System

Abstract

An object of a system according to an embodiment is to support effective facilitation in an online conference.SOLUTION: A system according to an embodiment includes a scenario creation support unit, a punch picture generation support unit, a summary support unit, a timekeeper unit, and a minutes creation support unit. The scenario creation support unit creates a scenario by inputting an agenda and items in a wizard format. A punch picture generation support part generates a punch picture during a conference. The summarization support unit summarizes the content of the discussion during the conference. The timekeeper unit monitors the progress of the conference and manages the time allocation. The minutes creation support unit creates minutes after the end of the conference.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to effectively facilitate online meetings, resulting in insufficient progress and results.

[0005] The system according to the embodiment aims to support effective facilitation in online conferences. [Means for solving the problem]

[0006] The system according to the embodiment comprises a scenario creation support unit, a cartoon generation support unit, a summarization support unit, a timekeeper unit, and a minutes creation support unit. The scenario creation support unit creates a scenario by inputting an agenda and items in a wizard format. The cartoon generation support unit generates cartoons during a meeting. The summarization support unit summarizes the contents of discussions during the meeting. The timekeeper unit monitors the progress of the meeting and manages time allocation. The minutes creation support unit creates minutes after the meeting ends. [Effects of the Invention]

[0007] The system according to the embodiment can support effective facilitation in online meetings. [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 facilitation support system according to an embodiment of the present invention is a system that efficiently and effectively supports facilitation in online meetings. This system provides functions such as scenario creation support, support for creating outline drawings during meetings, support for summarizing, timekeeping, and support for creating minutes by inputting agendas and items in a wizard format. As a result, the facilitation support system can smoothly progress online meetings and provide an environment where all participants can concentrate on the discussion. In addition, by automating the creation of minutes and summarizing, it can reduce the workload after the meeting.

[0029] A facilitation support system according to an embodiment includes a scenario creation support unit, a cartoon generation support unit, a summary support unit, a timekeeper unit, and a minutes creation support unit. The scenario creation support unit creates a scenario by inputting an agenda and items using a wizard. For example, when a user inputs information such as the "meeting purpose," "topic," and "participants," a generation AI generates a meeting progress scenario based on that information. The generation AI generates a scenario using a text generation AI (e.g., LLM). The generation AI can also learn from the user's past meeting data and automatically generate a scenario tailored to the user's preferences and style. Furthermore, the generation AI can incorporate external industry news and trend information in real time to provide a scenario that reflects the latest information. The cartoon generation support unit generates cartoons during a meeting. For example, the generation AI can analyze the content of discussions during a meeting in real time, extract important keywords, and reflect them in the cartoon. The generation AI can also refer to past meeting data and provide illustrations based on similar discussion content. Furthermore, the generation AI can use its emotion estimation function to analyze the emotions of participants during discussions and incorporate visual expressions corresponding to their emotions into the cartoons. The summary support unit summarizes the content of discussions during meetings. For example, the generation AI can individually summarize the content of each speaker's remarks during a meeting and provide a summary for each speaker. The generation AI can also consider the purpose and goals of the meeting when generating summaries and prioritize summaries that are necessary to achieve those goals. The generation AI can also use its emotion estimation function to analyze changes in emotions during discussions and generate summaries that emphasize emotionally important points. The timekeeper unit monitors the progress of meetings and manages time allocation. For example, the generation AI can analyze the progress of meetings in real time and automatically make suggestions to speed things up if the meeting is behind schedule. The generation AI can also learn from past meeting data and suggest optimal time allocation. The generation AI can also use its emotion estimation function to analyze participants' emotions and speed things up at emotionally appropriate times. The minutes creation support unit creates minutes after the meeting ends. For example, the generation AI can analyze the content of comments made during a meeting in real time and automatically highlight important comments.The generation AI can also refer to past meeting data to provide consistent minutes. Furthermore, the generation AI can also use an emotion estimation function to reflect the emotional nuances of remarks in the minutes. This allows the facilitation support system according to the embodiment to smoothly progress online meetings and provide an environment where all participants can concentrate on the discussion. Furthermore, by automating the creation and summarization of minutes, the workload after the meeting can be reduced.

