system

Abstract

The system according to this embodiment aims to efficiently create an educational system for reskilling and promote human resource development. [Solution] The system according to the embodiment comprises a generation unit, a learning unit, an evaluation unit, and a proposal unit. The generation unit automatically generates roadmaps and materials using internal company content. The learning unit proceeds with learning based on the roadmap generated by the generation unit. The evaluation unit evaluates the progress of learning carried out by the learning unit. The proposal unit proposes an optimal learning plan based on the progress evaluated by 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 persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to 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 prior art, there is a problem that creating an education system for reskilling and the process of cultivating human resources require time and effort and are difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently create an education system for reskilling and promote the cultivation of human resources.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a learning unit, an evaluation unit, and a proposal unit. The generation unit automatically generates roadmaps and materials using internal company content. The learning unit proceeds with learning based on the roadmaps generated by the generation unit. The evaluation unit evaluates the progress of learning carried out by the learning unit. The proposal unit proposes an optimal learning plan based on the progress evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create an educational system for reskilling and promote human resource development. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied 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 reskilling system according to an embodiment of the present invention is a system that utilizes generative AI to develop the reskilling market in Japan. This reskilling system automatically generates roadmaps and materials using internal company content with generative AI, and each individual works on a roadmap linked to organizational goals. For example, the reskilling system takes input such as the company's training policy and the individual's training policy, and the generative AI creates an optimal roadmap based on that. If a company aims to develop DX personnel, a roadmap is generated that includes specific learning steps and goals based on that policy. Next, the reskilling system allows individuals to proceed with their learning according to the generated roadmap. For example, learning content for acquiring specific skills is automatically recommended. This content is generated using existing company documents, videos, audio, etc. Furthermore, the reskilling system also creates supervisor confirmation reports and skill belts. It is linked with the HR system to manage employee training status and individual growth goals. For example, employees who acquire specific skills are awarded skill belts, and the AI ​​proposes optimal personnel placement based on their achievements. This system can spread the significance of human resource development in the reskilling market throughout Japan. By semi-automating their proprietary training systems, companies can efficiently develop talent and promote DX (Digital Transformation) across their entire organization. Furthermore, in Japan, where reskilling is often associated with the concept of changing jobs, creating a positive image for reskilling will encourage multiple companies to adopt it more readily. This enables the reskilling system to semi-automatically create company-specific training systems and integrate them into HR and organizational evaluations.

[0029] The reskilling system according to this embodiment comprises a generation unit, a learning unit, an evaluation unit, and a proposal unit. The generation unit automatically generates roadmaps and materials utilizing internal company content. For example, the generation unit takes input such as the company's training policy or an individual's training policy and generates an optimal roadmap based on it. The generation unit uses a generation AI to create a roadmap that includes specific learning steps and goals based on the company's training policy. For example, if a company aims to develop DX personnel, the generation unit generates a roadmap based on that policy. The generation unit can also provide an individualized learning plan based on an individual's training policy. The learning unit proceeds with learning based on the generated roadmap. For example, the learning unit automatically recommends learning content for acquiring specific skills. The learning unit generates learning content utilizing existing company materials, videos, audio, etc. The learning unit can also use a generation AI to manage an individual's learning progress and provide an optimal learning plan. The evaluation unit evaluates the learning progress. For example, the evaluation unit records and evaluates the learning progress. The evaluation unit manages an individual's growth goals based on the learning progress. The evaluation unit can use generative AI to assess learning progress and propose an optimal learning plan. The proposal unit proposes an optimal learning plan based on the assessed progress. For example, the proposal unit proposes a learning plan tailored to the individual's skill level based on the assessed progress. The proposal unit can also use generative AI to provide a learning plan based on the individual's growth goals. As a result, the reskilling system according to this embodiment improves the efficiency of reskilling by automatically generating roadmaps and materials utilizing internal content, evaluating learning progress, and proposing an optimal learning plan.

[0030] The generation unit automatically generates roadmaps and materials utilizing internal company content. Specifically, it takes the company's training policies and individual training policies as input and generates an optimal roadmap based on that information. The generation unit uses a generation AI to create roadmaps that include specific learning steps and goals based on the company's training policies. For example, if a company aims to develop DX (Digital Transformation) talent, it will generate a roadmap based on that policy. This generation AI utilizes natural language processing technology to understand the company's training policies and individual goals and proposes the most suitable learning steps. Specifically, if "promoting digital transformation (DX)" is input as the company's training policy, the generation AI will generate a roadmap based on that policy that includes learning steps such as data analysis, cloud computing, and AI technology. It can also provide personalized learning plans based on individual training policies. For example, if an individual has the goal of "becoming a data scientist," the generation AI will generate a personalized roadmap based on that goal that includes learning steps such as statistics, programming, and machine learning. Furthermore, to maximize the use of existing internal content, the generation unit can analyze internal documents and past training data and combine them in the most optimal way to generate new learning materials. This allows the generation unit to automatically generate learning roadmaps optimized for the training policies of companies and individuals, significantly improving the efficiency of reskilling.

[0031] The learning department guides learners through a generated roadmap. Specifically, it automatically recommends learning content to acquire specific skills. The learning department generates learning content by utilizing existing company documents, videos, and audio. For example, it analyzes internal technical documents, past training videos, and expert lecture recordings, and combines them in the most optimal way to create new learning content. The learning department can also use generative AI to manage individual learning progress and provide optimal learning plans. Specifically, it analyzes an individual's learning history and current skill level and recommends what they should learn next. For example, if an individual has finished learning the basics of programming, it will recommend learning data structures and algorithms next. Furthermore, the learning department can collect learner feedback and continuously improve the quality of learning content based on it. For example, if a learner finds a particular piece of content "difficult," the content is restructured based on that feedback and improved to be easier to understand. In addition, the learning department provides features to facilitate communication among learners. For example, it creates an environment where learners can exchange questions and opinions through online forums and chat functions. This allows the learning unit to provide support for efficient and effective learning based on the generated roadmap, thereby assisting learners in improving their skills.

