the training had on them. Based on workers' emotional data, the emotional analytics unit can quantitatively evaluate the effectiveness of training and certifications. Furthermore, the emotional analytics unit can provide workers' emotional data to corporate sponsors, who can use it to evaluate the effectiveness of training programs. This enables the evaluation of training and certification effectiveness based on workers' emotional data.

[0113] The skill-based reward token ecosystem can also incorporate an emotional incentive component. This component estimates the worker's emotions and provides incentives based on those estimates. For example, if a worker is stressed, the emotional incentive component provides a relaxing incentive. If a worker is relaxed, the emotional incentive component provides a challenging incentive. The emotional incentive component can collect worker emotional data and optimize the content of the incentives. Furthermore, the emotional incentive component can provide worker emotional data to corporate sponsors to help design incentive programs. This enables the provision of incentives tailored to the worker's emotions.

[0114] The skill-up reward token ecosystem can also include an emotional support unit. This unit estimates the worker's emotions and provides support based on those estimates. For example, if a worker is stressed, the emotional support unit provides counseling and mental health support. If a worker is relaxed, the emotional support unit provides support to encourage further growth. The emotional support unit can collect worker emotional data and provide appropriate support. Furthermore, the emotional support unit can provide worker emotional data to corporate sponsors and design programs to support workers' mental health. This ensures that support is tailored to the worker's emotions, improving their mental health.

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

[0116] Step 1: The Skills Demand Analysis Department analyzes skills demand. Specifically, it collects labor market data and identifies which skills are in high demand. For example, it analyzes the demand for IT skills and digital skills among workers in industries where these skills are required. Step 2: The Skill Recommendation Department analyzes the skill sets of workers based on the skill demands analyzed by the Skill Demand Analysis Department and recommends the most suitable training and certifications. Specifically, it collects worker skill data and identifies which skills are lacking. For example, it recommends the training and certifications necessary for workers to improve their IT skills. Step 3: The rewards department provides token rewards to workers who complete training or certifications recommended by the skills recommendation department. Specifically, it optimizes incentives and issues tokens according to the worker's progress. For example, if a worker obtains an IT certification, a token is issued and the worker is rewarded. Step 4: The monetization unit generates revenue through corporate sponsorships and educational content. Specifically, companies enter into sponsorship agreements to support the skill development of workers and provide educational content. For example, a company provides IT training to workers and issues tokens as compensation.

