system

Abstract

The system according to the embodiment aims to analyze information related to human resource development at each organization, company, and school, and provide the most suitable program for each user. [Solution] The system according to the embodiment includes an information collection unit, an analysis unit, a generation unit, and a provision unit. The information collection unit collects information related to human resource development from various organizations, companies, and schools. The analysis unit analyzes the information collected by the information collection unit. The generation unit generates an appropriate program based on the information analyzed by the analysis unit. The provision unit provides the program generated by the generation unit to a user.

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 does not adequately consolidate information on human resource development from various organizations, companies, and schools, and provide optimal programs for individual users, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze information related to human resource development at each organization, company, and school, and provide the most suitable program for each user. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a generation unit, and a provision unit. The information collection unit collects information related to human resource development from organizations, companies, and schools. The analysis unit analyzes the information collected by the information collection unit. The generation unit generates an appropriate program based on the information analyzed by the analysis unit. The provision unit provides the program generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information related to human resource development at each organization, company, and school, and provide the most suitable program for each user. [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 human resource development system according to an embodiment of the present invention centrally aggregates information on human resource development from organizations, companies, and schools in Japan and overseas, and automatically provides "advanced human resource development programs" tailored to each individual, from students to working adults around the world, through a generation AI. The human resource development system collects information on human resource development from each organization, company, and school, analyzes it using a generation AI, and generates an optimal program based on the user's needs and characteristics. For example, the human resource development system collects detailed information about the programs offered by each organization, company, and school, such as the content, target audience, and implementation method. The human resource development system then analyzes the collected information using a generation AI to analyze the characteristics of each program and the needs of the target audience. For example, the human resource development system can propose an optimal internship program for students with specific skills. Furthermore, the human resource development system uses a generation AI to generate an optimal program based on the user's needs and characteristics. For example, if a user wants to acquire a specific skill, the system can propose a program related to that skill. It can also generate a program tailored to the user's career path. The generated program is then provided to the user, who can gain new learning and experience through the provided program. For example, by taking online courses or participating in internships, users can acquire practical skills. This allows the human resource development system to provide users with creative value and career paths. This allows the human resource development system to automatically provide each user with an optimal "advanced human resource generation program," providing creative value and career paths through new learning and experiences. For example, acquiring new skills broadens the scope of one's career and creates more options. Furthermore, participating in programs in different fields allows users to gain new perspectives and ideas.

[0029] The human resource development system according to the embodiment includes an information collection unit, an analysis unit, a generation unit, and a provision unit. The information collection unit collects information related to human resource development from organizations, companies, and schools. The information collection unit acquires data from organizations, companies, and schools, for example, via an API. The information collection unit can also collect publicly available information using web scraping technology. The information collection unit can also periodically collect the latest information through collaboration with organizations, companies, and schools. The analysis unit analyzes the collected information using a generation AI. For example, the analysis unit analyzes the characteristics of each program and the needs of the target users based on the collected information. For example, the generation AI analyzes the content of the program using a text generation AI (e.g., LLM) to identify the program that is optimal for a user with specific skills. The analysis unit can also analyze information such as images and audio, in addition to text, using a multimodal generation AI. The generation unit generates an optimal program based on the information analyzed by the analysis unit using the generation AI. The generation unit customizes the program based on the user's needs and characteristics, for example. For example, the generation AI generates a program tailored to the user's career path. The generation unit can also use the generation AI to generate a program based on the user's interests and concerns. The provision unit provides the generated program to the user. The provision unit provides the program, for example, via email, a dedicated app, or a website. The provision unit can also collect user feedback, and the generation AI can improve the program based on that feedback. For example, the provision unit adjusts the content and provision method of the program based on user feedback. As a result, the human resource development system according to the embodiment can provide optimal programs for each user and provide creative value and career paths through new learning and experiences.

[0030] The information collection unit can acquire data from each organization, company, or school through an API. Examples of APIs include, but are not limited to, a REST API and a SOAP API. The information collection unit can acquire data from each organization, company, or school using, for example, a REST API. The information collection unit can also acquire data using a SOAP API. For example, the information collection unit can acquire data by sending a request to an API endpoint provided by each organization, company, or school. This allows efficient information collection by acquiring data through an API. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI. For example, the information collection unit can input data acquired through the API into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the characteristics of each program and the needs of the subjects based on the collected information. The analysis unit, for example, analyzes the characteristics of each program based on the collected information. For example, the analysis unit analyzes the content and implementation method of the program, the characteristics of the subjects, etc. The analysis unit can also analyze the needs of the subjects based on the collected information. For example, the analysis unit analyzes the skill level, interests, career path, etc. of the subjects. The analysis unit can also analyze the effectiveness and evaluation of the program based on the collected information. For example, the analysis unit analyzes feedback and evaluation data after the program is implemented and evaluates the effectiveness of the program. In this way, by analyzing the collected information, the characteristics of each program and the needs of the subjects can be understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI and cause the generation AI to analyze the characteristics of the program and the needs of the subjects.

[0032] The generation unit can generate an appropriate program based on the user's needs and characteristics. The generation unit, for example, generates a program based on the user's needs. For example, if the user wants to acquire a specific skill, the generation unit generates a program related to that skill. The generation unit can also generate a program based on the user's characteristics. For example, the generation unit generates a program tailored to the user's career path. Furthermore, the generation unit can also generate a program based on the user's interests and concerns. For example, the generation unit generates a program related to a field in which the user is interested. This allows for generating an optimal program based on the user's needs and characteristics, thereby providing personalized learning and experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's needs and characteristics into a generation AI and cause the generation AI to generate an optimal program.

[0033] The providing unit can provide the generated program to the user via email, a dedicated app, or a website. The providing unit, for example, provides the generated program to the user via email. For example, the providing unit sends the user an email including a link and detailed information about the generated program. The providing unit can also provide the program via a dedicated app. For example, the providing unit provides a program notification and an access link through the dedicated app. The providing unit can also provide the program via a website. For example, the providing unit posts detailed information about the program and a registration link on the website. This makes it easier for users to access the generated program by providing it in various ways. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a method for providing the generated program to the generation AI and cause the generation AI to select the optimal method for providing the program.