[0032] The evaluation department assesses learning progress. Specifically, it records and evaluates learning progress. Based on learning progress, the evaluation department manages individual growth goals. The evaluation department can also use generative AI to evaluate learning progress and propose optimal learning plans. For example, it can evaluate whether learners have acquired specific skills through tests and quizzes, and determine the next learning step based on the results. The evaluation department collects and analyzes learner performance data to quantitatively evaluate the effectiveness of learning. Specifically, it measures how much time learners spend studying specific content, the accuracy rate of tests, and their level of understanding, and evaluates learning progress based on this data. The evaluation department also collects learners' self-assessments and feedback and reflects them comprehensively in the evaluation. For example, if a learner feels that their understanding is still insufficient in their self-assessment, additional learning content will be provided based on that feedback. Furthermore, the evaluation department regularly reviews the learner's progress toward their growth goals and resets goals as needed. For example, if a learner achieves their goal earlier than planned, a new goal will be set and a plan for the next step will be proposed. This allows the evaluation unit to accurately assess learning progress and effectively support individual growth.

[0033] The suggestion department proposes the optimal learning plan based on the assessed progress. Specifically, it proposes a learning plan tailored to the individual's skill level based on the assessed progress. The suggestion department can also use generative AI to provide learning plans based on the individual's growth goals. For example, if the assessment department evaluates the learner's progress and determines that a particular skill has not yet been sufficiently acquired, the suggestion department will propose additional learning content to strengthen that skill. The suggestion department analyzes the learner's past learning history and current skill level and proposes specific content to learn next based on that. For example, after a learner has acquired the basics of programming, it will propose a learning plan to deepen their knowledge of databases. The suggestion department can also customize the optimal learning plan considering the learner's interests and career goals. For example, if a learner is interested in data science, it will prioritize suggesting learning content related to that field. Furthermore, the suggestion department continuously improves the learning plan based on the learner's feedback. For example, if a learner finds a particular piece of content "difficult," the suggestion department will restructure the content based on that feedback and improve it to make it easier to understand. In this way, the suggestion department can provide the optimal learning plan based on assessed progress and effectively support the learner's skill improvement.

[0034] The generation unit takes corporate and individual training policies as input and generates an optimal roadmap based on that input. For example, the generation unit takes corporate training policies as input and generates an optimal roadmap based on that input. The generation unit uses generation AI to create a roadmap that includes specific learning steps and goals based on corporate training policies. For example, if a company aims to develop DX (Digital Transformation) personnel, the generation unit will generate a roadmap based on that policy. The generation unit can also provide individualized learning plans based on individual training policies. In this way, by generating an optimal roadmap based on corporate and individual training policies, it is possible to provide individualized learning plans.

[0035] The learning unit proceeds with learning according to the generated roadmap. For example, the learning unit can manage individual learning progress and provide an optimal learning plan using generating AI. This enables systematic learning by following the generated roadmap.

[0036] The evaluation unit records and evaluates learning progress. For example, the evaluation unit records and evaluates learning progress. Based on learning progress, the evaluation unit manages individual growth goals. The evaluation unit can also use generative AI to evaluate learning progress and propose optimal learning plans. This allows for understanding the effectiveness of learning by recording and evaluating learning progress.

[0037] The suggestion department proposes an optimal learning plan based on the assessed progress. For example, it proposes a learning plan tailored to the individual's skill level based on the assessed progress. The suggestion department can also use generative AI to provide a learning plan based on the individual's growth goals. This enables efficient learning by proposing an optimal learning plan based on assessed progress.

[0038] The generation unit analyzes a company's past training data and automatically proposes the optimal training policy. For example, the generation unit's AI analyzes past training data, extracts commonalities from successful training programs, and proposes a new training policy. The generation unit's AI can also propose the optimal learning steps for a specific skill set based on past training data. The generation unit's AI can also analyze past training data and propose improvements to failed training programs. In this way, by analyzing a company's past training data, it is possible to propose an effective training policy.

[0039] The generation unit provides different learning steps depending on the individual's skill level when generating a roadmap. For example, the generation unit's generating AI can assess the individual's skill level and provide basic learning steps for beginners. The generation unit can also assess the individual's skill level and provide advanced learning steps for intermediate learners. The generation unit can also assess the individual's skill level and provide advanced learning steps for advanced learners. This enables effective learning by providing learning steps tailored to the individual's skill level.

[0040] The generation unit incorporates the latest skills, taking into account the company's industry trends, when generating roadmaps. For example, the generation unit's AI can analyze the latest industry trends and generate a roadmap that includes the necessary skills. The generation unit's AI can also consider technological innovations in the industry and generate a roadmap that includes new technological skills. The generation unit's AI can also analyze the competitive landscape of the industry and generate a roadmap that includes skills to enhance competitiveness. This allows the company to provide learning plans that include the latest skills by taking industry trends into account.

[0041] The generation unit provides a customized learning plan based on individual career goals when generating a roadmap. For example, the generation unit's AI analyzes an individual's career goals and provides a learning plan aligned with those goals. The generation unit's AI can also consider an individual's career goals and provide a learning plan for acquiring the necessary skills. The generation unit's AI can also provide a learning plan that supports a long-term career path based on an individual's career goals. This enables effective career development by providing a learning plan based on individual career goals.

[0042] The learning unit assesses the user's understanding in real time during the learning process and provides supplementary materials as needed. For example, the learning unit uses AI to assess the user's understanding in real time and provide supplementary materials if understanding is insufficient. The learning unit can also use AI to monitor the user's learning progress and provide additional learning content according to their level of understanding. Furthermore, the learning unit can use AI to assess the user's understanding and provide personalized instruction as needed. This real-time assessment of user understanding and provision of supplementary materials enables effective learning.

