System

Abstract

The system according to the embodiment aims to learn basic knowledge and techniques for utilizing AI and apply them to practice.SOLUTION: The system according to the embodiment includes a curriculum providing unit, an exercise / project providing unit, and a new business discovery unit. The curriculum providing unit provides a curriculum for a person or an organization to learn basic knowledge and technology for utilizing AI. The exercise / project providing unit provides an exercise or a project for applying the knowledge provided by the curriculum providing unit to practice. The new business discovery unit discovers an idea of a new business based on the result obtained by the exercise / project providing unit, and evaluates the feasibility.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks a systematic curriculum for learning the basic knowledge and skills needed to utilize AI and apply them in practice, leaving room for improvement in improving the productivity of individuals and organizations and promoting new businesses.

[0005] The system according to the embodiment aims to teach basic knowledge and techniques for utilizing AI and to apply them in practice. [Means for solving the problem]

[0006] The system according to the embodiment includes a curriculum provider, an exercise and project provider, and a new business discovery unit. The curriculum provider provides a curriculum for individuals or organizations to learn basic knowledge and skills for utilizing AI. The exercise and project provider provides exercises or projects for applying the knowledge provided by the curriculum provider to practice. The new business discovery unit discovers new business ideas based on the results obtained by the exercise and project provider and evaluates their feasibility. [Effects of the Invention]

[0007] The system according to the embodiment allows users to learn basic knowledge and techniques for utilizing AI and apply them in practice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A training platform according to an embodiment of the present invention is a system designed to develop businesspeople who can properly utilize AI and to discover new talent and businesses. This system provides a curriculum for individuals and organizations to learn basic knowledge and skills for utilizing AI and provides opportunities to apply the knowledge learned through exercises and projects based on actual business scenarios. It also provides a platform for discovering new business ideas using AI and evaluating their feasibility. For example, the training platform provides opportunities to learn the basics of AI through online courses and workshops. Next, participants can work on projects to solve problems related to their work using AI. Furthermore, participants can share their proposed ideas for new services or products using AI with other participants and experts and receive feedback. This allows the training platform to help individuals and organizations improve their productivity and promote new businesses. This allows the training platform to develop businesspeople who can properly utilize AI and to discover new talent and businesses. For example, participants can learn basic AI knowledge and skills and apply them in practice through exercises and projects based on actual business scenarios. Furthermore, by discovering new business ideas using AI and evaluating their feasibility, new business opportunities can be discovered.

[0029] A training platform according to an embodiment includes a curriculum provider, an exercise and project provider, and a new business discovery unit. The curriculum provider provides a curriculum for individuals or organizations to learn basic knowledge and skills for utilizing AI. The curriculum includes, for example, basic AI concepts, algorithms, and data processing techniques. The curriculum provider provides participants with opportunities to learn basic AI knowledge and skills through online courses and workshops. For example, the curriculum provider may provide live sessions, recorded lectures, and interactive exercises. The exercise and project provider provides exercises or projects for applying the knowledge provided by the curriculum provider to practice. Exercises and projects include, for example, case studies, practical projects, and simulations based on actual business scenarios. The exercise and project provider provides participants with opportunities to work on projects that use AI to solve problems related to their work. For example, participants can work on projects aimed at improving specific business processes or increasing business efficiency. The new business discovery unit discovers new business ideas and evaluates their feasibility based on the results obtained by the exercise and project provider. The new business discovery unit evaluates the proposed ideas and provides feedback. For example, the New Business Discovery Department evaluates ideas based on evaluation criteria such as technical feasibility, marketability, innovativeness, etc. As a result, the training platform according to the embodiment enables individuals and organizations to learn basic knowledge and skills for properly utilizing AI, apply them in practice, and discover and evaluate new business ideas.

[0030] The curriculum provider provides online courses or workshops. Online courses or workshops include, for example, live sessions, recorded lectures, and interactive exercises. The curriculum provider provides online courses and workshops for participants to learn fundamental knowledge and skills of AI. For example, the curriculum provider may deliver lectures in real time through live sessions, providing participants with opportunities to ask questions and hold discussions. Alternatively, the curriculum provider may provide recorded lectures so that participants can learn at their own pace. Furthermore, the curriculum provider may provide participants with opportunities to learn hands-on through interactive exercises. This allows participants to learn fundamental knowledge and skills of AI through online courses and workshops.

[0031] The Exercise and Project Department offers exercises or projects based on real business scenarios. Examples of real business scenarios include case studies and practical projects in specific industries. The Exercise and Project Department offers exercises and projects that allow students to apply their knowledge in practice. For example, the Exercise and Project Department provides students with the opportunity to tackle problems based on real business scenarios through case studies in specific industries. Furthermore, through practical projects, students can work on projects that use AI to solve problems related to their own work. Furthermore, through simulations, students can acquire skills to utilize AI in virtual business environments. This allows students to apply their knowledge in practice through exercises and projects based on real business scenarios.

[0032] The New Business Identification Department evaluates proposed ideas and provides feedback. The proposed ideas include evaluation criteria such as technical feasibility, marketability, and innovativeness. The New Business Identification Department evaluates new business ideas proposed by trainees and provides feedback. For example, to evaluate technical feasibility, the New Business Identification Department evaluates whether the proposed idea is feasible from a technical perspective. To evaluate marketability, the department can also evaluate whether the proposed idea will be accepted in the market. Furthermore, to evaluate innovativeness, the department can evaluate how well the proposed idea is differentiated from existing products and services. This allows trainees to receive evaluation and feedback on their proposed ideas.

[0033] The curriculum provider provides a curriculum that includes basic AI concepts and technologies, methods for handling data, and methods for building AI models. Examples of basic AI concepts and technologies include machine learning, deep learning, and natural language processing. Examples of methods for handling data include data collection, preprocessing, and analysis. Examples of methods for building AI models include model selection, training, and evaluation. The curriculum provider provides a curriculum that allows participants to learn basic AI concepts and technologies, methods for handling data, and methods for building AI models. For example, the curriculum provider may provide lectures to learn basic machine learning concepts and algorithms. It may also provide practical training to learn data collection and preprocessing methods for handling data. It may also provide exercises to learn model selection, training, and evaluation methods for building AI models. This allows participants to learn basic AI concepts and technologies, methods for handling data, and methods for building AI models.

