System

Abstract

An object of a system according to an embodiment is to automatically generate an optimal personal property raising program for a user.SOLUTION: A system according to an embodiment includes an information aggregation unit, a profile collection unit, and a program generation unit. The information aggregation part unitarily aggregates information on personal property raising programs provided by Japanese organizations, enterprises and schools or overseas organizations, enterprises and schools. The profile collection unit collects profile information of a user. A program generation part automatically generates a personal property raising program optimal to the user on the basis of the information aggregated by the information aggregation part and the information collected by the profile collection part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology required users to manually collect and analyze a large amount of information to find the optimal human resource development program, which was inefficient.

[0005] The system according to the embodiment aims to automatically generate a human resource development program that is optimal for the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an information aggregation unit, a profile collection unit, and a program generation unit. The information aggregation unit centrally aggregates information about human resource development programs offered by Japanese organizations, companies, and schools or overseas organizations, companies, and schools. The profile collection unit collects user profile information. The program generation unit automatically generates a human resource development program optimal for the user based on the information aggregated by the information aggregation unit and the information collected by the profile collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a human resource development program that is optimal for the 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The human resource development program automatic generation system according to an embodiment of the present invention is a system that centrally collects information on human resource development from organizations, companies, and schools in Japan and overseas, and uses a generation AI to automatically generate the optimal human resource development program for the user. As a result, the human resource development program automatic generation system can provide users with new learning and experiences, and help them discover creative values ​​and career paths.

[0029] A human resource development program automatic generation system according to an embodiment includes an information aggregation unit, a profile collection unit, and a program generation unit. The information aggregation unit centrally aggregates information about human resource development programs offered by organizations, companies, and schools in Japan and overseas. For example, it collects information such as the content, target audience, duration, cost, and delivery method (online or offline) of each program. The information aggregation unit stores the collected information in a database and analyzes and updates it as needed. The profile collection unit collects user profile information. For example, it collects information such as the user's age, educational background, work history, interests, skills, and career goals. The profile collection unit also provides an interface for inputting profile information when the user accesses the system. The program generation unit automatically generates a human resource development program optimal for the user based on the information aggregated by the information aggregation unit and the information collected by the profile collection unit. For example, the generation AI generates a program based on user prompts. As a result, the human resource development program automatic generation system according to an embodiment can automatically generate an optimal human resource development program for the user, providing new learning and experiences and helping the user discover creative value and career paths.

[0030] The information aggregation unit stores the aggregated human resource development information in a database and can analyze and update it as needed. For example, the information aggregation unit collects feedback provided by past participants, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it quantifies participants' satisfaction and learning outcomes and identifies areas for improvement in the program. The information aggregation unit also collects information on participants' skill improvement and career progress as outcome data, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it evaluates based on employment and promotion rates after program completion. The information aggregation unit also builds a system in which the generation AI evaluates the effectiveness of the program based on feedback and outcome data and prioritizes and proposes highly effective programs. For example, it automatically selects the optimal program based on past data. This allows for efficient management of aggregated information and provides the latest information.

[0031] The profile collection unit can collect profile information including the user's age, educational background, work history, interests, skills, and career goals. The profile collection unit, for example, provides an interface for collecting the user's age, educational background, work history, interests, skills, and career goals. For example, it provides a form for the user to enter and collect the necessary information. The profile collection unit also stores the information entered by the user in a database, and the generation AI suggests the most appropriate program based on that information. For example, if a user enters "I'm interested in data science," the generation AI searches for programs related to data science and suggests programs that match the user's skill level and career goals. The profile collection unit also regularly updates the user's profile information and suggests programs based on the latest information. This allows the collection of detailed profile information for the user and suggests more appropriate programs.

[0032] The program generation unit can generate a program based on a user's prompt. For example, if a user inputs "I'm interested in data science," the generation AI searches for programs related to data science and suggests programs that match the user's skill level and career goals. For example, the generation AI generates a program based on the user's prompt. For example, the generation AI receives a prompt such as "Please suggest the best data science program for this user" and generates a program. The program generation unit also implements an algorithm that enables the generation AI to generate a program based on the user's prompt. For example, the program generation unit uses a machine learning algorithm to generate an optimal program based on the user's prompt. This allows the generation AI to generate a program based on the user's specific instructions.

[0033] The information aggregation unit collects feedback or outcome data from past participants, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, the information aggregation unit collects feedback provided by past participants, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it quantifies participants' satisfaction and learning outcomes and identifies areas for improvement in the program. The information aggregation unit also collects information on participants' skill improvement and career progress as outcome data, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it evaluates based on employment rate and promotion rate after completion of the program. The information aggregation unit also builds a system in which the generation AI evaluates the effectiveness of the program based on feedback and outcome data and prioritizes proposing highly effective programs. For example, it automatically selects the optimal program based on past data. This makes it possible to evaluate the effectiveness of the program and provide highly effective programs.

