system

Abstract

The system according to this embodiment aims to provide employees with incentives for reskilling and acquiring new skills. [Solution] The system according to the embodiment comprises a learning unit, a posting unit, an evaluation unit, and a distribution unit. The learning unit is where employees take AI learning courses. The posting unit is where employees post new content. The evaluation unit evaluates the data provided by the learning unit and the posting unit. The distribution unit distributes rewards based on the evaluation results from the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, incentives for employees' reskilling and acquisition of new skills are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide incentives for employees' reskilling and acquisition of new skills.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a posting unit, an evaluation unit, and a distribution unit. The learning unit is where employees take AI learning courses. The posting unit is where employees post new content. The evaluation unit evaluates the data provided by the learning unit and the posting unit. The distribution unit distributes rewards based on the evaluation results from the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide incentives for employees to reskill and acquire new skills. [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 system according to an embodiment of the present invention focuses on the business affinity between corporate employee reskilling and providing incentives for acquiring new skills. This system evaluates employee activities such as taking AI learning materials or posting new content, and distributes appropriate royalties (rewards) to a digital payroll account. For example, when an employee takes AI learning materials from SoftBank's corporate AI / DX talent development platform "Axross Recipe," they create their own account and begin learning. The learning content is provided based on a dynamic knowledge graph utilizing generative AI. Next, the employee posts new content. For example, the employee posts a report summarizing their learning results or an article about new AI technology. This posted content is evaluated by generative AI. The generative AI analyzes the quality and usefulness of the posted content and makes an appropriate evaluation. Once the evaluation is complete, the AI ​​calculates appropriate royalties (rewards). For example, the reward is determined based on the content of the learning materials taken by the employee and the evaluation of the content posted. This reward is distributed to the digital payroll account. This system allows employees to earn rewards through their learning activities. This will improve employees' motivation to learn and raise the overall skill level of the company. Furthermore, by utilizing generative AI, learning content and posted content are evaluated dynamically, enabling evaluations that are always based on the latest information. In addition, this system contributes to the company's growth strategy. For example, by utilizing SoftBank's domestically produced LLM and next-generation AI infrastructure, companies can improve their AI literacy and mindset. This allows companies to effectively utilize AI technology and enhance their competitiveness. Thus, this invention is a system that promotes employee reskilling and the acquisition of new skills, and supports the growth of the company. By utilizing generative AI, the evaluation of learning activities and the distribution of rewards are performed dynamically, improving employee motivation to learn and the company's competitiveness. As a result, the system can promote employee skill improvement and company growth by dynamically evaluating employee learning activities and distributing rewards.

[0029] The system according to this embodiment comprises a learning unit, a posting unit, an evaluation unit, and a distribution unit. The learning unit allows employees to take AI learning courses. When employees take AI learning courses, this includes, but is not limited to, online courses, video lectures, and interactive materials. The learning unit provides, for example, online courses, allowing employees to learn at their own pace. The learning unit also provides video lectures, allowing employees to watch lectures by experts. Furthermore, the learning unit provides interactive materials, allowing employees to learn by actually working through them. The posting unit allows employees to post new content. When employees post new content, this includes, but is not limited to, text, video, audio, and interactive content. The posting unit allows, for example, employees to post reports summarizing their learning outcomes. The posting unit also allows employees to post articles about new AI technologies. Furthermore, the posting unit allows employees to share their learning content through video and audio. The evaluation unit evaluates the data provided by the learning unit and the posting unit. The evaluation unit uses generative AI to analyze the quality and usefulness of course materials and submitted content, and to make appropriate evaluations. For example, the evaluation unit uses generative AI to evaluate the accuracy and reliability of course materials. The evaluation unit can also use generative AI to evaluate the practicality and usefulness of submitted content. Furthermore, the evaluation unit can use generative AI to evaluate the overall quality of course materials and submitted content. The distribution unit distributes rewards based on the results evaluated by the evaluation unit. The distribution unit distributes monetary rewards, point systems, perks, etc., based on the evaluation results. For example, the distribution unit provides monetary rewards to employees based on the evaluation results. The distribution unit can also award points to employees based on the evaluation results. Furthermore, the distribution unit can provide perks to employees based on the evaluation results. In this way, the system according to the embodiment can dynamically evaluate employees' learning activities and distribute rewards, thereby promoting employee skill improvement and corporate growth.

[0030] The learning department provides employees with access to AI learning materials. These materials may include, but are not limited to, online courses, video lectures, and interactive materials. For example, the learning department can offer online courses, allowing employees to learn at their own pace. These online courses are delivered through a web-based platform, accessible from anywhere with an internet connection. This allows employees to learn according to their own schedules, enabling efficient learning. The learning department can also provide video lectures, allowing employees to watch expert lectures. These video lectures are delivered in high-resolution video and clear audio, effectively conveying information through both sight and sound. Furthermore, the learning department can provide interactive materials, allowing employees to learn through hands-on activities. These interactive materials include simulations, quizzes, and practical exercises, enabling employees to hone their skills in an environment similar to their actual work. This allows the learning department to accommodate diverse learning styles and provide an effective learning experience. Additionally, the learning department can track learning progress, providing real-time insights into how far employees are progressing. This allows employees to check their learning progress and adjust their learning plans as needed.

[0031] The contribution section allows employees to post new content. This content can include, but is not limited to, text, video, audio, and interactive content. For example, employees can post reports summarizing their learning outcomes. These reports, in text format, can include detailed analysis and insights, serving as valuable resources for sharing knowledge with other employees. Employees can also post articles on new AI technologies, sharing the latest technological trends and research findings within the company and improving the overall knowledge level. Furthermore, employees can share their learning content through video and audio. Video and audio content is easier to understand and beneficial to other employees because it conveys information through both sight and sound. Interactive content includes quizzes, simulations, and practical exercises, allowing employees to test their knowledge and skills. The contribution section embraces these diverse formats, providing an environment where employees can freely share knowledge and learn from each other. Additionally, the contribution section centrally manages posted content and includes a function to notify other employees as needed. This allows the posting department to promote knowledge sharing among employees and enhance the overall learning effect.