[0034] The providing unit collects user feedback, and the generation AI can improve the program based on that feedback. The providing unit, for example, collects user feedback in the form of a questionnaire. For example, the providing unit may send a questionnaire to users after the program is completed to collect feedback. The providing unit may also collect user comments and ratings. For example, the providing unit may provide a function that allows users to post comments and ratings on a dedicated app or website. The providing unit may also collect user behavioral data and use it as feedback. For example, the providing unit may collect data such as users' program usage status and completion rate. This allows the program to be improved based on user feedback, thereby providing a more appropriate program. Some or all of the above-described processes for collecting feedback and improving the program may be performed using, or without, AI. For example, the providing unit may input the collected feedback into the generation AI and cause the generation AI to improve the program.

[0035] The information collection unit can analyze the past data provision history of each organization, company, and school and select an appropriate information collection method. The information collection unit, for example, analyzes the past data provision history of each organization, company, and school. For example, the information collection unit prioritizes collecting information from organizations that have provided data quickly in the past. The information collection unit can also prioritize collecting information from companies that have provided high-quality data in the past. Furthermore, the information collection unit can collect information from schools that provided a lot of data during a specific period based on the past data provision history. This allows for efficient information collection by analyzing the past data provision history. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without AI. For example, the information collection unit can input the past data provision history into the generation AI and have the generation AI select the optimal information collection method.

[0036] The information collection unit can filter information based on the current projects and areas of interest of each organization, company, or school. For example, the information collection unit may prioritize information related to each organization's ongoing projects. The information collection unit can also filter and collect relevant information based on each company's areas of interest. Furthermore, the information collection unit can filter and collect relevant information based on each school's research theme. This allows highly relevant information to be collected by filtering information based on current projects and areas of interest. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI. For example, the information collection unit may input data on the current projects and areas of interest of each organization, company, or school into the generation AI and have the generation AI perform information filtering.

[0037] The information collection unit can select an appropriate collection method depending on the data format provided by each organization, company, or school. For example, the information collection unit selects the optimal collection method depending on the data format (text, image, audio, etc.) provided by each organization, company, or school. For example, the information collection unit collects text data via an API from organizations that provide text data. The information collection unit can also collect image data using image recognition technology from companies that provide image data. Furthermore, the information collection unit can collect audio data using voice recognition technology from schools that provide audio data. This allows for efficient information collection by selecting the optimal collection method depending on the data format. Some or all of the above-described processing in the information collection unit may be performed using, for example, AI, or may be performed without AI. For example, the information collection unit can input the data format provided by each organization, company, or school into a generation AI and have the generation AI select the optimal collection method.

[0038] The information collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each organization, company, and school. The information collection unit, for example, collects information by taking into account the geographical location information of each organization, company, and school. For example, the information collection unit prioritizes collecting information from geographically close organizations. The information collection unit can also prioritize collecting information from geographically related companies. Furthermore, the information collection unit can prioritize collecting information from geographically related schools. This allows for efficient collection of highly relevant information by taking geographical location information into account. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the geographical location information of each organization, company, and school into the generation AI and cause the generation AI to collect information.

[0039] The information collection unit can analyze the social media activities of each organization, company, and school and collect related information. The information collection unit, for example, analyzes the social media activities of each organization, company, and school. For example, the information collection unit analyzes the social media posts of each organization and collects related information. The information collection unit can also analyze the social media activities of each company and collect related information. Furthermore, the information collection unit can analyze the social media activities of each school and collect related information. In this way, by analyzing social media activities, the latest related information can be collected. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input data on the social media activities of each organization, company, and school into the generation AI and have the generation AI collect information.

[0040] The information collection unit can customize the collection method by reflecting past feedback from each organization, company, or school. The information collection unit, for example, analyzes past feedback from each organization, company, or school. For example, the information collection unit adjusts the frequency of information collection based on past feedback. The information collection unit can also customize the means of information collection based on past feedback. Furthermore, the information collection unit can also select targets for information collection based on past feedback. This allows the collection method to be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the information collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the information collection unit can input past feedback into the generation AI and have the generation AI customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of each program. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of each program. For example, the analysis unit performs a detailed analysis on programs with high importance. The analysis unit can also perform a concise analysis on programs with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. As a result, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of each program to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of each program. For example, the analysis unit applies different analysis algorithms depending on the category of each program. For example, the analysis unit applies a technical analysis algorithm to a technical program. The analysis unit can also apply a business analysis algorithm to a business program. Furthermore, the analysis unit can apply an art analysis algorithm to an art program. In this way, by applying an analysis algorithm depending on the category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of each program to a generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results, for example. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of analysis detail by referring to the user's past analysis results. Furthermore, the analysis unit can determine the priority of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the release date of each program. The analysis unit determines the analysis priority based on, for example, the release date of each program. For example, the analysis unit prioritizes analysis of programs that will be released soon. The analysis unit can also postpone analysis of programs that will be released further in the future. Furthermore, the analysis unit can adjust the analysis schedule according to the release date. In this way, by determining the analysis priority based on the release date, analysis can be performed in a timely manner. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input release date data of each program to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of each program. The analysis unit adjusts the order of analysis based on, for example, the relevance of each program. For example, the analysis unit prioritizes analysis of highly relevant programs. The analysis unit can also postpone analysis of less relevant programs. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. As a result, analysis can be performed efficiently by adjusting the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of each program to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise, for example. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with a high level of expertise. The analysis unit can also provide analysis results that are concise and easy to understand to a user with a low level of expertise. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the level of expertise. In this way, by adjusting the use of technical terminology according to the level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The generation unit can adjust the level of detail of the program to be generated based on the user's career path. The generation unit, for example, adjusts the level of detail of the program based on the user's career path. For example, the generation unit generates a program that emphasizes skills related to the user's career path. The generation unit can also generate a program that includes detailed explanations according to the user's career path. Furthermore, the generation unit can determine the priority of the program based on the user's career path. This makes it possible to provide an optimal program for the user by adjusting the level of detail of the program based on the career path. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's career path data into the generation AI and cause the generation AI to adjust the level of detail of the program.