[0034] The information aggregation unit can prioritize the aggregation of highly reliable information, taking into account the reliability and evaluation of the program provider. For example, the information aggregation unit collects past performance and evaluation data to evaluate the reliability of the program provider, and the generation AI analyzes the data to prioritize the aggregation of highly reliable information. For example, the information aggregation unit filters information based on the provider's evaluation score. The information aggregation unit also builds a system that prioritizes the aggregation of highly reliable information, taking into account the reliability of the provider. For example, it selects information based on the provider's authentication information and industry evaluation. The information aggregation unit also prioritizes the aggregation of highly reliable program information based on the provider's evaluation data. For example, it evaluates the reliability of the provider based on the evaluations and feedback of past participants. This makes it possible to provide highly reliable information and select programs that are beneficial to users.

[0035] The information aggregating unit aggregates multimedia information including video and audio data, and can convey the appeal of the program through visual and audio information as well. The information aggregating unit aggregates multimedia information including video and audio data when collecting program information, for example. For example, it collects program introduction videos and audio interviews of instructors, and conveys the appeal of the program through visual and audio information. The information aggregating unit also builds a system for conveying the appeal of the program based on the multimedia information. For example, it analyzes video and audio data and extracts the features and appeal of the program. The information aggregating unit also aggregates multimedia information including video and audio data, and conveys the appeal of the program through visual and audio information. For example, it provides program demo videos and audio lectures by instructors. This makes it possible to effectively convey the appeal of the program through visual and audio information.

[0036] The information aggregating unit can aggregate programs from different industries or fields across a cross-section, allowing users to choose from a variety of options. The information aggregating unit, for example, builds a system that aggregates programs from different industries or fields across a cross-section, allowing users to choose from a variety of options. For example, it centrally aggregates technical, design, and business programs. The information aggregating unit also collects program information from different industries or fields, allowing users to choose from a variety of options. For example, it organizes information so that programs from different industries can be compared. The information aggregating unit also develops a system that allows users to choose from a variety of options based on the program information aggregated across a cross-section. For example, it provides programs from different fields on a single platform. This allows users to select the most suitable program from a variety of options.

[0037] The profile collection unit records the user's past learning history or work history in detail, and the generation AI can suggest the optimal program based on that data. The profile collection unit, for example, records the user's past learning history in detail, and the generation AI can suggest the optimal program based on that data. For example, a program may be selected based on courses taken in the past or qualifications obtained. The profile collection unit also records the user's work history in detail, and the generation AI can suggest the optimal program based on that data. For example, a program may be selected based on past job content and industry experience. The profile collection unit also builds a system in which the generation AI suggests the optimal program based on the learning history and work history. For example, it analyzes past data and suggests a program that matches the user's skills and career goals. This makes it possible to suggest more appropriate programs based on the user's past learning history and work history.

[0038] The profile collection unit can conduct regular surveys or interviews to gain a deeper understanding of the user's interests and reflect the results in the profile. For example, the profile collection unit can conduct regular surveys to gain a deeper understanding of the user's interests and reflect the results in the profile. For example, the survey can collect information about areas of interest and skills the user wants to learn. The profile collection unit can also conduct regular interviews to gain a detailed understanding of the user's interests and reflect the results in the profile, and the generation AI can suggest optimal programs. For example, the program can be selected based on information obtained from the interview. The profile collection unit can also reflect the results of surveys or interviews in the profile, building a system in which the generation AI can suggest programs based on the user's interests and concerns. For example, the user's interests can be updated regularly. This allows for a deeper understanding of the user's interests and suggests more appropriate programs.

[0039] The profile collection unit can also add data about the user's health condition or lifestyle to the user's profile information, allowing for more comprehensive program suggestions. For example, the profile collection unit adds data about the user's health condition to the user's profile information, and the generation AI then suggests the optimal program based on that data. For example, it selects a program that suits the health condition. The profile collection unit also adds data about the user's lifestyle to the profile information, and the generation AI then suggests a comprehensive program based on that data. For example, it suggests a learning format that suits the lifestyle. The profile collection unit also adds data about the health condition or lifestyle to the profile information, creating a system that allows the generation AI to suggest more comprehensive programs. For example, it suggests a program that matches the user's lifestyle. This makes it possible to suggest comprehensive programs that take the user's health condition and lifestyle into consideration.

