System

Abstract

An object of a system according to an embodiment is to efficiently obtain feedback or an idea from a multiple perspectives for a promotion posted by a user.SOLUTION: A system according to an embodiment includes a AI agent, a AI moderator, a relevant information providing unit, and a Miro collaboration AI. The AI agent gives feedback or an idea to the plan posted by the user. The AI moderator manages the progress of the AI by the dialog agent. A related information providing part provides related information from the past discussion. The Miro collaborative AI converts and organizes ideas made during brainstorming into text and charts in real time.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, it was difficult to efficiently obtain feedback and ideas from multiple perspectives during brainstorming.

[0005] The system according to the embodiment aims to efficiently obtain feedback and ideas from multiple perspectives for plans posted by users. [Means for solving the problem]

[0006] The system according to the embodiment comprises an AI agent, an AI moderator, a related information provider, and a Miro-linked AI. The AI ​​agent provides feedback and ideas for projects posted by users. The AI ​​moderator manages the progress of the dialogue between the AI ​​agents. The related information provider provides related information from past discussions. The Miro-linked AI converts and organizes ideas generated during brainstorming into text and diagrams in real time. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently obtain feedback and ideas from multiple perspectives for a plan posted by a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A brainstorming system according to an embodiment of the present invention is a system that realizes multi-perspective brainstorming by having multiple AI agents with different specific knowledge areas and thinking styles exchange feedback and ideas in a chat format for a plan posted by a user. As a result, the brainstorming system enables one person or a small number of people to improve the quality of ideas by brainstorming from multiple perspectives and to make the decision-making process more efficient.

[0029] A brainstorming system according to an embodiment of the present invention includes an AI agent, an AI moderator, a related information provider, and a Miro-linked AI. The AI ​​agents provide feedback and ideas for projects submitted by users. For example, AI agents with technical perspectives, marketing perspectives, and creative perspectives collaborate to brainstorm. The AI ​​moderator manages the progress of the dialogue between the AI ​​agents. For example, the AI ​​moderator ensures that the discussion does not go off track and focuses on key points. The AI ​​moderator also uses facilitation techniques to support all AI agents in effectively sharing their opinions. The related information provider provides relevant information from past discussions. For example, the AI ​​moderator references ideas and discussions from previous brainstorming sessions to provide new perspectives. The Miro-linked AI converts ideas generated during brainstorming into text and diagrams in real time and organizes them. For example, the AI ​​moderator automatically extracts keywords and phrases from discussions and visually organizes them. The AI ​​moderator also uses diagrams to show the relationships between ideas, aiding visual understanding. As a result, the brainstorming system according to the embodiment enables brainstorming from multiple perspectives and can improve the quality of ideas. For example, by combining technical and marketing perspectives, more feasible and marketable plans can be created. Also, by referring to past discussions, new ideas can be generated while utilizing existing knowledge. Furthermore, visual organization makes it easier to intuitively understand the content of the discussion.

[0030] AI agents can have different knowledge areas and thinking styles. For example, AI agents with a technical perspective, an AI agent with a marketing perspective, and an AI agent with a creative perspective can collaborate in brainstorming. This allows for opinions and ideas from multiple perspectives to be gathered, improving the quality of the plan.

[0031] The AI ​​moderator can manage the progress of the discussion and encourage it to proceed appropriately. For example, the AI ​​moderator pays attention to prevent the discussion from going off track and focuses on important points. In addition, the AI ​​moderator uses facilitation techniques to support all AI agents in effectively exchanging opinions. This results in an efficient brainstorming session.

[0032] The related information provision unit can analyze past discussions and provide related information. For example, the related information provision unit can refer to ideas and discussions that came up in previous brainstorming sessions to provide new perspectives. This allows for deeper discussions while utilizing past knowledge.

[0033] Miro-linked AI can convert ideas generated during brainstorming into text and diagrams in real time. For example, Miro-linked AI can automatically extract keywords and phrases that arise during discussions and organize them visually. It also aids visual understanding by showing the relationships between ideas using diagrams. This aids visual understanding and makes it easier to intuitively grasp the content of the discussion.

[0034] AI agents can customize ideas based on a user's past posting history and interests, providing more personalized feedback. For example, an AI agent can analyze a user's past posting history and customize the ideas it generates based on their interests. For example, a user who posts a lot of technical content can be given preferential treatment in technical ideas. This allows for more relevant ideas to be provided by providing feedback based on the user's past posting history and interests.