[0030] The scenario creation support unit can learn from the user's past meeting data and automatically generate scenarios that match the user's preferences and style. For example, the scenario creation support unit uses a generation AI to analyze the user's past meeting data and learn the user's speech patterns and preferences. For example, it identifies the user's frequently used phrases and progress methods and automatically generates a scenario based on them. This makes it possible to provide the optimal scenario based on the user's past meeting data.

[0031] The scenario creation support unit can incorporate external industry news and trend information in real time and provide scenarios that reflect the latest information. For example, the scenario creation support unit uses a generation AI to collect external industry news and trend information in real time and reflect this in the scenario. For example, it generates scenarios that incorporate the latest technological trends and market changes. This makes it possible to provide scenarios that reflect the latest information.

[0032] The scenario creation support unit can also be applied to creating progress scenarios for online classes and seminars in the field of education. For example, in online classes in the field of education, the generation AI provides a scenario creation support function. For example, it automatically generates an optimal progress scenario based on the purpose and content of the class. This makes it applicable to online classes and seminars in the field of education.

[0033] The scenario creation support unit supports the generation AI in creating scenarios in different languages, making it possible to accommodate international conferences.The scenario creation support unit, for example, supports the generation AI in creating scenarios in different languages, making it possible to accommodate international conferences.For example, it automatically generates scenarios in multiple languages, such as English, Japanese, and French.This makes it possible to provide scenarios that are suitable for international conferences.

[0034] The cartoon generation support unit can analyze the audio data during a meeting in real time, extract important keywords, and reflect them in the cartoon. For example, the generation AI in the cartoon generation support unit analyzes the audio data during a meeting in real time and extracts important keywords. For example, it identifies words and phrases that frequently appear in a discussion and reflects them in the cartoon. This makes it possible to visually show important keywords during a meeting.

[0035] The cartoon generation support unit can refer to past meeting data and provide illustrations based on similar discussion content. For example, the generation AI refers to past meeting data and provides illustrations based on similar discussion content. For example, it refers to cartoons used in past meetings. This makes it possible to provide illustrations based on past meeting data.

[0036] The cartoon generation support unit can also be applied to marketing presentations and product demonstrations. For example, in marketing presentations, the generation AI provides a cartoon generation support function. For example, it generates cartoons that visually show product features and market data. This makes it applicable to marketing presentations and product demonstrations.

[0037] The cartoon generation support unit can respond to the terminology and illustration styles of different industries and provide industry-specific cartoons. For example, the generation AI can respond to the terminology and illustration styles of different industries and provide industry-specific cartoons. For example, it can generate illustrations specialized for the medical industry or the financial industry. This makes it possible to provide industry-specific cartoons.

[0038] The summary support unit can individually summarize the content of each speaker's speech during a meeting and provide a summary for each speaker. For example, the summary support unit uses a generation AI to analyze the content of each speaker's speech during a meeting in real time and summarize it individually. For example, it extracts the important points of each speaker and generates a summary. This makes it possible to provide a summary for each speaker.

[0039] The summary support unit takes into consideration the purpose and goals of the meeting when generating a summary, and can prioritize summarizing information necessary to achieve that purpose. For example, the summary support unit uses a generation AI to consider the purpose and goals of the meeting and prioritize summarizing information necessary to achieve that purpose. For example, it can highlight the conclusions and action items of the meeting. This allows information necessary to achieve the purpose to be summarized with priority.

[0040] The summary support unit can also be applied to generating summaries of academic papers and technical reports. For example, when generating summaries of academic papers, the generation AI provides summary support functions. For example, it concisely summarizes the main points and conclusions of the paper. This makes it applicable to generating summaries of academic papers and technical reports.