[0040] The profile collection unit can make the profile information input form multilingual so as to accommodate users of different languages ​​or cultural backgrounds. The profile collection unit, for example, makes the profile information input form multilingual so as to accommodate users of different languages ​​or cultural backgrounds. For example, it allows input in multiple languages ​​such as English, French, and Chinese. The profile collection unit also provides a multilingual input form so as to accommodate users of different cultural backgrounds. For example, it sets questions that take cultural backgrounds into consideration. The profile collection unit also makes the profile information input form multilingual so that the generation AI can propose optimal programs for users of different languages ​​or cultural backgrounds. For example, it automatically translates and analyzes the input content. This makes it possible to accommodate users of different languages ​​and cultural backgrounds.

[0041] When the generation AI automatically generates a program, the program generation unit can learn from past success stories or failure stories and generate a more effective program. For example, the program generation unit has the generation AI learn from past success stories and failure stories and generate an effective program when automatically generating a program. For example, it analyzes the factors for success and failure and reflects them in the program. The program generation unit also builds a system where the generation AI automatically generates effective programs based on past success stories and failure stories. For example, it extracts common points between success stories and incorporates them into the program. The program generation unit also has the generation AI learn from past data and automatically generate an effective program. For example, it incorporates measures to avoid failure stories into the program. In this way, it is possible to learn from past success stories and failure stories and generate a more effective program.

[0042] The program generation unit can add customization functions that match the user's learning style or pace when generating a program. For example, the program generation unit uses a generation AI to add customization functions that match the user's learning style and pace, and automatically generates a program. For example, the program content is adjusted according to the user's learning speed and preferences. The program generation unit also builds a system in which the generation AI automatically generates a customized program based on the user's learning style. For example, the program generation unit suggests a program that includes a lot of video content to a user who prefers visual learning. The program generation unit also adds customization functions that match the user's learning pace, and automatically generates a program. For example, the difficulty of the program is adjusted according to the user's progress. This makes it possible to customize the program to match the user's learning style and pace.

[0043] The program generation unit can combine programs from different fields or industries to provide new learning opportunities. For example, the program generation unit builds a system in which the generative AI combines programs from different fields or industries to provide new learning opportunities. For example, it proposes a learning course that combines technical and design programs. The program generation unit also provides new learning opportunities by combining programs from different industries. For example, it generates a program that allows students to learn business skills and creative skills at the same time. The program generation unit also provides new learning opportunities by combining programs from different fields. For example, it proposes a program that combines knowledge from the medical and IT fields. This makes it possible to provide new learning opportunities by combining programs from different fields or industries.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The information aggregator can also collect the user's geographical location information and suggest programs tailored to the characteristics of each region. For example, it can prioritize suggesting programs that are popular in a particular region or programs specialized in that region's industry. The information aggregator can also collect information on educational institutions and companies in each region and offer programs that are rooted in the region. For example, it can suggest programs in collaboration with local companies or learning programs based on the local culture. The information aggregator can also collect event information for each region and suggest local events that the user can participate in. For example, it can provide information on local seminars and workshops. This allows the user to have learning opportunities that are closely tied to the local area.

[0046] The information aggregating unit can also suggest new programs based on the user's learning history, taking into account their relevance to programs previously taken. For example, it can analyze the content and results of programs previously taken and suggest new programs related to them. The information aggregating unit can also grasp the user's learning progress based on the user's learning history and suggest the next program to take. For example, it can suggest a step-by-step program for acquiring a specific skill. The information aggregating unit can also reflect evaluations of programs previously taken based on the user's learning history and use this information to help select a new program. For example, it can suggest a program similar to a program that has received high evaluations in the past. This allows the user to take the optimal program based on their own learning history.

[0047] The profile collection unit can also collect information about the user's hobbies and lifestyle and suggest programs based on that information. For example, if the user is interested in sports or music, it can suggest programs related to those fields. The profile collection unit can also collect information about the user's lifestyle and suggest learning formats that suit that lifestyle. For example, it can suggest programs that can be learned in a short amount of time to a busy user. The profile collection unit can also suggest programs that allow learning in a relaxing environment based on information about the user's hobbies and lifestyle. For example, it can suggest workshops held in nature or learning programs at resorts. This allows the user to obtain learning opportunities that suit their hobbies and lifestyle.

[0048] The program generation unit can also provide a customization function according to the user's learning style. For example, it can suggest a program that includes many videos and illustrations to a user who prefers visual learning. The program generation unit can also adjust the progress of the program to match the user's learning pace. For example, it can adjust the difficulty level of the program according to the user's progress. The program generation unit can also suggest programs that incorporate interactive elements based on the user's learning style. For example, it can suggest programs that include quizzes and simulations. This allows the user to take the program that best suits their learning style.

