System

Abstract

An object of the system according to the embodiment is to create an idea by obtaining feedback from various viewpoints without being restricted by time and place.SOLUTION: A system includes an idea input part, a discussion simulation part, an idea arrangement part, and an evaluation feedback part. The idea input unit receives an idea of a user. The discussion simulation unit provides feedback from the viewpoint of another person to the idea received by the idea input unit. The idea organizer visually organizes the ideas based on the feedback provided by the discussion simulator. The evaluation feedback unit evaluates the idea organized by the idea organizing unit and provides feedback.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] With conventional technology, the idea generation process was constrained by time and location, making it difficult to obtain feedback from diverse perspectives.

[0005] The system according to the embodiment aims to generate ideas by obtaining feedback from a variety of perspectives without being restricted by time or place. [Means for solving the problem]

[0006] The system according to the embodiment includes an idea input unit, a discussion simulation unit, an idea organization unit, and an evaluation feedback unit. The idea input unit accepts ideas from users. The discussion simulation unit provides feedback from other people's perspectives on the ideas accepted by the idea input unit. The idea organization unit visually organizes the ideas based on the feedback provided by the discussion simulation unit. The evaluation feedback unit evaluates the ideas organized by the idea organization unit and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can generate ideas by receiving feedback from a variety of perspectives without being restricted by time or place. [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 virtual brainstorming system according to an embodiment of the present invention is a system that streamlines the process of users generating ideas, and uses a generative AI to provide feedback from other people's perspectives, visually organize ideas, and evaluate and provide feedback. As a result, the virtual brainstorming system can streamline the user's idea generation process and accelerate innovation.

[0029] A virtual brainstorming system according to an embodiment includes an idea input unit, a discussion simulation unit, an idea organization unit, and an evaluation feedback unit. The idea input unit accepts a user's idea. For example, the user inputs the idea in text format. The idea input unit can also accept the user's idea using voice input. For example, the user dictates the idea using a microphone and converts it into text. The idea input unit can also accept ideas using images or diagrams. For example, the user scans handwritten notes and inputs them as ideas. The discussion simulation unit provides feedback from other people's perspectives on the ideas accepted by the idea input unit. For example, the generation AI poses questions such as, "What market will this idea be sold to?" or "What is your point of differentiation from competitors?" The generation AI can also provide new perspectives and ideas for the user's idea. For example, the generation AI suggests, "What technology is needed to realize this idea?" The discussion simulation unit can also automatically search for related patent information and academic papers and present them as reference materials to provide feedback from other people's perspectives on the user's idea. The idea organization unit visually organizes ideas based on the feedback provided by the discussion simulation unit. For example, the generation AI organizes the user's ideas in the form of a mind map or flowchart and displays them visually in an easy-to-understand manner. The generation AI can also analyze the relevance of ideas and group related ideas. Furthermore, the idea organization unit can use an emotion estimation function to customize the interface according to the user's emotional state and improve the efficiency of idea creation. The evaluation feedback unit evaluates the ideas organized by the idea organization unit and provides feedback. For example, the generation AI evaluates the feasibility and marketability of the ideas and provides feedback on the results to the user. The generation AI can also propose new business models and marketing strategies for the user's ideas.Furthermore, the evaluation feedback unit can use the emotion estimation function to estimate the user's emotions in real time when entering ideas and make suggestions to elicit positive emotions. This allows the virtual brainstorming system according to the embodiment to streamline the user's idea creation process and accelerate innovation. For example, users can exchange ideas wherever they are, such as at home, in the office, or at a cafe, and the generation AI provides feedback from other people's perspectives and new ideas. The generation AI also organizes ideas and displays them visually in an easy-to-understand manner, making them easy for users to understand. Furthermore, the generation AI evaluates the feasibility and marketability of ideas and provides feedback, allowing users to understand the strengths and weaknesses of their ideas and identify areas for improvement. This allows users to build competitive businesses.

[0030] The idea input unit can analyze the user's past idea history and provide idea generation hints optimized for each individual user. The idea input unit, for example, stores the user's past idea history in a database, and the generation AI analyzes that data. For example, it learns the trends and patterns of ideas submitted in the past and proposes new ideas to the user. The idea input unit also provides idea generation hints optimized for each individual user based on the user's past idea history. For example, it provides specific advice to the user by referring to past success stories and failure stories. The idea input unit also analyzes the user's past idea history, and the generation AI generates new ideas based on that data. For example, it extracts keywords and themes from past ideas and proposes new ideas based on them. This makes it possible to provide the user with more accurate idea generation hints.

