System

Abstract

An object of a system according to an embodiment is to easily obtain expertise by using a generated AI on which a person has performed original additional learning.SOLUTION: A system according to an embodiment includes a generation AI, an additional learning function, and a sales platform. The generation AI provides a generation AI. The incremental learning function enables individuals to perform original incremental learning in the generation AI. The sales platform sells the generated AI learned by the additional learning feature.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 has limited means for connecting with experts with knowledge in new fields, posing a high hurdle.

[0005] The system of the embodiment aims to easily obtain expert knowledge by using generative AI that has undergone original additional learning by individuals. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an additional learning function, and a sales platform. The generation AI provides the generation AI. The additional learning function allows individuals to perform original additional learning on the generation AI. The sales platform sells the generation AI trained by the additional learning function. [Effects of the Invention]

[0007] The system of the embodiment uses generative AI that has undergone original additional learning by individuals, making it easy to obtain expert knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The bouncing support system according to an embodiment of the present invention is a system for people who want to use generative AI to bouncing ideas off of CEOs and experts in various fields. This system adds a function to the generative AI that allows individuals to perform original additional learning, and allows CEOs and experts in various fields to use this function. This allows the bouncing support system to sell the generative AI used by CEOs and experts in various fields by field.

[0029] The system for supporting users to provide feedback includes a generating AI, an additional learning function, and a sales platform. The generating AI allows users to perform original additional learning. For example, a user can have the generating AI learn information related to their field of expertise or field of interest. The input to the generating AI is a prompt containing instructions on what the user wants the generating AI to do, and the generating AI performs additional learning based on the prompt. The additional learning function allows individuals to perform original additional learning on the generating AI. For example, an expert in the semiconductor field can create a generating AI specialized in that field by having the generating AI learn their own knowledge and experience. The sales platform is a platform for selling the generating AI trained by the additional learning function. For example, by purchasing a generating AI used by a professor specializing in the semiconductor field, a user can gain advanced knowledge in that field. This allows the system for supporting users to provide additional learning and sell the generating AI.

[0030] The generation AI can propose individually customized study plans based on the user's past learning history and performance data. The generation AI proposes individually customized study plans based on the user's past learning history and performance data. For example, the system analyzes the user's past learning history to identify areas of strength and weakness. For example, it prioritizes incorporating areas in which the user has previously scored highly into the study plan. Based on the user's performance data, it monitors learning progress in real time and suggests review or additional study at the appropriate time. For example, it re-incorporates areas that have not been studied for a certain period of time into the study plan. It automatically generates customized study plans to suit the user's learning style and pace. For example, it suggests a short-term intensive plan for a user who likes to study intensively for short periods of time. This makes it possible to propose customized study plans based on the user's past learning history.

[0031] Generative AI can automatically retrieve the latest research papers and patent information in the field the user wants to study. Add a function to the generative AI that automatically retrieves the latest research papers and patent information in the field the user wants to study. For example, add a function to the generative AI that connects with the latest research paper database, automatically retrieving the latest research in the field the user wants to study. For example, regularly update the latest papers in the semiconductor field. Add a function that connects with a patent database, automatically retrieving the latest patent information in fields the user is interested in. For example, incorporate patent information related to new technologies and inventions into the learning content. Based on keywords specified by the user, automatically collect the latest related research papers and patent information and reflect it in the learning content. For example, automatically retrieve the latest information on "quantum computers." This makes it possible to automatically retrieve the latest research papers and patent information.

[0032] Generative AI can be equipped with a collaboration function that allows experts from different fields to work together on additional learning. A collaboration function that allows experts from different fields to work together on additional learning is added to generative AI. For example, an online platform is built for experts from different fields to work together on additional learning. For example, a collaboration tool is provided to enable experts from the medical and engineering fields to work together on research. A function is added to the generative AI that allows experts to exchange opinions and discuss in real time while conducting additional learning. For example, video conferencing and chat functions are integrated. A task management function is added that allows experts from different fields to work together on projects. For example, a function is provided to share project progress and allocate tasks. This allows experts from different fields to work together on additional learning.

[0033] The generative AI can be equipped with a community function that enables users to share what they have learned with other users and receive feedback. A community function that enables users to share what they have learned with other users and receive feedback is added to the generative AI. For example, an online forum is created where users can share what they have learned with other users. For example, a bulletin board is provided where users can ask questions and exchange opinions about the learning content. A function is added that enables users to receive feedback from other users in real time when sharing the learning content. For example, feedback is obtained through comments and ratings. The community function allows users to collaborate to brush up on the learning content. For example, a function is provided where users can collaboratively edit documents or create presentations. This allows users to share the learning content and receive feedback.