[0049] The program generation unit can also combine programs from different fields and industries to provide new learning opportunities. For example, it can propose a learning course that combines technical and design programs. The program generation unit can also provide new learning opportunities by combining programs from different industries. For example, it can generate a program that allows students to learn business skills and creative skills at the same time. The program generation unit can also provide new learning opportunities by combining programs from different fields using the generation AI. For example, it can propose a program that combines knowledge from the medical and IT fields. This makes it possible to provide new learning opportunities by combining programs from different fields and industries.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The Information Aggregation Department centrally collects information about human resource development programs offered by organizations, companies, and schools in Japan and overseas. For example, it collects information such as the content, target audience, duration, cost, and delivery method (online, offline) of each program, and stores it in a database. It also analyzes and updates the collected information as needed. Step 2: The profile collection unit collects user profile information, such as the user's age, educational background, work history, interests, skills, and career goals. It also provides an interface for users to enter their profile information when they access the system. Step 3: The program generation unit automatically generates a human resource development program optimal for the user based on the information aggregated by the information aggregation unit and the information collected by the profile collection unit. For example, the generation AI generates a program based on user prompts.

[0052] (Example 2) The human resource development program automatic generation system according to an embodiment of the present invention is a system that centrally collects information on human resource development from organizations, companies, and schools in Japan and overseas, and uses a generation AI to automatically generate the optimal human resource development program for the user. As a result, the human resource development program automatic generation system can provide users with new learning and experiences, and help them discover creative values ​​and career paths.

[0053] A human resource development program automatic generation system according to an embodiment includes an information aggregation unit, a profile collection unit, and a program generation unit. The information aggregation unit centrally aggregates information about human resource development programs offered by organizations, companies, and schools in Japan and overseas. For example, it collects information such as the content, target audience, duration, cost, and delivery method (online or offline) of each program. The information aggregation unit stores the collected information in a database and analyzes and updates it as needed. The profile collection unit collects user profile information. For example, it collects information such as the user's age, educational background, work history, interests, skills, and career goals. The profile collection unit also provides an interface for inputting profile information when the user accesses the system. The program generation unit automatically generates a human resource development program optimal for the user based on the information aggregated by the information aggregation unit and the information collected by the profile collection unit. For example, the generation AI generates a program based on user prompts. As a result, the human resource development program automatic generation system according to an embodiment can automatically generate an optimal human resource development program for the user, providing new learning and experiences and helping the user discover creative value and career paths.

[0054] The information aggregation unit stores the aggregated human resource development information in a database and can analyze and update it as needed. For example, the information aggregation unit collects feedback provided by past participants, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it quantifies participants' satisfaction and learning outcomes and identifies areas for improvement in the program. The information aggregation unit also collects information on participants' skill improvement and career progress as outcome data, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it evaluates based on employment and promotion rates after program completion. The information aggregation unit also builds a system in which the generation AI evaluates the effectiveness of the program based on feedback and outcome data and prioritizes and proposes highly effective programs. For example, it automatically selects the optimal program based on past data. This allows for efficient management of aggregated information and provides the latest information.

[0055] The profile collection unit can collect profile information including the user's age, educational background, work history, interests, skills, and career goals. The profile collection unit, for example, provides an interface for collecting the user's age, educational background, work history, interests, skills, and career goals. For example, it provides a form for the user to enter and collect the necessary information. The profile collection unit also stores the information entered by the user in a database, and the generation AI suggests the most appropriate program based on that information. For example, if a user enters "I'm interested in data science," the generation AI searches for programs related to data science and suggests programs that match the user's skill level and career goals. The profile collection unit also regularly updates the user's profile information and suggests programs based on the latest information. This allows the collection of detailed profile information for the user and suggests more appropriate programs.

[0056] The program generation unit can generate a program based on a user's prompt. For example, if a user inputs "I'm interested in data science," the generation AI searches for programs related to data science and suggests programs that match the user's skill level and career goals. For example, the generation AI generates a program based on the user's prompt. For example, the generation AI receives a prompt such as "Please suggest the best data science program for this user" and generates a program. The program generation unit also implements an algorithm that enables the generation AI to generate a program based on the user's prompt. For example, the program generation unit uses a machine learning algorithm to generate an optimal program based on the user's prompt. This allows the generation AI to generate a program based on the user's specific instructions.

[0057] The information aggregation unit collects feedback or outcome data from past participants, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, the information aggregation unit collects feedback provided by past participants, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it quantifies participants' satisfaction and learning outcomes and identifies areas for improvement in the program. The information aggregation unit also collects information on participants' skill improvement and career progress as outcome data, and the generation AI analyzes that data to evaluate the effectiveness of the program. For example, it evaluates based on employment rate and promotion rate after completion of the program. The information aggregation unit also builds a system in which the generation AI evaluates the effectiveness of the program based on feedback and outcome data and prioritizes proposing highly effective programs. For example, it automatically selects the optimal program based on past data. This makes it possible to evaluate the effectiveness of the program and provide highly effective programs.