[0032] The evaluation department evaluates the data provided by the learning and posting departments. The evaluation department uses generative AI to analyze the quality and usefulness of the learning and posting content and provide appropriate evaluations. For example, the evaluation department uses generative AI to evaluate the accuracy and reliability of the learning content. The generative AI utilizes natural language processing technology to analyze the content of text data and determine the accuracy of specialized knowledge and information. The evaluation department can also use generative AI to evaluate the practicality and usefulness of posting content. The generative AI analyzes posted video and audio data and evaluates how useful the content is in actual work. Furthermore, the evaluation department can use generative AI to evaluate the overall quality of the learning and posting content. Based on past data and evaluation criteria, the generative AI comprehensively judges the quality of each piece of content and assigns a score. This allows the evaluation department to accurately evaluate the quality of content provided by employees and provide appropriate feedback. Furthermore, based on the evaluation results, the evaluation department can visualize employees' learning progress and skill levels and assist in developing individual learning plans. This allows the evaluation department to comprehensively support employees' learning activities and promote skill improvement.

[0033] The distribution department distributes rewards based on the results evaluated by the evaluation department. For example, the distribution department distributes monetary rewards, point systems, and perks based on evaluation results. For instance, the distribution department provides monetary rewards to employees based on evaluation results. Monetary rewards are an important element in increasing employee motivation and promoting learning activities. The distribution department can also award points to employees based on evaluation results. A point system motivates employees to continue learning activities, and allows them to exchange accumulated points for perks or rewards. Furthermore, the distribution department can offer perks to employees based on evaluation results. Perks may include, for example, participation in special training programs, invitations to company events, or gift certificates. This allows the distribution department to dynamically evaluate employee learning activities and distribute rewards, thereby promoting employee skill development and company growth. Additionally, the distribution department has mechanisms in place to ensure a transparent and fair reward distribution process, allowing employees to review their evaluation results and reward details. This allows the distribution department to gain employee trust and support the continuous improvement of learning activities.

[0034] The learning unit can analyze an employee's past learning history and suggest the optimal learning sequence. For example, the learning unit can use AI to suggest what an employee should learn next based on what they have previously learned. The learning unit can also use AI to suggest a sequence to strengthen a specific skill set based on the employee's learning history. Furthermore, the learning unit can analyze an employee's past learning history and have AI design an efficient learning path. This allows for skill improvement by designing an efficient learning path based on the employee's past learning history. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input employee learning history data into a generating AI and have the generating AI suggest an optimal learning sequence.

[0035] The learning unit can provide customized learning paths based on employees' current projects and work content. For example, the AI ​​can prioritize providing content related to the projects the employee is currently working on. The learning unit can also have the AI ​​design learning paths to reinforce necessary skills based on the employee's work content. Furthermore, the AI ​​can suggest appropriate learning content according to the employee's project progress. This allows employees to efficiently acquire necessary skills by providing learning paths tailored to their work content. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input employee project data into a generating AI and have the generating AI provide customized learning paths.

[0036] The learning department can prioritize providing highly relevant learning content by taking into account the employee's geographical location. For example, if an employee is in a specific region, the AI ​​can provide learning content relevant to that region. The learning department can also use the AI ​​to suggest content for learning region-specific skills and knowledge based on the employee's location. Furthermore, if an employee is on a business trip, the AI ​​can provide learning content relevant to their destination. This enhances the effectiveness of learning by providing highly relevant learning content based on the employee's location. Some or all of the above processing in the learning department may be performed using AI, or not. For example, the learning department can input employee location data into a generating AI and have the generating AI provide highly relevant learning content.

[0037] The learning department can analyze employees' social media activity and suggest relevant learning content. For example, the learning department can use AI to provide learning content related to topics that employees have shown interest in on social media. The learning department can also use AI to suggest learning content that employees might be interested in based on their social media activity. Furthermore, the learning department can use AI to provide learning content related to experts and influencers that employees follow. This can improve employees' motivation to learn by providing learning content that they might be interested in based on their social media activity. Some or all of the above processes in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input employee social media data into a generating AI and have the generating AI suggest relevant learning content.

[0038] The posting function can analyze an employee's past posting history and suggest the optimal posting format. For example, the AI ​​can suggest the optimal format based on the employee's past successful posting formats. The posting function can also suggest a specific format if it is effective based on the employee's posting history. Furthermore, the AI ​​can design the most effective posting format based on the employee's past posting history. This enhances the effectiveness of information dissemination by suggesting effective posting formats based on the employee's past posting history. Some or all of the above processes in the posting function may be performed using AI, or not. For example, the posting function can input employee posting history data into a generating AI and have the generating AI suggest the optimal posting format.

[0039] The posting function can customize post content based on the employee's current work. For example, the AI ​​can prioritize suggesting content related to the project the employee is currently working on. The AI ​​can also design posts containing necessary information based on the employee's work. Furthermore, the AI ​​can suggest appropriate post content according to the employee's project progress. This allows for the effective dissemination of necessary information by providing posts tailored to the employee's work. Some or all of the above processes in the posting function may be performed using AI, or not. For example, the posting function can input employee work data into a generating AI and have the generating AI provide customized post content.

[0040] The evaluation unit can improve the accuracy of its evaluations by referring to employees' past learning and posting histories. For example, the evaluation unit can use a generating AI to improve the accuracy of evaluations based on employees' past learning histories. The evaluation unit can also use a generating AI to improve the accuracy of evaluations by referring to employees' posting histories. Furthermore, the evaluation unit can use a generating AI to improve the accuracy of evaluations by comprehensively analyzing employees' past histories. This allows for more accurate evaluations by improving the accuracy of evaluations based on employees' past histories. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employees' past history data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0041] The evaluation department can customize evaluation criteria based on an employee's current work. For example, the evaluation department can use a generating AI to set evaluation criteria related to a project the employee is currently working on. The evaluation department can also use a generating AI to design appropriate evaluation criteria based on the employee's work. Furthermore, the evaluation department can use a generating AI to customize evaluation criteria according to the employee's project progress. This allows for more appropriate evaluations by providing evaluation criteria tailored to the employee's work. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input employee work data into a generating AI and have the generating AI customize the evaluation criteria.

