System

Abstract

An object of the system according to the embodiment is to automate the creation of materials for a meeting, the progress of a discussion, and the summarization of a conclusion.SOLUTION: A system includes a material creation part, a discussion progress part, and a conclusion summarizing part. The material creation unit automatically creates a material based on a purpose or an agenda of a meeting. The discussion progression unit monitors the discussion during the conference in real time and progresses the discussion at an appropriate timing. A conclusion summarizing part analyzes the discussion contents of the conference and automatically summarizes the conclusion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem that it took a lot of time and effort to prepare meeting materials, lead discussions, and reach conclusions.

[0005] The system according to the embodiment aims to automate the creation of meeting materials, the progress of discussions, and the summarization of conclusions. [Means for solving the problem]

[0006] The system according to the embodiment includes a material creation unit, a discussion promotion unit, and a conclusion summary unit. The material creation unit automatically creates materials based on the purpose or agenda of the meeting. The discussion promotion unit monitors the discussion during the meeting in real time and advances the agenda at the appropriate time. The conclusion summary unit analyzes the content of the discussion during the meeting and automatically summarizes a conclusion. [Effects of the Invention]

[0007] The system according to the embodiment can automate the creation of meeting materials, the progress of discussions, and the summary of conclusions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The moderating AI system according to an embodiment of the present invention is a system that solves various problems faced by meeting organizers and improves meeting efficiency. This system automates a series of meeting-related tasks, such as preparing meeting materials, leading discussions, and summarizing conclusions, creating an environment in which the organizer can focus on the tasks that should be prioritized. As a result, the moderating AI system frees the meeting organizer from tedious tasks such as preparing materials, leading discussions, and summarizing conclusions, creating an environment in which they can focus on the tasks that should be prioritized.

[0029] The AI ​​moderator system according to the embodiment includes a document creation unit, a discussion management unit, and a conclusion summary unit. The document creation unit automatically creates documents based on the purpose or agenda of the meeting. For example, the generation AI generates meeting agendas, presentation materials, and meeting minutes templates. The generation AI can also generate documents based on prompts containing instructions related to the purpose or agenda of the meeting. The discussion management unit monitors the discussion during the meeting in real time and advances the agenda at the appropriate time. For example, the generation AI analyzes the content of comments made during the meeting in real time and manages the progress of the discussion. The generation AI can also detect when participants are engaging in position-taking as part of internal politics and organize the discussion from a neutral standpoint. The conclusion summary unit analyzes the content of the meeting discussion and automatically summarizes the conclusion. For example, the generation AI can extract key points and present a clear conclusion even if the discussion has diverged. The generation AI can also analyze the content of comments made during the meeting and summarize the conclusion. As a result, the AI ​​moderator system of the embodiment can improve the efficiency of meetings and create an environment where the organizer can focus on the tasks that should be their priority.

[0030] The document creation unit can learn from past meeting materials and propose the optimal document format. For example, the generation AI in the document creation unit analyzes past meeting materials and learns frequently used formats and layouts. For example, it proposes the optimal document format based on templates commonly used in a particular industry or company. The generation AI can also automatically select the optimal document format based on the content of past meeting materials. This allows it to learn from past meeting materials and propose the optimal document format.

[0031] The material creation unit can automatically import external data related to the meeting materials. For example, the generation AI can automatically collect market research reports and industry news and reflect them in the meeting materials. For example, it can incorporate the latest market trends and competitive analysis into the materials. The generation AI can also learn how to collect external data and automatically import data related to the meeting materials. This makes it possible to automatically import external data related to the meeting materials.

[0032] The material creation unit automatically generates meeting materials in multiple languages, making it possible to support international conferences. For example, the material creation unit uses a generation AI to automatically translate meeting materials and provide them in multiple languages. For example, it supports major languages ​​such as English, Japanese, and Chinese. The generation AI can also automatically generate meeting materials in multiple languages ​​based on their contents. This allows meeting materials to be automatically generated in multiple languages, making it possible to support international conferences.

[0033] The material creation unit can automatically generate visually appealing infographics for meeting materials. In the material creation unit, for example, a generation AI analyzes data and automatically generates visually appealing infographics. For example, the data is visualized using graphs and charts. The generation AI can also automatically generate visually appealing infographics based on the contents of the meeting materials. In this way, the meeting materials can be automatically generated as visually appealing infographics.