[0034] Generative AI can learn from experts' past work data and success stories to provide more specific advice. Add a function to generative AI to learn from experts' past work data and success stories to provide more specific advice. For example, build a system that collects experts' past work data and lets the generative AI learn it. For example, use project progress data and deliverables as learning data. Add a function that allows generative AI to provide specific advice based on success stories. For example, analyze past success stories and suggest the optimal approach for similar situations. Develop a system that integrates experts' work data and success stories and allows generative AI to provide advice in real time. For example, suggest instant solutions to problems that arise during work. This makes it possible to provide specific advice based on experts' past work data and success stories.

[0035] Generative AI can predict problems and challenges that experts may face and propose countermeasures in advance. Add a function to generative AI that predicts problems and challenges that experts may face and proposes countermeasures in advance. For example, add a data analysis function to predict problems and challenges that experts may face. For example, calculate the probability of a problem occurring based on past data. Build a system in which generative AI proposes countermeasures in advance for predicted problems and challenges. For example, propose risk management plans and preventive measures. Add a function that monitors problems and challenges that experts face in real time and allows generative AI to immediately propose countermeasures. For example, integrate an anomaly detection system. This makes it possible to predict problems and challenges that experts may face and propose countermeasures in advance.

[0036] Generative AI can be equipped with an online conferencing function that allows experts to hold discussions with other experts in real time. An online conferencing function that allows experts to hold discussions with other experts in real time is added to the generative AI. For example, an online conferencing platform is built where experts can hold discussions with each other in real time. For example, video conferencing and chat functions are integrated. Functions are added that allow the generative AI to provide information in real time during online meetings and support discussions. For example, related data and materials are instantly displayed. A function is added that automatically records the contents of online meetings so that they can be referenced later. For example, meeting minutes are automatically generated and shared. This allows experts to hold discussions with other experts in real time.

[0037] Generative AI can be equipped with integration functions that work in conjunction with the tools and software used by experts to improve work efficiency. Integration functions can be added to generative AI to work in conjunction with the tools and software used by experts to improve work efficiency. For example, an integrated platform can be built that links generative AI with the tools and software used by experts. For example, linking with project management tools and data analysis software. A function can be added in which generative AI analyzes experts' work flows and suggests optimal ways to use tools and software. For example, proposing how to use tools to improve work efficiency. A system can be built in which generative AI automatically collects data from the tools and software used by experts and monitors the progress of work in real time. For example, project progress data can be automatically updated. This allows it to work in conjunction with the tools and software used by experts to improve work efficiency.

[0038] Sales platforms can be equipped with a recommendation function based on users' purchase history and ratings. A recommendation function based on users' purchase history and ratings is added to a sales platform. For example, a system is built that analyzes a user's purchase history and recommends related generative AI. For example, products similar to generative AIs purchased in the past are suggested. A function is added that recommends generative AIs that other users have given high ratings to, based on user rating data. For example, generative AIs with high ratings are displayed preferentially. A system is developed that integrates purchase history and rating data to provide personalized recommendations tailored to the user's interests and needs. For example, the optimal generative AI is suggested based on the user's past behavioral data. This makes it possible to provide a recommendation function based on a user's purchase history and ratings.

[0039] The sales platform can be equipped with a trial function that allows users to try out a demo of the generative AI. A trial function that allows users to try out a demo of the generative AI before purchasing can be added to the sales platform. For example, a function can be added that provides a demo version of the generative AI and allows users to try it out before purchasing. For example, a demo version that can be used free of charge for a certain period of time can be provided. A system can be built that allows users to check the performance and functions of the generative AI through the trial function. For example, the demo version can be used to perform specific tasks and evaluate the results. A function can be added that collects feedback from users during the trial period and uses the feedback to improve the generative AI. For example, the product can be improved based on the opinions and requests of users during the trial period. This allows users to try out a demo of the generative AI before purchasing.