[0042] The distribution unit can analyze an employee's past compensation history and propose the optimal distribution method. For example, the distribution unit's AI can propose the optimal distribution method based on an employee's past compensation history. The distribution unit can also have its AI propose a specific distribution method if it is effective based on an employee's compensation history. Furthermore, the distribution unit can analyze an employee's past compensation history and have its AI design the most efficient distribution method. This improves the efficiency of compensation distribution by proposing the optimal distribution method based on an employee's past compensation history. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input employee compensation history data into a generating AI and have the generating AI propose the optimal distribution method.

[0043] The distribution unit can customize the compensation distribution criteria based on the employee's current work. For example, the distribution unit's AI can set compensation distribution criteria related to the project the employee is currently working on. The distribution unit can also have its AI design appropriate compensation distribution criteria based on the employee's work. Furthermore, the distribution unit can have its AI customize the compensation distribution criteria according to the employee's project progress. This enables more appropriate compensation distribution by providing compensation distribution criteria that are tailored to the employee's work. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input employee work data into a generating AI and have the generating AI perform the customization of compensation distribution criteria.

[0044] The distribution unit can optimize the reward distribution method by taking into account the geographical location information of employees. For example, if an employee is in a specific region, the distribution unit's AI can set a reward distribution method relevant to that region. The distribution unit can also have the AI ​​suggest region-specific reward distribution methods based on the employee's location information. Furthermore, if an employee is on a business trip, the distribution unit's AI can set a reward distribution method relevant to the business trip destination. This allows for improved reward distribution effectiveness by optimizing the reward distribution method based on the employee's location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input employee location data into a generating AI and have the generating AI perform the optimization of the reward distribution method.

[0045] The distribution unit can analyze employees' social media activity and propose relevant compensation distribution criteria. For example, the distribution unit's AI can set compensation distribution criteria related to topics that employees have shown interest in on social media. The distribution unit can also have its AI propose compensation distribution criteria that employees might be interested in based on their social media activity. Furthermore, the distribution unit's AI can set compensation distribution criteria related to experts and influencers that employees follow. This allows for more appropriate compensation distribution by providing compensation distribution criteria based on employees' social media activity. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input employee social media data into a generating AI and have the generating AI propose relevant compensation distribution criteria.

[0046] The evaluation unit can analyze the quality and usefulness of course and posted content using a generative AI and perform appropriate evaluations. For example, the evaluation unit can use the generative AI to evaluate the accuracy and reliability of course content. The evaluation unit can also use the generative AI to evaluate the practicality and usefulness of posted content. Furthermore, the evaluation unit can use the generative AI to evaluate the overall quality of course and posted content. As a result, by using the generative AI, the quality and usefulness of course and posted content can be evaluated with high accuracy. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input course and posted content data into the generative AI and have the generative AI perform quality and usefulness evaluations.

[0047] The evaluation unit can analyze data to optimize employee learning paths. For example, the evaluation unit designs an optimal learning path based on an employee's learning goals, skill level, and past learning history. The evaluation unit can use generative AI to analyze data to optimize employee learning paths and provide efficient learning paths. This optimizes employee learning paths, enabling efficient learning. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employee learning data into a generative AI and have the generative AI perform the optimization of the learning path.

[0048] The distribution unit can distribute rewards to digital payroll accounts based on evaluation results. For example, the distribution unit can pay employees monetary rewards based on evaluation results. The distribution unit can also award employees points based on evaluation results. Furthermore, the distribution unit can offer employees perks based on evaluation results. This can improve employee motivation by distributing rewards based on evaluation results. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input evaluation result data into a generating AI and have the generating AI perform the reward distribution.

[0049] The distribution unit can calculate compensation. The distribution unit calculates compensation based on, for example, employee performance metrics, evaluation results, and distribution criteria. The distribution unit can use a generation AI to calculate compensation and achieve efficient compensation distribution. By automating compensation calculation, efficient compensation distribution becomes possible. Some or all of the above-described processes in the distribution unit may be performed using, for example, AI, or not using AI. For example, the distribution unit can input employee performance data into a generation AI and have the generation AI perform the compensation calculation.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The training department can analyze employees' learning styles and suggest the most suitable learning methods. For example, if an employee prefers visual learning, video lectures and infographics can be prioritized. If an employee prefers auditory learning, podcasts and audio materials can be provided. Furthermore, if an employee prefers hands-on learning, interactive simulations and exercises can be offered. This maximizes learning effectiveness by providing the most suitable learning methods tailored to each employee's learning style.

[0052] The posting section can provide real-time feedback on employee submissions. For example, when an employee submits an article about a new technology, the AI ​​can immediately provide feedback on the accuracy and usefulness of the content. Similarly, when an employee submits a report summarizing their learning outcomes, the AI ​​can offer advice on its structure and logic. Furthermore, when an employee shares their learning content via video or audio, the AI ​​can evaluate their presentation skills and suggest areas for improvement. This allows employees to instantly improve their submissions and deliver higher-quality content.

[0053] The learning department can monitor employees' learning progress in real time and adjust learning plans as needed. For example, if an employee is ahead of schedule, the AI ​​can suggest additional learning content. If an employee is behind schedule, the AI ​​can readjust the learning plan and suggest more efficient learning methods. Furthermore, if an employee is struggling with a particular topic, the AI ​​can provide supplementary materials or additional explanations. This maximizes learning effectiveness by providing flexible learning plans tailored to each employee's progress.

[0054] The posting department can collect and analyze feedback from other employees on employee posts. For example, when an employee posts an article about a new technology, the AI ​​can collect comments and ratings from other employees and analyze that feedback. Also, when an employee posts a report summarizing their learning outcomes, the AI ​​can suggest areas for improvement based on feedback from other employees. Furthermore, when an employee shares their learning content via video or audio, the AI ​​can collect feedback from other employees, analyze that feedback, and evaluate it. This allows employees to improve their posts based on feedback from other employees and provide higher quality content.