[0048] The generation unit can apply different generation algorithms depending on the user's interests. For example, the generation unit applies different generation algorithms depending on the user's interests. For example, the generation unit generates a relevant program based on the user's interests. The generation unit can also apply different generation algorithms depending on the user's interests. Furthermore, the generation unit can apply an optimal generation algorithm based on the user's past selection history. In this way, by applying a generation algorithm depending on the user's interests, it is possible to provide the user with an optimal program. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to apply the generation algorithm.

[0049] The generation unit can improve the accuracy of generation by referring to the user's past program usage history. The generation unit can improve the accuracy of generation by referring to, for example, the user's past program usage history. For example, the generation unit generates an optimal program based on the user's past program usage history. The generation unit can also adjust the level of detail of the program by referring to the user's past program usage history. Furthermore, the generation unit can also determine the priority of programs based on the user's past program usage history. In this way, the accuracy of generation can be improved by referring to the past program usage history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past program usage history data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0050] The generation unit can determine the priority of programs to be generated based on the user's skill level. The generation unit determines the priority of programs based on, for example, the user's skill level. For example, if the user's skill level is high, the generation unit can prioritize generating programs with high difficulty. Also, if the user's skill level is low, the generation unit can prioritize generating basic programs. Furthermore, the generation unit can adjust the priority of programs according to the user's skill level. In this way, by determining the priority of programs based on the skill level, it is possible to provide the user with an optimal program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's skill level data into the generation AI and cause the generation AI to determine the priority of programs.

[0051] The generation unit can adjust the order of the programs to be generated based on the user's related learning history. The generation unit, for example, adjusts the order of the programs based on the user's related learning history. For example, the generation unit prioritizes generating related programs based on the user's learning history. The generation unit can also adjust the order of the programs according to the user's learning history. Furthermore, the generation unit can also determine the optimal program order by referring to the user's learning history. In this way, by adjusting the program order based on the learning history, it is possible to provide the user with an optimal program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning history data into the generation AI and cause the generation AI to adjust the program order.

[0052] The generation unit can adjust the use of technical terminology in the generated program according to the user's level of expertise. The generation unit, for example, adjusts the use of technical terminology in the program according to the user's level of expertise. For example, the generation unit generates a program that uses a lot of technical terminology for a user with a high level of expertise. The generation unit can also generate a concise and easy-to-understand program for a user with a low level of expertise. Furthermore, the generation unit can adjust the level of detail of the program according to the level of expertise. This makes it possible to provide a program that is easy for the user to understand by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] The providing unit can select an appropriate delivery method by referring to the user's past program usage history. The providing unit, for example, selects the optimal delivery method by referring to the user's past program usage history. For example, the providing unit selects the optimal delivery method based on the user's past program usage history. The providing unit can also adjust the level of detail of delivery by referring to the user's past program usage history. Furthermore, the providing unit can also determine delivery priorities based on the user's past program usage history. In this way, the optimal delivery method can be selected by referring to the past program usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past program usage history data into a generation AI and cause the generation AI to select a delivery method.

[0054] The providing unit can customize the content to be provided according to the user's current task. The providing unit customizes the content to be provided according to, for example, the user's current task. For example, the providing unit prioritizes providing programs related to the task the user is currently working on. The providing unit can also customize the content to be provided according to the user's current task. Furthermore, the providing unit can provide an optimal program based on the user's current task. In this way, by customizing the content to be provided according to the current task, it is possible to provide an optimal program for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generating AI and cause the generating AI to customize the content to be provided.

[0055] The providing unit can improve the providing method by reflecting user feedback. The providing unit, for example, improves the providing method based on user feedback. For example, the providing unit improves the providing method based on user feedback. The providing unit can also adjust the level of detail of the providing by referring to the user feedback. Furthermore, the providing unit can also determine the priority of the providing based on the user feedback. In this way, by reflecting the feedback, the providing method can be improved and a more appropriate program can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0056] The providing unit can select an appropriate delivery method by taking into account the user's device information. The providing unit, for example, selects the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a delivery method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is concise and highly visible. This makes it possible to select the optimal delivery method for the user by taking into account the device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the delivery method.

[0057] The providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, makes the provided content multilingual in accordance with the user's language setting. For example, the providing unit automatically sets the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the provided content in that language. This makes it possible to provide a program that is easy for the user to understand by making the content multilingual in accordance with the language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute the multilingual provided content.

[0058] The providing unit can select an appropriate delivery method by taking into account the user's geographical location information. The providing unit selects the optimal delivery method by taking into account, for example, the user's geographical location information. For example, the providing unit provides a related program based on the user's geographical location information. The providing unit can also customize the content to be provided according to the user's geographical location information. Furthermore, the providing unit can select the optimal delivery method by referring to the user's geographical location information. In this way, the optimal program can be provided to the user by taking into account the geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into a generation AI and cause the generation AI to select a delivery method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The information collection unit can evaluate the reliability of data provided by each organization, company, and school and prioritize the collection of reliable information. For example, it can evaluate reliability based on past data provision history and prioritize the collection of information from reliable organizations. The information collection unit can also evaluate the frequency and accuracy of data provision and select reliable information. Furthermore, the information collection unit can prioritize the collection of reliable information based on an evaluation of the data provider. This allows for the provision of more accurate information by prioritizing the collection of reliable information. Some or all of the above-mentioned processing in the information collection unit can be performed, for example, using AI or without AI. For example, the information collection unit can input the reliability of data provided by each organization, company, and school into the generation AI and have the generation AI perform a reliability evaluation.