[0031] The discussion simulation unit can automatically search for related patent information and academic papers based on the user's input and present them as reference material. For example, the discussion simulation unit automatically searches for patent information related to an idea entered by the user, and the generation AI presents that information to the user. For example, it may search a patent database and list related patents. The discussion simulation unit can also automatically search for related academic papers based on the user's input, and the generation AI provides that information to the user. For example, it may search an academic database and list related papers. The discussion simulation unit can also automatically search for patent information and academic papers related to an idea entered by the user, and the generation AI presents that information as reference material. For example, it may display summaries of patents and papers to make them easy for the user to understand. This allows the user to easily obtain related information.

[0032] The system is customized for educational institutions and can be used when students brainstorm ideas for group projects. The system, for example, customizes virtual brainstorming sessions for educational institutions so that they can be used when students brainstorm ideas for group projects. For example, it provides a platform for students to collaborate online and share ideas. The system also customizes virtual brainstorming sessions for educational institutions so that they can be used when students brainstorm ideas for group projects. For example, it provides a function that allows students to share ideas in real time and receive feedback. The system also customizes virtual brainstorming sessions for educational institutions so that they can be used when students brainstorm ideas for group projects. For example, it provides a function that allows students to collaboratively organize ideas and display them in a visually easy-to-understand manner. In this way, the system can be customized for educational institutions so that it can be used when students brainstorm ideas for group projects.

[0033] The system is incorporated into a company's human resource development program and utilized as a tool to enhance employee creativity. For example, the system incorporates virtual brainstorming into a company's human resource development program and utilizes it as a tool to enhance employee creativity. For example, it provides a platform for employees to share ideas online and work on projects together. The system also incorporates virtual brainstorming into a company's human resource development program and utilizes it as a tool to enhance employee creativity. For example, it provides a function that allows employees to share ideas in real time and receive feedback. The system also incorporates virtual brainstorming into a company's human resource development program and utilizes it as a tool to enhance employee creativity. For example, it provides a function that allows employees to organize ideas together and display them in a visually easy-to-understand manner. As a result, the system can be incorporated into a company's human resource development program and utilized as a tool to enhance employee creativity.

[0034] The discussion simulation unit learns the user's past discussion history and can provide more accurate feedback. For example, the generation AI stores the user's past discussion history in a database and learns that data. For example, it analyzes past discussion content and feedback to provide more accurate feedback to the user. The discussion simulation unit also builds a system in which the generation AI provides more accurate feedback based on the user's past discussion history. For example, it provides specific advice to the user by referring to the content of past discussions. The discussion simulation unit also learns the user's past discussion history and provides new feedback based on that data. For example, it learns the trends and patterns of past discussions and provides appropriate feedback to the user. This makes it possible to provide more accurate feedback to the user.

[0035] The discussion simulation unit can simulate virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, the discussion simulation unit constructs a system in which a generation AI simulates virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, it generates virtual participants that reflect the cultures of different countries or regions. The discussion simulation unit also provides opinions from diverse perspectives by simulating virtual participants with different cultures and backgrounds. For example, it generates virtual participants from different industries or fields of expertise to provide diverse opinions to users. The discussion simulation unit also provides opinions from diverse perspectives by using a generation AI to simulate virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, it generates virtual participants with different values ​​and ways of thinking to provide users with new perspectives. This improves the quality of users' ideas by providing opinions from diverse perspectives.

[0036] The discussion simulation unit can simulate experts from different industries to hold discussions specialized for a specific industry. For example, the discussion simulation unit builds a system in which a generation AI simulates experts from different industries to hold discussions specialized for a specific industry. For example, it simulates experts from the medical industry or the IT industry to provide expert opinions to users. The discussion simulation unit also simulates experts from different industries to hold discussions specialized for a specific industry. For example, it simulates experts from the manufacturing industry or the service industry to provide users with industry-specific perspectives. The discussion simulation unit also simulates experts from different industries to hold discussions specialized for a specific industry. For example, it simulates experts from the financial industry or the education industry to provide users with industry-specific advice. In this way, expert opinions can be provided to users by holding discussions specialized for a specific industry.

[0037] The discussion simulation unit can deepen the discussion by automatically suggesting related video and audio content based on the user's input. For example, the discussion simulation unit constructs a system in which a generation AI analyzes the user's input and automatically suggests related video and audio content. For example, if users are discussing new technology, a related technical explanation video is suggested. Furthermore, the discussion simulation unit automatically suggests related video and audio content based on the user's input. For example, if users are discussing marketing strategy, an audio recording of a related marketing seminar is suggested. Furthermore, the discussion simulation unit analyzes the user's input and automatically suggests related video and audio content. For example, if users are discussing design, a video of a related design workshop is suggested. This allows the discussion to deepen by suggesting related video and audio content.