[0034] The Exercise and Project Department provides opportunities for students to work on projects using AI to solve problems related to their work. Problems related to their work include, for example, improving specific business processes and increasing work efficiency. The Exercise and Project Department provides opportunities for students to work on projects using AI to solve problems related to their work. For example, the Exercise and Project Department offers projects aimed at improving specific business processes, providing students with the opportunity to learn how to optimize business processes using AI. It also offers projects aimed at improving work efficiency, allowing students to learn how to use AI to improve work efficiency. Furthermore, by working on projects to solve problems related to their work, students can acquire the skills to use AI in real business environments. This allows students to acquire the skills to use AI to solve problems related to their work.

[0035] The New Business Discovery Department proposes ideas for new services or products using AI and holds discussions to evaluate their feasibility. Ideas for new services or products include, for example, technological innovation and improved user experience. The New Business Discovery Department allows participants to propose ideas for new services or products using AI and holds discussions to evaluate their feasibility. For example, the New Business Discovery Department discusses participants' proposed ideas for new AI-based services based on evaluation criteria such as technical feasibility, marketability, and innovativeness. It can also similarly evaluate participants' proposed ideas for new AI-based products and provide feedback. Furthermore, the New Business Discovery Department can provide participants with an opportunity to refine their ideas through discussions to evaluate the feasibility of their proposed ideas. This allows participants to propose ideas for new AI-based services or products and evaluate their feasibility.

[0036] The platform also serves as a forum for sharing success stories of AI-enabled businesses and the latest technological trends. Success stories include, for example, the success stories of specific companies and industry best practices. The latest technological trends include, for example, new algorithms and trends. The platform also serves as a forum for participants to learn about success stories of AI-enabled businesses and the latest technological trends. For example, the platform introduces examples of successful AI projects and shares their success factors and challenges so that participants can apply them to their own projects. It also provides information on the latest AI technologies and trends so that participants can always work with the latest knowledge. Furthermore, the platform can provide a discussion forum for participants to share success stories of AI-enabled businesses and the latest technological trends. This allows participants to exchange information and learn from other participants and experts. This allows participants to learn about success stories of AI-enabled businesses and the latest technological trends.

[0037] The curriculum provider analyzes the student's past learning history and automatically generates an appropriate curriculum. For example, the curriculum provider automatically selects the next topic the student should learn based on what the student has learned in the past. It can also analyze the student's past test results and generate a curriculum to strengthen weak points. It can also suggest new related curriculum based on the student's history of past workshops and online courses. The student's past learning history includes, for example, course history, grades, learning patterns, etc. This improves learning effectiveness by providing an optimal curriculum based on the student's past learning history.

[0038] When providing a curriculum, the curriculum provider customizes the curriculum content according to the student's occupation or work content. For example, if the student is an engineer, the curriculum provider may provide a curriculum to strengthen technical skills. If the student is a marketing professional, the curriculum provider may provide a curriculum on marketing strategies using data analysis and AI. Furthermore, if the student is a manager, the curriculum may provide a curriculum on management strategies and decision-making using AI. The student's occupation and work content include, for example, job type, work content, industry, etc. This improves learning effectiveness by providing a curriculum according to the student's occupation and work content.

[0039] When providing a curriculum, the curriculum provider selects an appropriate learning method according to the learning style of the student. For example, the curriculum provider may provide a curriculum that makes extensive use of videos and infographics to visual learners. Alternatively, the curriculum provider may provide a curriculum that focuses on podcasts and audio lectures to auditory learners. Furthermore, the curriculum provider may provide a curriculum that includes many practical exercises and projects to tactile learners. Student learning styles include, for example, visual learners, auditory learners, and tactile learners. This improves learning effectiveness by providing a learning method that suits the student's learning style.

[0040] When providing a curriculum, the curriculum provider takes into account the student's geographic location information and incorporates case studies specific to the region. For example, if the student is in Japan, the curriculum provider can incorporate case studies related to Japanese companies and markets into the curriculum. Also, if the student is in the United States, the curriculum can incorporate the American business environment and success stories. Furthermore, if the student is in Europe, the curriculum can incorporate case studies related to European regulations and market trends. The student's geographic location information includes, for example, GPS data, IP address, and case studies specific to the region. This improves learning effectiveness by providing case studies based on the student's geographic location information.

[0041] When providing a curriculum, the curriculum provider analyzes the student's social media activity and provides relevant learning resources. For example, the curriculum provider analyzes the content posted by experts and influencers that the student follows on social media and provides relevant learning resources. The curriculum provider can also suggest relevant curriculum based on the articles and posts that the student has shared on social media. Furthermore, the curriculum provider can analyze the student's social media activity history and provide learning resources that may be of interest to the student. The student's social media activity includes, for example, the content of posts, follower trends, and interests. This improves learning effectiveness by providing learning resources based on the student's social media activity.

[0042] The curriculum provider reflects learner feedback when providing the curriculum and continually improves the curriculum content. For example, the curriculum provider periodically reviews and improves the curriculum content based on feedback provided by learners. The curriculum provider can also analyze learner feedback and develop new curricula to address common challenges and requests. Learner feedback can also be reflected in the curriculum to improve the curriculum progression and the quality of the teaching materials. Learner feedback can be collected, for example, through surveys, feedback sessions, online forums, etc. By improving the curriculum content based on learner feedback, the learning effect can be improved.

[0043] When providing exercises or projects, the Exercise and Project Provision Department analyzes the student's past project history and selects appropriate assignments. For example, the Exercise and Project Provision Department selects the next assignment for the student based on the results of the student's past projects. The Department can also analyze the student's past project history and provide assignments that will help improve their skills. Furthermore, it can analyze the causes of the student's past failed projects and provide assignments that reflect areas for improvement. The student's past project history includes, for example, the type of project, results, and learning content. This improves learning effectiveness by providing assignments based on the student's past project history.

[0044] When providing exercises and projects, the Exercise and Project Provider customizes assignments based on the trainee's work. For example, if the trainee is an engineer, the Exercise and Project Provider can provide assignments to strengthen their technical skills. If the trainee is a marketer, the Exercise and Project Provider can provide assignments related to data analysis and marketing strategies using AI. Furthermore, if the trainee is a manager, the Exercise and Project Provider can provide assignments related to management strategies and decision-making using AI. The trainee's work may include, for example, their job type, business process, industry, etc. This improves learning effectiveness by providing assignments tailored to the trainee's work.