[0034] The discussion facilitator learns the speech patterns of participants and can propose the optimal speaking order. For example, the generation AI analyzes past meeting data and learns the speech patterns of participants. For example, it can propose the optimal speaking order based on the frequency and timing of speech. The generation AI can also optimize the progress of the discussion based on the speech patterns of participants. This allows it to learn the speech patterns of participants and propose the optimal speaking order.

[0035] The discussion moderator can present relevant data or materials in real time as the discussion progresses. For example, the generation AI automatically presents relevant data or materials in real time as the discussion progresses. For example, it displays relevant statistical data or research results depending on the content of the discussion. The generation AI can also automatically collect and present data or materials required as the discussion progresses. This allows relevant data or materials to be presented in real time as the discussion progresses.

[0036] The discussion facilitator can visualize the progress of the discussion, allowing participants to intuitively understand. For example, the generative AI can visualize the progress of the discussion in real time, allowing participants to intuitively understand. For example, it can display the flow of the discussion in a flowchart or mind map. The generative AI can also visually organize the progress of the discussion and present it to participants. This visualizes the progress of the discussion, allowing participants to intuitively understand.

[0037] The discussion facilitator can automatically present different viewpoints or opinions as the discussion progresses, deepening the discussion. For example, the generation AI can automatically present different viewpoints or opinions as the discussion progresses, deepening the discussion. For example, it can present a balanced mix of pro and con opinions. The generation AI can also automatically collect and present necessary viewpoints and opinions as the discussion progresses. This allows different viewpoints and opinions to be automatically presented as the discussion progresses, deepening the discussion.

[0038] The conclusion summary unit can automatically extract the main points of the discussion and organize them visually. For example, the generation AI can automatically extract the main points of the discussion and organize them visually. For example, it can show important points in bullet points or graphs. The generation AI can also automatically extract the main points based on the content of the discussion and organize them visually. This allows the main points of the discussion to be automatically extracted and organized visually.

[0039] The conclusion compilation unit can take relevant laws, regulations, and guidelines into consideration when automatically compiling a conclusion. For example, the conclusion compilation unit takes relevant laws, regulations, and guidelines into consideration when the generation AI automatically compiles a conclusion. For example, the generation AI can compile a conclusion by referring to industry regulations or internal company guidelines. The generation AI can also automatically apply relevant laws, regulations, and guidelines based on the content of the conclusion. This allows relevant laws, regulations, and guidelines to be taken into consideration when automatically compiling a conclusion.

[0040] The conclusion summary unit can automatically generate conclusions in multiple formats. In the conclusion summary unit, for example, the generation AI automatically generates conclusions in multiple formats and provides them to participants. For example, the generation AI creates conclusions in report format, presentation format, and email format. The generation AI can also automatically generate conclusions in multiple formats based on the content of the conclusion. This allows the conclusion to be automatically generated in multiple formats.

[0041] The conclusion summary unit can simulate different scenarios and select the optimal conclusion. For example, when the generation AI automatically summarizes a conclusion, the conclusion summary unit simulates different scenarios and selects the optimal conclusion. For example, it compares multiple scenarios and presents the most effective conclusion. The generation AI can also select the optimal conclusion based on the content of the scenario. This allows different scenarios to be simulated and the optimal conclusion to be selected.

[0042] The conclusion summary unit can automatically organize and prioritize post-meeting action items. In the conclusion summary unit, for example, the generation AI automatically organizes and prioritizes post-meeting action items. For example, it classifies action items based on importance and urgency. The generation AI can also automatically set priorities based on the content of the action items. This makes it possible to automatically organize and prioritize post-meeting action items.

[0043] The conclusion summary unit can automatically assign relevant resources and personnel in post-meeting follow-up. For example, the generation AI automatically assigns relevant resources and personnel in post-meeting follow-up. For example, it selects the most appropriate person in charge for an action item. The generation AI can also automatically assign the most appropriate person in charge based on the content of the resource. This makes it possible to automatically assign relevant resources and personnel in post-meeting follow-up.