[0061] The information collection unit can evaluate the update frequency of data provided by each organization, company, and school, and prioritize collecting the most recent information. For example, the information collection unit can evaluate the update frequency based on past data provision history and prioritize collecting information from organizations with high update frequencies. The information collection unit can also select information with high update frequencies based on an evaluation of the data provider. Furthermore, the information collection unit can also prioritize collecting the most recent information based on an evaluation of the data provider. This allows for the provision of more accurate information by prioritizing the collection of the most recent information. Some or all of the above-described processing in the information collection unit can be performed using, for example, AI, or without AI. For example, the information collection unit can input the update frequency of data provided by each organization, company, and school into the generation AI, and have the generation AI evaluate the update frequency.

[0062] The generation unit can customize the program content based on the user's past program usage history. For example, it can analyze the content of programs used in the past and generate a program related to the user's field of interest. It can also generate a program that complements the user's weak areas based on the user's past program usage history. It can also generate a program that focuses on topics in which the user is particularly interested based on the user's past program usage history. This allows the program content to be customized based on the user's past program usage history, thereby providing the user with the optimal program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past program usage history data into a generation AI and have the generation AI customize the program content.

[0063] The providing unit can adjust the providing method taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, a lightweight text-format program can be provided. Also, when the remaining battery level is sufficient, a video-format program can be provided. Furthermore, when the remaining battery level is moderate, an interactive program can be provided. In this way, the optimal providing method for the user can be selected by taking the remaining battery level of the device into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input remaining battery level data of the user's device to the generation AI and cause the generation AI to adjust the providing method.

[0064] The information collection unit can select the optimal analysis method based on the format of the data provided by each organization, company, or school. For example, in the case of text data, analysis can be performed using natural language processing technology. In addition, in the case of image data, analysis can be performed using image recognition technology. Furthermore, in the case of audio data, analysis can be performed using voice recognition technology. In this way, by selecting the optimal analysis method based on the data format, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or without AI. For example, the information collection unit can input the format of the data provided by each organization, company, or school into the generation AI and have the generation AI select the optimal analysis method.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The information gathering department collects information on human resource development from each organization, company, and school. For example, the information gathering department obtains data from each organization, company, and school through an API. The information gathering department can also collect publicly available information using web scraping technology. Furthermore, the information gathering department can regularly collect the latest information through collaboration with each organization, company, and school. Step 2: The analysis unit uses the generation AI to analyze the collected information. For example, the analysis unit analyzes the characteristics of each program and the needs of the target users based on the collected information. For example, the generation AI uses text generation AI (e.g., LLM) to analyze the content of the program and identify the program that is best suited to users with specific skills. The analysis unit can also use multimodal generation AI to analyze information such as images and audio in addition to text. Step 3: The generation unit uses the generation AI to generate an optimal program based on the information analyzed by the analysis unit. The generation unit customizes the program based on, for example, the user's needs and characteristics. For example, the generation AI generates a program tailored to the user's career path. The generation unit can also use the generation AI to generate a program based on the user's interests and concerns. Step 4: The providing unit provides the generated program to the user. The providing unit provides the program via email, a dedicated app, or a website, for example. The providing unit can also collect user feedback, and the generation AI can improve the program based on that feedback. For example, the providing unit can adjust the content and delivery method of the program based on user feedback.

[0067] (Example 2) A human resource development system according to an embodiment of the present invention centrally aggregates information on human resource development from organizations, companies, and schools in Japan and overseas, and automatically provides "advanced human resource development programs" tailored to each individual, from students to working adults around the world, through a generation AI. The human resource development system collects information on human resource development from each organization, company, and school, analyzes it using a generation AI, and generates an optimal program based on the user's needs and characteristics. For example, the human resource development system collects detailed information about the programs offered by each organization, company, and school, such as the content, target audience, and implementation method. The human resource development system then analyzes the collected information using a generation AI to analyze the characteristics of each program and the needs of the target audience. For example, the human resource development system can propose an optimal internship program for students with specific skills. Furthermore, the human resource development system uses a generation AI to generate an optimal program based on the user's needs and characteristics. For example, if a user wants to acquire a specific skill, the system can propose a program related to that skill. It can also generate a program tailored to the user's career path. The generated program is then provided to the user, who can gain new learning and experience through the provided program. For example, by taking online courses or participating in internships, users can acquire practical skills. This allows the human resource development system to provide users with creative value and career paths. This allows the human resource development system to automatically provide each user with an optimal "advanced human resource generation program," providing creative value and career paths through new learning and experiences. For example, acquiring new skills broadens the scope of one's career and creates more options. Furthermore, participating in programs in different fields allows users to gain new perspectives and ideas.

[0068] The human resource development system according to the embodiment includes an information collection unit, an analysis unit, a generation unit, and a provision unit. The information collection unit collects information related to human resource development from organizations, companies, and schools. The information collection unit acquires data from organizations, companies, and schools, for example, via an API. The information collection unit can also collect publicly available information using web scraping technology. The information collection unit can also periodically collect the latest information through collaboration with organizations, companies, and schools. The analysis unit analyzes the collected information using a generation AI. For example, the analysis unit analyzes the characteristics of each program and the needs of the target users based on the collected information. For example, the generation AI analyzes the content of the program using a text generation AI (e.g., LLM) to identify the program that is optimal for a user with specific skills. The analysis unit can also analyze information such as images and audio, in addition to text, using a multimodal generation AI. The generation unit generates an optimal program based on the information analyzed by the analysis unit using the generation AI. The generation unit customizes the program based on the user's needs and characteristics, for example. For example, the generation AI generates a program tailored to the user's career path. The generation unit can also use the generation AI to generate a program based on the user's interests and concerns. The provision unit provides the generated program to the user. The provision unit provides the program, for example, via email, a dedicated app, or a website. The provision unit can also collect user feedback, and the generation AI can improve the program based on that feedback. For example, the provision unit adjusts the content and provision method of the program based on user feedback. As a result, the human resource development system according to the embodiment can provide optimal programs for each user and provide creative value and career paths through new learning and experiences.