[0055] The distribution unit can analyze an employee's past compensation history and optimize the frequency of compensation distribution. For example, if an employee's motivation improved when they received frequent compensation in the past, the AI ​​will increase the frequency of compensation distribution. Also, if an employee was more satisfied when they received a lump-sum payment in the past, the AI ​​can suggest a lump-sum payment. Furthermore, the AI ​​can comprehensively analyze an employee's past compensation history and design the most effective compensation distribution frequency. This maximizes the effectiveness of compensation distribution by providing the optimal distribution frequency based on an employee's past compensation history.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The training department ensures that employees access AI learning materials. These materials may include online courses, video lectures, and interactive learning materials. The training department provides online courses, allowing employees to learn at their own pace. Video lectures are also provided, allowing employees to watch expert presentations. Furthermore, interactive learning materials are offered, enabling employees to learn through hands-on activities. Step 2: The posting section is where employees submit new content. This content can include text, video, audio, and interactive content. Employees can submit reports summarizing their learning outcomes, articles about new AI technologies, and even videos and audio recordings of their learning progress. Step 3: The evaluation team evaluates the data provided by the learning and posting teams. The evaluation team uses generative AI to analyze the quality and usefulness of the learning and posting content and make appropriate evaluations. For example, they evaluate the accuracy and reliability of the learning content, the practicality and usefulness of the posting content, and the overall quality. Step 4: The distribution department distributes rewards based on the evaluation results from the evaluation department. The distribution department distributes monetary rewards, point systems, perks, etc., based on the evaluation results. For example, it may provide employees with monetary rewards, award points, or offer perks.

[0058] (Example of form 2) The system according to an embodiment of the present invention focuses on the business affinity between corporate employee reskilling and providing incentives for acquiring new skills. This system evaluates employee activities such as taking AI learning materials or posting new content, and distributes appropriate royalties (rewards) to a digital payroll account. For example, when an employee takes AI learning materials from SoftBank's corporate AI / DX talent development platform "Axross Recipe," they create their own account and begin learning. The learning content is provided based on a dynamic knowledge graph utilizing generative AI. Next, the employee posts new content. For example, the employee posts a report summarizing their learning results or an article about new AI technology. This posted content is evaluated by generative AI. The generative AI analyzes the quality and usefulness of the posted content and makes an appropriate evaluation. Once the evaluation is complete, the AI ​​calculates appropriate royalties (rewards). For example, the reward is determined based on the content of the learning materials taken by the employee and the evaluation of the content posted. This reward is distributed to the digital payroll account. This system allows employees to earn rewards through their learning activities. This will improve employees' motivation to learn and raise the overall skill level of the company. Furthermore, by utilizing generative AI, learning content and posted content are evaluated dynamically, enabling evaluations that are always based on the latest information. In addition, this system contributes to the company's growth strategy. For example, by utilizing SoftBank's domestically produced LLM and next-generation AI infrastructure, companies can improve their AI literacy and mindset. This allows companies to effectively utilize AI technology and enhance their competitiveness. Thus, this invention is a system that promotes employee reskilling and the acquisition of new skills, and supports the growth of the company. By utilizing generative AI, the evaluation of learning activities and the distribution of rewards are performed dynamically, improving employee motivation to learn and the company's competitiveness. As a result, the system can promote employee skill improvement and company growth by dynamically evaluating employee learning activities and distributing rewards.

[0059] The system according to this embodiment comprises a learning unit, a posting unit, an evaluation unit, and a distribution unit. The learning unit allows employees to take AI learning courses. When employees take AI learning courses, this includes, but is not limited to, online courses, video lectures, and interactive materials. The learning unit provides, for example, online courses, allowing employees to learn at their own pace. The learning unit also provides video lectures, allowing employees to watch lectures by experts. Furthermore, the learning unit provides interactive materials, allowing employees to learn by actually working through them. The posting unit allows employees to post new content. When employees post new content, this includes, but is not limited to, text, video, audio, and interactive content. The posting unit allows, for example, employees to post reports summarizing their learning outcomes. The posting unit also allows employees to post articles about new AI technologies. Furthermore, the posting unit allows employees to share their learning content through video and audio. The evaluation unit evaluates the data provided by the learning unit and the posting unit. The evaluation unit uses generative AI to analyze the quality and usefulness of course materials and submitted content, and to make appropriate evaluations. For example, the evaluation unit uses generative AI to evaluate the accuracy and reliability of course materials. The evaluation unit can also use generative AI to evaluate the practicality and usefulness of submitted content. Furthermore, the evaluation unit can use generative AI to evaluate the overall quality of course materials and submitted content. The distribution unit distributes rewards based on the results evaluated by the evaluation unit. The distribution unit distributes monetary rewards, point systems, perks, etc., based on the evaluation results. For example, the distribution unit provides monetary rewards to employees based on the evaluation results. The distribution unit can also award points to employees based on the evaluation results. Furthermore, the distribution unit can provide perks to employees based on the evaluation results. In this way, the system according to the embodiment can dynamically evaluate employees' learning activities and distribute rewards, thereby promoting employee skill improvement and corporate growth.

[0060] The learning department provides employees with access to AI learning materials. These materials may include, but are not limited to, online courses, video lectures, and interactive materials. For example, the learning department can offer online courses, allowing employees to learn at their own pace. These online courses are delivered through a web-based platform, accessible from anywhere with an internet connection. This allows employees to learn according to their own schedules, enabling efficient learning. The learning department can also provide video lectures, allowing employees to watch expert lectures. These video lectures are delivered in high-resolution video and clear audio, effectively conveying information through both sight and sound. Furthermore, the learning department can provide interactive materials, allowing employees to learn through hands-on activities. These interactive materials include simulations, quizzes, and practical exercises, enabling employees to hone their skills in an environment similar to their actual work. This allows the learning department to accommodate diverse learning styles and provide an effective learning experience. Additionally, the learning department can track learning progress, providing real-time insights into how far employees are progressing. This allows employees to check their learning progress and adjust their learning plans as needed.