[0043] The learning unit analyzes the user's learning history during the learning process and proposes an optimal learning pace. For example, the learning unit uses AI to analyze the user's learning history and propose an optimal learning pace. The learning unit can also use AI to propose an efficient learning pace based on the user's past learning data. The learning unit can also use AI to consider the user's learning history and propose an individually customized learning pace. This enables effective learning by analyzing the user's learning history and proposing an optimal learning pace.

[0044] The learning unit collects user feedback during the learning process and continuously improves the learning content. For example, the learning unit uses AI to collect user feedback and identify areas for improvement in the learning content. The learning unit can also use AI to update the learning content based on user feedback. The learning unit can also use AI to analyze user feedback and improve the quality of the learning content. This allows for effective learning by collecting user feedback and continuously improving the learning content.

[0045] The learning unit recommends relevant additional learning content based on the user's interests during the learning process. For example, the learning unit uses AI to analyze the user's interests and recommend relevant additional learning content. The learning unit can also use AI to provide additional learning content tailored to the user's interests based on their learning history. The learning unit can also use AI to recommend additional learning content based on user feedback. This enables effective learning by recommending relevant additional learning content based on the user's interests.

[0046] The evaluation unit ensures consistency in evaluations by referring to past evaluation data when assessing learning progress. For example, the evaluation unit uses AI to refer to past evaluation data to ensure consistency. The evaluation unit can also use AI to adjust evaluation criteria based on past evaluation data. Furthermore, the evaluation unit can use AI to analyze past evaluation data to ensure fairness in evaluations. This ensures consistency in evaluations by referring to past evaluation data.

[0047] The evaluation unit applies customized evaluation criteria based on individual growth goals when assessing learning progress. For example, the evaluation unit uses AI to analyze individual growth goals and apply evaluation criteria based on those goals. The evaluation unit can also use AI to customize evaluation criteria based on individual growth goals. The evaluation unit can also use AI to adjust evaluation criteria based on individual growth goals. This allows for appropriate evaluation by applying evaluation criteria based on individual growth goals.

[0048] The evaluation department considers the overall team performance when assessing learning progress. For example, the evaluation department may use AI to analyze the overall team performance and reflect it in individual evaluations. The evaluation department can also use AI to consider the team's overall goal achievement and adjust individual evaluations accordingly. The evaluation department can also use AI to perform individual evaluations based on the overall team performance data. This makes individual evaluations fairer by considering the overall team performance.

[0049] The evaluation unit provides evaluation results when assessing learning progress, comparing them to industry standards. For example, the evaluation unit uses AI to reference industry standard data and provide individual evaluation results. The evaluation unit can also adjust individual evaluation results by having the AI ​​compare them to industry standards. Furthermore, the evaluation unit can have the AI ​​analyze individual evaluation results based on industry standard data. This improves the reliability of the evaluation results by comparing them to industry standards.

[0050] The proposal department analyzes past proposal history to provide the optimal learning plan. For example, the proposal department uses AI to analyze past proposal history and make new proposals based on successful learning plans. The proposal department can also use AI to refer to past proposal history and provide a learning plan best suited to the individual. The proposal department can also use AI to analyze past proposal history and make proposals that reflect improvements from unsuccessful learning plans. In this way, by analyzing past proposal history, it is possible to provide an effective learning plan.

[0051] The proposal department, when making a proposal, suggests a customized learning plan based on the individual's career goals. For example, the proposal department can use AI to analyze an individual's career goals and propose a learning plan aligned with those goals. The proposal department can also use AI to consider an individual's career goals and propose a learning plan to acquire the necessary skills. The proposal department can also use AI to suggest a learning plan that supports a long-term career path based on an individual's career goals. This enables effective career development by proposing a learning plan based on an individual's career goals.

[0052] The proposal department incorporates the latest skills, taking into account the company's industry trends, when making proposals. For example, the proposal department can use AI to analyze the latest industry trends and make proposals that include the necessary skills. The proposal department can also use AI to consider technological innovations in the industry and make proposals that include new technical skills. The proposal department can also use AI to analyze the competitive landscape of the industry and make proposals that include skills to enhance competitiveness. This allows the department to provide learning plans that include the latest skills, taking into account the company's industry trends.

[0053] The proposal department proposes optimal personnel allocation based on the individual skill level achieved during the proposal process. For example, the proposal department can use AI to analyze individual skill levels and propose optimal personnel allocation. The proposal department can also use AI to consider individual skill levels and propose the most suitable personnel for a project. The proposal department can also use AI to propose personnel allocation that considers team balance based on individual skill levels. This enables effective personnel allocation by proposing optimal personnel allocation based on individual skill levels.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The generation unit can analyze a company's performance data and automatically propose optimal training strategies. For example, the generation AI can analyze a company's past performance data, extract commonalities from successful projects, and propose new training strategies. The generation AI can also propose optimal learning steps for specific skill sets based on the company's performance data. The generation AI can also analyze a company's performance data and suggest improvements for failed projects. In this way, by analyzing a company's performance data, effective training strategies can be proposed.

[0056] The generation unit can also provide different learning steps depending on the individual's learning style when generating a roadmap. For example, the generation AI can evaluate the individual's learning style and provide visual learning steps. The generation AI can also evaluate the individual's learning style and provide auditory learning steps. The generation AI can also evaluate the individual's learning style and provide experiential learning steps. This enables effective learning by providing learning steps tailored to the individual's learning style.

[0057] The generation unit can also provide learning steps that take into account the company's future goals when generating a roadmap. For example, the generation AI can analyze the company's future goals and provide learning steps aligned with those goals. The generation AI can also consider the company's future goals and provide learning steps to acquire the necessary skills. Based on the company's future goals, the generation AI can provide learning steps that support long-term career paths. This enables effective talent development by providing learning steps based on the company's future goals.