[0044] The conclusion summary unit can automatically carry out post-meeting follow-up through different communication channels. For example, the generation AI can automatically carry out post-meeting follow-up through different communication channels. For example, it can notify action items via email, chat, or social media. The generation AI can also select the optimal communication channel based on the content of the follow-up and carry out the follow-up automatically. This allows post-meeting follow-up to be carried out automatically through different communication channels.

[0045] The conclusion summary unit can monitor the progress in real time during post-meeting follow-up and send reminders as necessary. For example, the generation AI can monitor the progress in real time during post-meeting follow-up and send reminders as necessary. For example, it can send reminders for action items whose deadlines are approaching. The generation AI can also automatically adjust and send the content of reminders based on the progress. This makes it possible to monitor the progress in real time during post-meeting follow-up and send reminders as necessary.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The moderator AI system also includes a schedule adjustment unit for participants. The schedule adjustment unit can obtain the participants' calendar information and propose the optimal meeting date and time. For example, the generation AI can analyze the free time of each participant and automatically select a date and time when everyone can attend. The generation AI can also prioritize and propose the optimal date and time based on the importance and urgency of the meeting. This makes the schedule adjustment for participants more efficient and allows meetings to be held more smoothly.

[0048] The moderator AI system also includes a meeting recording and transcription unit. The recording and transcription unit can record and transcribe meeting audio in real time. For example, the generation AI can automatically convert statements made during a meeting into text and save it as minutes. The generation AI can also analyze the recording data and highlight important statements and points of discussion. This ensures that the contents of the meeting are accurately recorded, making it useful for later review.

[0049] The moderator AI system also has a participant profile management section. The profile management section stores participants' expertise and past comments in a database, which can be used to help progress the meeting. For example, the generation AI can nominate appropriate speakers during a discussion based on participants' areas of expertise and past comments. The generation AI can also optimize the progress of the discussion based on the participants' profiles. This improves the quality of the meeting and enables more efficient discussions.

[0050] The AI ​​moderator system also includes a meeting feedback collection unit. This can automatically collect and analyze feedback from participants after the meeting. For example, the generation AI can automatically generate a questionnaire and send it to participants. The generation AI can also analyze the collected feedback and identify areas for improvement and successes in the meeting. This allows for specific improvement measures to be taken to improve the quality of the next meeting.

[0051] The AI ​​moderator system also includes a meeting rehearsal support section. The rehearsal support section simulates a meeting, allowing organizers and participants to rehearse in advance. For example, the generation AI can automatically generate a meeting scenario and conduct a virtual meeting. The generation AI can also provide feedback on areas for improvement and caution based on the results of the rehearsal. This ensures that meetings are thoroughly prepared and the actual meeting proceeds smoothly.

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

[0053] Step 1: The material creation unit automatically creates materials based on the purpose or agenda of the meeting. For example, the generation AI generates meeting agendas, presentation materials, and meeting minutes templates. The generation AI can also generate materials based on prompts containing instructions about the purpose or agenda of the meeting. Step 2: The discussion moderator monitors the discussion during the meeting in real time and moves the agenda forward at the appropriate time. For example, the generation AI analyzes the content of comments made during the meeting in real time and manages the progress of the discussion. The generation AI can also detect when participants are engaging in position-taking as part of internal politics and organize the discussion from a neutral standpoint. Step 3: The conclusion summary section analyzes the content of the meeting discussion and automatically summarizes the conclusion. For example, even if the discussion diverges, the generation AI can extract the main points and present a clear conclusion. The generation AI can also analyze the content of comments made during the meeting and summarize the conclusion.

[0054] (Example 2) The moderating AI system according to an embodiment of the present invention is a system that solves various problems faced by meeting organizers and improves meeting efficiency. This system automates a series of meeting-related tasks, such as preparing meeting materials, leading discussions, and summarizing conclusions, creating an environment in which the organizer can focus on the tasks that should be prioritized. As a result, the moderating AI system frees the meeting organizer from tedious tasks such as preparing materials, leading discussions, and summarizing conclusions, creating an environment in which they can focus on the tasks that should be prioritized.