[0040] The sales platform can be equipped with a bundle sales function that allows users to purchase generative AIs in different fields in combination. A bundle sales function that allows users to purchase generative AIs in different fields in combination is added to the sales platform. For example, a bundle sales function is added that allows users to purchase generative AIs in different fields in combination. For example, generative AIs in the medical and engineering fields are sold as a set. A system is built through the bundle sales function that allows users to purchase multiple generative AIs at once. For example, generative AIs related to a specific field are offered together. A function is added that offers discounts and benefits to users when selling bundles. For example, a discount price is applied when purchasing a bundle. This allows users to purchase generative AIs in different fields in combination.

[0041] The sales platform can be equipped with a marketplace function that allows users to customize and sell generated AI. A marketplace function that allows users to customize and sell generated AI is added to the sales platform. For example, a marketplace is built where users can customize generated AI and sell their own original generated AI. For example, a generated AI specialized in a specific field is created and sold. A function is added that allows other users to purchase customized generated AI. For example, a generated AI created by a user is put up for sale on the marketplace. A rating system is introduced to support transactions on the marketplace. For example, buyers can rate the quality and performance of generated AI and provide feedback. This allows users to customize and sell generated AI.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] Generative AI can propose individually customized study plans based on a user's past learning history and performance data. For example, it can analyze a user's past learning history to identify areas of strength and weakness. For example, it can prioritize areas in which the user has previously scored highly in the study plan. It can monitor the user's learning progress in real time based on the user's performance data and suggest review or additional study at the appropriate time. For example, it can re-incorporate areas that have not been studied for a certain period of time into the study plan. It can automatically generate customized study plans to suit the user's learning style and pace. For example, it can suggest a short-term, intensive plan to a user who likes to study intensively for short periods of time. This makes it possible to propose customized study plans based on the user's past learning history.

[0044] Generative AI can automatically incorporate the latest research papers and patent information in the fields that users want to study. For example, adding a function to the generative AI that connects with the latest research paper database will automatically incorporate the latest research in the fields that users want to study. For example, regularly updating the latest papers in the semiconductor field. Adding a function to connect with a patent database and automatically incorporating the latest patent information in fields that users are interested in will incorporate patent information related to new technologies and inventions into the learning content. Based on keywords specified by the user, the latest related research papers and patent information will be automatically collected and reflected in the learning content. For example, automatically incorporating the latest information on "quantum computers." This will allow the latest research papers and patent information to be incorporated automatically.

[0045] Generative AI can be equipped with collaboration functions that allow experts from different fields to work together on additional learning. For example, an online platform can be built for experts from different fields to work together on additional learning. For example, a collaboration tool can be provided to enable experts from the medical and engineering fields to work together on research. A function can be added to the generative AI that allows experts to exchange opinions and discuss in real time while conducting additional learning. For example, video conferencing and chat functions can be integrated. A task management function can be added that allows experts from different fields to work together on a project. For example, a function can be provided to share project progress and allocate tasks. This allows experts from different fields to work together on additional learning.

[0046] Generative AI can be equipped with a community function that allows users to share what they have learned with other users and receive feedback. For example, an online forum can be created where users can share what they have learned with other users. For example, a bulletin board can be provided where users can ask questions and exchange opinions about the learning content. When sharing the learning content, a function can be added to receive feedback from other users in real time. For example, feedback can be obtained through comments and ratings. The community function can be used to enable users to collaborate to brush up on the learning content. For example, a function can be provided where users can collaboratively edit documents or create presentations. This allows users to share the learning content and receive feedback.

[0047] Generative AI can learn from experts' past work data and success stories to provide more specific advice. For example, a system can be built that collects experts' past work data and has the generative AI learn from it. For example, project progress data and deliverables can be used as learning data. A function can be added that allows the generative AI to provide specific advice based on success stories. For example, past success stories can be analyzed to suggest the optimal approach for similar situations. A system can be developed that integrates experts' work data and success stories and allows the generative AI to provide advice in real time. For example, it can immediately suggest solutions to problems that arise during work. This makes it possible to provide specific advice based on experts' past work data and success stories.

[0048] Generative AI can predict problems and challenges that experts may face and propose countermeasures in advance. For example, add a data analysis function to predict problems and challenges that experts may face. For example, calculate the probability of a problem occurring based on past data. Build a system in which generative AI proposes countermeasures in advance for predicted problems and challenges. For example, propose risk management plans and preventive measures. Add a function to monitor problems and challenges that experts face in real time and have generative AI immediately propose countermeasures. For example, integrate an anomaly detection system. This will enable predicting problems and challenges that experts may face and proposing countermeasures in advance.