[0069] The information collection unit can acquire data from each organization, company, or school through an API. Examples of APIs include, but are not limited to, a REST API and a SOAP API. The information collection unit can acquire data from each organization, company, or school using, for example, a REST API. The information collection unit can also acquire data using a SOAP API. For example, the information collection unit can acquire data by sending a request to an API endpoint provided by each organization, company, or school. This allows efficient information collection by acquiring data through an API. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI. For example, the information collection unit can input data acquired through the API into a generation AI and have the generation AI analyze the data.

[0070] The analysis unit can analyze the characteristics of each program and the needs of the subjects based on the collected information. The analysis unit, for example, analyzes the characteristics of each program based on the collected information. For example, the analysis unit analyzes the content and implementation method of the program, the characteristics of the subjects, etc. The analysis unit can also analyze the needs of the subjects based on the collected information. For example, the analysis unit analyzes the skill level, interests, career path, etc. of the subjects. The analysis unit can also analyze the effectiveness and evaluation of the program based on the collected information. For example, the analysis unit analyzes feedback and evaluation data after the program is implemented and evaluates the effectiveness of the program. In this way, by analyzing the collected information, the characteristics of each program and the needs of the subjects can be understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI and cause the generation AI to analyze the characteristics of the program and the needs of the subjects.

[0071] The generation unit can generate an appropriate program based on the user's needs and characteristics. The generation unit, for example, generates a program based on the user's needs. For example, if the user wants to acquire a specific skill, the generation unit generates a program related to that skill. The generation unit can also generate a program based on the user's characteristics. For example, the generation unit generates a program tailored to the user's career path. Furthermore, the generation unit can also generate a program based on the user's interests and concerns. For example, the generation unit generates a program related to a field in which the user is interested. This allows for generating an optimal program based on the user's needs and characteristics, thereby providing personalized learning and experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's needs and characteristics into a generation AI and cause the generation AI to generate an optimal program.

[0072] The providing unit can provide the generated program to the user via email, a dedicated app, or a website. The providing unit, for example, provides the generated program to the user via email. For example, the providing unit sends the user an email including a link and detailed information about the generated program. The providing unit can also provide the program via a dedicated app. For example, the providing unit provides a program notification and an access link through the dedicated app. The providing unit can also provide the program via a website. For example, the providing unit posts detailed information about the program and a registration link on the website. This makes it easier for users to access the generated program by providing it in various ways. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a method for providing the generated program to the generation AI and cause the generation AI to select the optimal method for providing the program.

[0073] The providing unit collects user feedback, and the generation AI can improve the program based on that feedback. The providing unit, for example, collects user feedback in the form of a questionnaire. For example, the providing unit may send a questionnaire to users after the program is completed to collect feedback. The providing unit may also collect user comments and ratings. For example, the providing unit may provide a function that allows users to post comments and ratings on a dedicated app or website. The providing unit may also collect user behavioral data and use it as feedback. For example, the providing unit may collect data such as users' program usage status and completion rate. This allows the program to be improved based on user feedback, thereby providing a more appropriate program. Some or all of the above-described processes for collecting feedback and improving the program may be performed using, or without, AI. For example, the providing unit may input the collected feedback into the generation AI and cause the generation AI to improve the program.

[0074] The information collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The information collection unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the information collection unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The information collection unit can also adjust the timing of information collection based on the user's emotion data. For example, if the user is stressed, the frequency of information collection can be reduced and information can be collected when the user is relaxed. Also, if the user is excited, information can be collected in real time and provided immediately. Furthermore, if the user is concentrating, information collection can be temporarily stopped and information can be collected when the user's concentration is broken. This allows information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the information collection unit can be performed, for example, using AI or without AI. For example, the information collection unit can input the user's emotional data into the generation AI and have the generation AI adjust the timing of information collection.

[0075] The information collection unit can analyze the past data provision history of each organization, company, and school and select an appropriate information collection method. The information collection unit, for example, analyzes the past data provision history of each organization, company, and school. For example, the information collection unit prioritizes collecting information from organizations that have provided data quickly in the past. The information collection unit can also prioritize collecting information from companies that have provided high-quality data in the past. Furthermore, the information collection unit can collect information from schools that provided a lot of data during a specific period based on the past data provision history. This allows for efficient information collection by analyzing the past data provision history. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without AI. For example, the information collection unit can input the past data provision history into the generation AI and have the generation AI select the optimal information collection method.

[0076] The information collection unit can filter information based on the current projects and areas of interest of each organization, company, or school. For example, the information collection unit may prioritize information related to each organization's ongoing projects. The information collection unit can also filter and collect relevant information based on each company's areas of interest. Furthermore, the information collection unit can filter and collect relevant information based on each school's research theme. This allows highly relevant information to be collected by filtering information based on current projects and areas of interest. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI. For example, the information collection unit may input data on the current projects and areas of interest of each organization, company, or school into the generation AI and have the generation AI perform information filtering.

[0077] The information collection unit can select an appropriate collection method depending on the data format provided by each organization, company, or school. For example, the information collection unit selects the optimal collection method depending on the data format (text, image, audio, etc.) provided by each organization, company, or school. For example, the information collection unit collects text data via an API from organizations that provide text data. The information collection unit can also collect image data using image recognition technology from companies that provide image data. Furthermore, the information collection unit can collect audio data using voice recognition technology from schools that provide audio data. This allows for efficient information collection by selecting the optimal collection method depending on the data format. Some or all of the above-described processing in the information collection unit may be performed using, for example, AI, or may be performed without AI. For example, the information collection unit can input the data format provided by each organization, company, or school into a generation AI and have the generation AI select the optimal collection method.