[0061] The contribution section allows employees to post new content. This content can include, but is not limited to, text, video, audio, and interactive content. For example, employees can post reports summarizing their learning outcomes. These reports, in text format, can include detailed analysis and insights, serving as valuable resources for sharing knowledge with other employees. Employees can also post articles on new AI technologies, sharing the latest technological trends and research findings within the company and improving the overall knowledge level. Furthermore, employees can share their learning content through video and audio. Video and audio content is easier to understand and beneficial to other employees because it conveys information through both sight and sound. Interactive content includes quizzes, simulations, and practical exercises, allowing employees to test their knowledge and skills. The contribution section embraces these diverse formats, providing an environment where employees can freely share knowledge and learn from each other. Additionally, the contribution section centrally manages posted content and includes a function to notify other employees as needed. This allows the posting department to promote knowledge sharing among employees and enhance the overall learning effect.

[0062] The evaluation department evaluates the data provided by the learning and posting departments. The evaluation department uses generative AI to analyze the quality and usefulness of the learning and posting content and provide appropriate evaluations. For example, the evaluation department uses generative AI to evaluate the accuracy and reliability of the learning content. The generative AI utilizes natural language processing technology to analyze the content of text data and determine the accuracy of specialized knowledge and information. The evaluation department can also use generative AI to evaluate the practicality and usefulness of posting content. The generative AI analyzes posted video and audio data and evaluates how useful the content is in actual work. Furthermore, the evaluation department can use generative AI to evaluate the overall quality of the learning and posting content. Based on past data and evaluation criteria, the generative AI comprehensively judges the quality of each piece of content and assigns a score. This allows the evaluation department to accurately evaluate the quality of content provided by employees and provide appropriate feedback. Furthermore, based on the evaluation results, the evaluation department can visualize employees' learning progress and skill levels and assist in developing individual learning plans. This allows the evaluation department to comprehensively support employees' learning activities and promote skill improvement.

[0063] The distribution department distributes rewards based on the results evaluated by the evaluation department. For example, the distribution department distributes monetary rewards, point systems, and perks based on evaluation results. For instance, the distribution department provides monetary rewards to employees based on evaluation results. Monetary rewards are an important element in increasing employee motivation and promoting learning activities. The distribution department can also award points to employees based on evaluation results. A point system motivates employees to continue learning activities, and allows them to exchange accumulated points for perks or rewards. Furthermore, the distribution department can offer perks to employees based on evaluation results. Perks may include, for example, participation in special training programs, invitations to company events, or gift certificates. This allows the distribution department to dynamically evaluate employee learning activities and distribute rewards, thereby promoting employee skill development and company growth. Additionally, the distribution department has mechanisms in place to ensure a transparent and fair reward distribution process, allowing employees to review their evaluation results and reward details. This allows the distribution department to gain employee trust and support the continuous improvement of learning activities.

[0064] The learning unit can estimate an employee's emotions and adjust the difficulty level of the learning content based on the estimated emotions. For example, if an employee is stressed, the AI ​​can prioritize providing easier content to reduce the learning burden. Conversely, if an employee is relaxed, the AI ​​can provide more difficult content to promote skill improvement. Furthermore, if an employee is focused, the AI ​​can provide content of moderate difficulty to support efficient learning. In this way, by adjusting the difficulty level of the learning content according to the employee's emotions, the learning burden can be reduced and efficient learning can be supported. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The learning unit can analyze an employee's past learning history and suggest the optimal learning sequence. For example, the learning unit can use AI to suggest what an employee should learn next based on what they have previously learned. The learning unit can also use AI to suggest a sequence to strengthen a specific skill set based on the employee's learning history. Furthermore, the learning unit can analyze an employee's past learning history and have AI design an efficient learning path. This allows for skill improvement by designing an efficient learning path based on the employee's past learning history. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input employee learning history data into a generating AI and have the generating AI suggest an optimal learning sequence.

[0066] The learning unit can provide customized learning paths based on employees' current projects and work content. For example, the AI ​​can prioritize providing content related to the projects the employee is currently working on. The learning unit can also have the AI ​​design learning paths to reinforce necessary skills based on the employee's work content. Furthermore, the AI ​​can suggest appropriate learning content according to the employee's project progress. This allows employees to efficiently acquire necessary skills by providing learning paths tailored to their work content. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input employee project data into a generating AI and have the generating AI provide customized learning paths.

[0067] The learning unit can estimate an employee's emotions and adjust the timing of their lessons based on those emotions. For example, if an employee is tired, the AI ​​can suggest a break and resume lessons at an appropriate time. The learning unit can also suggest that an employee continue lessons if they are focused. Furthermore, if an employee is stressed, the AI ​​can suggest that they resume lessons at a time when they can relax. This allows for improved learning efficiency by adjusting the timing of lessons according to the employee'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 learning unit may be performed using AI or not. For example, the learning unit can input employee biometric data into a generative AI and have the generative AI perform emotion estimation.

[0068] The learning department can prioritize providing highly relevant learning content by taking into account the employee's geographical location. For example, if an employee is in a specific region, the AI ​​can provide learning content relevant to that region. The learning department can also use the AI ​​to suggest content for learning region-specific skills and knowledge based on the employee's location. Furthermore, if an employee is on a business trip, the AI ​​can provide learning content relevant to their destination. This enhances the effectiveness of learning by providing highly relevant learning content based on the employee's location. Some or all of the above processing in the learning department may be performed using AI, or not. For example, the learning department can input employee location data into a generating AI and have the generating AI provide highly relevant learning content.

[0069] The learning department can analyze employees' social media activity and suggest relevant learning content. For example, the learning department can use AI to provide learning content related to topics that employees have shown interest in on social media. The learning department can also use AI to suggest learning content that employees might be interested in based on their social media activity. Furthermore, the learning department can use AI to provide learning content related to experts and influencers that employees follow. This can improve employees' motivation to learn by providing learning content that they might be interested in based on their social media activity. Some or all of the above processes in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input employee social media data into a generating AI and have the generating AI suggest relevant learning content.