[0058] The generation unit can also provide learning steps that take into account an individual's learning history when generating a roadmap. For example, the generating AI can analyze an individual's learning history and provide learning steps based on past learning content. The generating AI can also consider an individual's learning history and provide learning steps that reflect past learning achievements. The generating AI can also provide efficient learning steps based on an individual's learning history. As a result, providing learning steps based on an individual's learning history enables effective learning.

[0059] The generation unit can also provide learning steps that take into account a company's culture and values ​​when generating a roadmap. For example, the generation AI can analyze a company's culture and values ​​and provide learning steps that align with those culture and values. The generation AI can also consider a company's culture and values ​​and provide learning steps to acquire necessary skills. Based on a company's culture and values, the generation AI can provide learning steps that support long-term career paths. This enables effective talent development by providing learning steps based on a company's culture and values.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The generation unit automatically generates roadmaps and materials using internal company content. The generation unit takes input on the company's training policies and individual training policies, and generates an optimal roadmap based on that. Using generation AI, the generation unit creates a roadmap that includes specific learning steps and goals based on the company's training policies. For example, if a company aims to develop DX (Digital Transformation) talent, it will generate a roadmap based on that policy. It can also provide individualized learning plans based on individual training policies. Step 2: The learning department proceeds with learning based on the generated roadmap. The learning department automatically recommends learning content to acquire specific skills. The learning department generates learning content using existing company documents, videos, audio, etc. The learning department can also use generating AI to manage individual learning progress and provide the optimal learning plan. Step 3: The evaluation unit assesses learning progress. The evaluation unit records and evaluates learning progress. Based on learning progress, the evaluation unit manages individual growth goals. The evaluation unit can also use generative AI to evaluate learning progress and suggest the optimal learning plan. Step 4: The suggestion department proposes the optimal learning plan based on the assessed progress. The suggestion department proposes a learning plan tailored to the individual's skill level based on the assessed progress. The suggestion department can also use generative AI to provide a learning plan based on the individual's growth goals.

[0062] (Example of form 2) The reskilling system according to an embodiment of the present invention is a system that utilizes generative AI to develop the reskilling market in Japan. This reskilling system automatically generates roadmaps and materials using internal company content with generative AI, and each individual works on a roadmap linked to organizational goals. For example, the reskilling system takes input such as the company's training policy and the individual's training policy, and the generative AI creates an optimal roadmap based on that. If a company aims to develop DX personnel, a roadmap is generated that includes specific learning steps and goals based on that policy. Next, the reskilling system allows individuals to proceed with their learning according to the generated roadmap. For example, learning content for acquiring specific skills is automatically recommended. This content is generated using existing company documents, videos, audio, etc. Furthermore, the reskilling system also creates supervisor confirmation reports and skill belts. It is linked with the HR system to manage employee training status and individual growth goals. For example, employees who acquire specific skills are awarded skill belts, and the AI ​​proposes optimal personnel placement based on their achievements. This system can spread the significance of human resource development in the reskilling market throughout Japan. By semi-automating their proprietary training systems, companies can efficiently develop talent and promote DX (Digital Transformation) across their entire organization. Furthermore, in Japan, where reskilling is often associated with the concept of changing jobs, creating a positive image for reskilling will encourage multiple companies to adopt it more readily. This enables the reskilling system to semi-automatically create company-specific training systems and integrate them into HR and organizational evaluations.

[0063] The reskilling system according to this embodiment comprises a generation unit, a learning unit, an evaluation unit, and a proposal unit. The generation unit automatically generates roadmaps and materials utilizing internal company content. For example, the generation unit takes input such as the company's training policy or an individual's training policy and generates an optimal roadmap based on it. The generation unit uses a generation AI to create a roadmap that includes specific learning steps and goals based on the company's training policy. For example, if a company aims to develop DX personnel, the generation unit generates a roadmap based on that policy. The generation unit can also provide an individualized learning plan based on an individual's training policy. The learning unit proceeds with learning based on the generated roadmap. For example, the learning unit automatically recommends learning content for acquiring specific skills. The learning unit generates learning content utilizing existing company materials, videos, audio, etc. The learning unit can also use a generation AI to manage an individual's learning progress and provide an optimal learning plan. The evaluation unit evaluates the learning progress. For example, the evaluation unit records and evaluates the learning progress. The evaluation unit manages an individual's growth goals based on the learning progress. The evaluation unit can use generative AI to assess learning progress and propose an optimal learning plan. The proposal unit proposes an optimal learning plan based on the assessed progress. For example, the proposal unit proposes a learning plan tailored to the individual's skill level based on the assessed progress. The proposal unit can also use generative AI to provide a learning plan based on the individual's growth goals. As a result, the reskilling system according to this embodiment improves the efficiency of reskilling by automatically generating roadmaps and materials utilizing internal content, evaluating learning progress, and proposing an optimal learning plan.

[0064] The generation unit automatically generates roadmaps and materials utilizing internal company content. Specifically, it takes the company's training policies and individual training policies as input and generates an optimal roadmap based on that information. The generation unit uses a generation AI to create roadmaps that include specific learning steps and goals based on the company's training policies. For example, if a company aims to develop DX (Digital Transformation) talent, it will generate a roadmap based on that policy. This generation AI utilizes natural language processing technology to understand the company's training policies and individual goals and proposes the most suitable learning steps. Specifically, if "promoting digital transformation (DX)" is input as the company's training policy, the generation AI will generate a roadmap based on that policy that includes learning steps such as data analysis, cloud computing, and AI technology. It can also provide personalized learning plans based on individual training policies. For example, if an individual has the goal of "becoming a data scientist," the generation AI will generate a personalized roadmap based on that goal that includes learning steps such as statistics, programming, and machine learning. Furthermore, to maximize the use of existing internal content, the generation unit can analyze internal documents and past training data and combine them in the most optimal way to generate new learning materials. This allows the generation unit to automatically generate learning roadmaps optimized for the training policies of companies and individuals, significantly improving the efficiency of reskilling.