[0038] The idea organization unit can analyze the relevance of ideas and automatically suggest related ideas that users tend to overlook. For example, the idea organization unit constructs a system in which a generation AI analyzes a user's ideas and automatically suggests related ideas. For example, when a user submits an idea for a new product, the generation AI will suggest similar ideas from the past. The idea organization unit also automatically suggests related ideas that users tend to overlook. For example, when a user submits an idea for a specific technology, the generation AI will suggest other ideas that apply that technology. The idea organization unit also analyzes the relevance of ideas and automatically suggests related ideas that users tend to overlook. For example, when a user comes up with a new business model, the generation AI will suggest related marketing strategies. This improves the quality of ideas by suggesting related ideas that users tend to overlook.

[0039] The idea organization unit can track the progress of an idea and periodically send reminders to the user. The idea organization unit, for example, builds a system in which a generation AI tracks the progress of a user's idea and periodically sends reminders. For example, if progress on an idea is behind schedule, a reminder is sent to the user. The idea organization unit also tracks the progress of a user's idea using the generation AI and periodically sends reminders. For example, if the deadline for executing the idea is approaching, a reminder is sent to the user. The idea organization unit also tracks the progress of an idea using the generation AI and periodically sends reminders to the user. For example, if progress on an idea is going well, a reminder is sent to the user suggesting the next step. In this way, tracking the progress of an idea and sending reminders to the user promotes the execution of the idea.

[0040] The idea organization unit can introduce encryption technology to protect the user's privacy when sharing ideas. The idea organization unit, for example, builds a system that introduces encryption technology to protect the user's privacy when the generation AI shares ideas. For example, the idea data is encrypted and stored and shared. The idea organization unit also introduces encryption technology to protect the user's privacy when the generation AI shares ideas. For example, the idea data is encrypted and sent so that only the recipient can decrypt it. The idea organization unit also introduces encryption technology to protect the user's privacy when the generation AI shares ideas. For example, the idea data is encrypted and shared so that third parties cannot access it. This allows ideas to be shared while protecting the user's privacy.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The virtual brainstorming system can analyze a user's past idea history and provide idea generation hints optimized for each individual user. For example, it can learn the trends and patterns of ideas submitted in the past and suggest new ideas to the user. It can also provide specific advice to the user by referring to past successes and failures. It can also extract keywords and themes from past ideas and suggest new ideas based on them. This allows it to provide users with more accurate idea generation hints.

[0043] Based on the user's input, the virtual brainstorming system can automatically search for relevant patent information and academic papers and present them as reference material. For example, it can search patent databases and list relevant patents. It can also search academic databases and list relevant papers. It can also display abstracts of patents and papers so that users can easily understand them. This allows users to easily obtain relevant information.

[0044] Virtual brainstorming systems can be customized for educational institutions and used when students brainstorm ideas for group projects. For example, they can provide a platform for students to collaborate online and share ideas. They can also provide features that allow students to share ideas in real time and receive feedback. They can also provide features that allow students to collaboratively organize their ideas and display them visually in an easy-to-understand manner. These features make them suitable for educational institutions and used when students brainstorm ideas for group projects.

[0045] Virtual brainstorming systems can be incorporated into corporate human resource development programs and used as a tool to enhance employee creativity. For example, they can provide a platform for employees to share ideas online and work on projects together. They can also provide functions that allow employees to share ideas in real time and receive feedback. They can also provide functions that allow employees to collaboratively organize ideas and display them visually in an easy-to-understand manner. As a result, virtual brainstorming systems can be incorporated into corporate human resource development programs and used as a tool to enhance employee creativity.

[0046] The virtual brainstorming system can learn from a user's past discussion history and provide more accurate feedback. For example, it can analyze past discussion content and feedback to provide more accurate feedback to the user. It can also provide specific advice to the user by referring to the content of past discussions. It can also learn trends and patterns in past discussions and provide appropriate feedback to the user. This allows it to provide more accurate feedback to the user.