[0049] Sales platforms can be equipped with a recommendation function based on users' purchase history and ratings. For example, a system can be built that analyzes a user's purchase history and recommends related generative AI. For example, products similar to generative AIs purchased in the past can be suggested. A function can be added that recommends generative AIs that other users have given high ratings to, based on user rating data. For example, highly rated generative AIs can be displayed preferentially. A system can be developed that integrates purchase history and rating data to provide personalized recommendations tailored to the user's interests and needs. For example, the optimal generative AI can be suggested based on the user's past behavioral data. This makes it possible to provide a recommendation function based on a user's purchase history and ratings.

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

[0051] Step 1: The user can perform original additional learning on the generating AI. For example, the user can have the generating AI learn information about their field of expertise or area of ​​interest. The input to the generating AI is a prompt containing instructions on what the user wants the generating AI to do, and the generating AI performs additional learning based on that prompt. Step 2: The additional learning function allows individuals to perform original additional learning on the generative AI. For example, an expert in the semiconductor field can create a generative AI specialized in that field by teaching the generative AI his or her own knowledge and experience. Step 3: The sales platform is a platform for selling generative AI trained through additional learning functions. For example, by purchasing generative AI used by a professor specializing in the semiconductor field, you can gain advanced knowledge in that field.

[0052] (Example 2) The bouncing support system according to an embodiment of the present invention is a system for people who want to use generative AI to bouncing ideas off of CEOs and experts in various fields. This system adds a function to the generative AI that allows individuals to perform original additional learning, and allows CEOs and experts in various fields to use this function. This allows the bouncing support system to sell the generative AI used by CEOs and experts in various fields by field.

[0053] The system for supporting users to provide feedback includes a generating AI, an additional learning function, and a sales platform. The generating AI allows users to perform original additional learning. For example, a user can have the generating AI learn information related to their field of expertise or field of interest. The input to the generating AI is a prompt containing instructions on what the user wants the generating AI to do, and the generating AI performs additional learning based on the prompt. The additional learning function allows individuals to perform original additional learning on the generating AI. For example, an expert in the semiconductor field can create a generating AI specialized in that field by having the generating AI learn their own knowledge and experience. The sales platform is a platform for selling the generating AI trained by the additional learning function. For example, by purchasing a generating AI used by a professor specializing in the semiconductor field, a user can gain advanced knowledge in that field. This allows the system for supporting users to provide additional learning and sell the generating AI.

[0054] The generative AI is equipped with an emotion estimation function that estimates the user's emotions, and the emotion estimation function can optimize learning content based on the user's emotions. An emotion estimation function is added to the generative AI to analyze the emotions felt by the user while studying in real time. For example, if a user shows positive emotions about content that interests them, learning content in that field will be provided preferentially. The emotion estimation function is used to detect stress or fatigue felt by the user while studying and suggest appropriate breaks and ways to refresh. For example, if the user feels tired, content that helps them relax will be provided. A system will be built that dynamically adjusts learning content based on user emotion data. For example, if a user shows negative emotions, learning content with a lower level of difficulty will be provided. This makes it possible to optimize learning content based on the user's emotions.

[0055] The generation AI can propose individually customized study plans based on the user's past learning history and performance data. The generation AI proposes individually customized study plans based on the user's past learning history and performance data. For example, the system analyzes the user's past learning history to identify areas of strength and weakness. For example, it prioritizes incorporating areas in which the user has previously scored highly into the study plan. Based on the user's performance data, it monitors learning progress in real time and suggests review or additional study at the appropriate time. For example, it re-incorporates areas that have not been studied for a certain period of time into the study plan. It automatically generates customized study plans to suit the user's learning style and pace. For example, it suggests a short-term intensive plan for a user who likes to study intensively for short periods of time. This makes it possible to propose customized study plans based on the user's past learning history.

[0056] Generative AI can automatically retrieve the latest research papers and patent information in the field the user wants to study. Add a function to the generative AI that automatically retrieves the latest research papers and patent information in the field the user wants to study. For example, add a function to the generative AI that connects with the latest research paper database, automatically retrieving the latest research in the field the user wants to study. For example, regularly update the latest papers in the semiconductor field. Add a function that connects with a patent database, automatically retrieving the latest patent information in fields the user is interested in. For example, incorporate patent information related to new technologies and inventions into the learning content. Based on keywords specified by the user, automatically collect the latest related research papers and patent information and reflect it in the learning content. For example, automatically retrieve the latest information on "quantum computers." This makes it possible to automatically retrieve the latest research papers and patent information.