[0045] The exercise and project delivery department continuously improves the content of exercises and projects by incorporating student feedback. For example, the exercise and project delivery department periodically reviews and improves the content of assignments based on student feedback. The department can also analyze student feedback and develop new assignments to address common challenges and requests. Furthermore, student feedback can be incorporated into the progress of assignments and the quality of the materials. Student feedback can be collected, for example, through surveys, feedback sessions, online forums, etc. By incorporating student feedback into the content of assignments, the learning effect can be improved.

[0046] When providing exercises and projects, the Exercise and Project Provider takes into account the student's geographic location information and incorporates region-specific assignments. For example, if a student is in Japan, the Exercise and Project Provider can provide assignments related to Japanese companies and markets. Also, if a student is in the United States, the Exercise and Project Provider can provide assignments based on the American business environment and success stories. Furthermore, if a student is in Europe, the Exercise and Project Provider can provide assignments related to European regulations and market trends. The student's geographic location information includes, for example, GPS data, IP address, and region-specific case studies. This improves learning effectiveness by providing assignments based on the student's geographic location information.

[0047] The exercise and project provider analyzes students' social media activity and provides relevant assignments when providing exercises and projects. For example, the exercise and project provider analyzes the posts of experts and influencers that students follow on social media and provides relevant assignments. It can also suggest related assignments based on the articles and posts that students have shared on social media. Furthermore, it can analyze students' social media activity history and provide assignments that may be of interest. Students' social media activity includes, for example, the content of posts, follower trends, and interests. This improves learning effectiveness by providing assignments based on students' social media activity.

[0048] The exercise and project delivery department customizes the assignment content by reflecting student feedback when providing exercises and projects. For example, the exercise and project delivery department periodically reviews and customizes the content of assignments based on feedback provided by students. The department can also analyze student feedback and develop new assignments to address common challenges and requests. Furthermore, student feedback can be reflected in the delivery of assignments and the quality of the materials. Student feedback can be collected, for example, through surveys, feedback sessions, online forums, etc. By customizing the assignment content based on student feedback, learning effectiveness can be improved.

[0049] When identifying new businesses, the New Business Discovery Department evaluates the feasibility of ideas by referring to past success stories. For example, the New Business Discovery Department evaluates the feasibility of proposed ideas based on examples of past successful AI projects. The Department can also analyze data obtained from past success stories to evaluate the risks and benefits of proposed ideas. Furthermore, by referring to past success stories, the Department can evaluate the resources and skills required to realize the proposed idea. Past success stories include, for example, success stories of specific companies and industry best practices. This allows the feasibility of ideas to be objectively evaluated by referring to past success stories.

[0050] When discovering new businesses, the New Business Discovery Department customizes ideas based on the trainee's work content or expertise. For example, if the trainee is an engineer, the New Business Discovery Department will propose new business ideas that utilize their technical skills. If the trainee is a marketer, the Department can also propose new business ideas based on marketing strategies that utilize data analysis and AI. Furthermore, if the trainee is a manager, the Department can propose new business ideas based on management strategies and decision-making that utilize AI. Trainees' work content and expertise include, for example, their job type, business processes, and areas of expertise. This increases the feasibility of ideas by providing ideas based on the trainee's work content and expertise.

[0051] The New Business Identification Department continuously improves the evaluation criteria by reflecting the opinions of trainees when identifying new businesses. For example, the New Business Identification Department periodically reviews and improves the evaluation criteria based on feedback provided by trainees. The New Business Identification Department can also analyze trainee feedback and develop new evaluation criteria to address common challenges and requests. Furthermore, the transparency and fairness of the evaluation criteria can be improved by reflecting trainee feedback. Trainee opinions can be collected, for example, through questionnaires, feedback sessions, online forums, etc. In this way, the fairness of evaluation is improved by improving the evaluation criteria by reflecting trainee feedback.

[0052] When identifying new businesses, the New Business Identification Department takes into account the participants' geographic location information to incorporate ideas specific to the region. For example, if a participant is in Japan, the New Business Identification Department can propose new business ideas suited to the Japanese market and culture. If a participant is in the United States, the Department can also propose new business ideas based on the American business environment and consumer trends. Furthermore, if a participant is in Europe, the Department can propose new business ideas suited to European regulations and market trends. The participants' geographic location information includes, for example, GPS data, IP address, and regional examples. This increases the feasibility of ideas by providing them based on the participants' geographic location information.

[0053] The New Business Discovery Department analyzes participants' social media activity when identifying new businesses and provides relevant ideas. For example, the New Business Discovery Department analyzes the posts of experts and influencers that participants follow on social media and provides relevant new business ideas. It can also suggest related new business ideas based on the articles and posts that participants have shared on social media. It can also analyze participants' social media activity history and provide new business ideas that may interest them. A participant's social media activity includes, for example, the content of posts, follower trends, and interests. This increases the feasibility of providing ideas based on the participant's social media activity.

[0054] When identifying new businesses, the New Business Identification Department customizes the evaluation criteria for ideas by reflecting the opinions of the participants. For example, the New Business Identification Department periodically reviews and customizes the evaluation criteria based on feedback provided by the participants. The New Business Identification Department can also analyze the participants' feedback and develop new evaluation criteria to address common challenges and requests. Furthermore, the transparency and fairness of the evaluation criteria can be improved by reflecting the participants' feedback. The participants' opinions can be collected, for example, through questionnaires, feedback sessions, online forums, etc. In this way, the fairness of evaluation can be improved by customizing the evaluation criteria by reflecting the participants' feedback.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The curriculum provider can monitor learners' learning progress in real time and dynamically adjust the curriculum content based on their progress. For example, if a learner is struggling with a particular topic, it can provide additional learning resources or supplementary materials related to that topic. Also, if a learner quickly understands a particular topic, it can provide a test to move on to the next topic, supporting smooth learning progress. It can also provide individual feedback and advice based on the learner's learning progress. This allows learners to learn efficiently at their own pace.