[0047] The virtual brainstorming system can simulate virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, it can generate virtual participants that reflect the cultures of different countries or regions. It can also generate virtual participants from different industries or fields of expertise to provide users with diverse opinions. It can also generate virtual participants with different values ​​and ways of thinking to provide users with new perspectives. This can improve the quality of users' ideas by providing opinions from diverse perspectives.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The idea input unit accepts a user's idea. For example, the user inputs the idea in text format. The idea input unit can also accept a user's idea using voice input. For example, the user dictates the idea using a microphone and converts it into text. The idea input unit can also accept an idea using an image or diagram. For example, the user scans handwritten notes and inputs them as an idea. Step 2: The discussion simulation unit provides feedback from other people's perspectives on the ideas accepted by the idea input unit. For example, the generation AI poses questions such as, "What market will this idea be sold to?" or "What is its point of differentiation from competitors?" The generation AI can also provide new perspectives and ideas on the user's idea. For example, the generation AI may suggest, "What technology is needed to realize this idea?" Furthermore, the discussion simulation unit can automatically search for related patent information and academic papers and present them as reference materials to provide feedback on the user's idea from other people's perspectives. Step 3: The idea organization unit visually organizes the ideas based on the feedback provided by the discussion simulation unit. For example, the generation AI may organize the user's ideas in the form of a mind map or flowchart, displaying them in a visually easy-to-understand format. The generation AI may also analyze the relevance of ideas and group related ideas. Furthermore, the idea organization unit may use an emotion estimation function to customize the interface according to the user's emotional state, improving the efficiency of idea generation. Step 4: The evaluation feedback unit evaluates the ideas organized by the idea organization unit and provides feedback. For example, the generation AI evaluates the feasibility and marketability of the idea and provides feedback on the results to the user. The generation AI can also propose new business models and marketing strategies for the user's ideas. Furthermore, the evaluation feedback unit can use an emotion estimation function to estimate the emotions the user is feeling in real time when entering their idea, and make suggestions that will elicit positive emotions.

[0050] (Example 2) The virtual brainstorming system according to an embodiment of the present invention is a system that streamlines the process of users generating ideas, and uses a generative AI to provide feedback from other people's perspectives, visually organize ideas, and evaluate and provide feedback. As a result, the virtual brainstorming system can streamline the user's idea generation process and accelerate innovation.

[0051] A virtual brainstorming system according to an embodiment includes an idea input unit, a discussion simulation unit, an idea organization unit, and an evaluation feedback unit. The idea input unit accepts a user's idea. For example, the user inputs the idea in text format. The idea input unit can also accept the user's idea using voice input. For example, the user dictates the idea using a microphone and converts it into text. The idea input unit can also accept ideas using images or diagrams. For example, the user scans handwritten notes and inputs them as ideas. The discussion simulation unit provides feedback from other people's perspectives on the ideas accepted by the idea input unit. For example, the generation AI poses questions such as, "What market will this idea be sold to?" or "What is your point of differentiation from competitors?" The generation AI can also provide new perspectives and ideas for the user's idea. For example, the generation AI suggests, "What technology is needed to realize this idea?" The discussion simulation unit can also automatically search for related patent information and academic papers and present them as reference materials to provide feedback from other people's perspectives on the user's idea. The idea organization unit visually organizes ideas based on the feedback provided by the discussion simulation unit. For example, the generation AI organizes the user's ideas in the form of a mind map or flowchart and displays them visually in an easy-to-understand manner. The generation AI can also analyze the relevance of ideas and group related ideas. Furthermore, the idea organization unit can use an emotion estimation function to customize the interface according to the user's emotional state and improve the efficiency of idea creation. The evaluation feedback unit evaluates the ideas organized by the idea organization unit and provides feedback. For example, the generation AI evaluates the feasibility and marketability of the ideas and provides feedback on the results to the user. The generation AI can also propose new business models and marketing strategies for the user's ideas.Furthermore, the evaluation feedback unit can use the emotion estimation function to estimate the user's emotions in real time when entering ideas and make suggestions to elicit positive emotions. This allows the virtual brainstorming system according to the embodiment to streamline the user's idea creation process and accelerate innovation. For example, users can exchange ideas wherever they are, such as at home, in the office, or at a cafe, and the generation AI provides feedback from other people's perspectives and new ideas. The generation AI also organizes ideas and displays them visually in an easy-to-understand manner, making them easy for users to understand. Furthermore, the generation AI evaluates the feasibility and marketability of ideas and provides feedback, allowing users to understand the strengths and weaknesses of their ideas and identify areas for improvement. This allows users to build competitive businesses.

[0052] The idea input unit can analyze the user's past idea history and provide idea generation hints optimized for each individual user. The idea input unit, for example, stores the user's past idea history in a database, and the generation AI analyzes that data. For example, it learns the trends and patterns of ideas submitted in the past and proposes new ideas to the user. The idea input unit also provides idea generation hints optimized for each individual user based on the user's past idea history. For example, it provides specific advice to the user by referring to past success stories and failure stories. The idea input unit also analyzes the user's past idea history, and the generation AI generates new ideas based on that data. For example, it extracts keywords and themes from past ideas and proposes new ideas based on them. This makes it possible to provide the user with more accurate idea generation hints.