[0057] Generative AI can be equipped with a collaboration function that allows experts from different fields to work together on additional learning. A collaboration function that allows experts from different fields to work together on additional learning is added to generative AI. For example, an online platform is built for experts from different fields to work together on additional learning. For example, a collaboration tool is provided to enable experts from the medical and engineering fields to work together on research. A function is added to the generative AI that allows experts to exchange opinions and discuss in real time while conducting additional learning. For example, video conferencing and chat functions are integrated. A task management function is added that allows experts from different fields to work together on projects. For example, a function is provided to share project progress and allocate tasks. This allows experts from different fields to work together on additional learning.

[0058] The generative AI can be equipped with a community function that enables users to share what they have learned with other users and receive feedback. A community function that enables users to share what they have learned with other users and receive feedback is added to the generative AI. For example, an online forum is created where users can share what they have learned with other users. For example, a bulletin board is provided where users can ask questions and exchange opinions about the learning content. A function is added that enables users to receive feedback from other users in real time when sharing the learning content. For example, feedback is obtained through comments and ratings. The community function allows users to collaborate to brush up on the learning content. For example, a function is provided where users can collaboratively edit documents or create presentations. This allows users to share the learning content and receive feedback.

[0059] Using its emotion estimation function, the generative AI can detect the stress and fatigue a user feels while studying and suggest ways to take a break or refresh themselves. Using the emotion estimation function in the generative AI, it can detect the stress and fatigue a user feels while studying and suggest appropriate ways to take a break or refresh themselves. For example, using the emotion estimation function, we can build a system that detects the stress and fatigue a user feels while studying in real time. For example, we can analyze the user's facial expressions and voice to measure their stress level. We can add a function that suggests appropriate ways to take a break or refresh themselves when the user feels stressed or fatigued. For example, we can suggest short breaks or relaxation exercises. We can provide interactive content to reduce stress and fatigue while studying. For example, we can provide relaxing music or meditation guides. This can detect the user's stress and fatigue and suggest appropriate ways to take a break or refresh themselves.

[0060] Generative AI can learn from experts' past work data and success stories to provide more specific advice. Add a function to generative AI to learn from experts' past work data and success stories to provide more specific advice. For example, build a system that collects experts' past work data and lets the generative AI learn it. For example, use project progress data and deliverables as learning data. Add a function that allows generative AI to provide specific advice based on success stories. For example, analyze past success stories and suggest the optimal approach for similar situations. Develop a system that integrates experts' work data and success stories and allows generative AI to provide advice in real time. For example, suggest instant solutions to problems that arise during work. This makes it possible to provide specific advice based on experts' past work data and success stories.

[0061] Generative AI can predict problems and challenges that experts may face and propose countermeasures in advance. Add a function to generative AI that predicts problems and challenges that experts may face and proposes countermeasures in advance. For example, add a data analysis function to predict problems and challenges that experts may face. For example, calculate the probability of a problem occurring based on past data. Build a system in which generative AI proposes countermeasures in advance for predicted problems and challenges. For example, propose risk management plans and preventive measures. Add a function that monitors problems and challenges that experts face in real time and allows generative AI to immediately propose countermeasures. For example, integrate an anomaly detection system. This makes it possible to predict problems and challenges that experts may face and propose countermeasures in advance.

[0062] Generative AI can be equipped with an online conferencing function that allows experts to hold discussions with other experts in real time. An online conferencing function that allows experts to hold discussions with other experts in real time is added to the generative AI. For example, an online conferencing platform is built where experts can hold discussions with each other in real time. For example, video conferencing and chat functions are integrated. Functions are added that allow the generative AI to provide information in real time during online meetings and support discussions. For example, related data and materials are instantly displayed. A function is added that automatically records the contents of online meetings so that they can be referenced later. For example, meeting minutes are automatically generated and shared. This allows experts to hold discussions with other experts in real time.

[0063] Generative AI can be equipped with integration functions that work in conjunction with the tools and software used by experts to improve work efficiency. Integration functions can be added to generative AI to work in conjunction with the tools and software used by experts to improve work efficiency. For example, an integrated platform can be built that links generative AI with the tools and software used by experts. For example, linking with project management tools and data analysis software. A function can be added in which generative AI analyzes experts' work flows and suggests optimal ways to use tools and software. For example, proposing how to use tools to improve work efficiency. A system can be built in which generative AI automatically collects data from the tools and software used by experts and monitors the progress of work in real time. For example, project progress data can be automatically updated. This allows it to work in conjunction with the tools and software used by experts to improve work efficiency.