[0065] The learning department guides learners through a generated roadmap. Specifically, it automatically recommends learning content to acquire specific skills. The learning department generates learning content by utilizing existing company documents, videos, and audio. For example, it analyzes internal technical documents, past training videos, and expert lecture recordings, and combines them in the most optimal way to create new learning content. The learning department can also use generative AI to manage individual learning progress and provide optimal learning plans. Specifically, it analyzes an individual's learning history and current skill level and recommends what they should learn next. For example, if an individual has finished learning the basics of programming, it will recommend learning data structures and algorithms next. Furthermore, the learning department can collect learner feedback and continuously improve the quality of learning content based on it. For example, if a learner finds a particular piece of content "difficult," the content is restructured based on that feedback and improved to be easier to understand. In addition, the learning department provides features to facilitate communication among learners. For example, it creates an environment where learners can exchange questions and opinions through online forums and chat functions. This allows the learning unit to provide support for efficient and effective learning based on the generated roadmap, thereby assisting learners in improving their skills.

[0066] The evaluation department assesses learning progress. Specifically, it records and evaluates learning progress. Based on learning progress, the evaluation department manages individual growth goals. The evaluation department can also use generative AI to evaluate learning progress and propose optimal learning plans. For example, it can evaluate whether learners have acquired specific skills through tests and quizzes, and determine the next learning step based on the results. The evaluation department collects and analyzes learner performance data to quantitatively evaluate the effectiveness of learning. Specifically, it measures how much time learners spend studying specific content, the accuracy rate of tests, and their level of understanding, and evaluates learning progress based on this data. The evaluation department also collects learners' self-assessments and feedback and reflects them comprehensively in the evaluation. For example, if a learner feels that their understanding is still insufficient in their self-assessment, additional learning content will be provided based on that feedback. Furthermore, the evaluation department regularly reviews the learner's progress toward their growth goals and resets goals as needed. For example, if a learner achieves their goal earlier than planned, a new goal will be set and a plan for the next step will be proposed. This allows the evaluation unit to accurately assess learning progress and effectively support individual growth.

[0067] The suggestion department proposes the optimal learning plan based on the assessed progress. Specifically, it proposes a learning plan tailored to the individual's skill level based on the assessed progress. The suggestion department can also use generative AI to provide learning plans based on the individual's growth goals. For example, if the assessment department evaluates the learner's progress and determines that a particular skill has not yet been sufficiently acquired, the suggestion department will propose additional learning content to strengthen that skill. The suggestion department analyzes the learner's past learning history and current skill level and proposes specific content to learn next based on that. For example, after a learner has acquired the basics of programming, it will propose a learning plan to deepen their knowledge of databases. The suggestion department can also customize the optimal learning plan considering the learner's interests and career goals. For example, if a learner is interested in data science, it will prioritize suggesting learning content related to that field. Furthermore, the suggestion department continuously improves the learning plan based on the learner's feedback. For example, if a learner finds a particular piece of content "difficult," the suggestion department will restructure the content based on that feedback and improve it to make it easier to understand. In this way, the suggestion department can provide the optimal learning plan based on assessed progress and effectively support the learner's skill improvement.

[0068] The generation unit takes corporate and individual training policies as input and generates an optimal roadmap based on that input. For example, the generation unit takes corporate training policies as input and generates an optimal roadmap based on that input. The generation unit uses generation AI to create a roadmap that includes specific learning steps and goals based on corporate training policies. For example, if a company aims to develop DX (Digital Transformation) personnel, the generation unit will generate a roadmap based on that policy. The generation unit can also provide individualized learning plans based on individual training policies. In this way, by generating an optimal roadmap based on corporate and individual training policies, it is possible to provide individualized learning plans.

[0069] The learning unit proceeds with learning according to the generated roadmap. For example, the learning unit can manage individual learning progress and provide an optimal learning plan using generating AI. This enables systematic learning by following the generated roadmap.

[0070] The evaluation unit records and evaluates learning progress. For example, the evaluation unit records and evaluates learning progress. Based on learning progress, the evaluation unit manages individual growth goals. The evaluation unit can also use generative AI to evaluate learning progress and propose optimal learning plans. This allows for understanding the effectiveness of learning by recording and evaluating learning progress.

[0071] The suggestion department proposes an optimal learning plan based on the assessed progress. For example, it proposes a learning plan tailored to the individual's skill level based on the assessed progress. The suggestion department can also use generative AI to provide a learning plan based on the individual's growth goals. This enables efficient learning by proposing an optimal learning plan based on assessed progress.

[0072] The generation unit estimates the user's emotions and adjusts the roadmap content based on the estimated emotions. For example, if the user is stressed, the generation unit generates a roadmap with simple, short-term learning steps. If the user is relaxed, the generation unit can also generate a roadmap with detailed, long-term learning steps. If the user is excited, the generation unit can also generate a roadmap with challenging tasks. This allows for the provision of a learning plan tailored to the user by adjusting the roadmap content based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The generation unit analyzes a company's past training data and automatically proposes the optimal training policy. For example, the generation unit's AI analyzes past training data, extracts commonalities from successful training programs, and proposes a new training policy. The generation unit's AI can also propose the optimal learning steps for a specific skill set based on past training data. The generation unit's AI can also analyze past training data and propose improvements to failed training programs. In this way, by analyzing a company's past training data, it is possible to propose an effective training policy.

[0074] The generation unit provides different learning steps depending on the individual's skill level when generating a roadmap. For example, the generation unit's generating AI can assess the individual's skill level and provide basic learning steps for beginners. The generation unit can also assess the individual's skill level and provide advanced learning steps for intermediate learners. The generation unit can also assess the individual's skill level and provide advanced learning steps for advanced learners. This enables effective learning by providing learning steps tailored to the individual's skill level.