[0057] The New Business Discovery Department analyzes the trainees' past project history and can propose new business ideas based on past successes and failures. For example, they can propose new business ideas that incorporate elements of past successful projects. They can also analyze the causes of past failed projects and propose new business ideas that reflect those areas for improvement. Furthermore, they can propose business ideas that take into account the trainees' strengths and weaknesses based on their past project history. This makes it possible to propose new business ideas that make use of the trainees' past experience.

[0058] The Exercise and Project Provider can provide exercises and projects that incorporate business issues specific to the region, taking into account the student's geographic location. For example, if a student is in Asia, the department can provide exercises that incorporate business issues specific to the Asian market. If a student is in Europe, the department can provide projects based on European regulations and market trends. Furthermore, if a student is in the Americas, the department can provide projects based on the American business environment and consumer trends. This improves learning effectiveness by providing exercises and projects based on the student's geographic location.

[0059] The curriculum provider can analyze students' social media activities and incorporate topics that are likely to interest them into the curriculum. For example, they can analyze the posts of experts and influencers that students follow on social media and add related topics to the curriculum. They can also provide related learning resources based on the articles and posts that students share on social media. They can also analyze students' social media activity history and suggest topics that they might be interested in. This improves learning effectiveness by providing a curriculum based on students' social media activity.

[0060] The New Business Discovery Department can analyze a student's past learning history and propose new business ideas based on their past learning content and achievements. For example, it can automatically select the next topic to learn based on what they have learned in the past and propose new business ideas related to it. It can also analyze past test results and propose business ideas to strengthen weak points. It can also propose related new business ideas based on the history of workshops and online courses they have participated in in the past. This makes it possible to propose new business ideas based on a student's past learning history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The curriculum provider provides a curriculum for individuals or organizations to learn the basic knowledge and skills required to utilize AI. The curriculum includes basic AI concepts, algorithms, data processing techniques, etc. The curriculum provider provides participants with opportunities to learn basic AI knowledge and skills through online courses and workshops. For example, the curriculum provider can provide live sessions, recorded lectures, and interactive exercises. Step 2: The Exercise and Project Department provides exercises or projects to apply the knowledge provided by the Curriculum Department to practice. Exercises and projects include case studies, practical projects, and simulations based on real business scenarios. Participants have the opportunity to work on projects that use AI to solve problems related to their work. For example, they can work on projects aimed at improving specific business processes or increasing business efficiency. Step 3: The New Business Discovery Department will discover new business ideas based on the results of the Exercises and Projects Department and evaluate their feasibility. The New Business Discovery Department will evaluate the proposed ideas based on criteria such as technical feasibility, marketability, and innovativeness, and provide feedback.

[0063] (Example 2) A training platform according to an embodiment of the present invention is a system designed to develop businesspeople who can properly utilize AI and to discover new talent and businesses. This system provides a curriculum for individuals and organizations to learn basic knowledge and skills for utilizing AI and provides opportunities to apply the knowledge learned through exercises and projects based on actual business scenarios. It also provides a platform for discovering new business ideas using AI and evaluating their feasibility. For example, the training platform provides opportunities to learn the basics of AI through online courses and workshops. Next, participants can work on projects to solve problems related to their work using AI. Furthermore, participants can share their proposed ideas for new services or products using AI with other participants and experts and receive feedback. This allows the training platform to help individuals and organizations improve their productivity and promote new businesses. This allows the training platform to develop businesspeople who can properly utilize AI and to discover new talent and businesses. For example, participants can learn basic AI knowledge and skills and apply them in practice through exercises and projects based on actual business scenarios. Furthermore, by discovering new business ideas using AI and evaluating their feasibility, new business opportunities can be discovered.

[0064] A training platform according to an embodiment includes a curriculum provider, an exercise and project provider, and a new business discovery unit. The curriculum provider provides a curriculum for individuals or organizations to learn basic knowledge and skills for utilizing AI. The curriculum includes, for example, basic AI concepts, algorithms, and data processing techniques. The curriculum provider provides participants with opportunities to learn basic AI knowledge and skills through online courses and workshops. For example, the curriculum provider may provide live sessions, recorded lectures, and interactive exercises. The exercise and project provider provides exercises or projects for applying the knowledge provided by the curriculum provider to practice. Exercises and projects include, for example, case studies, practical projects, and simulations based on actual business scenarios. The exercise and project provider provides participants with opportunities to work on projects that use AI to solve problems related to their work. For example, participants can work on projects aimed at improving specific business processes or increasing business efficiency. The new business discovery unit discovers new business ideas and evaluates their feasibility based on the results obtained by the exercise and project provider. The new business discovery unit evaluates the proposed ideas and provides feedback. For example, the New Business Discovery Department evaluates ideas based on evaluation criteria such as technical feasibility, marketability, innovativeness, etc. As a result, the training platform according to the embodiment enables individuals and organizations to learn basic knowledge and skills for properly utilizing AI, apply them in practice, and discover and evaluate new business ideas.

[0065] The curriculum provider provides online courses or workshops. Online courses or workshops include, for example, live sessions, recorded lectures, and interactive exercises. The curriculum provider provides online courses and workshops for participants to learn fundamental knowledge and skills of AI. For example, the curriculum provider may deliver lectures in real time through live sessions, providing participants with opportunities to ask questions and hold discussions. Alternatively, the curriculum provider may provide recorded lectures so that participants can learn at their own pace. Furthermore, the curriculum provider may provide participants with opportunities to learn hands-on through interactive exercises. This allows participants to learn fundamental knowledge and skills of AI through online courses and workshops.

[0066] The Exercise and Project Department offers exercises or projects based on real business scenarios. Examples of real business scenarios include case studies and practical projects in specific industries. The Exercise and Project Department offers exercises and projects that allow students to apply their knowledge in practice. For example, the Exercise and Project Department provides students with the opportunity to tackle problems based on real business scenarios through case studies in specific industries. Furthermore, through practical projects, students can work on projects that use AI to solve problems related to their own work. Furthermore, through simulations, students can acquire skills to utilize AI in virtual business environments. This allows students to apply their knowledge in practice through exercises and projects based on real business scenarios.