[0064] Using its emotion estimation function, the generation AI can detect fluctuations in the motivation of experts while they are working and suggest measures to maintain their motivation. Using the emotion estimation function in the generation AI, it can detect fluctuations in the motivation of experts while they are working and suggest appropriate measures to maintain their motivation. For example, using the emotion estimation function, a system can be built that detects fluctuations in the motivation of experts while they are working in real time. For example, by analyzing the experts' facial expressions and voice and calculating a motivation score. A function can be added that suggests appropriate measures to maintain motivation based on the experts' motivation state. For example, by presenting encouraging messages or success stories. Based on the emotion estimation data, a system can be developed that continuously monitors the experts' motivation and provides a long-term motivation maintenance plan. For example, by conducting regular motivation checks. This makes it possible to detect fluctuations in the motivation of experts while they are working and suggest appropriate measures to maintain their motivation.

[0065] Sales platforms can be equipped with a recommendation function based on users' purchase history and ratings. A recommendation function based on users' purchase history and ratings is added to a sales platform. For example, a system is built that analyzes a user's purchase history and recommends related generative AI. For example, products similar to generative AIs purchased in the past are suggested. A function is added that recommends generative AIs that other users have given high ratings to, based on user rating data. For example, generative AIs with high ratings are displayed preferentially. A system is developed that integrates purchase history and rating data to provide personalized recommendations tailored to the user's interests and needs. For example, the optimal generative AI is suggested based on the user's past behavioral data. This makes it possible to provide a recommendation function based on a user's purchase history and ratings.

[0066] The sales platform can be equipped with a trial function that allows users to try out a demo of the generative AI. A trial function that allows users to try out a demo of the generative AI before purchasing can be added to the sales platform. For example, a function can be added that provides a demo version of the generative AI and allows users to try it out before purchasing. For example, a demo version that can be used free of charge for a certain period of time can be provided. A system can be built that allows users to check the performance and functions of the generative AI through the trial function. For example, the demo version can be used to perform specific tasks and evaluate the results. A function can be added that collects feedback from users during the trial period and uses the feedback to improve the generative AI. For example, the product can be improved based on the opinions and requests of users during the trial period. This allows users to try out a demo of the generative AI before purchasing.

[0067] A sales platform can use the emotion estimation function to analyze users' purchasing intent and satisfaction in real time and optimize marketing strategies. A sales platform can use the emotion estimation function to analyze users' purchasing intent and satisfaction in real time and optimize marketing strategies. For example, a system can be built that uses the emotion estimation function to analyze users' purchasing intent in real time. For example, a user's facial expressions and voice can be analyzed to calculate a purchasing intent score. A function can be added that monitors user satisfaction in real time based on emotion data and optimizes marketing strategies. For example, special offers can be provided to highly satisfied users. A system can be developed that predicts user purchasing behavior based on emotion estimation data and proposes optimal marketing measures. For example, targeted advertisements can be delivered to users with a high purchasing intent. This makes it possible to analyze users' purchasing intent and satisfaction in real time and optimize marketing strategies.

[0068] The sales platform can be equipped with a bundle sales function that allows users to purchase generative AIs in different fields in combination. A bundle sales function that allows users to purchase generative AIs in different fields in combination is added to the sales platform. For example, a bundle sales function is added that allows users to purchase generative AIs in different fields in combination. For example, generative AIs in the medical and engineering fields are sold as a set. A system is built through the bundle sales function that allows users to purchase multiple generative AIs at once. For example, generative AIs related to a specific field are offered together. A function is added that offers discounts and benefits to users when selling bundles. For example, a discount price is applied when purchasing a bundle. This allows users to purchase generative AIs in different fields in combination.

[0069] The sales platform can be equipped with a marketplace function that allows users to customize and sell generated AI. A marketplace function that allows users to customize and sell generated AI is added to the sales platform. For example, a marketplace is built where users can customize generated AI and sell their own original generated AI. For example, a generated AI specialized in a specific field is created and sold. A function is added that allows other users to purchase customized generated AI. For example, a generated AI created by a user is put up for sale on the marketplace. A rating system is introduced to support transactions on the marketplace. For example, buyers can rate the quality and performance of generated AI and provide feedback. This allows users to customize and sell generated AI.