[0058] The information aggregation unit can prioritize the aggregation of highly reliable information, taking into account the reliability and evaluation of the program provider. For example, the information aggregation unit collects past performance and evaluation data to evaluate the reliability of the program provider, and the generation AI analyzes the data to prioritize the aggregation of highly reliable information. For example, the information aggregation unit filters information based on the provider's evaluation score. The information aggregation unit also builds a system that prioritizes the aggregation of highly reliable information, taking into account the reliability of the provider. For example, it selects information based on the provider's authentication information and industry evaluation. The information aggregation unit also prioritizes the aggregation of highly reliable program information based on the provider's evaluation data. For example, it evaluates the reliability of the provider based on the evaluations and feedback of past participants. This makes it possible to provide highly reliable information and select programs that are beneficial to users.

[0059] The information aggregating unit can use the emotion estimation function to extract emotional elements from the program provider's presentation or explanatory text, and identify programs that are attractive to users. The information aggregating unit, for example, analyzes the program provider's presentation or explanatory text and extracts emotional elements using the emotion estimation function. For example, it preferentially suggests programs that have a strong positive emotion. The information aggregating unit also uses the emotion estimation function to extract emotional elements from the program provider's explanatory text, and identifies programs that are attractive to users. For example, it evaluates the attractiveness of the program based on an emotion score. The information aggregating unit also builds a system that analyzes the program provider's presentation and extracts emotional elements using the emotion estimation function. For example, it preferentially displays programs that have a high positive emotion. This makes it possible to identify and provide programs that are attractive to users.

[0060] The information aggregating unit aggregates multimedia information including video and audio data, and can convey the appeal of the program through visual and audio information as well. The information aggregating unit aggregates multimedia information including video and audio data when collecting program information, for example. For example, it collects program introduction videos and audio interviews of instructors, and conveys the appeal of the program through visual and audio information. The information aggregating unit also builds a system for conveying the appeal of the program based on the multimedia information. For example, it analyzes video and audio data and extracts the features and appeal of the program. The information aggregating unit also aggregates multimedia information including video and audio data, and conveys the appeal of the program through visual and audio information. For example, it provides program demo videos and audio lectures by instructors. This makes it possible to effectively convey the appeal of the program through visual and audio information.

[0061] The information aggregating unit can aggregate programs from different industries or fields across a cross-section, allowing users to choose from a variety of options. The information aggregating unit, for example, builds a system that aggregates programs from different industries or fields across a cross-section, allowing users to choose from a variety of options. For example, it centrally aggregates technical, design, and business programs. The information aggregating unit also collects program information from different industries or fields, allowing users to choose from a variety of options. For example, it organizes information so that programs from different industries can be compared. The information aggregating unit also develops a system that allows users to choose from a variety of options based on the program information aggregated across a cross-section. For example, it provides programs from different fields on a single platform. This allows users to select the most suitable program from a variety of options.

[0062] The profile collection unit records the user's past learning history or work history in detail, and the generation AI can suggest the optimal program based on that data. The profile collection unit, for example, records the user's past learning history in detail, and the generation AI can suggest the optimal program based on that data. For example, a program may be selected based on courses taken in the past or qualifications obtained. The profile collection unit also records the user's work history in detail, and the generation AI can suggest the optimal program based on that data. For example, a program may be selected based on past job content and industry experience. The profile collection unit also builds a system in which the generation AI suggests the optimal program based on the learning history and work history. For example, it analyzes past data and suggests a program that matches the user's skills and career goals. This makes it possible to suggest more appropriate programs based on the user's past learning history and work history.

[0063] The profile collection unit can conduct regular surveys or interviews to gain a deeper understanding of the user's interests and reflect the results in the profile. For example, the profile collection unit can conduct regular surveys to gain a deeper understanding of the user's interests and reflect the results in the profile. For example, the survey can collect information about areas of interest and skills the user wants to learn. The profile collection unit can also conduct regular interviews to gain a detailed understanding of the user's interests and reflect the results in the profile, and the generation AI can suggest optimal programs. For example, the program can be selected based on information obtained from the interview. The profile collection unit can also reflect the results of surveys or interviews in the profile, building a system in which the generation AI can suggest programs based on the user's interests and concerns. For example, the user's interests can be updated regularly. This allows for a deeper understanding of the user's interests and suggests more appropriate programs.