[0067] The New Business Identification Department evaluates proposed ideas and provides feedback. The proposed ideas include evaluation criteria such as technical feasibility, marketability, and innovativeness. The New Business Identification Department evaluates new business ideas proposed by trainees and provides feedback. For example, to evaluate technical feasibility, the New Business Identification Department evaluates whether the proposed idea is feasible from a technical perspective. To evaluate marketability, the department can also evaluate whether the proposed idea will be accepted in the market. Furthermore, to evaluate innovativeness, the department can evaluate how well the proposed idea is differentiated from existing products and services. This allows trainees to receive evaluation and feedback on their proposed ideas.

[0068] The curriculum provider provides a curriculum that includes basic AI concepts and technologies, methods for handling data, and methods for building AI models. Examples of basic AI concepts and technologies include machine learning, deep learning, and natural language processing. Examples of methods for handling data include data collection, preprocessing, and analysis. Examples of methods for building AI models include model selection, training, and evaluation. The curriculum provider provides a curriculum that allows participants to learn basic AI concepts and technologies, methods for handling data, and methods for building AI models. For example, the curriculum provider may provide lectures to learn basic machine learning concepts and algorithms. It may also provide practical training to learn data collection and preprocessing methods for handling data. It may also provide exercises to learn model selection, training, and evaluation methods for building AI models. This allows participants to learn basic AI concepts and technologies, methods for handling data, and methods for building AI models.

[0069] The Exercise and Project Department provides opportunities for students to work on projects using AI to solve problems related to their work. Problems related to their work include, for example, improving specific business processes and increasing work efficiency. The Exercise and Project Department provides opportunities for students to work on projects using AI to solve problems related to their work. For example, the Exercise and Project Department offers projects aimed at improving specific business processes, providing students with the opportunity to learn how to optimize business processes using AI. It also offers projects aimed at improving work efficiency, allowing students to learn how to use AI to improve work efficiency. Furthermore, by working on projects to solve problems related to their work, students can acquire the skills to use AI in real business environments. This allows students to acquire the skills to use AI to solve problems related to their work.

[0070] The New Business Discovery Department proposes ideas for new services or products using AI and holds discussions to evaluate their feasibility. Ideas for new services or products include, for example, technological innovation and improved user experience. The New Business Discovery Department allows participants to propose ideas for new services or products using AI and holds discussions to evaluate their feasibility. For example, the New Business Discovery Department discusses participants' proposed ideas for new AI-based services based on evaluation criteria such as technical feasibility, marketability, and innovativeness. It can also similarly evaluate participants' proposed ideas for new AI-based products and provide feedback. Furthermore, the New Business Discovery Department can provide participants with an opportunity to refine their ideas through discussions to evaluate the feasibility of their proposed ideas. This allows participants to propose ideas for new AI-based services or products and evaluate their feasibility.

[0071] The platform also serves as a forum for sharing success stories of AI-enabled businesses and the latest technological trends. Success stories include, for example, the success stories of specific companies and industry best practices. The latest technological trends include, for example, new algorithms and trends. The platform also serves as a forum for participants to learn about success stories of AI-enabled businesses and the latest technological trends. For example, the platform introduces examples of successful AI projects and shares their success factors and challenges so that participants can apply them to their own projects. It also provides information on the latest AI technologies and trends so that participants can always work with the latest knowledge. Furthermore, the platform can provide a discussion forum for participants to share success stories of AI-enabled businesses and the latest technological trends. This allows participants to exchange information and learn from other participants and experts. This allows participants to learn about success stories of AI-enabled businesses and the latest technological trends.

[0072] The curriculum providing unit estimates the emotions of the student and adjusts the pace of the curriculum based on the estimated emotions of the student. For example, if the student is feeling stressed, the curriculum providing unit slows down the pace of the curriculum and provides additional materials to deepen understanding. Also, if the student is relaxed, the curriculum can be accelerated and a test can be provided to advance to the next step. Furthermore, if the student is excited, the curriculum can be adjusted and interactive content can be added to attract the student's interest. The emotions of the student can be estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. As a result, the learning effect can be improved by adjusting the pace of the curriculum according to the student's emotions.

[0073] The curriculum provider analyzes the student's past learning history and automatically generates an appropriate curriculum. For example, the curriculum provider automatically selects the next topic the student should learn based on what the student has learned in the past. It can also analyze the student's past test results and generate a curriculum to strengthen weak points. It can also suggest new related curriculum based on the student's history of past workshops and online courses. The student's past learning history includes, for example, course history, grades, learning patterns, etc. This improves learning effectiveness by providing an optimal curriculum based on the student's past learning history.

[0074] When providing a curriculum, the curriculum provider customizes the curriculum content according to the student's occupation or work content. For example, if the student is an engineer, the curriculum provider may provide a curriculum to strengthen technical skills. If the student is a marketing professional, the curriculum provider may provide a curriculum on marketing strategies using data analysis and AI. Furthermore, if the student is a manager, the curriculum may provide a curriculum on management strategies and decision-making using AI. The student's occupation and work content include, for example, job type, work content, industry, etc. This improves learning effectiveness by providing a curriculum according to the student's occupation and work content.

[0075] When providing a curriculum, the curriculum provider selects an appropriate learning method according to the learning style of the student. For example, the curriculum provider may provide a curriculum that makes extensive use of videos and infographics to visual learners. Alternatively, the curriculum provider may provide a curriculum that focuses on podcasts and audio lectures to auditory learners. Furthermore, the curriculum provider may provide a curriculum that includes many practical exercises and projects to tactile learners. Student learning styles include, for example, visual learners, auditory learners, and tactile learners. This improves learning effectiveness by providing a learning method that suits the student's learning style.

[0076] The curriculum provider estimates the student's emotions and adjusts the details of the curriculum based on the estimated student's emotions. For example, if the student is feeling stressed, the curriculum provider can provide less difficult content and add supplementary materials to deepen understanding. If the student is relaxed, the curriculum provider can provide more difficult content and add challenging tasks. Furthermore, if the student is excited, the curriculum provider can add interactive content and practical exercises to make the content more interesting. The student's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This improves learning effectiveness by adjusting the curriculum content according to the student's emotions.