[0070] The sales platform can use the emotion estimation function to collect users' post-purchase satisfaction and dissatisfaction as feedback and use it for product improvements. The emotion estimation function can be used on the sales platform to collect users' post-purchase satisfaction and dissatisfaction as feedback and use it for product improvements. For example, the emotion estimation function can be used to build a system that monitors the satisfaction that users feel with generative AI in real time after purchasing. For example, the user's facial expressions and voice can be analyzed to calculate a satisfaction score. A function can be added to collect user satisfaction and dissatisfaction based on emotion data and use it for product improvement. For example, a product can be improved based on feedback from users with low satisfaction. A system can be developed that analyzes user feedback based on emotion estimation data and helps improve the quality of generative AI. For example, the system can identify user dissatisfaction and suggest improvements. This allows users' post-purchase satisfaction and dissatisfaction to be collected as feedback and used for product improvement.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] Generative AI can propose individually customized study plans based on a user's past learning history and performance data. For example, it can analyze a user's past learning history to identify areas of strength and weakness. For example, it can prioritize areas in which the user has previously scored highly in the study plan. It can monitor the user's learning progress in real time based on the user's performance data and suggest review or additional study at the appropriate time. For example, it can re-incorporate areas that have not been studied for a certain period of time into the study plan. It can automatically generate customized study plans to suit the user's learning style and pace. For example, it can suggest a short-term, intensive plan to a user who likes to study intensively for short periods of time. This makes it possible to propose customized study plans based on the user's past learning history.

[0073] The generative AI is equipped with an emotion estimation function that estimates the user's emotions, and this emotion estimation function can optimize learning content based on the user's emotions. For example, if a user shows positive emotions about content that interests them, learning content in that field can be provided preferentially. The emotion estimation function can be used to detect stress or fatigue felt by the user while studying, and suggest appropriate breaks or ways to refresh. For example, if the user feels fatigued, content that helps them relax can be provided. A system can be built that dynamically adjusts learning content based on user emotion data. For example, if a user shows negative emotions, learning content with a lower level of difficulty can be provided. This makes it possible to optimize learning content based on the user's emotions.

[0074] Generative AI can automatically incorporate the latest research papers and patent information in the fields that users want to study. For example, adding a function to the generative AI that connects with the latest research paper database will automatically incorporate the latest research in the fields that users want to study. For example, regularly updating the latest papers in the semiconductor field. Adding a function to connect with a patent database and automatically incorporating the latest patent information in fields that users are interested in will incorporate patent information related to new technologies and inventions into the learning content. Based on keywords specified by the user, the latest related research papers and patent information will be automatically collected and reflected in the learning content. For example, automatically incorporating the latest information on "quantum computers." This will allow the latest research papers and patent information to be incorporated automatically.

[0075] Generative AI can be equipped with collaboration functions that allow experts from different fields to work together on additional learning. For example, an online platform can be built for experts from different fields to work together on additional learning. For example, a collaboration tool can be provided to enable experts from the medical and engineering fields to work together on research. A function can be added to the generative AI that allows experts to exchange opinions and discuss in real time while conducting additional learning. For example, video conferencing and chat functions can be integrated. A task management function can be added that allows experts from different fields to work together on a project. For example, a function can be provided to share project progress and allocate tasks. This allows experts from different fields to work together on additional learning.

[0076] Generative AI can be equipped with a community function that allows users to share what they have learned with other users and receive feedback. For example, an online forum can be created where users can share what they have learned with other users. For example, a bulletin board can be provided where users can ask questions and exchange opinions about the learning content. When sharing the learning content, a function can be added to receive feedback from other users in real time. For example, feedback can be obtained through comments and ratings. The community function can be used to enable users to collaborate to brush up on the learning content. For example, a function can be provided where users can collaboratively edit documents or create presentations. This allows users to share the learning content and receive feedback.

[0077] Using its emotion estimation function, the generative AI can detect the stress and fatigue a user feels while studying and suggest ways to take a break and refresh themselves. For example, using the emotion estimation function, a system can be built that detects the stress and fatigue a user feels while studying in real time. For example, the system can analyze the user's facial expressions and voice to measure their stress level. A function can be added to suggest appropriate ways to take a break or refresh themselves when the user feels stressed or fatigued. For example, the system can suggest short breaks or relaxation exercises. The system can provide interactive content to reduce stress and fatigue while studying. For example, the system can provide relaxing music or meditation guides. This makes it possible to detect the user's stress and fatigue and suggest appropriate ways to take a break or refresh themselves.