[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 skill demand analysis unit, skill recommendation unit, reward provision unit, monetization unit, and reward system design unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the skill demand analysis unit is implemented by the control unit 46A of the smart device 14, which collects labor market data and identifies which skills are in high demand. The skill recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the skill sets of workers and recommends the most suitable training and qualifications. The reward provision unit is implemented by, for example, the control unit 46A of the smart device 14, which provides token rewards to workers who complete training and qualifications. The monetization unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which monetizes through corporate sponsorships and educational content. The reward system design unit is implemented by, for example, the control unit 46A of the smart device 14, which designs the types of rewards, methods of provision, evaluation criteria, etc. The correspondence between each unit and the device or control unit is not limited to the examples 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 skill demand analysis unit, skill recommendation unit, reward provision unit, monetization unit, and reward system design unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the skill demand analysis unit is implemented by the control unit 46A of the smart glasses 214, which collects labor market data and identifies which skills are in high demand. The skill recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the skill set of workers and recommends the most suitable training and qualifications. The reward provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides token rewards to workers who complete training or qualifications. The monetization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which monetizes through corporate sponsorships and educational content. The reward system design unit is implemented, for example, by the control unit 46A of the smart glasses 214, which designs the types of rewards, methods of provision, evaluation criteria, etc. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications 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 skill demand analysis unit, skill recommendation unit, reward provision unit, monetization unit, and reward system design unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the skill demand analysis unit is implemented by the control unit 46A of the headset terminal 314, which collects labor market data and identifies which skills are in high demand. The skill recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the skill set of workers and recommends the most suitable training and qualifications. The reward provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides token rewards to workers who complete training and qualifications. The monetization unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which monetizes through corporate sponsorships and educational content. The reward system design unit is implemented by, for example, the control unit 46A of the headset terminal 314, which designs the types of rewards, methods of provision, evaluation criteria, etc. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications 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 skill demand analysis unit, skill recommendation unit, reward provision unit, monetization unit, and reward system design unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the skill demand analysis unit is implemented by the control unit 46A of the robot 414, which collects labor market data and identifies which skills are in high demand. The skill recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the skill sets of workers and recommends the most suitable training and qualifications. The reward provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides token rewards to workers who complete training and qualifications. The monetization unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which monetizes through corporate sponsorships and educational content. The reward system design unit is implemented by, for example, the control unit 46A of the robot 414, which designs the types of rewards, methods of provision, evaluation criteria, etc. The correspondence between each unit and the device or control unit is not limited to the examples 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 Skills Demand Analysis Department analyzes the demand for skills, Based on the skill demands analyzed by the aforementioned Skill Demand Analysis Department, the Skill Recommendation Department analyzes the skill sets of workers and recommends the most suitable training and qualifications. A reward provision unit provides token rewards to workers who complete training or qualifications recommended by the aforementioned skill recommendation unit, It includes a monetization department that generates revenue through corporate sponsorships and educational content. A system characterized by the following features. (Note 2) Furthermore, the company has a Reward System Design Department that designs the reward system. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Skill Demand Analysis Department Collect labor market data and identify which skills are in high demand. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned skill recommendation unit is, Collect worker skills data and identify which skills are lacking. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned compensation provision unit, Optimize incentives and issue tokens based on worker growth. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned revenue generation unit is, Revenue is generated through corporate sponsorship deals and the provision of educational content. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned Skill Demand Analysis Department We estimate workers' sentiments and adjust the analysis results of skill demand based on the estimated workers' sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned Skill Demand Analysis Department Collect labor market data in real time and instantly reflect changes in skill demand. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned Skill Demand Analysis Department When analyzing skill demand, we conduct a detailed analysis of skill demand specific to particular industries or regions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned Skill Demand Analysis Department Estimate workers' sentiments and prioritize skill demands based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned Skill Demand Analysis Department When analyzing skill demand, forecast skill demand by taking into account companies' growth strategies and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned Skill Demand Analysis Department When analyzing skill demand, we refer to past skill demand data to predict future demand. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned skill recommendation unit is, It estimates the workers' emotions and adjusts the recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned skill recommendation unit is, When analyzing a worker's skill set, consider their past training history and certification history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned skill recommendation unit is, When making recommendations, the system suggests the most suitable training and qualifications based on the worker's career path and goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned skill recommendation unit is, It estimates the workers' emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned skill recommendation unit is, When making recommendations, the system takes into account the worker's geographical location to suggest region-specific training and qualifications. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned skill recommendation unit is, When making recommendations, the system analyzes the worker's social media activity and suggests relevant training and qualifications. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned compensation provision unit, Estimate workers' sentiments and adjust compensation methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned compensation provision unit, When providing compensation, optimize it by taking into account the worker's past performance and growth rate. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned compensation provision unit, When providing compensation, offer special rewards for specific skills or qualifications. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned compensation provision unit, The system estimates workers' emotions and determines reward priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned compensation provision unit, When providing compensation, we will provide region-specific compensation that takes into account the geographical location of the worker. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned compensation provision unit, When providing compensation, we analyze the worker's social media activity and adjust the compensation method accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned revenue generation unit is, Estimate workers' sentiments and adjust monetization methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned revenue generation unit is, When monetizing, optimize your monetization strategy by considering the needs of corporate sponsors and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned revenue generation unit is, When monetizing, provide special monetization methods for specific educational content or training programs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned revenue generation unit is, Estimate workers' sentiments and determine revenue stream priorities based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned revenue generation unit is, When monetizing, diversify monetization methods while considering the company's growth strategy and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned revenue generation unit is, When monetizing, historical revenue data is used to predict future revenue. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reward system design unit, Estimate workers' emotions and adjust the design of the reward system based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned reward system design unit, When designing a compensation system, optimize it by taking into account the worker's past performance and growth rate. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned reward system design unit, The system estimates workers' emotions and prioritizes the reward system based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned reward system design unit, When designing a compensation system, diversify the system by taking into account the company's growth strategy and market trends. The system described in Appendix 2, 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 Skills Demand Analysis Department analyzes the demand for skills, Based on the skill demands analyzed by the aforementioned Skill Demand Analysis Department, the Skill Recommendation Department analyzes the skill sets of workers and recommends the most suitable training and qualifications. A reward provision unit provides token rewards to workers who complete training or qualifications recommended by the aforementioned skill recommendation unit, It includes a monetization department that generates revenue through corporate sponsorships and educational content. A system characterized by the following features.

2. Furthermore, the company has a Reward System Design Department that designs the reward system. The system according to feature 1.

3. The aforementioned Skill Demand Analysis Department Collect labor market data and identify which skills are in high demand. The system according to feature 1.

4. The aforementioned skill recommendation unit is, Collect worker skills data and identify which skills are lacking. The system according to feature 1.

5. The aforementioned compensation provision unit, Optimize incentives and issue tokens based on worker growth. The system according to feature 1.

6. The aforementioned revenue generation unit is, Revenue is generated through corporate sponsorship deals and the provision of educational content. The system according to feature 1.

7. The aforementioned Skill Demand Analysis Department We estimate workers' sentiments and adjust the analysis results of skill demand based on the estimated workers' sentiments. The system according to feature 1.

8. The aforementioned Skill Demand Analysis Department Collect labor market data in real time and instantly reflect changes in skill demand. The system according to feature 1.

9. The aforementioned Skill Demand Analysis Department When analyzing skill demand, we conduct a detailed analysis of skill demand specific to particular industries or regions. The system according to feature 1.

10. The aforementioned Skill Demand Analysis Department Estimate workers' sentiments and prioritize skill demands based on those estimated sentiments. The system according to feature 1.

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