[0035] AI agents can automatically combine ideas from other AI agents to generate new perspectives and ideas. For example, an AI agent can automatically combine the ideas it generates with the ideas of other AI agents to generate new perspectives and ideas. For example, it can combine technical ideas and marketing ideas to propose a new product concept. In this way, new perspectives and ideas can be generated by combining ideas from different perspectives.

[0036] An AI moderator can analyze past discussion data, learn the discussion progression patterns, and automatically select the optimal facilitation method. For example, an AI moderator can analyze past discussion data, learn the discussion progression patterns, and automatically select the optimal facilitation method. For example, it can adjust the way the discussion progresses based on past success stories. This makes it possible to optimize the progress of the discussion by selecting the optimal facilitation method based on past discussion data.

[0037] The AI ​​moderator can evaluate the content of each AI agent's comments in real time and highlight important points and new perspectives. For example, the AI ​​moderator can highlight important comments from a technical perspective. This improves the quality of the discussion by highlighting important points and new perspectives.

[0038] The related information providing unit can automatically extract related topics and keywords based on past discussion data and generate new perspectives and ideas. The related information providing unit, for example, automatically extracts related topics and keywords based on past discussion data and generates new perspectives and ideas. For example, it proposes new ideas based on keywords that frequently appeared in past discussions. In this way, generating new perspectives and ideas based on past discussion data increases the depth of the discussion.

[0039] Miro-linked AI can customize the diagrams it generates based on a user's past posting history and interests, providing more personalized visual information. For example, Miro-linked AI can analyze a user's past posting history and customize the diagrams it generates based on their interests. For example, a user who posts a lot of technical content can be given priority in displaying technical diagrams. This allows for more relevant information to be provided by providing visual information based on a user's past posting history and interests.

[0040] Miro-collaborating AI can automatically combine ideas from other AI agents to visually present new perspectives and ideas. For example, Miro-collaborating AI can automatically combine the diagrams it generates with ideas from other AI agents to visually present new perspectives and ideas. For example, it can display a diagram that combines technical ideas and marketing ideas. This allows new perspectives and ideas to be provided by combining ideas from different perspectives and visually presenting them.

[0041] Miro-linked AI can optimize diagrams for specific themes or issues selected by the user, providing visual information that is aligned with the theme. For example, if a user selects a theme related to environmental issues, Miro-linked AI will prioritize displaying diagrams from an environmental science perspective. This allows the user to provide more relevant information by providing visual information based on the theme or issue selected by the user.

[0042] The related information providing unit analyzes past discussion data in chronological order, grasps the progress patterns and trends of the discussion, and can use this information to help with future discussions. For example, the related information providing unit analyzes past discussion data in chronological order, grasps the progress patterns and trends of the discussion, and can use this information to help with future discussions. For example, it suggests how to proceed with future discussions based on the flow of past discussions. This improves the quality of discussions by optimizing future discussions based on past discussion data.

[0043] The Related Information Providing Department can integrate past discussion data from different industries and fields to promote crossover innovation. For example, the Related Information Providing Department can integrate past discussion data from different industries and fields to promote crossover innovation. For example, it can integrate discussion data from the technology field and the design field to propose new product ideas. In this way, new innovation can be promoted by integrating discussion data from different industries and fields.

[0044] The related information providing unit can visualize past discussion data to enable users to intuitively understand it. The related information providing unit, for example, visualizes past discussion data to enable users to intuitively understand it. For example, it displays the flow of the discussion in a flowchart. In this way, visualizing past discussion data makes it easier for users to intuitively understand it.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The brainstorming system may further include a biometric information acquisition unit that acquires biometric information of the user. The biometric information acquisition unit acquires biometric information such as the user's heart rate and galvanic skin response in real time to measure the user's stress level and concentration. For example, if the heart rate increases, it is determined that the user is excited, and the pace of the discussion is adjusted accordingly. Also, if the galvanic skin response decreases, it is determined that the user is relaxed, and deeper discussion is promoted. This makes it possible to optimize the progress of the discussion based on the user's biometric information.

[0047] The brainstorming system can further include a summarization unit that automatically summarizes what users say. The summarization unit analyzes what users say in real time, extracts important points, and summarizes them. For example, it can summarize long comments in short sentences, making the flow of discussion smoother. In addition, the summarization can be shared with other users, making it easier for everyone to understand the content of the discussion. This can improve the efficiency of discussions.