[0053] The discussion simulation unit can automatically search for related patent information and academic papers based on the user's input and present them as reference material. For example, the discussion simulation unit automatically searches for patent information related to an idea entered by the user, and the generation AI presents that information to the user. For example, it may search a patent database and list related patents. The discussion simulation unit can also automatically search for related academic papers based on the user's input, and the generation AI provides that information to the user. For example, it may search an academic database and list related papers. The discussion simulation unit can also automatically search for patent information and academic papers related to an idea entered by the user, and the generation AI presents that information as reference material. For example, it may display summaries of patents and papers to make them easy for the user to understand. This allows the user to easily obtain related information.

[0054] The idea organizing unit uses the emotion estimation function to customize the interface according to the user's emotional state, thereby improving the efficiency of idea creation. The idea organizing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and customize the interface based on the results. For example, if the user is feeling stressed, it provides a relaxing interface. The idea organizing unit also builds a system that dynamically changes the color and layout of the interface according to the user's emotional state. For example, if the user is feeling positive, it provides a brightly colored interface. The idea organizing unit also uses the emotion estimation function to customize the interface based on the user's emotional state, thereby improving the efficiency of idea creation. For example, it optimizes interface elements to provide an environment that makes it easier for the user to concentrate. This makes it possible to customize the interface according to the user's emotional state.

[0055] The system is customized for educational institutions and can be used when students brainstorm ideas for group projects. The system, for example, customizes virtual brainstorming sessions for educational institutions so that they can be used when students brainstorm ideas for group projects. For example, it provides a platform for students to collaborate online and share ideas. The system also customizes virtual brainstorming sessions for educational institutions so that they can be used when students brainstorm ideas for group projects. For example, it provides a function that allows students to share ideas in real time and receive feedback. The system also customizes virtual brainstorming sessions for educational institutions so that they can be used when students brainstorm ideas for group projects. For example, it provides a function that allows students to collaboratively organize ideas and display them in a visually easy-to-understand manner. In this way, the system can be customized for educational institutions so that it can be used when students brainstorm ideas for group projects.

[0056] The system is incorporated into a company's human resource development program and utilized as a tool to enhance employee creativity. For example, the system incorporates virtual brainstorming into a company's human resource development program and utilizes it as a tool to enhance employee creativity. For example, it provides a platform for employees to share ideas online and work on projects together. The system also incorporates virtual brainstorming into a company's human resource development program and utilizes it as a tool to enhance employee creativity. For example, it provides a function that allows employees to share ideas in real time and receive feedback. The system also incorporates virtual brainstorming into a company's human resource development program and utilizes it as a tool to enhance employee creativity. For example, it provides a function that allows employees to organize ideas together and display them in a visually easy-to-understand manner. As a result, the system can be incorporated into a company's human resource development program and utilized as a tool to enhance employee creativity.

[0057] The evaluation feedback unit can use the emotion estimation function to estimate the emotion a user is feeling when inputting an idea in real time and make suggestions to elicit positive emotions. For example, the evaluation feedback unit uses the emotion estimation function to estimate the emotion a user is feeling when inputting an idea in real time and make suggestions to elicit positive emotions based on the results. For example, if the user is feeling negative emotions, it displays an encouraging message. The evaluation feedback unit also uses the emotion estimation function to analyze the emotion a user is feeling when inputting an idea in real time and make suggestions to elicit positive emotions. For example, if the user is feeling stressed, it makes suggestions to help the user relax. The evaluation feedback unit also uses the emotion estimation function to estimate the emotion a user is feeling when inputting an idea in real time and make suggestions to elicit positive emotions. For example, interface elements are optimized to provide an environment that makes it easier for the user to concentrate. This elicits positive emotions from the user and improves the efficiency of idea creation.

[0058] The discussion simulation unit learns the user's past discussion history and can provide more accurate feedback. For example, the generation AI stores the user's past discussion history in a database and learns that data. For example, it analyzes past discussion content and feedback to provide more accurate feedback to the user. The discussion simulation unit also builds a system in which the generation AI provides more accurate feedback based on the user's past discussion history. For example, it provides specific advice to the user by referring to the content of past discussions. The discussion simulation unit also learns the user's past discussion history and provides new feedback based on that data. For example, it learns the trends and patterns of past discussions and provides appropriate feedback to the user. This makes it possible to provide more accurate feedback to the user.