[0077] When providing a curriculum, the curriculum provider takes into account the student's geographic location information and incorporates case studies specific to the region. For example, if the student is in Japan, the curriculum provider can incorporate case studies related to Japanese companies and markets into the curriculum. Also, if the student is in the United States, the curriculum can incorporate the American business environment and success stories. Furthermore, if the student is in Europe, the curriculum can incorporate case studies related to European regulations and market trends. The student's geographic location information includes, for example, GPS data, IP address, and case studies specific to the region. This improves learning effectiveness by providing case studies based on the student's geographic location information.

[0078] When providing a curriculum, the curriculum provider analyzes the student's social media activity and provides relevant learning resources. For example, the curriculum provider analyzes the content posted by experts and influencers that the student follows on social media and provides relevant learning resources. The curriculum provider can also suggest relevant curriculum based on the articles and posts that the student has shared on social media. Furthermore, the curriculum provider can analyze the student's social media activity history and provide learning resources that may be of interest to the student. The student's social media activity includes, for example, the content of posts, follower trends, and interests. This improves learning effectiveness by providing learning resources based on the student's social media activity.

[0079] The curriculum provider reflects learner feedback when providing the curriculum and continually improves the curriculum content. For example, the curriculum provider periodically reviews and improves the curriculum content based on feedback provided by learners. The curriculum provider can also analyze learner feedback and develop new curricula to address common challenges and requests. Learner feedback can also be reflected in the curriculum to improve the curriculum progression and the quality of the teaching materials. Learner feedback can be collected, for example, through surveys, feedback sessions, online forums, etc. By improving the curriculum content based on learner feedback, the learning effect can be improved.

[0080] The exercise and project provision unit estimates the student's emotions and adjusts the difficulty of exercises and projects based on the estimated student's emotions. For example, if the student is feeling stressed, the exercise and project provision unit can provide less difficult exercises and projects to help the student gain experience of success. Alternatively, if the student is relaxed, the exercise and project provision unit can provide more difficult exercises and projects to help the student tackle challenging tasks. Furthermore, if the student is excited, the exercise and project provision unit can provide interactive and stimulating exercises and projects to adjust the content to attract the student's attention. The student's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This improves learning effectiveness by adjusting the difficulty of exercises and projects according to the student's emotions.

[0081] When providing exercises or projects, the Exercise and Project Provision Department analyzes the student's past project history and selects appropriate assignments. For example, the Exercise and Project Provision Department selects the next assignment for the student based on the results of the student's past projects. The Department can also analyze the student's past project history and provide assignments that will help improve their skills. Furthermore, it can analyze the causes of the student's past failed projects and provide assignments that reflect areas for improvement. The student's past project history includes, for example, the type of project, results, and learning content. This improves learning effectiveness by providing assignments based on the student's past project history.

[0082] When providing exercises and projects, the Exercise and Project Provider customizes assignments based on the trainee's work. For example, if the trainee is an engineer, the Exercise and Project Provider can provide assignments to strengthen their technical skills. If the trainee is a marketer, the Exercise and Project Provider can provide assignments related to data analysis and marketing strategies using AI. Furthermore, if the trainee is a manager, the Exercise and Project Provider can provide assignments related to management strategies and decision-making using AI. The trainee's work may include, for example, their job type, business process, industry, etc. This improves learning effectiveness by providing assignments tailored to the trainee's work.

[0083] The exercise and project delivery department continuously improves the content of exercises and projects by incorporating student feedback. For example, the exercise and project delivery department periodically reviews and improves the content of assignments based on student feedback. The department can also analyze student feedback and develop new assignments to address common challenges and requests. Furthermore, student feedback can be incorporated into the progress of assignments and the quality of the materials. Student feedback can be collected, for example, through surveys, feedback sessions, online forums, etc. By incorporating student feedback into the content of assignments, the learning effect can be improved.

[0084] The exercise and project provider estimates the student's emotions and adjusts the progress of exercises and projects based on the estimated student's emotions. For example, if the student is feeling stressed, the exercise and project provider can slow down the progress and provide additional materials to deepen understanding. Alternatively, if the student is relaxed, the exercise and project provider can speed up the progress and provide a test to advance to the next step. Furthermore, if the student is excited, the provider can adjust the progress and add interactive content to pique the student's interest. Student emotions can be estimated using, for example, facial expression recognition, voice analysis, and survey results. This improves learning effectiveness by adjusting the progress of exercises and projects according to the student's emotions.

[0085] When providing exercises and projects, the Exercise and Project Provider takes into account the student's geographic location information and incorporates region-specific assignments. For example, if a student is in Japan, the Exercise and Project Provider can provide assignments related to Japanese companies and markets. Also, if a student is in the United States, the Exercise and Project Provider can provide assignments based on the American business environment and success stories. Furthermore, if a student is in Europe, the Exercise and Project Provider can provide assignments related to European regulations and market trends. The student's geographic location information includes, for example, GPS data, IP address, and region-specific case studies. This improves learning effectiveness by providing assignments based on the student's geographic location information.

[0086] The exercise and project provider analyzes students' social media activity and provides relevant assignments when providing exercises and projects. For example, the exercise and project provider analyzes the posts of experts and influencers that students follow on social media and provides relevant assignments. It can also suggest related assignments based on the articles and posts that students have shared on social media. Furthermore, it can analyze students' social media activity history and provide assignments that may be of interest. Students' social media activity includes, for example, the content of posts, follower trends, and interests. This improves learning effectiveness by providing assignments based on students' social media activity.

[0087] The exercise and project delivery department customizes the assignment content by reflecting student feedback when providing exercises and projects. For example, the exercise and project delivery department periodically reviews and customizes the content of assignments based on feedback provided by students. The department can also analyze student feedback and develop new assignments to address common challenges and requests. Furthermore, student feedback can be reflected in the delivery of assignments and the quality of the materials. Student feedback can be collected, for example, through surveys, feedback sessions, online forums, etc. By customizing the assignment content based on student feedback, learning effectiveness can be improved.

[0088] The new business discovery department estimates the emotions of the trainees and adjusts the evaluation criteria for ideas based on the estimated emotions of the trainees. For example, if the trainees are feeling stressed, the new business discovery department can relax the evaluation criteria and emphasize positive feedback. Also, if the trainees are relaxed, the evaluation criteria can be set at normal levels to provide an objective evaluation. Furthermore, if the trainees are excited, the evaluation criteria can be tightened to emphasize challenging ideas. The emotions of the trainees can be estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. In this way, the fairness of evaluations can be improved by adjusting the evaluation criteria for ideas according to the trainees' emotions.