[0048] The brainstorming system can further include an external resource providing unit that analyzes the content of user comments and automatically provides relevant external resources. The external resource providing unit analyzes the content of user comments and automatically searches for and provides external resources such as related papers, articles, and databases. For example, if a technical discussion is taking place, the latest related research papers may be presented. Also, if a marketing discussion is taking place, relevant market data may be provided. This can improve the quality of the discussion.

[0049] The brainstorming system can further include a recording unit that analyzes the content of user comments and automatically records the progress of the discussion. The recording unit analyzes the content of user comments in real time and automatically records the progress of the discussion. For example, it can automatically record important points and decisions of the discussion so that they can be referenced later. In addition, the recorded content can be shared with other users, making it easier for everyone to understand the content of the discussion. This can improve the efficiency of the discussion.

[0050] The brainstorming system can further include an evaluation unit that analyzes the content of user comments and automatically evaluates the progress of the discussion. The evaluation unit analyzes the content of user comments in real time and automatically evaluates the progress of the discussion. For example, it evaluates whether the discussion is progressing smoothly and suggests improvements as needed. It also evaluates whether the content of the discussion is substantial and provides new perspectives as needed. This can improve the quality of the discussion.

[0051] The brainstorming system can further include a visualization unit that analyzes user comments and automatically visualizes the progress of the discussion. The visualization unit analyzes user comments in real time and automatically visualizes the progress of the discussion. For example, it can display the flow of the discussion in a flowchart, making it easier for everyone to grasp the progress of the discussion. It can also highlight important points and decisions, making it easier for people to intuitively understand the content of the discussion. This can improve the efficiency of the discussion.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The AI ​​agents provide feedback and ideas for the proposals posted by users. For example, AI agents with technical perspectives, AI agents with marketing perspectives, and AI agents with creative perspectives may collaborate to brainstorm. Step 2: The AI ​​moderator manages the progress of the dialogue among the AI ​​agents, for example, by making sure the discussion doesn't go off track and by keeping the focus on important points. They also use facilitation techniques to support all AI agents in effectively exchanging their opinions. Step 3: The relevant information provider provides relevant information from past discussions, for example, referencing ideas and discussions from previous brainstorming sessions to provide new perspectives. Step 4: Miro-linked AI converts ideas generated during brainstorming into text and diagrams in real time and organizes them. For example, it automatically extracts keywords and phrases that arise during discussions and organizes them visually. It also uses diagrams to show the relationships between ideas, aiding visual understanding.

[0054] (Example 2) A brainstorming system according to an embodiment of the present invention is a system that realizes multi-perspective brainstorming by having multiple AI agents with different specific knowledge areas and thinking styles exchange feedback and ideas in a chat format for a plan posted by a user. As a result, the brainstorming system enables one person or a small number of people to improve the quality of ideas by brainstorming from multiple perspectives and to make the decision-making process more efficient.

[0055] A brainstorming system according to an embodiment of the present invention includes an AI agent, an AI moderator, a related information provider, and a Miro-linked AI. The AI ​​agents provide feedback and ideas for projects submitted by users. For example, AI agents with technical perspectives, marketing perspectives, and creative perspectives collaborate to brainstorm. The AI ​​moderator manages the progress of the dialogue between the AI ​​agents. For example, the AI ​​moderator ensures that the discussion does not go off track and focuses on key points. The AI ​​moderator also uses facilitation techniques to support all AI agents in effectively sharing their opinions. The related information provider provides relevant information from past discussions. For example, the AI ​​moderator references ideas and discussions from previous brainstorming sessions to provide new perspectives. The Miro-linked AI converts ideas generated during brainstorming into text and diagrams in real time and organizes them. For example, the AI ​​moderator automatically extracts keywords and phrases from discussions and visually organizes them. The AI ​​moderator also uses diagrams to show the relationships between ideas, aiding visual understanding. As a result, the brainstorming system according to the embodiment enables brainstorming from multiple perspectives and can improve the quality of ideas. For example, by combining technical and marketing perspectives, more feasible and marketable plans can be created. Also, by referring to past discussions, new ideas can be generated while utilizing existing knowledge. Furthermore, visual organization makes it easier to intuitively understand the content of the discussion.