[0041] The summary support unit enables the generation AI to support the generation of summaries in different languages, making it possible to handle international documents. For example, the summary support unit enables the generation AI to support the generation of summaries in different languages, making it possible to handle international documents. For example, it automatically generates summaries in multiple languages, such as English, Japanese, and French. This makes it possible to provide summaries that can be handled even for international documents.

[0042] The timekeeper unit analyzes the progress of the meeting in real time and can automatically make suggestions to speed things up if the meeting is behind schedule. For example, the timekeeper unit uses a generation AI to analyze the progress of the meeting in real time and can automatically make suggestions to speed things up if the meeting is behind schedule. For example, it adjusts the time allocation for agenda items. This makes it possible to automatically make suggestions to speed things up if the meeting is behind schedule.

[0043] The timekeeping unit can learn from past meeting data and propose optimal time allocation. For example, the generation AI in the timekeeping unit learns from past meeting data and proposes optimal time allocation. For example, it refers to time allocation in past meetings. This makes it possible to propose optimal time allocation based on past meeting data.

[0044] The timekeeper unit can also be applied to event and seminar progress management. For example, in event progress management, the generation AI provides a timekeeper function. For example, it automatically adjusts the time allocation for each session. This makes it applicable to event and seminar progress management.

[0045] The timekeeper section allows the generated AI to adapt to different time zones, supporting time management at international conferences. For example, the timekeeper section allows the generated AI to adapt to different time zones, supporting time management at international conferences. For example, it provides a schedule tailored to each participant's local time. This supports time management at international conferences.

[0046] The minutes-taking support unit can analyze what is said during a meeting in real time and automatically highlight important remarks. For example, the minutes-taking support unit uses a generation AI to analyze what is said during a meeting in real time and automatically highlight important remarks. For example, it can highlight the main points and conclusions of a discussion. This makes it possible to automatically highlight important remarks.

[0047] The minutes creation support unit can refer to past meeting data and provide consistent minutes. For example, the generation AI can refer to past meeting data and provide consistent minutes. For example, it can maintain consistency with past minutes. This makes it possible to provide consistent minutes.

[0048] The minutes-taking support unit can also be applied to creating lesson records and seminar records in the field of education. For example, in the case of lesson records in the field of education, the generation AI provides a minutes-taking support function. For example, it automatically records the content of the lesson and important points. This makes it applicable to creating lesson records and seminar records in the field of education.

[0049] The minutes creation support unit allows the generation AI to support the creation of minutes in different languages, making it possible to accommodate international conferences.The minutes creation support unit, for example, allows the generation AI to support the creation of minutes in different languages, making it possible to accommodate international conferences.For example, minutes can be automatically generated in multiple languages, such as English, Japanese, and French.This makes it possible to provide minutes that are suitable for international conferences.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The facilitation support system can also be equipped with a function to track participants' gazes during meetings and analyze their level of focus. For example, the generation AI can use a camera to track participants' gazes in real time and identify areas where they are focused and areas where they are distracted. It can also adjust the progress of the meeting and the way materials are displayed based on the gaze data. Furthermore, if participants' focus levels drop, interactive elements can be introduced to attract their attention.

[0052] The facilitation support system can also be equipped with a function that automatically tags what participants say during a meeting, making it easier to search for later. For example, the generation AI can analyze what is said in real time and automatically assign tags based on important keywords and topics. It can also make it easy to search for specific topics or keywords after the meeting based on the tagged comments. This allows for efficient organization of meeting content and makes it easier to refer to later.

[0053] The facilitation support system can also be equipped with a function that summarizes what participants say during a meeting and displays it in real time. For example, the generation AI can analyze what is said in real time, extract important points, and generate a summary. Displaying the summary during the meeting also makes it easier for all participants to understand the progress of the discussion. Furthermore, the summary can be automatically reflected in the minutes. This helps smooth the progress of the meeting and clarify the key points of the discussion.