[0089] When identifying new businesses, the New Business Discovery Department evaluates the feasibility of ideas by referring to past success stories. For example, the New Business Discovery Department evaluates the feasibility of proposed ideas based on examples of past successful AI projects. The Department can also analyze data obtained from past success stories to evaluate the risks and benefits of proposed ideas. Furthermore, by referring to past success stories, the Department can evaluate the resources and skills required to realize the proposed idea. Past success stories include, for example, success stories of specific companies and industry best practices. This allows the feasibility of ideas to be objectively evaluated by referring to past success stories.

[0090] When discovering new businesses, the New Business Discovery Department customizes ideas based on the trainee's work content or expertise. For example, if the trainee is an engineer, the New Business Discovery Department will propose new business ideas that utilize their technical skills. If the trainee is a marketer, the Department can also propose new business ideas based on marketing strategies that utilize data analysis and AI. Furthermore, if the trainee is a manager, the Department can propose new business ideas based on management strategies and decision-making that utilize AI. Trainees' work content and expertise include, for example, their job type, business processes, and areas of expertise. This increases the feasibility of ideas by providing ideas based on the trainee's work content and expertise.

[0091] The New Business Identification Department continuously improves the evaluation criteria by reflecting the opinions of trainees when identifying new businesses. For example, the New Business Identification Department periodically reviews and improves the evaluation criteria based on feedback provided by trainees. The New Business Identification Department can also analyze trainee feedback and develop new evaluation criteria to address common challenges and requests. Furthermore, the transparency and fairness of the evaluation criteria can be improved by reflecting trainee feedback. Trainee opinions can be collected, for example, through questionnaires, feedback sessions, online forums, etc. In this way, the fairness of evaluation is improved by improving the evaluation criteria by reflecting trainee feedback.

[0092] The New Business Discovery Department estimates the emotions of the trainees and adjusts the way in which they provide feedback on ideas based on the estimated emotions of the trainees. For example, if the trainees are feeling stressed, the New Business Discovery Department will emphasize positive feedback and gently suggest areas for improvement. If the trainees are relaxed, the Department can provide objective feedback and suggest specific areas for improvement. Furthermore, if the trainees are excited, the Department can provide challenging feedback to encourage further growth. The emotions of trainees can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This improves the effectiveness of feedback by adjusting the feedback method according to the trainees' emotions.

[0093] When identifying new businesses, the New Business Identification Department takes into account the participants' geographic location information to incorporate ideas specific to the region. For example, if a participant is in Japan, the New Business Identification Department can propose new business ideas suited to the Japanese market and culture. If a participant is in the United States, the Department can also propose new business ideas based on the American business environment and consumer trends. Furthermore, if a participant is in Europe, the Department can propose new business ideas suited to European regulations and market trends. The participants' geographic location information includes, for example, GPS data, IP address, and regional examples. This increases the feasibility of ideas by providing them based on the participants' geographic location information.

[0094] The New Business Discovery Department analyzes participants' social media activity when identifying new businesses and provides relevant ideas. For example, the New Business Discovery Department analyzes the posts of experts and influencers that participants follow on social media and provides relevant new business ideas. It can also suggest related new business ideas based on the articles and posts that participants have shared on social media. It can also analyze participants' social media activity history and provide new business ideas that may interest them. A participant's social media activity includes, for example, the content of posts, follower trends, and interests. This increases the feasibility of providing ideas based on the participant's social media activity.

[0095] When identifying new businesses, the New Business Identification Department customizes the evaluation criteria for ideas by reflecting the opinions of the participants. For example, the New Business Identification Department periodically reviews and customizes the evaluation criteria based on feedback provided by the participants. The New Business Identification Department can also analyze the participants' feedback and develop new evaluation criteria to address common challenges and requests. Furthermore, the transparency and fairness of the evaluation criteria can be improved by reflecting the participants' feedback. The participants' opinions can be collected, for example, through questionnaires, feedback sessions, online forums, etc. In this way, the fairness of evaluation can be improved by customizing the evaluation criteria by reflecting the participants' feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the curriculum providing unit, exercise / project providing unit, and new business discovery unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the curriculum providing unit is realized by the control unit 46A of the smart device 14 and provides online courses and workshops. The exercise / project providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides exercises and projects based on actual business scenarios. The new business discovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates and provides feedback on proposed ideas. === Hard Collateral 1-2 === Each of the multiple elements, including the curriculum providing unit, exercise / project providing unit, and new business discovery unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the curriculum providing unit is realized by the control unit 46A of the smart glasses 214 and provides online courses and workshops. The exercise / project providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides exercises and projects based on actual business scenarios. The new business discovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates and provides feedback on proposed ideas. === Hard Collateral 1-3 === Each of the multiple elements including the curriculum providing unit, exercise / project providing unit, and new business discovery unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the curriculum providing unit is realized by the control unit 46A of the headset-type terminal 314 and provides online courses and workshops. The exercise / project providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides exercises and projects based on actual business scenarios. The new business discovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates and provides feedback on proposed ideas. === Hard Collateral 1-4 === Each of the multiple elements including the curriculum providing unit, exercise / project providing unit, and new business discovery unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the curriculum providing unit is realized by the control unit 46A of the robot 414 and provides online courses and workshops. The exercise / project providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides exercises and projects based on actual business scenarios. The new business discovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates and provides feedback on proposed ideas.

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

[0097] The curriculum provider can monitor learners' learning progress in real time and dynamically adjust the curriculum content based on their progress. For example, if a learner is struggling with a particular topic, it can provide additional learning resources or supplementary materials related to that topic. Also, if a learner quickly understands a particular topic, it can provide a test to move on to the next topic, supporting smooth learning progress. It can also provide individual feedback and advice based on the learner's learning progress. This allows learners to learn efficiently at their own pace.