[0056] AI agents can have different knowledge areas and thinking styles. For example, AI agents with a technical perspective, an AI agent with a marketing perspective, and an AI agent with a creative perspective can collaborate in brainstorming. This allows for opinions and ideas from multiple perspectives to be gathered, improving the quality of the plan.

[0057] The AI ​​moderator can manage the progress of the discussion and encourage it to proceed appropriately. For example, the AI ​​moderator pays attention to prevent the discussion from going off track and focuses on important points. In addition, the AI ​​moderator uses facilitation techniques to support all AI agents in effectively exchanging opinions. This results in an efficient brainstorming session.

[0058] The related information provision unit can analyze past discussions and provide related information. For example, the related information provision unit can refer to ideas and discussions that came up in previous brainstorming sessions to provide new perspectives. This allows for deeper discussions while utilizing past knowledge.

[0059] Miro-linked AI can convert ideas generated during brainstorming into text and diagrams in real time. For example, Miro-linked AI can automatically extract keywords and phrases that arise during discussions and organize them visually. It also aids visual understanding by showing the relationships between ideas using diagrams. This aids visual understanding and makes it easier to intuitively grasp the content of the discussion.

[0060] The AI ​​agent can use the emotion estimation function to analyze the user's emotional reactions in real time and prioritize presenting ideas that elicit a positive reaction. For example, the AI ​​agent can use the emotion estimation function to analyze the user's facial expressions and voice in real time for ideas generated by each AI agent and prioritize presenting ideas that elicit a positive emotional reaction. For example, ideas that make the user smile are displayed preferentially. This allows for more effective brainstorming by prioritizing the presentation of ideas based on the user's emotions.

[0061] AI agents can customize ideas based on a user's past posting history and interests, providing more personalized feedback. For example, an AI agent can analyze a user's past posting history and customize the ideas it generates based on their interests. For example, a user who posts a lot of technical content can be given preferential treatment in technical ideas. This allows for more relevant ideas to be provided by providing feedback based on the user's past posting history and interests.

[0062] AI agents can automatically combine ideas from other AI agents to generate new perspectives and ideas. For example, an AI agent can automatically combine the ideas it generates with the ideas of other AI agents to generate new perspectives and ideas. For example, it can combine technical ideas and marketing ideas to propose a new product concept. In this way, new perspectives and ideas can be generated by combining ideas from different perspectives.

[0063] The AI ​​moderator can use the emotion estimation function to grasp the user's emotional state in real time and intervene or provide support at the appropriate time. For example, the AI ​​moderator can use the emotion estimation function to grasp the user's emotional state in real time and intervene or provide support at the appropriate time. For example, it can suggest ways to relax when the user is feeling stressed. This improves the quality of discussions by providing appropriate support according to the user's emotional state.

[0064] An AI moderator can analyze past discussion data, learn the discussion progression patterns, and automatically select the optimal facilitation method. For example, an AI moderator can analyze past discussion data, learn the discussion progression patterns, and automatically select the optimal facilitation method. For example, it can adjust the way the discussion progresses based on past success stories. This makes it possible to optimize the progress of the discussion by selecting the optimal facilitation method based on past discussion data.

[0065] The AI ​​moderator can evaluate the content of each AI agent's comments in real time and highlight important points and new perspectives. For example, the AI ​​moderator can highlight important comments from a technical perspective. This improves the quality of the discussion by highlighting important points and new perspectives.

[0066] The related information providing unit can automatically extract related topics and keywords based on past discussion data and generate new perspectives and ideas. The related information providing unit, for example, automatically extracts related topics and keywords based on past discussion data and generates new perspectives and ideas. For example, it proposes new ideas based on keywords that frequently appeared in past discussions. In this way, generating new perspectives and ideas based on past discussion data increases the depth of the discussion.

[0067] Miro-linked AI can customize the diagrams it generates based on a user's past posting history and interests, providing more personalized visual information. For example, Miro-linked AI can analyze a user's past posting history and customize the diagrams it generates based on their interests. For example, a user who posts a lot of technical content can be given priority in displaying technical diagrams. This allows for more relevant information to be provided by providing visual information based on a user's past posting history and interests.