[0054] The facilitation support system can also be equipped with a function to translate what participants say during a meeting and display it in different languages. For example, the generation AI can analyze what is said in real time and translate it into different languages ​​for display. The translated content can also be used to facilitate smooth communication, even in international meetings. The translated content can also be automatically reflected in the minutes of the meeting. This allows discussions to proceed smoothly even between participants who speak different languages.

[0055] The facilitation support system can also be equipped with a function to analyze what participants say during a meeting and adjust the balance of speech. For example, the generation AI can analyze what is being said in real time, and if a particular participant is speaking too much, it can encourage other participants to speak up. It can also display messages encouraging participants who are not speaking much to speak at the appropriate time. This makes the meeting proceed fairly and provides an environment where everyone can easily express their opinions.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The scenario creation support unit creates a scenario by inputting the agenda and items in a wizard format. For example, when a user inputs information such as the "purpose of the meeting," "topic," and "participants," the generation AI generates a meeting progress scenario based on that information. The generation AI generates the scenario using a text generation AI (e.g., LLM). The generation AI can also learn from the user's past meeting data and automatically generate a scenario that matches the user's preferences and style. Furthermore, the generation AI can also incorporate external industry news and trend information in real time and provide a scenario that reflects the latest information. Step 2: The cartoon generation support unit generates cartoons during the meeting. For example, the generation AI analyzes the content of discussions during the meeting in real time, extracts important keywords, and reflects them in the cartoon. The generation AI can also refer to past meeting data and provide illustrations based on similar discussion content. Furthermore, the generation AI can use an emotion estimation function to analyze the emotions of participants during the discussion and incorporate visual expressions corresponding to their emotions into the cartoon. Step 3: The summary support unit summarizes the content of discussions during the meeting. For example, the generation AI can individually summarize the content of each speaker's remarks during the meeting and provide a summary for each speaker. The generation AI can also take into account the purpose and goals of the meeting when generating the summary, prioritizing the summary of information necessary to achieve that purpose. Furthermore, the generation AI can use its emotion estimation function to analyze changes in emotions during the discussion and generate a summary that emphasizes emotionally important points. Step 4: The timekeeper monitors the progress of the meeting and manages time allocation. For example, the generation AI analyzes the progress of the meeting in real time and automatically makes suggestions to speed things up if the meeting is behind schedule. The generation AI can also learn from past meeting data and suggest optimal time allocation. Furthermore, the generation AI can use its emotion estimation function to analyze the emotions of participants and prompt them to proceed at an emotionally appropriate time. Step 5: The minutes creation support unit creates minutes after the meeting ends. For example, the generation AI analyzes what is said during the meeting in real time and automatically highlights important comments. The generation AI can also refer to past meeting data to provide consistent minutes. Furthermore, the generation AI can use emotion estimation functionality to reflect the emotional nuances of what is said in the minutes.

[0058] (Example 2) A facilitation support system according to an embodiment of the present invention is a system that efficiently and effectively supports facilitation in online meetings. This system provides functions such as scenario creation support, support for creating outline drawings during meetings, support for summarizing, timekeeping, and support for creating minutes by inputting agendas and items in a wizard format. As a result, the facilitation support system can smoothly progress online meetings and provide an environment where all participants can concentrate on the discussion. In addition, by automating the creation of minutes and summarizing, it can reduce the workload after the meeting.