[0075] The generation unit estimates the user's emotions and determines the priority of the roadmap based on the estimated emotions. For example, if the user is stressed, the generation unit generates a roadmap that prioritizes easy tasks. If the user is relaxed, the generation unit can also generate a roadmap that prioritizes important tasks. If the user is excited, the generation unit can also generate a roadmap that prioritizes challenging tasks. This allows for the provision of a learning plan tailored to the user by prioritizing the roadmap based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The generation unit incorporates the latest skills, taking into account the company's industry trends, when generating roadmaps. For example, the generation unit's AI can analyze the latest industry trends and generate a roadmap that includes the necessary skills. The generation unit's AI can also consider technological innovations in the industry and generate a roadmap that includes new technological skills. The generation unit's AI can also analyze the competitive landscape of the industry and generate a roadmap that includes skills to enhance competitiveness. This allows the company to provide learning plans that include the latest skills by taking industry trends into account.

[0077] The generation unit provides a customized learning plan based on individual career goals when generating a roadmap. For example, the generation unit's AI analyzes an individual's career goals and provides a learning plan aligned with those goals. The generation unit's AI can also consider an individual's career goals and provide a learning plan for acquiring the necessary skills. The generation unit's AI can also provide a learning plan that supports a long-term career path based on an individual's career goals. This enables effective career development by providing a learning plan based on individual career goals.

[0078] The learning unit estimates the user's emotions and adjusts the difficulty level of the learning content based on the estimated emotions. For example, if the user is stressed, the AI ​​will provide easy learning content. If the user is relaxed, the AI ​​can also provide learning content of moderate difficulty. If the user is excited, the AI ​​can also provide learning content of high difficulty. This allows for effective learning by adjusting the difficulty level of the learning content based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The learning unit assesses the user's understanding in real time during the learning process and provides supplementary materials as needed. For example, the learning unit uses AI to assess the user's understanding in real time and provide supplementary materials if understanding is insufficient. The learning unit can also use AI to monitor the user's learning progress and provide additional learning content according to their level of understanding. Furthermore, the learning unit can use AI to assess the user's understanding and provide personalized instruction as needed. This real-time assessment of user understanding and provision of supplementary materials enables effective learning.

[0080] The learning unit analyzes the user's learning history during the learning process and proposes an optimal learning pace. For example, the learning unit uses AI to analyze the user's learning history and propose an optimal learning pace. The learning unit can also use AI to propose an efficient learning pace based on the user's past learning data. The learning unit can also use AI to consider the user's learning history and propose an individually customized learning pace. This enables effective learning by analyzing the user's learning history and proposing an optimal learning pace.

[0081] The learning unit estimates the user's emotions and adjusts the learning pace based on the estimated emotions. For example, if the user is stressed, the AI ​​will slow down the learning pace. If the user is relaxed, the AI ​​can maintain a normal learning pace. If the user is excited, the AI ​​can speed up the learning pace. This allows for effective learning by adjusting the learning pace based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The learning unit collects user feedback during the learning process and continuously improves the learning content. For example, the learning unit uses AI to collect user feedback and identify areas for improvement in the learning content. The learning unit can also use AI to update the learning content based on user feedback. The learning unit can also use AI to analyze user feedback and improve the quality of the learning content. This allows for effective learning by collecting user feedback and continuously improving the learning content.

[0083] The learning unit recommends relevant additional learning content based on the user's interests during the learning process. For example, the learning unit uses AI to analyze the user's interests and recommend relevant additional learning content. The learning unit can also use AI to provide additional learning content tailored to the user's interests based on their learning history. The learning unit can also use AI to recommend additional learning content based on user feedback. This enables effective learning by recommending relevant additional learning content based on the user's interests.

[0084] The evaluation unit estimates the user's emotions and adjusts the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the AI ​​will relax the evaluation criteria. If the user is relaxed, the AI ​​can apply normal evaluation criteria. If the user is excited, the AI ​​can apply strict evaluation criteria. This allows for appropriate evaluation by adjusting the evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The evaluation unit ensures consistency in evaluations by referring to past evaluation data when assessing learning progress. For example, the evaluation unit uses AI to refer to past evaluation data to ensure consistency. The evaluation unit can also use AI to adjust evaluation criteria based on past evaluation data. Furthermore, the evaluation unit can use AI to analyze past evaluation data to ensure fairness in evaluations. This ensures consistency in evaluations by referring to past evaluation data.

[0086] The evaluation unit applies customized evaluation criteria based on individual growth goals when assessing learning progress. For example, the evaluation unit uses AI to analyze individual growth goals and apply evaluation criteria based on those goals. The evaluation unit can also use AI to customize evaluation criteria based on individual growth goals. The evaluation unit can also use AI to adjust evaluation criteria based on individual growth goals. This allows for appropriate evaluation by applying evaluation criteria based on individual growth goals.

[0087] The evaluation unit estimates the user's emotions and adjusts the feedback method of the evaluation results based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can use the AI ​​to provide feedback in gentle language. If the user is relaxed, the evaluation unit can also use the AI ​​to provide detailed feedback. If the user is agitated, the evaluation unit can also use the AI ​​to provide quick and concise feedback. This allows for appropriate feedback by adjusting the feedback method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The evaluation department considers the overall team performance when assessing learning progress. For example, the evaluation department may use AI to analyze the overall team performance and reflect it in individual evaluations. The evaluation department can also use AI to consider the team's overall goal achievement and adjust individual evaluations accordingly. The evaluation department can also use AI to perform individual evaluations based on the overall team performance data. This makes individual evaluations fairer by considering the overall team performance.