[0055] The AI ​​moderator system according to the embodiment includes a document creation unit, a discussion management unit, and a conclusion summary unit. The document creation unit automatically creates documents based on the purpose or agenda of the meeting. For example, the generation AI generates meeting agendas, presentation materials, and meeting minutes templates. The generation AI can also generate documents based on prompts containing instructions related to the purpose or agenda of the meeting. The discussion management unit monitors the discussion during the meeting in real time and advances the agenda at the appropriate time. For example, the generation AI analyzes the content of comments made during the meeting in real time and manages the progress of the discussion. The generation AI can also detect when participants are engaging in position-taking as part of internal politics and organize the discussion from a neutral standpoint. The conclusion summary unit analyzes the content of the meeting discussion and automatically summarizes the conclusion. For example, the generation AI can extract key points and present a clear conclusion even if the discussion has diverged. The generation AI can also analyze the content of comments made during the meeting and summarize the conclusion. As a result, the AI ​​moderator system of the embodiment can improve the efficiency of meetings and create an environment where the organizer can focus on the tasks that should be their priority.

[0056] The document creation unit can learn from past meeting materials and propose the optimal document format. For example, the generation AI in the document creation unit analyzes past meeting materials and learns frequently used formats and layouts. For example, it proposes the optimal document format based on templates commonly used in a particular industry or company. The generation AI can also automatically select the optimal document format based on the content of past meeting materials. This allows it to learn from past meeting materials and propose the optimal document format.

[0057] The material creation unit can automatically import external data related to the meeting materials. For example, the generation AI can automatically collect market research reports and industry news and reflect them in the meeting materials. For example, it can incorporate the latest market trends and competitive analysis into the materials. The generation AI can also learn how to collect external data and automatically import data related to the meeting materials. This makes it possible to automatically import external data related to the meeting materials.

[0058] The material creation unit can use the emotion estimation function to customize the material content according to the interests or expectations of the participants. For example, the generation AI analyzes the participants' past comments and feedback and customizes the material content according to their interests and expectations. For example, it can highlight topics that interest specific participants. The generation AI can also use the emotion estimation function to analyze the participants' interests and expectations in real time and customize the material content. This makes it possible to customize the material content according to the participants' interests and expectations.

[0059] The material creation unit automatically generates meeting materials in multiple languages, making it possible to support international conferences. For example, the material creation unit uses a generation AI to automatically translate meeting materials and provide them in multiple languages. For example, it supports major languages ​​such as English, Japanese, and Chinese. The generation AI can also automatically generate meeting materials in multiple languages ​​based on their contents. This allows meeting materials to be automatically generated in multiple languages, making it possible to support international conferences.

[0060] The material creation unit can automatically generate visually appealing infographics for meeting materials. In the material creation unit, for example, a generation AI analyzes data and automatically generates visually appealing infographics. For example, the data is visualized using graphs and charts. The generation AI can also automatically generate visually appealing infographics based on the contents of the meeting materials. In this way, the meeting materials can be automatically generated as visually appealing infographics.

[0061] The material creation unit can use the emotion estimation function to consider participants' emotions when creating materials and suggest content that will elicit a positive response. For example, the material creation unit can use the emotion estimation function to analyze participants' emotions in real time when creating materials and suggest content that will elicit a positive response. For example, it can highlight topics that are likely to interest participants. The generation AI can also customize the content of materials based on participants' emotions to elicit a positive response. This makes it possible to consider participants' emotions when creating materials and suggest content that will elicit a positive response.

[0062] The discussion facilitator learns the speech patterns of participants and can propose the optimal speaking order. For example, the generation AI analyzes past meeting data and learns the speech patterns of participants. For example, it can propose the optimal speaking order based on the frequency and timing of speech. The generation AI can also optimize the progress of the discussion based on the speech patterns of participants. This allows it to learn the speech patterns of participants and propose the optimal speaking order.

[0063] The discussion moderator can present relevant data or materials in real time as the discussion progresses. For example, the generation AI automatically presents relevant data or materials in real time as the discussion progresses. For example, it displays relevant statistical data or research results depending on the content of the discussion. The generation AI can also automatically collect and present data or materials required as the discussion progresses. This allows relevant data or materials to be presented in real time as the discussion progresses.