[0078] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The information collection unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the information collection unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The information collection unit can also determine the priority of information to be collected based on the user's emotion data. For example, if the user is stressed, the information collection unit can prioritize collecting relaxing information. If the user is excited, the information collection unit can prioritize collecting stimulating information. Furthermore, if the user is concentrating, the information collection unit can prioritize collecting information useful for learning. This allows more appropriate information to be collected by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the information collection unit can be performed, for example, using AI or without AI. For example, the information collection unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the information.

[0079] The information collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each organization, company, and school. The information collection unit, for example, collects information by taking into account the geographical location information of each organization, company, and school. For example, the information collection unit prioritizes collecting information from geographically close organizations. The information collection unit can also prioritize collecting information from geographically related companies. Furthermore, the information collection unit can prioritize collecting information from geographically related schools. This allows for efficient collection of highly relevant information by taking geographical location information into account. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the geographical location information of each organization, company, and school into the generation AI and cause the generation AI to collect information.

[0080] The information collection unit can analyze the social media activities of each organization, company, and school and collect related information. The information collection unit, for example, analyzes the social media activities of each organization, company, and school. For example, the information collection unit analyzes the social media posts of each organization and collects related information. The information collection unit can also analyze the social media activities of each company and collect related information. Furthermore, the information collection unit can analyze the social media activities of each school and collect related information. In this way, by analyzing social media activities, the latest related information can be collected. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input data on the social media activities of each organization, company, and school into the generation AI and have the generation AI collect information.

[0081] The information collection unit can customize the collection method by reflecting past feedback from each organization, company, or school. The information collection unit, for example, analyzes past feedback from each organization, company, or school. For example, the information collection unit adjusts the frequency of information collection based on past feedback. The information collection unit can also customize the means of information collection based on past feedback. Furthermore, the information collection unit can also select targets for information collection based on past feedback. This allows the collection method to be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the information collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the information collection unit can input past feedback into the generation AI and have the generation AI customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The analysis unit can also adjust the presentation method of the analysis based on the user's emotion data. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of each program. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of each program. For example, the analysis unit performs a detailed analysis on programs with high importance. The analysis unit can also perform a concise analysis on programs with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. As a result, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of each program to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of each program. For example, the analysis unit applies different analysis algorithms depending on the category of each program. For example, the analysis unit applies a technical analysis algorithm to a technical program. The analysis unit can also apply a business analysis algorithm to a business program. Furthermore, the analysis unit can apply an art analysis algorithm to an art program. In this way, by applying an analysis algorithm depending on the category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of each program to a generation AI and cause the generation AI to apply the analysis algorithm.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results, for example. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of analysis detail by referring to the user's past analysis results. Furthermore, the analysis unit can determine the priority of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The analysis unit can also adjust the length of the analysis based on the user's emotion data. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] The analysis unit can determine the analysis priority based on the release date of each program. The analysis unit determines the analysis priority based on, for example, the release date of each program. For example, the analysis unit prioritizes analysis of programs that will be released soon. The analysis unit can also postpone analysis of programs that will be released further in the future. Furthermore, the analysis unit can adjust the analysis schedule according to the release date. In this way, by determining the analysis priority based on the release date, analysis can be performed in a timely manner. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input release date data of each program to the generation AI and have the generation AI determine the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of each program. The analysis unit adjusts the order of analysis based on, for example, the relevance of each program. For example, the analysis unit prioritizes analysis of highly relevant programs. The analysis unit can also postpone analysis of less relevant programs. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. As a result, analysis can be performed efficiently by adjusting the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of each program to the generation AI and cause the generation AI to adjust the order of analysis.

[0089] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise, for example. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with a high level of expertise. The analysis unit can also provide analysis results that are concise and easy to understand to a user with a low level of expertise. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the level of expertise. In this way, by adjusting the use of technical terminology according to the level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0090] The generation unit can estimate the user's emotions and adjust the expression method of the generated program based on the estimated user emotions. The generation unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the generation unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The generation unit can also adjust the expression method of the generated program based on the user's emotion data. For example, if the user is relaxed, the generation unit can generate a program that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a program that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a program that adds visually stimulating effects. This allows the program to be adjusted according to the user's emotions, providing a more appropriate program. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the program's expression method.

[0091] The generation unit can adjust the level of detail of the program to be generated based on the user's career path. The generation unit, for example, adjusts the level of detail of the program based on the user's career path. For example, the generation unit generates a program that emphasizes skills related to the user's career path. The generation unit can also generate a program that includes detailed explanations according to the user's career path. Furthermore, the generation unit can determine the priority of the program based on the user's career path. This makes it possible to provide an optimal program for the user by adjusting the level of detail of the program based on the career path. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's career path data into the generation AI and cause the generation AI to adjust the level of detail of the program.

[0092] The generation unit can apply different generation algorithms depending on the user's interests. For example, the generation unit applies different generation algorithms depending on the user's interests. For example, the generation unit generates a relevant program based on the user's interests. The generation unit can also apply different generation algorithms depending on the user's interests. Furthermore, the generation unit can apply an optimal generation algorithm based on the user's past selection history. In this way, by applying a generation algorithm depending on the user's interests, it is possible to provide the user with an optimal program. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to apply the generation algorithm.

[0093] The generation unit can improve the accuracy of generation by referring to the user's past program usage history. The generation unit can improve the accuracy of generation by referring to, for example, the user's past program usage history. For example, the generation unit generates an optimal program based on the user's past program usage history. The generation unit can also adjust the level of detail of the program by referring to the user's past program usage history. Furthermore, the generation unit can also determine the priority of programs based on the user's past program usage history. In this way, the accuracy of generation can be improved by referring to the past program usage history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past program usage history data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0094] The generation unit can estimate the user's emotions and adjust the length of the generated program based on the estimated user emotions. The generation unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the generation unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The generation unit can also adjust the length of the generated program based on the user's emotion data. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point program. If the user is relaxed, the generation unit can generate a longer program with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a program with visually stimulating effects. This allows the program length to be adjusted according to the user's emotions, providing a more appropriate program. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the program.