[0089] The evaluation unit provides evaluation results when assessing learning progress, comparing them to industry standards. For example, the evaluation unit uses AI to reference industry standard data and provide individual evaluation results. The evaluation unit can also adjust individual evaluation results by having the AI ​​compare them to industry standards. Furthermore, the evaluation unit can have the AI ​​analyze individual evaluation results based on industry standard data. This improves the reliability of the evaluation results by comparing them to industry standards.

[0090] The suggestion unit estimates the user's emotions and adjusts the suggestions based on those emotions. For example, if the user is stressed, the AI ​​might suggest a simple, short-term learning plan. If the user is relaxed, the AI ​​might suggest a detailed, long-term learning plan. If the user is excited, the AI ​​might suggest a challenging learning plan. This allows for the provision of an appropriate learning plan by adjusting suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The proposal department analyzes past proposal history to provide the optimal learning plan. For example, the proposal department uses AI to analyze past proposal history and make new proposals based on successful learning plans. The proposal department can also use AI to refer to past proposal history and provide a learning plan best suited to the individual. The proposal department can also use AI to analyze past proposal history and make proposals that reflect improvements from unsuccessful learning plans. In this way, by analyzing past proposal history, it is possible to provide an effective learning plan.

[0092] The proposal department, when making a proposal, suggests a customized learning plan based on the individual's career goals. For example, the proposal department can use AI to analyze an individual's career goals and propose a learning plan aligned with those goals. The proposal department can also use AI to consider an individual's career goals and propose a learning plan to acquire the necessary skills. The proposal department can also use AI to suggest a learning plan that supports a long-term career path based on an individual's career goals. This enables effective career development by proposing a learning plan based on an individual's career goals.

[0093] The suggestion unit estimates the user's emotions and prioritizes suggestions based on those emotions. For example, if the user is stressed, the suggestion unit might suggest prioritizing easy tasks. If the user is relaxed, the suggestion unit might suggest prioritizing important tasks. If the user is excited, the suggestion unit might suggest prioritizing challenging tasks. By prioritizing suggestions based on the user's emotions, an appropriate learning plan can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The proposal department incorporates the latest skills, taking into account the company's industry trends, when making proposals. For example, the proposal department can use AI to analyze the latest industry trends and make proposals that include the necessary skills. The proposal department can also use AI to consider technological innovations in the industry and make proposals that include new technical skills. The proposal department can also use AI to analyze the competitive landscape of the industry and make proposals that include skills to enhance competitiveness. This allows the department to provide learning plans that include the latest skills, taking into account the company's industry trends.

[0095] The proposal department proposes optimal personnel allocation based on the individual skill level achieved during the proposal process. For example, the proposal department can use AI to analyze individual skill levels and propose optimal personnel allocation. The proposal department can also use AI to consider individual skill levels and propose the most suitable personnel for a project. The proposal department can also use AI to propose personnel allocation that considers team balance based on individual skill levels. This enables effective personnel allocation by proposing optimal personnel allocation based on individual skill levels.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The generation unit can also estimate the user's emotions and adjust the format of the learning content based on those emotions. For example, if the user is stressed, the generation AI will generate a learning plan with a lot of visual content. If the user is relaxed, the generation AI can also generate a detailed, text-based learning plan. If the user is excited, the generation AI can also generate a learning plan with a lot of interactive content. This allows for the provision of a learning plan tailored to the user by adjusting the format of the learning content based on their emotions.

[0098] The generation unit can also estimate the user's emotions and adjust the learning timing based on those emotions. For example, if the user is stressed, the generation AI suggests a short learning session. If the user is relaxed, the generation AI may suggest a longer learning session. If the user is excited, the generation AI may suggest a learning session that requires concentration. By adjusting the learning timing based on the user's emotions, effective learning becomes possible.

[0099] The generation unit can also estimate the user's emotions and adjust the learning process based on those emotions. For example, if the user is stressed, the generation AI will generate a learning plan that progresses gradually. If the user is relaxed, the generation AI can generate a learning plan that progresses quickly. If the user is excited, the generation AI can generate a learning plan that includes challenging progression. In this way, by adjusting the learning process based on the user's emotions, a learning plan that is suitable for the user can be provided.

[0100] The generation unit can also estimate the user's emotions and adjust the learning feedback method based on those emotions. For example, if the user is stressed, the generation AI will generate a learning plan that includes a lot of positive feedback. If the user is relaxed, the generation AI can also generate a learning plan that provides detailed feedback. If the user is excited, the generation AI can also generate a learning plan that provides quick and concise feedback. This allows for more effective learning by adjusting the learning feedback method based on the user's emotions.

[0101] The generation unit can estimate the user's emotions and incorporate elements to maintain learning motivation based on those emotions. For example, if the user is stressed, the generation AI can generate a learning plan that includes relaxation techniques. If the user is relaxed, the generation AI can also generate a learning plan that includes goal setting to boost motivation. If the user is excited, the generation AI can also generate a learning plan that includes challenging tasks. This allows for more effective learning by incorporating elements to maintain learning motivation based on the user's emotions.

[0102] The generation unit can analyze a company's performance data and automatically propose optimal training strategies. For example, the generation AI can analyze a company's past performance data, extract commonalities from successful projects, and propose new training strategies. The generation AI can also propose optimal learning steps for specific skill sets based on the company's performance data. The generation AI can also analyze a company's performance data and suggest improvements for failed projects. In this way, by analyzing a company's performance data, effective training strategies can be proposed.

[0103] The generation unit can also provide different learning steps depending on the individual's learning style when generating a roadmap. For example, the generation AI can evaluate the individual's learning style and provide visual learning steps. The generation AI can also evaluate the individual's learning style and provide auditory learning steps. The generation AI can also evaluate the individual's learning style and provide experiential learning steps. This enables effective learning by providing learning steps tailored to the individual's learning style.