[0064] The profile collection unit can use the emotion estimation function to analyze the emotion a user feels when entering profile information and provide an interface for eliciting positive emotions. The profile collection unit, for example, uses the emotion estimation function to analyze the emotion a user feels when entering profile information. For example, it analyzes the user's facial expressions and voice and provides an interface for eliciting positive emotions. The profile collection unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when a user enters profile information. For example, it displays encouraging messages and success stories. The profile collection unit also uses the emotion estimation function to build a system that analyzes the user's emotions in real time and provides an interface for eliciting positive emotions. For example, it provides feedback according to the user's input content. This allows the user to enter profile information with positive emotions.

[0065] The profile collection unit can also add data about the user's health condition or lifestyle to the user's profile information, allowing for more comprehensive program suggestions. For example, the profile collection unit adds data about the user's health condition to the user's profile information, and the generation AI then suggests the optimal program based on that data. For example, it selects a program that suits the health condition. The profile collection unit also adds data about the user's lifestyle to the profile information, and the generation AI then suggests a comprehensive program based on that data. For example, it suggests a learning format that suits the lifestyle. The profile collection unit also adds data about the health condition or lifestyle to the profile information, creating a system that allows the generation AI to suggest more comprehensive programs. For example, it suggests a program that matches the user's lifestyle. This makes it possible to suggest comprehensive programs that take the user's health condition and lifestyle into consideration.

[0066] The profile collection unit can make the profile information input form multilingual so as to accommodate users of different languages ​​or cultural backgrounds. The profile collection unit, for example, makes the profile information input form multilingual so as to accommodate users of different languages ​​or cultural backgrounds. For example, it allows input in multiple languages ​​such as English, French, and Chinese. The profile collection unit also provides a multilingual input form so as to accommodate users of different cultural backgrounds. For example, it sets questions that take cultural backgrounds into consideration. The profile collection unit also makes the profile information input form multilingual so that the generation AI can propose optimal programs for users of different languages ​​or cultural backgrounds. For example, it automatically translates and analyzes the input content. This makes it possible to accommodate users of different languages ​​and cultural backgrounds.

[0067] The profile collection unit can use the emotion estimation function to suggest programs that are likely to be emotionally relatable based on the user's profile information. The profile collection unit, for example, uses the emotion estimation function to build a system that suggests programs that are likely to be emotionally relatable based on the user's profile information. For example, the program is selected based on an emotion score. The profile collection unit also analyzes the user's profile information and uses the emotion estimation function to suggest programs that are likely to be emotionally relatable. For example, the program is selected based on the user's interests and concerns. The profile collection unit also uses the emotion estimation function to develop a system that suggests programs that are likely to be emotionally relatable based on the user's profile information. For example, the program is selected based on the user's emotional response. This makes it possible to suggest programs that are likely to be emotionally relatable to the user.

[0068] When the generation AI automatically generates a program, the program generation unit can learn from past success stories or failure stories and generate a more effective program. For example, the program generation unit has the generation AI learn from past success stories and failure stories and generate an effective program when automatically generating a program. For example, it analyzes the factors for success and failure and reflects them in the program. The program generation unit also builds a system where the generation AI automatically generates effective programs based on past success stories and failure stories. For example, it extracts common points between success stories and incorporates them into the program. The program generation unit also has the generation AI learn from past data and automatically generate an effective program. For example, it incorporates measures to avoid failure stories into the program. In this way, it is possible to learn from past success stories and failure stories and generate a more effective program.

[0069] The program generation unit can add customization functions that match the user's learning style or pace when generating a program. For example, the program generation unit uses a generation AI to add customization functions that match the user's learning style and pace, and automatically generates a program. For example, the program content is adjusted according to the user's learning speed and preferences. The program generation unit also builds a system in which the generation AI automatically generates a customized program based on the user's learning style. For example, the program generation unit suggests a program that includes a lot of video content to a user who prefers visual learning. The program generation unit also adds customization functions that match the user's learning pace, and automatically generates a program. For example, the difficulty of the program is adjusted according to the user's progress. This makes it possible to customize the program to match the user's learning style and pace.

[0070] The program generation unit uses the emotion estimation function to monitor the emotions of a user when using a program in real time, and can adjust the program content as needed. The program generation unit, for example, uses the emotion estimation function to build a system that monitors the emotions of a user when using a program in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The program generation unit also monitors the user's emotions in real time and adjusts the program content as needed. For example, it lowers the difficulty of the program if the user has strong negative emotions. The program generation unit also uses the emotion estimation function to develop a system that monitors the user's emotions in real time and dynamically adjusts the program content. For example, it speeds up the progress of the program if the user has strong positive emotions. This makes it possible to monitor the user's emotions in real time and dynamically adjust the program content.