[0070] The posting system can estimate an employee's emotions and adjust the way the post is expressed based on that estimation. For example, if an employee is relaxed, the AI ​​might suggest a post with a detailed explanation. If an employee is in a hurry, the AI ​​might suggest a concise and to-the-point post. Furthermore, if an employee is excited, the AI ​​might suggest a post with visually appealing content. By adjusting the way the post is expressed according to the employee's emotions, more effective information dissemination becomes possible. 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 posting system may be performed using AI or not. For example, the posting system can input employee emotion data into a generative AI and have the generative AI adjust the way the post is expressed.

[0071] The posting function can analyze an employee's past posting history and suggest the optimal posting format. For example, the AI ​​can suggest the optimal format based on the employee's past successful posting formats. The posting function can also suggest a specific format if it is effective based on the employee's posting history. Furthermore, the AI ​​can design the most effective posting format based on the employee's past posting history. This enhances the effectiveness of information dissemination by suggesting effective posting formats based on the employee's past posting history. Some or all of the above processes in the posting function may be performed using AI, or not. For example, the posting function can input employee posting history data into a generating AI and have the generating AI suggest the optimal posting format.

[0072] The posting function can customize post content based on the employee's current work. For example, the AI ​​can prioritize suggesting content related to the project the employee is currently working on. The AI ​​can also design posts containing necessary information based on the employee's work. Furthermore, the AI ​​can suggest appropriate post content according to the employee's project progress. This allows for the effective dissemination of necessary information by providing posts tailored to the employee's work. Some or all of the above processes in the posting function may be performed using AI, or not. For example, the posting function can input employee work data into a generating AI and have the generating AI provide customized post content.

[0073] The evaluation unit can estimate an employee's emotions and adjust evaluation criteria based on those emotions. For example, if an employee is relaxed, the evaluation unit can use a generating AI to set detailed evaluation criteria. If an employee is in a hurry, the evaluation unit can use the generating AI to set concise evaluation criteria. Furthermore, if an employee is excited, the evaluation unit can use the generating AI to set visually appealing evaluation criteria. This allows for more appropriate evaluations by adjusting evaluation criteria according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating 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 evaluation unit may be performed using AI or not. For example, the evaluation unit can input employee emotion data into a generating AI and have the generating AI adjust the evaluation criteria.

[0074] The evaluation unit can improve the accuracy of its evaluations by referring to employees' past learning and posting histories. For example, the evaluation unit can use a generating AI to improve the accuracy of evaluations based on employees' past learning histories. The evaluation unit can also use a generating AI to improve the accuracy of evaluations by referring to employees' posting histories. Furthermore, the evaluation unit can use a generating AI to improve the accuracy of evaluations by comprehensively analyzing employees' past histories. This allows for more accurate evaluations by improving the accuracy of evaluations based on employees' past histories. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employees' past history data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0075] The evaluation department can customize evaluation criteria based on an employee's current work. For example, the evaluation department can use a generating AI to set evaluation criteria related to a project the employee is currently working on. The evaluation department can also use a generating AI to design appropriate evaluation criteria based on the employee's work. Furthermore, the evaluation department can use a generating AI to customize evaluation criteria according to the employee's project progress. This allows for more appropriate evaluations by providing evaluation criteria tailored to the employee's work. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input employee work data into a generating AI and have the generating AI customize the evaluation criteria.

[0076] The distribution unit can estimate employees' emotions and adjust the reward distribution method based on the estimated emotions. For example, if an employee is relaxed, the distribution unit's AI can suggest a method of distributing rewards in stages. If an employee is in a hurry, the distribution unit's AI can suggest a method of distributing rewards in a lump sum. Furthermore, if an employee is excited, the distribution unit's AI can suggest a method of distributing rewards in a visually appealing way. This allows for more appropriate reward distribution by adjusting the reward distribution method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not using AI. For example, the distribution unit can input employee emotion data into a generative AI and have the generative AI adjust the reward distribution method.

[0077] The distribution unit can analyze an employee's past compensation history and propose the optimal distribution method. For example, the distribution unit's AI can propose the optimal distribution method based on an employee's past compensation history. The distribution unit can also have its AI propose a specific distribution method if it is effective based on an employee's compensation history. Furthermore, the distribution unit can analyze an employee's past compensation history and have its AI design the most efficient distribution method. This improves the efficiency of compensation distribution by proposing the optimal distribution method based on an employee's past compensation history. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input employee compensation history data into a generating AI and have the generating AI propose the optimal distribution method.

[0078] The distribution unit can customize the compensation distribution criteria based on the employee's current work. For example, the distribution unit's AI can set compensation distribution criteria related to the project the employee is currently working on. The distribution unit can also have its AI design appropriate compensation distribution criteria based on the employee's work. Furthermore, the distribution unit can have its AI customize the compensation distribution criteria according to the employee's project progress. This enables more appropriate compensation distribution by providing compensation distribution criteria that are tailored to the employee's work. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input employee work data into a generating AI and have the generating AI perform the customization of compensation distribution criteria.

[0079] The distribution unit can estimate the emotions of employees and adjust the timing of reward distribution based on the estimated emotions. For example, if an employee is tired, the distribution unit's AI can suggest a break and distribute rewards at an appropriate time. The distribution unit can also suggest that if an employee is focused, the AI ​​should distribute rewards continuously. Furthermore, if an employee is stressed, the distribution unit's AI can distribute rewards at a time when the employee can relax. This allows for more appropriate distribution of rewards by adjusting the timing of reward distribution according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not using AI. For example, the distribution unit can input employee emotion data into a generative AI and have the generative AI adjust the timing of reward distribution.

[0080] The distribution unit can optimize the reward distribution method by taking into account the geographical location information of employees. For example, if an employee is in a specific region, the distribution unit's AI can set a reward distribution method relevant to that region. The distribution unit can also have the AI ​​suggest region-specific reward distribution methods based on the employee's location information. Furthermore, if an employee is on a business trip, the distribution unit's AI can set a reward distribution method relevant to the business trip destination. This allows for improved reward distribution effectiveness by optimizing the reward distribution method based on the employee's location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input employee location data into a generating AI and have the generating AI perform the optimization of the reward distribution method.