[0104] The generation unit can also provide learning steps that take into account the company's future goals when generating a roadmap. For example, the generation AI can analyze the company's future goals and provide learning steps aligned with those goals. The generation AI can also consider the company's future goals and provide learning steps to acquire the necessary skills. Based on the company's future goals, the generation AI can provide learning steps that support long-term career paths. This enables effective talent development by providing learning steps based on the company's future goals.

[0105] The generation unit can also provide learning steps that take into account an individual's learning history when generating a roadmap. For example, the generating AI can analyze an individual's learning history and provide learning steps based on past learning content. The generating AI can also consider an individual's learning history and provide learning steps that reflect past learning achievements. The generating AI can also provide efficient learning steps based on an individual's learning history. As a result, providing learning steps based on an individual's learning history enables effective learning.

[0106] The generation unit can also provide learning steps that take into account a company's culture and values ​​when generating a roadmap. For example, the generation AI can analyze a company's culture and values ​​and provide learning steps that align with those culture and values. The generation AI can also consider a company's culture and values ​​and provide learning steps to acquire necessary skills. Based on a company's culture and values, the generation AI can provide learning steps that support long-term career paths. This enables effective talent development by providing learning steps based on a company's culture and values.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The generation unit automatically generates roadmaps and materials using internal company content. The generation unit takes input on the company's training policies and individual training policies, and generates an optimal roadmap based on that. Using generation AI, the generation unit creates a roadmap that includes specific learning steps and goals based on the company's training policies. For example, if a company aims to develop DX (Digital Transformation) talent, it will generate a roadmap based on that policy. It can also provide individualized learning plans based on individual training policies. Step 2: The learning department proceeds with learning based on the generated roadmap. The learning department automatically recommends learning content to acquire specific skills. The learning department generates learning content using existing company documents, videos, audio, etc. The learning department can also use generating AI to manage individual learning progress and provide the optimal learning plan. Step 3: The evaluation unit assesses learning progress. The evaluation unit records and evaluates learning progress. Based on learning progress, the evaluation unit manages individual growth goals. The evaluation unit can also use generative AI to evaluate learning progress and suggest the optimal learning plan. Step 4: The suggestion department proposes the optimal learning plan based on the assessed progress. The suggestion department proposes a learning plan tailored to the individual's skill level based on the assessed progress. The suggestion department can also use generative AI to provide a learning plan based on the individual's growth goals.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the generation unit, learning unit, evaluation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The learning unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements, including the generation unit, learning unit, evaluation unit, and proposal unit described above, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The learning unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The proposal unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements, including the generation unit, learning unit, evaluation unit, and proposal unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements, including the generation unit, learning unit, evaluation unit, and proposal unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A generation unit that automatically generates roadmaps and documents using internal company content, A learning unit that proceeds with learning based on the roadmap generated by the generation unit, An evaluation unit that evaluates the progress of learning carried out by the aforementioned learning unit, A proposal unit that proposes an optimal learning plan based on the progress evaluated by the evaluation unit, Equipped with A system characterized by the following features. (Note 2) The generating unit is Input your company's training policies and individual training policies, and generate an optimal roadmap based on them. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, Follow the generated roadmap to continue learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit described above, Record and evaluate learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose the optimal learning plan based on your assessed progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We estimate user sentiment and adjust the roadmap based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It analyzes a company's past training data and automatically proposes the optimal training policy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When generating a roadmap, provide different learning steps according to the individual's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is We estimate user sentiment and determine roadmap priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating a roadmap, include the latest skills, taking into account the company's industry trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a roadmap, we provide a customized learning plan based on individual career goals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, It estimates the user's emotions and adjusts the difficulty level of the learning content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During the learning process, the system assesses the user's understanding in real time and provides supplementary materials as needed. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During the learning process, the system analyzes the user's learning history and suggests the optimal learning pace. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning rate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During the learning process, we collect user feedback and continuously improve the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During the learning process, the system recommends relevant additional learning content based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit described above, When evaluating learning progress, refer to past evaluation data to ensure consistency in assessments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit described above, When evaluating learning progress, apply customized evaluation criteria based on individual growth goals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit described above, It estimates the user's emotions and adjusts the feedback method for evaluation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit described above, When evaluating learning progress, the overall team performance should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit described above, When evaluating learning progress, provide evaluation results in comparison to industry standards. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we analyze past proposal history and provide the optimal learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we will suggest a customized learning plan based on the individual's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, include the latest skills, taking into account the company's industry trends. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we will suggest the optimal staffing arrangement based on the individual's skill level. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. A generation unit that automatically generates roadmaps and documents using internal company content, A learning unit that proceeds with learning based on the roadmap generated by the generation unit, An evaluation unit that evaluates the progress of learning carried out by the aforementioned learning unit, A proposal unit that proposes an optimal learning plan based on the progress evaluated by the evaluation unit, Equipped with A system characterized by the following features.

2. The generating unit is Input your company's training policies and individual training policies, and generate an optimal roadmap based on them. The system according to feature 1.

3. The aforementioned learning unit, Follow the generated roadmap to continue learning. The system according to feature 1.

4. The evaluation unit described above, Record and evaluate learning progress. The system according to feature 1.

5. The aforementioned proposal section is, We propose the optimal learning plan based on your assessed progress. The system according to feature 1.

6. The generating unit is We estimate user sentiment and adjust the roadmap based on that estimated sentiment. The system according to feature 1.

7. The generating unit is It analyzes a company's past training data and automatically proposes the optimal training policy. The system according to feature 1.

8. The generating unit is When generating a roadmap, provide different learning steps according to the individual's skill level. The system according to feature 1.

9. The generating unit is We estimate user sentiment and determine roadmap priorities based on the estimated user sentiment. The system according to feature 1.

10. The generating unit is When generating a roadmap, include the latest skills, taking into account the company's industry trends. The system according to feature 1.

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