[0059] A facilitation support system according to an embodiment includes a scenario creation support unit, a cartoon generation support unit, a summary support unit, a timekeeper unit, and a minutes creation support unit. The scenario creation support unit creates a scenario by inputting an agenda and items using a wizard. For example, when a user inputs information such as the "meeting purpose," "topic," and "participants," a generation AI generates a meeting progress scenario based on that information. The generation AI generates a scenario using a text generation AI (e.g., LLM). The generation AI can also learn from the user's past meeting data and automatically generate a scenario tailored to the user's preferences and style. Furthermore, the generation AI can incorporate external industry news and trend information in real time to provide a scenario that reflects the latest information. The cartoon generation support unit generates cartoons during a meeting. For example, the generation AI can analyze the content of discussions during a meeting in real time, extract important keywords, and reflect them in the cartoon. The generation AI can also refer to past meeting data and provide illustrations based on similar discussion content. Furthermore, the generation AI can use its emotion estimation function to analyze the emotions of participants during discussions and incorporate visual expressions corresponding to their emotions into the cartoons. The summary support unit summarizes the content of discussions during meetings. For example, the generation AI can individually summarize the content of each speaker's remarks during a meeting and provide a summary for each speaker. The generation AI can also consider the purpose and goals of the meeting when generating summaries and prioritize summaries that are necessary to achieve those goals. The generation AI can also use its emotion estimation function to analyze changes in emotions during discussions and generate summaries that emphasize emotionally important points. The timekeeper unit monitors the progress of meetings and manages time allocation. For example, the generation AI can analyze the progress of meetings in real time and automatically make suggestions to speed things up if the meeting is behind schedule. The generation AI can also learn from past meeting data and suggest optimal time allocation. The generation AI can also use its emotion estimation function to analyze participants' emotions and speed things up at emotionally appropriate times. The minutes creation support unit creates minutes after the meeting ends. For example, the generation AI can analyze the content of comments made during a meeting in real time and automatically highlight important comments.The generation AI can also refer to past meeting data to provide consistent minutes. Furthermore, the generation AI can also use an emotion estimation function to reflect the emotional nuances of remarks in the minutes. This allows the facilitation support system according to the embodiment to smoothly progress online meetings and provide an environment where all participants can concentrate on the discussion. Furthermore, by automating the creation and summarization of minutes, the workload after the meeting can be reduced.

[0060] The scenario creation support unit can learn from the user's past meeting data and automatically generate scenarios that match the user's preferences and style. For example, the scenario creation support unit uses a generation AI to analyze the user's past meeting data and learn the user's speech patterns and preferences. For example, it identifies the user's frequently used phrases and progress methods and automatically generates a scenario based on them. This makes it possible to provide the optimal scenario based on the user's past meeting data.

[0061] The scenario creation support unit can incorporate external industry news and trend information in real time and provide scenarios that reflect the latest information. For example, the scenario creation support unit uses a generation AI to collect external industry news and trend information in real time and reflect this in the scenario. For example, it generates scenarios that incorporate the latest technological trends and market changes. This makes it possible to provide scenarios that reflect the latest information.

[0062] The scenario creation support unit can use the emotion estimation function to analyze the emotions felt by the user when they input information and generate a scenario that elicits positive emotions. For example, the scenario creation support unit can use the emotion estimation function to analyze the emotions felt by the user when they input information in real time and generate a scenario that elicits positive emotions. For example, if the user is feeling stressed, the scenario creation support unit can suggest a way to proceed that will help the user relax. This makes it possible to provide a scenario that matches the user's emotions.

[0063] The scenario creation support unit can also be applied to creating progress scenarios for online classes and seminars in the field of education. For example, in online classes in the field of education, the generation AI provides a scenario creation support function. For example, it automatically generates an optimal progress scenario based on the purpose and content of the class. This makes it applicable to online classes and seminars in the field of education.

[0064] The scenario creation support unit supports the generation AI in creating scenarios in different languages, making it possible to accommodate international conferences.The scenario creation support unit, for example, supports the generation AI in creating scenarios in different languages, making it possible to accommodate international conferences.For example, it automatically generates scenarios in multiple languages, such as English, Japanese, and French.This makes it possible to provide scenarios that are suitable for international conferences.