[0064] The discussion moderator uses the emotion estimation function to monitor the emotions of participants as the discussion progresses and intervene at the appropriate time. The discussion moderator, for example, uses the emotion estimation function to monitor the emotions of participants in real time as the discussion progresses and intervene at the appropriate time. For example, it can encourage calm opinions if the discussion becomes too heated. The generation AI can also adjust the progress of the discussion based on the emotions of participants. This makes it possible to monitor the emotions of participants as the discussion progresses and intervene at the appropriate time.

[0065] The discussion facilitator can visualize the progress of the discussion, allowing participants to intuitively understand. For example, the generative AI can visualize the progress of the discussion in real time, allowing participants to intuitively understand. For example, it can display the flow of the discussion in a flowchart or mind map. The generative AI can also visually organize the progress of the discussion and present it to participants. This visualizes the progress of the discussion, allowing participants to intuitively understand.

[0066] The discussion facilitator can automatically present different viewpoints or opinions as the discussion progresses, deepening the discussion. For example, the generation AI can automatically present different viewpoints or opinions as the discussion progresses, deepening the discussion. For example, it can present a balanced mix of pro and con opinions. The generation AI can also automatically collect and present necessary viewpoints and opinions as the discussion progresses. This allows different viewpoints and opinions to be automatically presented as the discussion progresses, deepening the discussion.

[0067] The discussion facilitator uses the emotion estimation function to analyze the emotions of participants as the discussion progresses, and can promote positive discussions. The discussion facilitator, for example, uses the emotion estimation function to analyze the emotions of participants in real time as the discussion progresses, and promote positive discussions. For example, it encourages calm opinions when emotions become heightened. The generative AI can also adjust the progress of the discussion based on the emotions of participants, and promote positive discussions. This makes it possible to analyze the emotions of participants as the discussion progresses, and promote positive discussions.

[0068] The conclusion summary unit can automatically extract the main points of the discussion and organize them visually. For example, the generation AI can automatically extract the main points of the discussion and organize them visually. For example, it can show important points in bullet points or graphs. The generation AI can also automatically extract the main points based on the content of the discussion and organize them visually. This allows the main points of the discussion to be automatically extracted and organized visually.

[0069] The conclusion compilation unit can take relevant laws, regulations, and guidelines into consideration when automatically compiling a conclusion. For example, the conclusion compilation unit takes relevant laws, regulations, and guidelines into consideration when the generation AI automatically compiles a conclusion. For example, the generation AI can compile a conclusion by referring to industry regulations or internal company guidelines. The generation AI can also automatically apply relevant laws, regulations, and guidelines based on the content of the conclusion. This allows relevant laws, regulations, and guidelines to be taken into consideration when automatically compiling a conclusion.

[0070] The conclusion summarizing unit can use the emotion estimation function to evaluate the emotional impact that the content of the conclusion has on participants and present the optimal conclusion. The conclusion summarizing unit, for example, uses the emotion estimation function to evaluate the emotional impact that the content of the conclusion has on participants in real time and present the optimal conclusion. For example, it selects a conclusion that elicits positive emotions. The generation AI can also adjust the content of the conclusion based on the emotions of the participants and present the optimal conclusion. This makes it possible to evaluate the emotional impact that the content of the conclusion has on participants and present the optimal conclusion.

[0071] The conclusion summary unit can automatically generate conclusions in multiple formats. In the conclusion summary unit, for example, the generation AI automatically generates conclusions in multiple formats and provides them to participants. For example, the generation AI creates conclusions in report format, presentation format, and email format. The generation AI can also automatically generate conclusions in multiple formats based on the content of the conclusion. This allows the conclusion to be automatically generated in multiple formats.

[0072] The conclusion summary unit can simulate different scenarios and select the optimal conclusion. For example, when the generation AI automatically summarizes a conclusion, the conclusion summary unit simulates different scenarios and selects the optimal conclusion. For example, it compares multiple scenarios and presents the most effective conclusion. The generation AI can also select the optimal conclusion based on the content of the scenario. This allows different scenarios to be simulated and the optimal conclusion to be selected.

[0073] The conclusion summarizing unit uses the emotion estimation function to analyze the emotional impact that the content of the conclusion has on participants and can derive a positive conclusion. The conclusion summarizing unit, for example, uses the emotion estimation function to analyze the emotional impact that the content of the conclusion has on participants in real time and derive a positive conclusion. For example, it preferentially presents conclusions with a high emotion score. The generation AI can also adjust the content of the conclusion based on the emotions of the participants and derive a positive conclusion. This makes it possible to analyze the emotional impact that the content of the conclusion has on participants and derive a positive conclusion.