[0068] Miro-collaborating AI can automatically combine ideas from other AI agents to visually present new perspectives and ideas. For example, Miro-collaborating AI can automatically combine the diagrams it generates with ideas from other AI agents to visually present new perspectives and ideas. For example, it can display a diagram that combines technical ideas and marketing ideas. This allows new perspectives and ideas to be provided by combining ideas from different perspectives and visually presenting them.

[0069] Miro-linked AI can optimize diagrams for specific themes or issues selected by the user, providing visual information that is aligned with the theme. For example, if a user selects a theme related to environmental issues, Miro-linked AI will prioritize displaying diagrams from an environmental science perspective. This allows the user to provide more relevant information by providing visual information based on the theme or issue selected by the user.

[0070] Miro-linked AI can use its emotion estimation function to identify the visual information that a user finds most interesting and generate diagrams based on that information. For example, Miro-linked AI can use its emotion estimation function to identify the visual information that a user finds most interesting and generate diagrams based on that information. For example, it can prioritize displaying visual information that makes a user excited. This allows for more effective brainstorming by providing visual information based on the user's emotions.

[0071] The related information providing unit analyzes past discussion data in chronological order, grasps the progress patterns and trends of the discussion, and can use this information to help with future discussions. For example, the related information providing unit analyzes past discussion data in chronological order, grasps the progress patterns and trends of the discussion, and can use this information to help with future discussions. For example, it suggests how to proceed with future discussions based on the flow of past discussions. This improves the quality of discussions by optimizing future discussions based on past discussion data.

[0072] The Related Information Providing Department can integrate past discussion data from different industries and fields to promote crossover innovation. For example, the Related Information Providing Department can integrate past discussion data from different industries and fields to promote crossover innovation. For example, it can integrate discussion data from the technology field and the design field to propose new product ideas. In this way, new innovation can be promoted by integrating discussion data from different industries and fields.

[0073] The related information providing unit can visualize past discussion data to enable users to intuitively understand it. The related information providing unit, for example, visualizes past discussion data to enable users to intuitively understand it. For example, it displays the flow of the discussion in a flowchart. In this way, visualizing past discussion data makes it easier for users to intuitively understand it.

[0074] The related information providing unit can use the emotion estimation function to identify the topic in which the user is most interested from past discussion data and provide information related to that topic. For example, the related information providing unit can use the emotion estimation function to identify the topic in which the user is most interested from past discussion data and provide information related to that topic. For example, it can preferentially present topics that excited the user in past discussions. This improves the quality of discussions by providing related information based on the user's interests.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The brainstorming system may further include a biometric information acquisition unit that acquires biometric information of the user. The biometric information acquisition unit acquires biometric information such as the user's heart rate and galvanic skin response in real time to measure the user's stress level and concentration. For example, if the heart rate increases, it is determined that the user is excited, and the pace of the discussion is adjusted accordingly. Also, if the galvanic skin response decreases, it is determined that the user is relaxed, and deeper discussion is promoted. This makes it possible to optimize the progress of the discussion based on the user's biometric information.

[0077] The brainstorming system can further include a summarization unit that automatically summarizes what users say. The summarization unit analyzes what users say in real time, extracts important points, and summarizes them. For example, it can summarize long comments in short sentences, making the flow of discussion smoother. In addition, the summarization can be shared with other users, making it easier for everyone to understand the content of the discussion. This can improve the efficiency of discussions.

[0078] The brainstorming system can further include an emotion estimation unit that estimates the user's emotion and adjusts the progress of the discussion based on the estimated emotion. The emotion estimation unit analyzes the user's facial expressions and voice to estimate the user's emotion in real time. For example, if the user is feeling dissatisfied, it makes a suggestion to change the direction of the discussion. Also, if the user is excited, it makes use of that emotion to deepen the discussion. In this way, it is possible to optimize the progress of the discussion based on the user's emotion.

[0079] The brainstorming system can further include an external resource providing unit that analyzes the content of user comments and automatically provides relevant external resources. The external resource providing unit analyzes the content of user comments and automatically searches for and provides external resources such as related papers, articles, and databases. For example, if a technical discussion is taking place, the latest related research papers may be presented. Also, if a marketing discussion is taking place, relevant market data may be provided. This can improve the quality of the discussion.

[0080] The brainstorming system may further include a feedback customization unit that estimates the user's emotions and customizes feedback based on the estimated emotions. The feedback customization unit analyzes the user's facial expressions and voice to estimate the user's emotions in real time. For example, if the user is expressing a positive emotion, the feedback customization unit provides feedback that reinforces the emotion. Also, if the user is expressing a negative emotion, the feedback customization unit provides feedback that alleviates the emotion. This makes it possible to optimize feedback based on the user's emotions.