[0065] The scenario creation support unit can use the emotion estimation function to predict the emotions of participants when creating a scenario and propose the optimal way to proceed. The scenario creation support unit can, for example, use the emotion estimation function to predict the emotions of participants when creating a scenario and propose the optimal way to proceed. For example, it can propose a way to proceed that will allow participants to relax. This makes it possible to provide the optimal way to proceed according to the emotions of the participants.

[0066] The cartoon generation support unit can analyze the audio data during a meeting in real time, extract important keywords, and reflect them in the cartoon. For example, the generation AI in the cartoon generation support unit analyzes the audio data during a meeting in real time and extracts important keywords. For example, it identifies words and phrases that frequently appear in a discussion and reflects them in the cartoon. This makes it possible to visually show important keywords during a meeting.

[0067] The cartoon generation support unit can refer to past meeting data and provide illustrations based on similar discussion content. For example, the generation AI refers to past meeting data and provides illustrations based on similar discussion content. For example, it refers to cartoons used in past meetings. This makes it possible to provide illustrations based on past meeting data.

[0068] The cartoon generation support unit can use the emotion estimation function to analyze the emotions of the participants during the discussion and incorporate visual expressions corresponding to the emotions into the cartoon. The cartoon generation support unit can, for example, use the emotion estimation function to analyze the emotions of the participants during the discussion in real time and incorporate visual expressions corresponding to the emotions into the cartoon. For example, bright colors can be used when a participant is excited. This makes it possible to provide visual expressions corresponding to the emotions of the participants.

[0069] The cartoon generation support unit can also be applied to marketing presentations and product demonstrations. For example, in marketing presentations, the generation AI provides a cartoon generation support function. For example, it generates cartoons that visually show product features and market data. This makes it applicable to marketing presentations and product demonstrations.

[0070] The cartoon generation support unit can respond to the terminology and illustration styles of different industries and provide industry-specific cartoons. For example, the generation AI can respond to the terminology and illustration styles of different industries and provide industry-specific cartoons. For example, it can generate illustrations specialized for the medical industry or the financial industry. This makes it possible to provide industry-specific cartoons.

[0071] The cartoon generation support unit can use the emotion estimation function to evaluate the emotional impact that the visual elements of the cartoon have on participants and propose an optimal design. The cartoon generation support unit, for example, uses the emotion estimation function to evaluate the emotional impact that the visual elements of the cartoon have on participants. For example, it analyzes the impact that colors and shapes have on participants' emotions. This makes it possible to propose an optimal design that takes into account the emotional impact on participants.

[0072] The summary support unit can individually summarize the content of each speaker's speech during a meeting and provide a summary for each speaker. For example, the summary support unit uses a generation AI to analyze the content of each speaker's speech during a meeting in real time and summarize it individually. For example, it extracts the important points of each speaker and generates a summary. This makes it possible to provide a summary for each speaker.

[0073] The summary support unit takes into consideration the purpose and goals of the meeting when generating a summary, and can prioritize summarizing information necessary to achieve that purpose. For example, the summary support unit uses a generation AI to consider the purpose and goals of the meeting and prioritize summarizing information necessary to achieve that purpose. For example, it can highlight the conclusions and action items of the meeting. This allows information necessary to achieve the purpose to be summarized with priority.

[0074] The summarization support unit can use the emotion estimation function to analyze changes in emotions during a discussion and generate a summary that emphasizes emotionally important points. For example, the summarization support unit can use the emotion estimation function to analyze changes in emotions during a discussion in real time and generate a summary that emphasizes emotionally important points. For example, the summarization support unit can reflect heightened emotions and tension. This makes it possible to provide a summary that emphasizes emotionally important points.

[0075] The summary support unit can also be applied to generating summaries of academic papers and technical reports. For example, when generating summaries of academic papers, the generation AI provides summary support functions. For example, it concisely summarizes the main points and conclusions of the paper. This makes it applicable to generating summaries of academic papers and technical reports.