[0074] The conclusion summary unit can automatically organize and prioritize post-meeting action items. In the conclusion summary unit, for example, the generation AI automatically organizes and prioritizes post-meeting action items. For example, it classifies action items based on importance and urgency. The generation AI can also automatically set priorities based on the content of the action items. This makes it possible to automatically organize and prioritize post-meeting action items.

[0075] The conclusion summary unit can automatically assign relevant resources and personnel in post-meeting follow-up. For example, the generation AI automatically assigns relevant resources and personnel in post-meeting follow-up. For example, it selects the most appropriate person in charge for an action item. The generation AI can also automatically assign the most appropriate person in charge based on the content of the resource. This makes it possible to automatically assign relevant resources and personnel in post-meeting follow-up.

[0076] The conclusion summary unit can use the emotion estimation function to evaluate the emotional impact that the follow-up content has on participants and propose the optimal follow-up. The conclusion summary unit, for example, uses the emotion estimation function to evaluate the emotional impact that the follow-up content has on participants in real time and propose the optimal follow-up. For example, it selects a follow-up that elicits positive emotions. The generation AI can also adjust the follow-up content based on the participants' emotions and propose the optimal follow-up. This makes it possible to evaluate the emotional impact that the follow-up content has on participants and propose the optimal follow-up.

[0077] The conclusion summary unit can automatically carry out post-meeting follow-up through different communication channels. For example, the generation AI can automatically carry out post-meeting follow-up through different communication channels. For example, it can notify action items via email, chat, or social media. The generation AI can also select the optimal communication channel based on the content of the follow-up and carry out the follow-up automatically. This allows post-meeting follow-up to be carried out automatically through different communication channels.

[0078] The conclusion summary unit can monitor the progress in real time during post-meeting follow-up and send reminders as necessary. For example, the generation AI can monitor the progress in real time during post-meeting follow-up and send reminders as necessary. For example, it can send reminders for action items whose deadlines are approaching. The generation AI can also automatically adjust and send the content of reminders based on the progress. This makes it possible to monitor the progress in real time during post-meeting follow-up and send reminders as necessary.

[0079] The conclusion summary unit can use the emotion estimation function to analyze the emotional impact that the follow-up content has on participants and promote positive follow-ups. The conclusion summary unit, for example, uses the emotion estimation function to analyze the emotional impact that the follow-up content has on participants in real time and promote positive follow-ups. For example, it can preferentially present follow-ups with high emotion scores. The generation AI can also adjust the follow-up content based on the participants' emotions and promote positive follow-ups. This makes it possible to analyze the emotional impact that the follow-up content has on participants and promote positive follow-ups.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The moderator AI system also includes a schedule adjustment unit for participants. The schedule adjustment unit can obtain the participants' calendar information and propose the optimal meeting date and time. For example, the generation AI can analyze the free time of each participant and automatically select a date and time when everyone can attend. The generation AI can also prioritize and propose the optimal date and time based on the importance and urgency of the meeting. This makes the schedule adjustment for participants more efficient and allows meetings to be held more smoothly.

[0082] The moderator AI system also includes a meeting recording and transcription unit. The recording and transcription unit can record and transcribe meeting audio in real time. For example, the generation AI can automatically convert statements made during a meeting into text and save it as minutes. The generation AI can also analyze the recording data and highlight important statements and points of discussion. This ensures that the contents of the meeting are accurately recorded, making it useful for later review.

[0083] The moderator AI system also has a participant profile management section. The profile management section stores participants' expertise and past comments in a database, which can be used to help progress the meeting. For example, the generation AI can nominate appropriate speakers during a discussion based on participants' areas of expertise and past comments. The generation AI can also optimize the progress of the discussion based on the participants' profiles. This improves the quality of the meeting and enables more efficient discussions.

[0084] The AI ​​moderator system also includes a meeting feedback collection unit. This can automatically collect and analyze feedback from participants after the meeting. For example, the generation AI can automatically generate a questionnaire and send it to participants. The generation AI can also analyze the collected feedback and identify areas for improvement and successes in the meeting. This allows for specific improvement measures to be taken to improve the quality of the next meeting.