[0071] The program generation unit can combine programs from different fields or industries to provide new learning opportunities. For example, the program generation unit builds a system in which the generative AI combines programs from different fields or industries to provide new learning opportunities. For example, it proposes a learning course that combines technical and design programs. The program generation unit also provides new learning opportunities by combining programs from different industries. For example, it generates a program that allows students to learn business skills and creative skills at the same time. The program generation unit also provides new learning opportunities by combining programs from different fields. For example, it proposes a program that combines knowledge from the medical and IT fields. This makes it possible to provide new learning opportunities by combining programs from different fields or industries.

[0072] The program generation unit can use the emotion estimation function to identify the combination of programs that will evoke the most positive emotions in the user and prioritize suggesting that combination. The program generation unit, for example, uses the emotion estimation function to build a system that identifies the combination of programs that will evoke the most positive emotions in the user. For example, the program generation unit suggests the optimal combination of programs based on emotion scores. The program generation unit also analyzes the user's emotional response to identify the combination of programs that will evoke the most positive emotions. For example, it selects programs that are likely to resonate emotionally with the user based on past data. The program generation unit also uses the emotion estimation function to develop a system that identifies the combination of programs that will evoke the most positive emotions in the user and prioritize suggesting that combination. For example, it monitors emotional responses in real time and selects the optimal combination. This allows the combination of programs that will evoke the most positive emotions in the user to be identified and prioritized.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The information aggregator can also collect the user's geographical location information and suggest programs tailored to the characteristics of each region. For example, it can prioritize suggesting programs that are popular in a particular region or programs specialized in that region's industry. The information aggregator can also collect information on educational institutions and companies in each region and offer programs that are rooted in the region. For example, it can suggest programs in collaboration with local companies or learning programs based on the local culture. The information aggregator can also collect event information for each region and suggest local events that the user can participate in. For example, it can provide information on local seminars and workshops. This allows the user to have learning opportunities that are closely tied to the local area.

[0075] The information aggregating unit can also suggest new programs based on the user's learning history, taking into account their relevance to programs previously taken. For example, it can analyze the content and results of programs previously taken and suggest new programs related to them. The information aggregating unit can also grasp the user's learning progress based on the user's learning history and suggest the next program to take. For example, it can suggest a step-by-step program for acquiring a specific skill. The information aggregating unit can also reflect evaluations of programs previously taken based on the user's learning history and use this information to help select a new program. For example, it can suggest a program similar to a program that has received high evaluations in the past. This allows the user to take the optimal program based on their own learning history.

[0076] The profile collection unit can also collect information about the user's hobbies and lifestyle and suggest programs based on that information. For example, if the user is interested in sports or music, it can suggest programs related to those fields. The profile collection unit can also collect information about the user's lifestyle and suggest learning formats that suit that lifestyle. For example, it can suggest programs that can be learned in a short amount of time to a busy user. The profile collection unit can also suggest programs that allow learning in a relaxing environment based on information about the user's hobbies and lifestyle. For example, it can suggest workshops held in nature or learning programs at resorts. This allows the user to obtain learning opportunities that suit their hobbies and lifestyle.

[0077] The program generation unit can also provide a customization function according to the user's learning style. For example, it can suggest a program that includes many videos and illustrations to a user who prefers visual learning. The program generation unit can also adjust the progress of the program to match the user's learning pace. For example, it can adjust the difficulty level of the program according to the user's progress. The program generation unit can also suggest programs that incorporate interactive elements based on the user's learning style. For example, it can suggest programs that include quizzes and simulations. This allows the user to take the program that best suits their learning style.

[0078] The information aggregating unit can use the emotion estimation function to analyze the user's emotional reactions to programs they have taken in the past and suggest new programs based on the results. For example, it can suggest a program similar to a program that they have previously shown high satisfaction with. The information aggregating unit can also use the emotion estimation function to analyze the user's emotional reactions to programs they have taken in the past and avoid programs that they have previously shown negative reactions to. For example, it can suggest a program with a different approach from a program that they have previously shown dissatisfaction with. The information aggregating unit can also use the emotion estimation function to monitor the user's emotional reactions in real time and adjust the progress of the program. For example, if the user is feeling stressed, it can slow down the progress of the program. This makes it possible to provide the optimal program based on the user's emotions.