[0098] The exercise and project provider can estimate the student's emotions and select exercise and project themes based on the estimated emotions. For example, if the student is feeling stressed, the provider can provide exercises with a relaxing theme, and if the student is relaxed, the provider can provide projects with a challenging theme. Also, if the student is excited, the provider can provide projects with a theme that will allow the student to demonstrate their creativity. This improves learning effectiveness by selecting themes that correspond to the student's emotions.

[0099] The New Business Discovery Department analyzes the trainees' past project history and can propose new business ideas based on past successes and failures. For example, they can propose new business ideas that incorporate elements of past successful projects. They can also analyze the causes of past failed projects and propose new business ideas that reflect those areas for improvement. Furthermore, they can propose business ideas that take into account the trainees' strengths and weaknesses based on their past project history. This makes it possible to propose new business ideas that make use of the trainees' past experience.

[0100] The curriculum provider can estimate the student's emotions and adjust the learning environment based on the estimated emotions. For example, if the student is feeling stressed, it can provide relaxing music or backgrounds. If the student is relaxed, it can also provide an environment that helps students concentrate. Furthermore, if the student is excited, it can provide a learning environment that incorporates interactive content or game elements. This improves learning effectiveness by providing a learning environment that suits the student's emotions.

[0101] The Exercise and Project Provider can provide exercises and projects that incorporate business issues specific to the region, taking into account the student's geographic location. For example, if a student is in Asia, the department can provide exercises that incorporate business issues specific to the Asian market. If a student is in Europe, the department can provide projects based on European regulations and market trends. Furthermore, if a student is in the Americas, the department can provide projects based on the American business environment and consumer trends. This improves learning effectiveness by providing exercises and projects based on the student's geographic location.

[0102] The New Business Discovery Department can estimate the emotions of trainees and adjust the feedback method for ideas based on the estimated emotions. For example, if a trainee is feeling stressed, they can emphasize positive feedback and gently point out areas for improvement. If a trainee is relaxed, they can provide objective feedback and indicate specific areas for improvement. Furthermore, if a trainee is excited, they can provide challenging feedback to encourage further growth. This improves the effectiveness of feedback by providing feedback methods that correspond to the trainee's emotions.

[0103] The curriculum provider can analyze students' social media activities and incorporate topics that are likely to interest them into the curriculum. For example, they can analyze the posts of experts and influencers that students follow on social media and add related topics to the curriculum. They can also provide related learning resources based on the articles and posts that students share on social media. They can also analyze students' social media activity history and suggest topics that they might be interested in. This improves learning effectiveness by providing a curriculum based on students' social media activity.

[0104] The exercise and project provider can estimate the student's emotions and adjust the progress of exercises and projects based on the estimated emotions. For example, if the student is feeling stressed, the progress can be slowed down and additional materials can be provided to deepen understanding. Alternatively, if the student is relaxed, the progress can be made faster than usual and a test can be provided to move on to the next step. Furthermore, if the student is excited, the progress can be adjusted and interactive content can be added to pique the student's interest. This improves learning effectiveness by providing progress that corresponds to the student's emotions.

[0105] The New Business Discovery Department can analyze a student's past learning history and propose new business ideas based on their past learning content and achievements. For example, it can automatically select the next topic to learn based on what they have learned in the past and propose new business ideas related to it. It can also analyze past test results and propose business ideas to strengthen weak points. It can also propose related new business ideas based on the history of workshops and online courses they have participated in in the past. This makes it possible to propose new business ideas based on a student's past learning history.

[0106] The curriculum provider can estimate the student's emotions and adjust the pace of the curriculum based on the estimated emotions. For example, if the student is feeling stressed, the curriculum provider can slow down the pace of the curriculum and provide additional materials to deepen understanding. Alternatively, if the student is relaxed, the curriculum provider can speed up the pace of the curriculum and provide a test to advance to the next step. Furthermore, if the student is excited, the curriculum provider can adjust the pace of the curriculum and add interactive content to attract the student's interest. This improves learning effectiveness by providing a curriculum pace that corresponds to the student's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The curriculum provider provides a curriculum for individuals or organizations to learn the basic knowledge and skills required to utilize AI. The curriculum includes basic AI concepts, algorithms, data processing techniques, etc. The curriculum provider provides participants with opportunities to learn basic AI knowledge and skills through online courses and workshops. For example, the curriculum provider can provide live sessions, recorded lectures, and interactive exercises. Step 2: The Exercise and Project Department provides exercises or projects to apply the knowledge provided by the Curriculum Department to practice. Exercises and projects include case studies, practical projects, and simulations based on real business scenarios. Participants have the opportunity to work on projects that use AI to solve problems related to their work. For example, they can work on projects aimed at improving specific business processes or increasing business efficiency. Step 3: The New Business Discovery Department will discover new business ideas based on the results of the Exercises and Projects Department and evaluate their feasibility. The New Business Discovery Department will evaluate the proposed ideas based on criteria such as technical feasibility, marketability, and innovativeness, and provide feedback.

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

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0130] 5, the data processing system 310 includes the 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0146] 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[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] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system characterized by comprising: a curriculum provision unit that provides a curriculum for individuals or organizations to learn basic knowledge and skills for utilizing AI; an exercise / project provision unit that provides exercises or projects for applying the knowledge provided by the curriculum provision unit in practice; and a new business discovery unit that discovers new business ideas and evaluates their feasibility based on the results obtained by the exercise / project provision unit.

2. The system of claim 1 , wherein the curriculum provider provides online courses or workshops.

3. The system according to claim 1 , wherein the exercise / project providing unit provides exercises or projects based on actual business scenarios.

4. 2. The system of claim 1, wherein the new business development department evaluates proposed ideas and provides feedback.

5. The system according to claim 1, wherein the curriculum provider provides a curriculum including basic concepts and techniques of AI, how to handle data, and how to build AI models.

6. The system according to claim 1, wherein the exercise / project providing unit provides an opportunity for students to work on a project using AI to solve a problem related to their work.

7. The system according to claim 1, wherein the new business discovery department proposes ideas for new services or products using AI and holds discussions to evaluate their feasibility.

8. The system according to claim 1, wherein the platform also serves as a forum for sharing success stories of businesses utilizing AI and the latest technological trends.

9. 2. The system according to claim 1, wherein the curriculum providing unit estimates the emotions of the students and adjusts the pace of the curriculum based on the estimated emotions of the students.

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