[0095] The generation unit can determine the priority of programs to be generated based on the user's skill level. The generation unit determines the priority of programs based on, for example, the user's skill level. For example, if the user's skill level is high, the generation unit can prioritize generating programs with high difficulty. Also, if the user's skill level is low, the generation unit can prioritize generating basic programs. Furthermore, the generation unit can adjust the priority of programs according to the user's skill level. In this way, by determining the priority of programs based on the skill level, it is possible to provide the user with an optimal program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's skill level data into the generation AI and cause the generation AI to determine the priority of programs.

[0096] The generation unit can adjust the order of the programs to be generated based on the user's related learning history. The generation unit, for example, adjusts the order of the programs based on the user's related learning history. For example, the generation unit prioritizes generating related programs based on the user's learning history. The generation unit can also adjust the order of the programs according to the user's learning history. Furthermore, the generation unit can also determine the optimal program order by referring to the user's learning history. In this way, by adjusting the program order based on the learning history, it is possible to provide the user with an optimal program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning history data into the generation AI and cause the generation AI to adjust the program order.

[0097] The generation unit can adjust the use of technical terminology in the generated program according to the user's level of expertise. The generation unit, for example, adjusts the use of technical terminology in the program according to the user's level of expertise. For example, the generation unit generates a program that uses a lot of technical terminology for a user with a high level of expertise. The generation unit can also generate a concise and easy-to-understand program for a user with a low level of expertise. Furthermore, the generation unit can adjust the level of detail of the program according to the level of expertise. This makes it possible to provide a program that is easy for the user to understand by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the program to be provided based on the estimated user's emotions. The providing unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the providing unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The providing unit can also adjust the display method of the program to be provided based on the user's emotion data. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for adjusting the display method according to the user's emotions to provide a more appropriate program. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's emotion data into the generating AI and cause the generating AI to adjust the display method.

[0099] The providing unit can select an appropriate delivery method by referring to the user's past program usage history. The providing unit, for example, selects the optimal delivery method by referring to the user's past program usage history. For example, the providing unit selects the optimal delivery method based on the user's past program usage history. The providing unit can also adjust the level of detail of delivery by referring to the user's past program usage history. Furthermore, the providing unit can also determine delivery priorities based on the user's past program usage history. In this way, the optimal delivery method can be selected by referring to the past program usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past program usage history data into a generation AI and cause the generation AI to select a delivery method.

[0100] The providing unit can customize the content to be provided according to the user's current task. The providing unit customizes the content to be provided according to, for example, the user's current task. For example, the providing unit prioritizes providing programs related to the task the user is currently working on. The providing unit can also customize the content to be provided according to the user's current task. Furthermore, the providing unit can provide an optimal program based on the user's current task. In this way, by customizing the content to be provided according to the current task, it is possible to provide an optimal program for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generating AI and cause the generating AI to customize the content to be provided.

[0101] The providing unit can improve the providing method by reflecting user feedback. The providing unit, for example, improves the providing method based on user feedback. For example, the providing unit improves the providing method based on user feedback. The providing unit can also adjust the level of detail of the providing by referring to the user feedback. Furthermore, the providing unit can also determine the priority of the providing based on the user feedback. In this way, by reflecting the feedback, the providing method can be improved and a more appropriate program can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0102] The providing unit can estimate the user's emotions and prioritize programs to provide based on the estimated user emotions. The providing unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the providing unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The providing unit can also prioritize programs to provide based on the user's emotion data. For example, if the user is stressed, it can prioritize relaxing programs to provide. If the user is excited, it can prioritize stimulating programs to provide. Furthermore, if the user is concentrating, it can prioritize programs that are useful for learning to provide. This allows more appropriate programs to be provided by prioritizing programs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the program priorities.

[0103] The providing unit can select an appropriate delivery method by taking into account the user's device information. The providing unit, for example, selects the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a delivery method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is concise and highly visible. This makes it possible to select the optimal delivery method for the user by taking into account the device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the delivery method.

[0104] The providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, makes the provided content multilingual in accordance with the user's language setting. For example, the providing unit automatically sets the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the provided content in that language. This makes it possible to provide a program that is easy for the user to understand by making the content multilingual in accordance with the language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute the multilingual provided content.

[0105] The providing unit can select an appropriate delivery method by taking into account the user's geographical location information. The providing unit selects the optimal delivery method by taking into account, for example, the user's geographical location information. For example, the providing unit provides a related program based on the user's geographical location information. The providing unit can also customize the content to be provided according to the user's geographical location information. Furthermore, the providing unit can select the optimal delivery method by referring to the user's geographical location information. In this way, the optimal program can be provided to the user by taking into account the geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into a generation AI and cause the generation AI to select a delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the information collection unit, analysis unit, generation unit, and provision 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 information collection unit can acquire data from organizations, companies, and schools through the communication I / F 44 of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a program based on the user's needs and characteristics. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated program to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the information collection unit, analysis unit, generation unit, and provision 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 information collection unit can acquire data from organizations, companies, and schools through the communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a program based on the user's needs and characteristics. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated program to the user. === Hard Collateral 1-3 === Each of the multiple elements including the information collection unit, analysis unit, generation unit, and provision 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 information collection unit can acquire data from organizations, companies, and schools through the communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a program based on the needs and characteristics of the user. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated program to the user. === Hard Collateral 1-4 === Each of the multiple elements including the information collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information collection unit can acquire data from organizations, companies, and schools through the communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a program based on the needs and characteristics of the user. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated program to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing relaxing content. Also, if the user is excited, the analysis unit can prioritize analyzing stimulating content. Furthermore, if the user is concentrating, the analysis unit can prioritize analyzing content that is useful for learning. By determining analysis priorities according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0108] The information collection unit can evaluate the reliability of data provided by each organization, company, and school and prioritize the collection of reliable information. For example, it can evaluate reliability based on past data provision history and prioritize the collection of information from reliable organizations. The information collection unit can also evaluate the frequency and accuracy of data provision and select reliable information. Furthermore, the information collection unit can prioritize the collection of reliable information based on an evaluation of the data provider. This allows for the provision of more accurate information by prioritizing the collection of reliable information. Some or all of the above-mentioned processing in the information collection unit can be performed, for example, using AI or without AI. For example, the information collection unit can input the reliability of data provided by each organization, company, and school into the generation AI and have the generation AI perform a reliability evaluation.