[0078] Generative AI can learn from experts' past work data and success stories to provide more specific advice. For example, a system can be built that collects experts' past work data and has the generative AI learn from it. For example, project progress data and deliverables can be used as learning data. A function can be added that allows the generative AI to provide specific advice based on success stories. For example, past success stories can be analyzed to suggest the optimal approach for similar situations. A system can be developed that integrates experts' work data and success stories and allows the generative AI to provide advice in real time. For example, it can immediately suggest solutions to problems that arise during work. This makes it possible to provide specific advice based on experts' past work data and success stories.

[0079] Generative AI can predict problems and challenges that experts may face and propose countermeasures in advance. For example, add a data analysis function to predict problems and challenges that experts may face. For example, calculate the probability of a problem occurring based on past data. Build a system in which generative AI proposes countermeasures in advance for predicted problems and challenges. For example, propose risk management plans and preventive measures. Add a function to monitor problems and challenges that experts face in real time and have generative AI immediately propose countermeasures. For example, integrate an anomaly detection system. This will enable predicting problems and challenges that experts may face and proposing countermeasures in advance.

[0080] Using its emotion estimation function, the generative AI can detect fluctuations in the motivation of experts while they are working and suggest measures to maintain their motivation. For example, a system can be built using the emotion estimation function to detect fluctuations in the motivation of experts while they are working in real time. For example, the system can analyze the experts' facial expressions and voices to calculate a motivation score. A function can be added to suggest appropriate measures to maintain motivation based on the experts' motivation state. For example, encouraging messages or success stories can be presented. A system can be developed that continuously monitors the experts' motivation based on the emotion estimation data and provides a long-term motivation maintenance plan. For example, regular motivation checks can be performed. This makes it possible to detect fluctuations in the experts' motivation while they are working and suggest appropriate measures to maintain their motivation.

[0081] Sales platforms can be equipped with a recommendation function based on users' purchase history and ratings. For example, a system can be built that analyzes a user's purchase history and recommends related generative AI. For example, products similar to generative AIs purchased in the past can be suggested. A function can be added that recommends generative AIs that other users have given high ratings to, based on user rating data. For example, highly rated generative AIs can be displayed preferentially. A system can be developed that integrates purchase history and rating data to provide personalized recommendations tailored to the user's interests and needs. For example, the optimal generative AI can be suggested based on the user's past behavioral data. This makes it possible to provide a recommendation function based on a user's purchase history and ratings.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The user can perform original additional learning on the generating AI. For example, the user can have the generating AI learn information about their field of expertise or area of ​​interest. The input to the generating AI is a prompt containing instructions on what the user wants the generating AI to do, and the generating AI performs additional learning based on that prompt. Step 2: The additional learning function allows individuals to perform original additional learning on the generative AI. For example, an expert in the semiconductor field can create a generative AI specialized in that field by teaching the generative AI his or her own knowledge and experience. Step 3: The sales platform is a platform for selling generative AI trained through additional learning functions. For example, by purchasing generative AI used by a professor specializing in the semiconductor field, you can gain advanced knowledge in that field.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] 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]

[0151] 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. Generative AI and An additional learning function that allows individuals to add original learning to the generating AI, A sales platform that sells the generation AI trained by the additional learning function. A system characterized by:

2. The generated AI is Equipped with an emotion estimation function that estimates the user's emotions, The emotion estimation function is Optimizing learning content based on the user's emotions 2. The system of claim 1.

3. The generated AI is Propose personalized learning plans based on users' past learning history and performance data 2. The system of claim 1.

4. The generated AI is Automatically pulls in the latest research papers and patent information in the field you want to learn about 2. The system of claim 1.

5. The generated AI is Equipped with collaboration functions that allow experts from different fields to collaborate on the additional learning.

2. The system of claim 1.

6. The generated AI is Community features that allow users to share their learning with other users and receive feedback 2. The system of claim 1.

7. The generated AI is Detects stress and fatigue felt by users while studying and suggests ways to take a break and refresh 2. The system of claim 1.

8. The generated AI is Learn from experts' past work data and success stories to provide more specific advice 2. The system of claim 1.

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