[0059] The discussion simulation unit can simulate virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, the discussion simulation unit constructs a system in which a generation AI simulates virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, it generates virtual participants that reflect the cultures of different countries or regions. The discussion simulation unit also provides opinions from diverse perspectives by simulating virtual participants with different cultures and backgrounds. For example, it generates virtual participants from different industries or fields of expertise to provide diverse opinions to users. The discussion simulation unit also provides opinions from diverse perspectives by using a generation AI to simulate virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, it generates virtual participants with different values ​​and ways of thinking to provide users with new perspectives. This improves the quality of users' ideas by providing opinions from diverse perspectives.

[0060] The discussion simulation unit can use the emotion estimation function to monitor changes in users' emotions while the discussion is in progress and pose questions to promote the discussion at appropriate times. The discussion simulation unit, for example, uses the emotion estimation function to monitor changes in users' emotions in real time while the discussion is in progress and poses questions at appropriate times based on the results. For example, if the user is excited, it asks questions to calm the user down. The discussion simulation unit also builds a system that monitors changes in users' emotions and poses questions at appropriate times while the discussion is in progress. For example, if the user is tired, it asks questions to refresh the user. The discussion simulation unit also uses the emotion estimation function to monitor changes in users' emotions while the discussion is in progress and poses questions to promote the discussion at appropriate times. For example, if the user is concentrating, it asks questions to dig deeper. This promotes the discussion in accordance with changes in users' emotions, thereby improving the quality of the discussion.

[0061] The discussion simulation unit can simulate experts from different industries to hold discussions specialized for a specific industry. For example, the discussion simulation unit builds a system in which a generation AI simulates experts from different industries to hold discussions specialized for a specific industry. For example, it simulates experts from the medical industry or the IT industry to provide expert opinions to users. The discussion simulation unit also simulates experts from different industries to hold discussions specialized for a specific industry. For example, it simulates experts from the manufacturing industry or the service industry to provide users with industry-specific perspectives. The discussion simulation unit also simulates experts from different industries to hold discussions specialized for a specific industry. For example, it simulates experts from the financial industry or the education industry to provide users with industry-specific advice. In this way, expert opinions can be provided to users by holding discussions specialized for a specific industry.

[0062] The discussion simulation unit can deepen the discussion by automatically suggesting related video and audio content based on the user's input. For example, the discussion simulation unit constructs a system in which a generation AI analyzes the user's input and automatically suggests related video and audio content. For example, if users are discussing new technology, a related technical explanation video is suggested. Furthermore, the discussion simulation unit automatically suggests related video and audio content based on the user's input. For example, if users are discussing marketing strategy, an audio recording of a related marketing seminar is suggested. Furthermore, the discussion simulation unit analyzes the user's input and automatically suggests related video and audio content. For example, if users are discussing design, a video of a related design workshop is suggested. This allows the discussion to deepen by suggesting related video and audio content.

[0063] The discussion simulation unit can use the emotion estimation function to suggest relaxation techniques to reduce the stress and anxiety felt by the user during the discussion. For example, the discussion simulation unit uses the emotion estimation function to monitor the stress and anxiety felt by the user during the discussion in real time and suggest relaxation techniques based on the results. For example, if the user is feeling stressed, the discussion simulation unit suggests deep breathing or meditation. The discussion simulation unit also monitors the user's emotional state and builds a system that suggests relaxation techniques to reduce the stress and anxiety felt during the discussion. For example, if the user is feeling anxious, the system suggests relaxing music. The discussion simulation unit also uses the emotion estimation function to suggest relaxation techniques to reduce the stress and anxiety felt by the user during the discussion. For example, if the user is tense, the system suggests stretching or exercises to help them relax. This reduces the user's stress and anxiety, thereby improving the quality of the discussion.

[0064] The idea organization unit can analyze the relevance of ideas and automatically suggest related ideas that users tend to overlook. For example, the idea organization unit constructs a system in which a generation AI analyzes a user's ideas and automatically suggests related ideas. For example, when a user submits an idea for a new product, the generation AI will suggest similar ideas from the past. The idea organization unit also automatically suggests related ideas that users tend to overlook. For example, when a user submits an idea for a specific technology, the generation AI will suggest other ideas that apply that technology. The idea organization unit also analyzes the relevance of ideas and automatically suggests related ideas that users tend to overlook. For example, when a user comes up with a new business model, the generation AI will suggest related marketing strategies. This improves the quality of ideas by suggesting related ideas that users tend to overlook.