[0079] The information aggregating unit can also use the emotion estimation function to extract emotional elements from the program provider's presentations and descriptions, and identify programs that are attractive to users. For example, it can preferentially suggest programs that evoke strong positive emotions. The information aggregating unit can also use the emotion estimation function to extract emotional elements from the program provider's descriptions, and identify programs that are attractive to users. For example, it can evaluate the attractiveness of a program based on an emotion score. The information aggregating unit can also build a system that analyzes the program provider's presentations and extracts emotional elements using the emotion estimation function. For example, it can preferentially display programs that evoke a lot of positive emotions. This makes it possible to identify and provide programs that are attractive to users.

[0080] The profile collection unit can also use the emotion estimation function to analyze the emotions a user has when entering profile information and provide an interface for eliciting positive emotions. For example, the interface can be provided by analyzing the user's facial expressions and voice and eliciting positive emotions. The profile collection unit can also use the emotion estimation function to provide an interface for eliciting positive emotions when a user enters profile information. For example, encouraging messages or success stories can be displayed. The profile collection unit can also use the emotion estimation function to build a system that analyzes a user's emotions in real time and provides an interface for eliciting positive emotions. For example, the system can provide feedback according to the user's input. This allows the user to enter profile information with positive emotions.

[0081] The program generation unit can use the emotion estimation function to monitor the emotions of the user when using the program in real time and adjust the program content as needed. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The program generation unit can also monitor the user's emotions in real time and adjust the program content as needed. For example, it can lower the difficulty of the program if the user's negative emotions are strong. The program generation unit can also use the emotion estimation function to develop a system that monitors the user's emotions in real time and dynamically adjusts the program content. For example, it can speed up the program if the user's positive emotions are strong. This makes it possible to monitor the user's emotions in real time and dynamically adjust the program content.

[0082] The program generation unit can also combine programs from different fields and industries to provide new learning opportunities. For example, it can propose a learning course that combines technical and design programs. The program generation unit can also provide new learning opportunities by combining programs from different industries. For example, it can generate a program that allows students to learn business skills and creative skills at the same time. The program generation unit can also provide new learning opportunities by combining programs from different fields using the generation AI. For example, it can propose a program that combines knowledge from the medical and IT fields. This makes it possible to provide new learning opportunities by combining programs from different fields and industries.

[0083] The program generation unit can use the emotion estimation function to identify the combination of programs that evokes the most positive emotions in the user and prioritize suggesting that combination. For example, the program generation unit can suggest the optimal combination of programs based on the emotion score. The program generation unit can also analyze the user's emotional response and identify the combination of programs that evokes the most positive emotions. For example, it can select programs that are likely to resonate with the user emotionally based on past data. The program generation unit can also use the emotion estimation function to develop a system that identifies the combination of programs that evokes the most positive emotions in the user and prioritize suggesting that combination. For example, it can monitor emotional responses in real time and select the optimal combination. This makes it possible to identify the combination of programs that evokes the most positive emotions in the user and prioritize suggesting that combination.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The Information Aggregation Department centrally collects information about human resource development programs offered by organizations, companies, and schools in Japan and overseas. For example, it collects information such as the content, target audience, duration, cost, and delivery method (online, offline) of each program, and stores it in a database. It also analyzes and updates the collected information as needed. Step 2: The profile collection unit collects user profile information, such as the user's age, educational background, work history, interests, skills, and career goals. It also provides an interface for users to enter their profile information when they access the system. Step 3: The program generation unit automatically generates a human resource development program optimal for the user based on the information aggregated by the information aggregation unit and the information collected by the profile collection unit. For example, the generation AI generates a program based on user prompts.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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, in order to avoid confusion and to 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.

[0152] 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. [Explanation of symbols]

[0153] 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. An information aggregation department that centrally collects information about human resource development programs offered by Japanese organizations, companies, schools, and overseas organizations, companies, and schools; a profile collection unit that collects user profile information; a program generation unit that automatically generates an optimal human resource development program for a user based on the information aggregated by the information aggregation unit and the information collected by the profile collection unit. A system characterized by:

2. The profile collection unit Collect profile information about the user, including their age, education, work history, interests, skills, and career goals 2. The system of claim 1.

3. The information aggregation unit Extract emotional elements from program provider presentations or descriptions to identify programs that are appealing to users 2. The system of claim 1.

4. The information aggregation unit Analyze the sentiment of providers when collecting program information and prioritize programs with positive sentiment.

2. The system of claim 1.

5. The profile collection unit Analyzes emotions when users enter profile information and provides an interface to elicit positive emotions 2. The system of claim 1.

6. The program generation unit When generative AI automatically generates a program, it learns from past successes and failures to generate a more effective program.

2. The system of claim 1.

7. The program generation unit Combine programs from different fields or industries to provide new learning opportunities 2. The system of claim 1.

8. The program generation unit Identify the combination of programs that elicits the most positive feelings from users and prioritize recommendations for that combination.

2. The system of claim 1.

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