[0076] The summary support unit enables the generation AI to support the generation of summaries in different languages, making it possible to handle international documents. For example, the summary support unit enables the generation AI to support the generation of summaries in different languages, making it possible to handle international documents. For example, it automatically generates summaries in multiple languages, such as English, Japanese, and French. This makes it possible to provide summaries that can be handled even for international documents.

[0077] The summarization support unit can use the emotion estimation function to evaluate the emotional impact of the summary content on the reader and propose the optimal expression. The summarization support unit, for example, uses the emotion estimation function to evaluate the emotional impact of the summary content on the reader. For example, it analyzes the emotions that the expression in the summary sentence has on the reader. This makes it possible to provide the optimal expression that takes into account the emotional impact on the reader.

[0078] The timekeeper unit analyzes the progress of the meeting in real time and can automatically make suggestions to speed things up if the meeting is behind schedule. For example, the timekeeper unit uses a generation AI to analyze the progress of the meeting in real time and can automatically make suggestions to speed things up if the meeting is behind schedule. For example, it adjusts the time allocation for agenda items. This makes it possible to automatically make suggestions to speed things up if the meeting is behind schedule.

[0079] The timekeeping unit can learn from past meeting data and propose optimal time allocation. For example, the generation AI in the timekeeping unit learns from past meeting data and proposes optimal time allocation. For example, it refers to time allocation in past meetings. This makes it possible to propose optimal time allocation based on past meeting data.

[0080] The timekeeper unit can use the emotion estimation function to analyze the emotions of the participants and encourage the progress of the meeting at an emotionally appropriate timing. The timekeeper unit can, for example, use the emotion estimation function to analyze the emotions of the participants in real time and encourage the progress of the meeting at an emotionally appropriate timing. For example, it can move on to the next topic when the participants are relaxed. This makes it possible to encourage the progress of the meeting at an appropriate timing according to the emotions of the participants.

[0081] The timekeeper unit can also be applied to event and seminar progress management. For example, in event progress management, the generation AI provides a timekeeper function. For example, it automatically adjusts the time allocation for each session. This makes it applicable to event and seminar progress management.

[0082] The timekeeper section allows the generated AI to adapt to different time zones, supporting time management at international conferences. For example, the timekeeper section allows the generated AI to adapt to different time zones, supporting time management at international conferences. For example, it provides a schedule tailored to each participant's local time. This supports time management at international conferences.

[0083] The timekeeper unit can use the emotion estimation function to suggest timing for alleviating ongoing emotional tension. For example, the timekeeper unit uses the emotion estimation function to analyze ongoing emotional tension in real time and suggest timing for alleviating it. For example, it can suggest taking a break when tension builds up. This makes it possible to suggest timing for alleviating emotional tension.

[0084] The minutes-taking support unit can analyze what is said during a meeting in real time and automatically highlight important remarks. For example, the minutes-taking support unit uses a generation AI to analyze what is said during a meeting in real time and automatically highlight important remarks. For example, it can highlight the main points and conclusions of a discussion. This makes it possible to automatically highlight important remarks.

[0085] The minutes creation support unit can refer to past meeting data and provide consistent minutes. For example, the generation AI can refer to past meeting data and provide consistent minutes. For example, it can maintain consistency with past minutes. This makes it possible to provide consistent minutes.

[0086] The minutes creation support unit can use the emotion estimation function to reflect the emotional nuances of the remarks in the minutes. For example, the minutes creation support unit uses the emotion estimation function to analyze the emotional nuances of the remarks in real time and reflect them in the minutes. For example, an annotation indicating the speaker's emotional state is added. This allows the emotional nuances of the remarks to be reflected in the minutes.

[0087] The minutes-taking support unit can also be applied to creating lesson records and seminar records in the field of education. For example, in the case of lesson records in the field of education, the generation AI provides a minutes-taking support function. For example, it automatically records the content of the lesson and important points. This makes it applicable to creating lesson records and seminar records in the field of education.