[0085] The AI ​​moderator system also includes a meeting rehearsal support section. The rehearsal support section simulates a meeting, allowing organizers and participants to rehearse in advance. For example, the generation AI can automatically generate a meeting scenario and conduct a virtual meeting. The generation AI can also provide feedback on areas for improvement and caution based on the results of the rehearsal. This ensures that meetings are thoroughly prepared and the actual meeting proceeds smoothly.

[0086] The moderator AI system can also use its emotion estimation function to monitor the stress levels of participants during a meeting and suggest breaks at appropriate times. For example, the generation AI can analyze participants' facial expressions and comments and suggest breaks when stress levels rise. The generation AI can also adjust the progress of the meeting based on participants' stress levels. This can reduce participants' stress and improve meeting efficiency.

[0087] The moderator AI system can also use its emotion estimation function to monitor the motivation of participants during a meeting and send encouraging messages at appropriate times. For example, the generation AI can analyze participants' facial expressions and comments and send encouraging messages when their motivation drops. The generation AI can also adjust the progress of the meeting based on the participants' motivation. This helps maintain participants' motivation and maximize the results of the meeting.

[0088] The moderator AI system can also use emotion estimation to monitor participants' interests during the meeting and suggest relevant topics at the appropriate time. For example, the generation AI can analyze participants' facial expressions and comments, and suggest relevant topics when interest increases. The generation AI can also adjust the progress of the meeting based on participants' interests. This can draw out participants' interests and make the meeting more lively.

[0089] The moderator AI system can also use its emotion estimation function to monitor the fatigue level of participants during a meeting and suggest refreshment at appropriate times. For example, the generation AI can analyze participants' facial expressions and comments and suggest refreshment when fatigue levels increase. The generation AI can also adjust the progress of the meeting based on participants' fatigue levels. This can reduce participant fatigue and improve meeting efficiency.

[0090] The moderator AI system can also use its emotion estimation function to monitor the emotions of participants during a meeting and suggest humorous remarks at appropriate times. For example, the generation AI can analyze participants' facial expressions and comments, and suggest humorous remarks when they become depressed. The generation AI can also adjust the progress of the meeting based on the participants' emotions. This can lighten the atmosphere of the meeting and encourage participants to relax.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The material creation unit automatically creates materials based on the purpose or agenda of the meeting. For example, the generation AI generates meeting agendas, presentation materials, and meeting minutes templates. The generation AI can also generate materials based on prompts containing instructions about the purpose or agenda of the meeting. Step 2: The discussion moderator monitors the discussion during the meeting in real time and moves the agenda forward at the appropriate time. For example, the generation AI analyzes the content of comments made during the meeting in real time and manages the progress of the discussion. The generation AI can also detect when participants are engaging in position-taking as part of internal politics and organize the discussion from a neutral standpoint. Step 3: The conclusion summary section analyzes the content of the meeting discussion and automatically summarizes the conclusion. For example, even if the discussion diverges, the generation AI can extract the main points and present a clear conclusion. The generation AI can also analyze the content of comments made during the meeting and summarize the conclusion.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. a material creation unit that automatically creates materials based on the purpose or agenda of the meeting; The discussion management department monitors the discussions during the meeting in real time and moves the agenda forward at the appropriate time. A conclusion summary unit that analyzes the contents of the discussion in the meeting and automatically summarizes the conclusion. A system characterized by:

2. The material creation unit Study past meeting materials and suggest the optimal format 2. The system of claim 1.

3. The discussion promotion section: Learns participants' speech patterns and suggests the optimal speaking order 2. The system of claim 1.

4. The conclusion summary section Automatically extract the main points of the discussion and organize them visually 2. The system of claim 1.

5. The conclusion summary section Automatically organize and prioritize post-meeting action items 2. The system of claim 1.

6. The material creation unit Customize content based on participants' interests or expectations 2. The system of claim 1.

7. The discussion promotion section: Monitor participants' emotions as the discussion progresses and intervene at the appropriate time 2. The system of claim 1.

8. The conclusion summary section Evaluate the emotional impact of the conclusion on participants and present the most appropriate conclusion 2. The system of claim 1.

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