[0065] The idea organization unit can track the progress of an idea and periodically send reminders to the user. The idea organization unit, for example, builds a system in which a generation AI tracks the progress of a user's idea and periodically sends reminders. For example, if progress on an idea is behind schedule, a reminder is sent to the user. The idea organization unit also tracks the progress of a user's idea using the generation AI and periodically sends reminders. For example, if the deadline for executing the idea is approaching, a reminder is sent to the user. The idea organization unit also tracks the progress of an idea using the generation AI and periodically sends reminders to the user. For example, if progress on an idea is going well, a reminder is sent to the user suggesting the next step. In this way, tracking the progress of an idea and sending reminders to the user promotes the execution of the idea.

[0066] The idea organizing unit can use the emotion estimation function to analyze the emotional state of the user when organizing ideas and propose an optimal organizing method. For example, the idea organizing unit uses the emotion estimation function to analyze the emotional state of the user when organizing ideas in real time and propose an optimal organizing method based on the results. For example, if the user is confused, it proposes a simple organizing method. The idea organizing unit also builds a system that analyzes the emotional state of the user and proposes an optimal organizing method. For example, if the user is feeling stressed, it proposes a relaxing organizing method. The idea organizing unit also uses the emotion estimation function to analyze the emotional state of the user when organizing ideas and proposes an optimal organizing method. For example, if the user is concentrating, it proposes a detailed organizing method. In this way, the efficiency of organizing ideas is improved by proposing an optimal organizing method according to the user's emotional state.

[0067] The idea organization unit can introduce encryption technology to protect the user's privacy when sharing ideas. The idea organization unit, for example, builds a system that introduces encryption technology to protect the user's privacy when the generation AI shares ideas. For example, the idea data is encrypted and stored and shared. The idea organization unit also introduces encryption technology to protect the user's privacy when the generation AI shares ideas. For example, the idea data is encrypted and sent so that only the recipient can decrypt it. The idea organization unit also introduces encryption technology to protect the user's privacy when the generation AI shares ideas. For example, the idea data is encrypted and shared so that third parties cannot access it. This allows ideas to be shared while protecting the user's privacy.

[0068] The evaluation feedback unit can use the emotion estimation function to collect other users' emotional reactions to the shared idea and improve the quality of the feedback. The evaluation feedback unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the shared idea in real time and improve the quality of the feedback based on the data. For example, ideas with many positive emotional reactions are preferentially evaluated. The evaluation feedback unit also collects other users' emotional reactions and builds a system that improves the quality of feedback for the shared idea. For example, the evaluation feedback unit adjusts the content of the feedback based on the emotion score. The evaluation feedback unit also uses the emotion estimation function to collect other users' emotional reactions to the shared idea and improve the quality of the feedback. For example, it makes improvement suggestions for ideas with many negative emotional reactions. In this way, the quality of feedback is improved by collecting other users' emotional reactions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The virtual brainstorming system can estimate a user's emotions and customize the interface based on the estimated emotions. For example, if a user is feeling stressed, it can provide a relaxing interface. If a user is concentrating, it can provide an interface that displays more detailed information. Furthermore, if a user is feeling positive, it can provide a brightly colored interface. This makes it possible to customize the interface according to the user's emotional state, thereby improving the efficiency of idea generation.

[0071] The virtual brainstorming system can analyze a user's past idea history and provide idea generation hints optimized for each individual user. For example, it can learn the trends and patterns of ideas submitted in the past and suggest new ideas to the user. It can also provide specific advice to the user by referring to past successes and failures. It can also extract keywords and themes from past ideas and suggest new ideas based on them. This allows it to provide users with more accurate idea generation hints.

[0072] Based on the user's input, the virtual brainstorming system can automatically search for relevant patent information and academic papers and present them as reference material. For example, it can search patent databases and list relevant patents. It can also search academic databases and list relevant papers. It can also display abstracts of patents and papers so that users can easily understand them. This allows users to easily obtain relevant information.

[0073] The virtual brainstorming system uses its emotion estimation function to customize the interface according to the user's emotional state, improving the efficiency of idea generation. For example, if the user is feeling stressed, a relaxing interface can be provided. If the user is feeling positive, a brightly colored interface can be provided. Furthermore, if the user is concentrating, an interface that displays detailed information can be provided. This makes it possible to customize the interface according to the user's emotional state, improving the efficiency of idea generation.

[0074] Virtual brainstorming systems can be customized for educational institutions and used when students brainstorm ideas for group projects. For example, they can provide a platform for students to collaborate online and share ideas. They can also provide features that allow students to share ideas in real time and receive feedback. They can also provide features that allow students to collaboratively organize their ideas and display them visually in an easy-to-understand manner. These features make them suitable for educational institutions and used when students brainstorm ideas for group projects.