[0109] The generation unit can estimate the user's emotions and adjust the difficulty of the generated program based on the estimated user's emotions. For example, if the user is relaxed, a more difficult program can be generated. Also, if the user is stressed, a less difficult program can be generated. Furthermore, if the user is excited, a more challenging program can be generated. This allows the program difficulty to be adjusted according to the user's emotions, thereby providing a more appropriate program. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the program difficulty.

[0110] The providing unit can estimate the user's emotions and adjust the format of the program to be provided based on the estimated user's emotions. For example, if the user is relaxed, a video-format program can be provided. If the user is in a hurry, a text-format program can be provided. Furthermore, if the user is excited, an interactive program can be provided. This allows a more appropriate program to be provided by adjusting the program format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the program format.

[0111] The information collection unit can evaluate the update frequency of data provided by each organization, company, and school, and prioritize collecting the most recent information. For example, the information collection unit can evaluate the update frequency based on past data provision history and prioritize collecting information from organizations with high update frequencies. The information collection unit can also select information with high update frequencies based on an evaluation of the data provider. Furthermore, the information collection unit can also prioritize collecting the most recent information based on an evaluation of the data provider. This allows for the provision of more accurate information by prioritizing the collection of the most recent information. Some or all of the above-described processing in the information collection unit can be performed using, for example, AI, or without AI. For example, the information collection unit can input the update frequency of data provided by each organization, company, and school into the generation AI, and have the generation AI evaluate the update frequency.

[0112] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed graphs and charts as analysis results. If the user is in a hurry, the analysis unit can provide simple icons and symbols as analysis results. If the user is excited, the analysis unit can provide visually stimulating effects as analysis results. This allows for more appropriate analysis results by adjusting the visual presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the visual presentation of the analysis.

[0113] The generation unit can customize the program content based on the user's past program usage history. For example, it can analyze the content of programs used in the past and generate a program related to the user's field of interest. It can also generate a program that complements the user's weak areas based on the user's past program usage history. It can also generate a program that focuses on topics in which the user is particularly interested based on the user's past program usage history. This allows the program content to be customized based on the user's past program usage history, thereby providing the user with the optimal program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past program usage history data into a generation AI and have the generation AI customize the program content.

[0114] The providing unit can adjust the providing method taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, a lightweight text-format program can be provided. Also, when the remaining battery level is sufficient, a video-format program can be provided. Furthermore, when the remaining battery level is moderate, an interactive program can be provided. In this way, the optimal providing method for the user can be selected by taking the remaining battery level of the device into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input remaining battery level data of the user's device to the generation AI and cause the generation AI to adjust the providing method.

[0115] The information collection unit can select the optimal analysis method based on the format of the data provided by each organization, company, or school. For example, in the case of text data, analysis can be performed using natural language processing technology. In addition, in the case of image data, analysis can be performed using image recognition technology. Furthermore, in the case of audio data, analysis can be performed using voice recognition technology. In this way, by selecting the optimal analysis method based on the data format, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or without AI. For example, the information collection unit can input the format of the data provided by each organization, company, or school into the generation AI and have the generation AI select the optimal analysis method.

[0116] The providing unit can estimate the user's emotions and adjust the notification method of the program to be provided based on the estimated user's emotions. For example, if the user is relaxed, the program can be provided via push notification. If the user is in a hurry, the program can be provided via email. Furthermore, if the user is excited, the program can be provided via in-app notification. This allows for providing a more appropriate program by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The information gathering department collects information on human resource development from each organization, company, and school. For example, the information gathering department obtains data from each organization, company, and school through an API. The information gathering department can also collect publicly available information using web scraping technology. Furthermore, the information gathering department can regularly collect the latest information through collaboration with each organization, company, and school. Step 2: The analysis unit uses the generation AI to analyze the collected information. For example, the analysis unit analyzes the characteristics of each program and the needs of the target users based on the collected information. For example, the generation AI uses text generation AI (e.g., LLM) to analyze the content of the program and identify the program that is best suited to users with specific skills. The analysis unit can also use multimodal generation AI to analyze information such as images and audio in addition to text. Step 3: The generation unit uses the generation AI to generate an optimal program based on the information analyzed by the analysis unit. The generation unit customizes the program based on, for example, the user's needs and characteristics. For example, the generation AI generates a program tailored to the user's career path. The generation unit can also use the generation AI to generate a program based on the user's interests and concerns. Step 4: The providing unit provides the generated program to the user. The providing unit provides the program via email, a dedicated app, or a website, for example. The providing unit can also collect user feedback, and the generation AI can improve the program based on that feedback. For example, the providing unit can adjust the content and delivery method of the program based on user feedback.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. The Information Collection Department collects information on human resource development from various organizations, companies, and schools. an analysis unit that analyzes the information collected by the information collection unit; a generation unit that generates an appropriate program based on the information analyzed by the analysis unit; a providing unit that provides a user with the program generated by the generating unit; Equipped with A system characterized by:

2. The information collecting unit Acquire data from organizations, companies, and schools through API 2. The system of claim 1.

3. The analysis unit Analyze the characteristics of each program and the needs of the target audience based on the collected information 2. The system of claim 1.

4. The generation unit Generate appropriate programs based on user needs and characteristics 2. The system of claim 1.

5. The providing unit The generated program is provided to the user via email, a dedicated app, or a website.

2. The system of claim 1.

6. The providing unit Collect user feedback and let the generative AI improve the program based on that feedback 2. The system of claim 1.

7. The information collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The information collecting unit Analyze the past data provision history of each organization, company, and school and select the appropriate information collection method.

2. The system of claim 1.

Images