[0081] The distribution unit can analyze employees' social media activity and propose relevant compensation distribution criteria. For example, the distribution unit's AI can set compensation distribution criteria related to topics that employees have shown interest in on social media. The distribution unit can also have its AI propose compensation distribution criteria that employees might be interested in based on their social media activity. Furthermore, the distribution unit's AI can set compensation distribution criteria related to experts and influencers that employees follow. This allows for more appropriate compensation distribution by providing compensation distribution criteria based on employees' social media activity. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input employee social media data into a generating AI and have the generating AI propose relevant compensation distribution criteria.

[0082] The evaluation unit can analyze the quality and usefulness of course and posted content using a generative AI and perform appropriate evaluations. For example, the evaluation unit can use the generative AI to evaluate the accuracy and reliability of course content. The evaluation unit can also use the generative AI to evaluate the practicality and usefulness of posted content. Furthermore, the evaluation unit can use the generative AI to evaluate the overall quality of course and posted content. As a result, by using the generative AI, the quality and usefulness of course and posted content can be evaluated with high accuracy. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input course and posted content data into the generative AI and have the generative AI perform quality and usefulness evaluations.

[0083] The evaluation unit can analyze data to optimize employee learning paths. For example, the evaluation unit designs an optimal learning path based on an employee's learning goals, skill level, and past learning history. The evaluation unit can use generative AI to analyze data to optimize employee learning paths and provide efficient learning paths. This optimizes employee learning paths, enabling efficient learning. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employee learning data into a generative AI and have the generative AI perform the optimization of the learning path.

[0084] The distribution unit can distribute rewards to digital payroll accounts based on evaluation results. For example, the distribution unit can pay employees monetary rewards based on evaluation results. The distribution unit can also award employees points based on evaluation results. Furthermore, the distribution unit can offer employees perks based on evaluation results. This can improve employee motivation by distributing rewards based on evaluation results. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input evaluation result data into a generating AI and have the generating AI perform the reward distribution.

[0085] The distribution unit can calculate compensation. The distribution unit calculates compensation based on, for example, employee performance metrics, evaluation results, and distribution criteria. The distribution unit can use a generation AI to calculate compensation and achieve efficient compensation distribution. By automating compensation calculation, efficient compensation distribution becomes possible. Some or all of the above-described processes in the distribution unit may be performed using, for example, AI, or not using AI. For example, the distribution unit can input employee performance data into a generation AI and have the generation AI perform the compensation calculation.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The training department can analyze employees' learning styles and suggest the most suitable learning methods. For example, if an employee prefers visual learning, video lectures and infographics can be prioritized. If an employee prefers auditory learning, podcasts and audio materials can be provided. Furthermore, if an employee prefers hands-on learning, interactive simulations and exercises can be offered. This maximizes learning effectiveness by providing the most suitable learning methods tailored to each employee's learning style.

[0088] The learning component can estimate an employee's emotions and provide feedback on the learning content based on those emotions. For example, if an employee is feeling anxious about learning, the AI ​​can provide encouraging messages to boost their motivation. If an employee is excited about learning, the AI ​​can also provide more challenging tasks. Furthermore, if an employee is feeling tired of learning, the AI ​​can suggest a break and provide content to help them refresh. This makes it easier to maintain learning motivation by providing feedback that is tailored to the employee's emotions.

[0089] The posting section can provide real-time feedback on employee submissions. For example, when an employee submits an article about a new technology, the AI ​​can immediately provide feedback on the accuracy and usefulness of the content. Similarly, when an employee submits a report summarizing their learning outcomes, the AI ​​can offer advice on its structure and logic. Furthermore, when an employee shares their learning content via video or audio, the AI ​​can evaluate their presentation skills and suggest areas for improvement. This allows employees to instantly improve their submissions and deliver higher-quality content.

[0090] The learning department can estimate employees' emotions and adjust the learning environment based on those estimates. For example, if an employee is stressed, the AI ​​can provide relaxing music and backgrounds to create a conducive learning environment. If an employee is focused, the AI ​​can turn off notifications to provide an environment conducive to learning. Furthermore, if an employee is tired, the AI ​​can suggest a break and provide content to help them refresh. This allows for improved learning efficiency by providing a learning environment tailored to employees' emotions.

[0091] The evaluation department can estimate employee emotions and adjust evaluation feedback based on those emotions. For example, if an employee is feeling anxious about their evaluation, the AI ​​can provide encouraging messages and present the results in a positive light. If an employee is excited about their evaluation, the AI ​​can suggest more challenging goals. Furthermore, if an employee is feeling exhausted by the evaluation, the AI ​​can suggest a break and provide content to help them refresh. By providing evaluation feedback that is tailored to the employee's emotions, the acceptance of evaluations can be improved.

[0092] The learning department can monitor employees' learning progress in real time and adjust learning plans as needed. For example, if an employee is ahead of schedule, the AI ​​can suggest additional learning content. If an employee is behind schedule, the AI ​​can readjust the learning plan and suggest more efficient learning methods. Furthermore, if an employee is struggling with a particular topic, the AI ​​can provide supplementary materials or additional explanations. This maximizes learning effectiveness by providing flexible learning plans tailored to each employee's progress.

[0093] The posting department can collect and analyze feedback from other employees on employee posts. For example, when an employee posts an article about a new technology, the AI ​​can collect comments and ratings from other employees and analyze that feedback. Also, when an employee posts a report summarizing their learning outcomes, the AI ​​can suggest areas for improvement based on feedback from other employees. Furthermore, when an employee shares their learning content via video or audio, the AI ​​can collect feedback from other employees, analyze that feedback, and evaluate it. This allows employees to improve their posts based on feedback from other employees and provide higher quality content.

[0094] The evaluation system can estimate employee emotions and adjust the timing of evaluations based on those emotions. For example, if an employee is tired, the AI ​​can postpone the evaluation and conduct it at an appropriate time. If an employee is focused, the AI ​​can conduct the evaluation immediately. Furthermore, if an employee is stressed, the AI ​​can conduct the evaluation at a time when they can relax. By providing evaluation timing that is tailored to the employee's emotions, the system can improve the acceptance of evaluations.