[0075] Virtual brainstorming systems can be incorporated into corporate human resource development programs and used as a tool to enhance employee creativity. For example, they can provide a platform for employees to share ideas online and work on projects together. They can also provide functions that allow employees to share ideas in real time and receive feedback. They can also provide functions that allow employees to collaboratively organize ideas and display them visually in an easy-to-understand manner. As a result, virtual brainstorming systems can be incorporated into corporate human resource development programs and used as a tool to enhance employee creativity.

[0076] The virtual brainstorming system uses an emotion estimation function to estimate the user's emotions in real time when they input their ideas, and can make suggestions to elicit positive emotions. For example, if the user is feeling negative, an encouraging message can be displayed. Also, if the user is feeling stressed, suggestions to help them relax can be made. Furthermore, the system can optimize interface elements to provide an environment that makes it easier for users to concentrate. This can improve the efficiency of idea generation by eliciting positive emotions from users.

[0077] The virtual brainstorming system can learn from a user's past discussion history and provide more accurate feedback. For example, it can analyze past discussion content and feedback to provide more accurate feedback to the user. It can also provide specific advice to the user by referring to the content of past discussions. It can also learn trends and patterns in past discussions and provide appropriate feedback to the user. This allows it to provide more accurate feedback to the user.

[0078] The virtual brainstorming system can simulate virtual participants with different cultures and backgrounds to provide opinions from diverse perspectives. For example, it can generate virtual participants that reflect the cultures of different countries or regions. It can also generate virtual participants from different industries or fields of expertise to provide users with diverse opinions. It can also generate virtual participants with different values ​​and ways of thinking to provide users with new perspectives. This can improve the quality of users' ideas by providing opinions from diverse perspectives.

[0079] Using its emotion estimation function, the virtual brainstorming system can monitor changes in users' emotions as the discussion progresses and ask questions at appropriate times to promote the discussion. For example, if a user is excited, it can ask questions to calm them down. If a user is tired, it can ask questions to refresh them. Furthermore, if a user is concentrating, it can ask questions to dig deeper. This can improve the quality of discussions by promoting discussions in response to changes in users' emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The idea input unit accepts a user's idea. For example, the user inputs the idea in text format. The idea input unit can also accept a user's idea using voice input. For example, the user dictates the idea using a microphone and converts it into text. The idea input unit can also accept an idea using an image or diagram. For example, the user scans handwritten notes and inputs them as an idea. Step 2: The discussion simulation unit provides feedback from other people's perspectives on the ideas accepted by the idea input unit. For example, the generation AI poses questions such as, "What market will this idea be sold to?" or "What is its point of differentiation from competitors?" The generation AI can also provide new perspectives and ideas on the user's idea. For example, the generation AI may suggest, "What technology is needed to realize this idea?" Furthermore, the discussion simulation unit can automatically search for related patent information and academic papers and present them as reference materials to provide feedback on the user's idea from other people's perspectives. Step 3: The idea organization unit visually organizes the ideas based on the feedback provided by the discussion simulation unit. For example, the generation AI may organize the user's ideas in the form of a mind map or flowchart, displaying them in a visually easy-to-understand format. The generation AI may also analyze the relevance of ideas and group related ideas. Furthermore, the idea organization unit may use an emotion estimation function to customize the interface according to the user's emotional state, improving the efficiency of idea generation. Step 4: The evaluation feedback unit evaluates the ideas organized by the idea organization unit and provides feedback. For example, the generation AI evaluates the feasibility and marketability of the idea and provides feedback on the results to the user. The generation AI can also propose new business models and marketing strategies for the user's ideas. Furthermore, the evaluation feedback unit can use an emotion estimation function to estimate the emotions the user is feeling in real time when entering their idea, and make suggestions that will elicit positive emotions.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an idea input unit that accepts ideas from users; a discussion simulation unit that provides feedback from other people's perspectives on the ideas received by the idea input unit; an idea organizing unit that visually organizes ideas based on the feedback provided by the discussion simulation unit; an evaluation and feedback unit that evaluates the ideas organized by the idea organization unit and provides feedback; A system characterized by:

2. The idea input unit Analyzing the user's past idea history and providing individual user with optimized idea generation hints 2. The system of claim 1.

3. The discussion simulation unit Based on the user's input, related patent information and academic papers are automatically searched and presented as reference material.

2. The system of claim 1.

4. The idea organizing unit The interface is customized according to the user's emotional state, thereby improving the efficiency of idea generation.

2. The system of claim 1.

5. The system comprises: Tailored for educational institutions, it helps students brainstorm ideas for group projects.

2. The system of claim 1.

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