[0081] The brainstorming system can further include a recording unit that analyzes the content of user comments and automatically records the progress of the discussion. The recording unit analyzes the content of user comments in real time and automatically records the progress of the discussion. For example, it can automatically record important points and decisions of the discussion so that they can be referenced later. In addition, the recorded content can be shared with other users, making it easier for everyone to understand the content of the discussion. This can improve the efficiency of the discussion.

[0082] The brainstorming system can further include a theme suggestion unit that estimates the user's emotions and suggests topics for discussion based on the estimated emotions. The theme suggestion unit analyzes the user's facial expressions and voice to estimate the user's emotions in real time. For example, if the user is excited, the system suggests a theme that matches the user's emotions. Also, if the user is relaxed, the system suggests a theme that can be discussed in a relaxed state. This makes it possible to optimize the topics for discussion based on the user's emotions.

[0083] The brainstorming system can further include an evaluation unit that analyzes the content of user comments and automatically evaluates the progress of the discussion. The evaluation unit analyzes the content of user comments in real time and automatically evaluates the progress of the discussion. For example, it evaluates whether the discussion is progressing smoothly and suggests improvements as needed. It also evaluates whether the content of the discussion is substantial and provides new perspectives as needed. This can improve the quality of the discussion.

[0084] The brainstorming system can further include a progress adjustment unit that estimates the user's emotions and adjusts the progress of the discussion based on the estimated emotions. The progress adjustment unit analyzes the user's facial expressions and voice to estimate the user's emotions in real time. For example, if the user is feeling stressed, the progress adjustment unit suggests slowing down the pace of the discussion. Also, if the user is excited, the progress adjustment unit makes use of the user's emotions to deepen the discussion. In this way, the progress of the discussion can be optimized based on the user's emotions.

[0085] The brainstorming system can further include a visualization unit that analyzes user comments and automatically visualizes the progress of the discussion. The visualization unit analyzes user comments in real time and automatically visualizes the progress of the discussion. For example, it can display the flow of the discussion in a flowchart, making it easier for everyone to grasp the progress of the discussion. It can also highlight important points and decisions, making it easier for people to intuitively understand the content of the discussion. This can improve the efficiency of the discussion.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The AI ​​agents provide feedback and ideas for the proposals posted by users. For example, AI agents with technical perspectives, AI agents with marketing perspectives, and AI agents with creative perspectives may collaborate to brainstorm. Step 2: The AI ​​moderator manages the progress of the dialogue among the AI ​​agents, for example, by making sure the discussion doesn't go off track and by keeping the focus on important points. They also use facilitation techniques to support all AI agents in effectively exchanging their opinions. Step 3: The relevant information provider provides relevant information from past discussions, for example, referencing ideas and discussions from previous brainstorming sessions to provide new perspectives. Step 4: Miro-linked AI converts ideas generated during brainstorming into text and diagrams in real time and organizes them. For example, it automatically extracts keywords and phrases that arise during discussions and organizes them visually. It also uses diagrams to show the relationships between ideas, aiding visual understanding.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 AI agent that provides feedback and ideas for projects posted by users, an AI moderator who manages the progress of the dialogue by the AI ​​agent; A related information section provides relevant information from past discussions; It is equipped with Miro-linked AI, which converts ideas generated during brainstorming into text and diagrams in real time. A system characterized by:

2. The AI ​​agent: have different knowledge areas and thinking styles 2. The system of claim 1.

3. The AI ​​moderator: Manage the discussion and encourage appropriate discussion 2. The system of claim 1.

4. The related information providing unit Analyze past discussions and provide relevant information 2. The system of claim 1.

5. The Miro collaborative AI is Convert ideas generated during brainstorming into text and diagrams in real time 2. The system of claim 1.

6. The AI ​​agent: Analyzing the user's emotional response in real time and preferentially presenting the ideas that elicit positive responses.

2. The system of claim 1.

7. The AI ​​moderator: Grasp the user's emotional state in real time and provide intervention and support at the appropriate time.

2. The system of claim 1.

8. The Miro collaborative AI is Identifying visual information that is most interesting to the user and generating the chart based on that information 2. The system of claim 1.

Images