[0095] The distribution unit can estimate employee emotions and adjust the type of reward based on those emotions. For example, if an employee is relaxed, the AI ​​can suggest long-term rewards (e.g., stock options). If an employee is in a hurry, the AI ​​can suggest immediate rewards (e.g., bonuses). Furthermore, if an employee is excited, the AI ​​can suggest special rewards (e.g., travel vouchers). This improves the acceptance of rewards by providing reward types that match employee emotions.

[0096] The distribution unit can analyze an employee's past compensation history and optimize the frequency of compensation distribution. For example, if an employee's motivation improved when they received frequent compensation in the past, the AI ​​will increase the frequency of compensation distribution. Also, if an employee was more satisfied when they received a lump-sum payment in the past, the AI ​​can suggest a lump-sum payment. Furthermore, the AI ​​can comprehensively analyze an employee's past compensation history and design the most effective compensation distribution frequency. This maximizes the effectiveness of compensation distribution by providing the optimal distribution frequency based on an employee's past compensation history.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The training department ensures that employees access AI learning materials. These materials may include online courses, video lectures, and interactive learning materials. The training department provides online courses, allowing employees to learn at their own pace. Video lectures are also provided, allowing employees to watch expert presentations. Furthermore, interactive learning materials are offered, enabling employees to learn through hands-on activities. Step 2: The posting section is where employees submit new content. This content can include text, video, audio, and interactive content. Employees can submit reports summarizing their learning outcomes, articles about new AI technologies, and even videos and audio recordings of their learning progress. Step 3: The evaluation team evaluates the data provided by the learning and posting teams. The evaluation team uses generative AI to analyze the quality and usefulness of the learning and posting content and make appropriate evaluations. For example, they evaluate the accuracy and reliability of the learning content, the practicality and usefulness of the posting content, and the overall quality. Step 4: The distribution department distributes rewards based on the evaluation results from the evaluation department. The distribution department distributes monetary rewards, point systems, perks, etc., based on the evaluation results. For example, it may provide employees with monetary rewards, award points, or offer perks.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the learning unit, posting unit, evaluation unit, and distribution unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14, allowing employees to take online courses or video lectures. The posting unit is implemented by, for example, the control unit 46A of the smart device 14, allowing employees to post their learning outcomes. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the learning and posting content using a generating AI. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which distributes rewards based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the learning unit, posting unit, evaluation unit, and distribution unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214, allowing employees to take online courses or video lectures. The posting unit is implemented by, for example, the control unit 46A of the smart glasses 214, allowing employees to post their learning outcomes. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the learning and posting content using a generating AI. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which distributes rewards based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the learning unit, posting unit, evaluation unit, and distribution unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314, allowing employees to take online courses and video lectures. The posting unit is implemented by the control unit 46A of the headset terminal 314, allowing employees to post their learning outcomes. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and evaluates the learning and posting content using a generating AI. The distribution unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and distributes rewards based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the learning unit, posting unit, evaluation unit, and distribution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414, allowing employees to take online courses or video lectures. The posting unit is implemented by, for example, the control unit 46A of the robot 414, allowing employees to post their learning outcomes. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the learning and posting content using a generating AI. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which distributes rewards based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The employee training section where employees take AI learning courses, The posting section where employees submit new content, An evaluation unit that evaluates the data provided by the aforementioned student unit and submission unit, The system comprises a distribution unit that distributes rewards based on the results evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned training section is, The system estimates employees' emotions and adjusts the difficulty level of the training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned training section is, We analyze employees' past learning history and suggest the optimal order for their courses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned training section is, We provide customized learning paths based on employees' current projects and responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned training section is, The system estimates employees' emotions and adjusts the timing of training based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned training section is, Prioritize providing highly relevant learning content by considering employees' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned training section is, Analyze employees' social media activity and suggest relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned submission section, We estimate employees' emotions and adjust the wording of posts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned submission section, We analyze employees' past posting history and suggest the most suitable posting format. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned submission section, Customize post content based on employees' current job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit described above, Estimate employee sentiment and adjust performance evaluation criteria based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit described above, Improve the accuracy of evaluations by referring to employees' past learning and posting history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit described above, Customize evaluation criteria based on employees' current job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned distribution unit is The system estimates employee sentiment and adjusts compensation distribution based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned distribution unit is We analyze employees' past compensation history and propose the optimal distribution method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned distribution unit is Customize compensation distribution criteria based on employees' current job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned distribution unit is The system estimates employee sentiment and adjusts the timing of compensation distribution based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned distribution unit is Optimize compensation distribution methods by taking into account employee geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned distribution unit is Analyze employees' social media activity and propose relevant compensation distribution criteria. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit described above, The AI ​​generates data to analyze the quality and usefulness of course materials and submitted content, and to provide appropriate evaluations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit described above, Analyze data to optimize employee learning paths. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned distribution unit is Based on the evaluation results, rewards will be distributed to the digital payroll account. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned distribution unit is Calculate the reward. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 employee training section where employees take AI learning courses, The posting section where employees submit new content, An evaluation unit that evaluates the data provided by the aforementioned student unit and submission unit, The system comprises a distribution unit that distributes rewards based on the results evaluated by the evaluation unit. A system characterized by the following features.

2. The aforementioned training section is, The system estimates employees' emotions and adjusts the difficulty level of the training content based on those estimated emotions. The system according to feature 1.

3. The aforementioned training section is, We analyze employees' past learning history and suggest the optimal order for their courses. The system according to feature 1.

4. The aforementioned training section is, We provide customized learning paths based on employees' current projects and responsibilities. The system according to feature 1.

5. The aforementioned training section is, The system estimates employees' emotions and adjusts the timing of training based on those estimated emotions. The system according to feature 1.

6. The aforementioned training section is, Prioritize providing highly relevant learning content by considering employees' geographical location. The system according to feature 1.

7. The aforementioned training section is, Analyze employees' social media activity and suggest relevant learning content. The system according to feature 1.

8. The aforementioned submission section, We estimate employees' emotions and adjust the wording of posts based on those estimated emotions. The system according to feature 1.

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