[0088] The minutes creation support unit allows the generation AI to support the creation of minutes in different languages, making it possible to accommodate international conferences.The minutes creation support unit, for example, allows the generation AI to support the creation of minutes in different languages, making it possible to accommodate international conferences.For example, minutes can be automatically generated in multiple languages, such as English, Japanese, and French.This makes it possible to provide minutes that are suitable for international conferences.

[0089] The minutes creation support unit can use the emotion estimation function to evaluate the emotional impact that the contents of the minutes have on the reader and propose optimal expressions. The minutes creation support unit can, for example, use the emotion estimation function to evaluate the emotional impact that the contents of the minutes have on the reader. For example, it can analyze the emotions that expressions in the minutes have on the reader. This makes it possible to provide optimal expressions that take into account the emotional impact on the reader.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The facilitation support system can also be equipped with a function to monitor the frequency of participants' speech and encourage those who speak less to speak. For example, the generation AI can analyze the number of times each participant speaks during a meeting in real time and display messages encouraging participants who speak less at the appropriate time. It can also learn from past meeting data to identify the times and topics when specific participants are most likely to speak and encourage them to speak based on that. Furthermore, it can use an emotion estimation function to analyze the emotions of participants who speak less and provide a relaxing environment.

[0092] The facilitation support system can also be equipped with a function to track participants' gazes during meetings and analyze their level of focus. For example, the generation AI can use a camera to track participants' gazes in real time and identify areas where they are focused and areas where they are distracted. It can also adjust the progress of the meeting and the way materials are displayed based on the gaze data. Furthermore, if participants' focus levels drop, interactive elements can be introduced to attract their attention.

[0093] The facilitation support system can also be equipped with a function to analyze what participants say during a meeting and provide appropriate feedback based on their tone and style. For example, the generative AI can analyze what is said in real time and suggest a more gentle way of expressing itself if the remark is offensive. It can also prompt participants to provide specific examples if the remark is ambiguous. Furthermore, it can use an emotion estimation function to analyze the emotions of the speaker and provide feedback according to their emotions.

[0094] The facilitation support system can also be equipped with a function to analyze the facial expressions of participants during a meeting and adjust the progress of the meeting based on changes in their expressions. For example, the generation AI can use a camera to analyze participants' expressions in real time, and if their expressions are stiff, it can suggest topics that will help them relax. If their expressions are cheerful, it can continue the meeting as is. Furthermore, it can use an emotion estimation function to estimate participants' emotions based on changes in their expressions and suggest ways to proceed based on their emotions.

[0095] The facilitation support system can also be equipped with a function to cluster the content of participants' comments during a meeting and group similar opinions. For example, the generative AI can analyze the content of comments in real time and automatically group similar opinions and suggestions. It can also visually display the grouped opinions to facilitate smooth discussion progress. Furthermore, it can use an emotion estimation function to analyze the emotional tendencies of each group and suggest ways to proceed based on their emotions.

[0096] The facilitation support system can also be equipped with a function that automatically tags what participants say during a meeting, making it easier to search for later. For example, the generation AI can analyze what is said in real time and automatically assign tags based on important keywords and topics. It can also make it easy to search for specific topics or keywords after the meeting based on the tagged comments. This allows for efficient organization of meeting content and makes it easier to refer to later.

[0097] The facilitation support system can also be equipped with a function that summarizes what participants say during a meeting and displays it in real time. For example, the generation AI can analyze what is said in real time, extract important points, and generate a summary. Displaying the summary during the meeting also makes it easier for all participants to understand the progress of the discussion. Furthermore, the summary can be automatically reflected in the minutes. This helps smooth the progress of the meeting and clarify the key points of the discussion.

[0098] The facilitation support system can also be equipped with a function to translate what participants say during a meeting and display it in different languages. For example, the generation AI can analyze what is said in real time and translate it into different languages ​​for display. The translated content can also be used to facilitate smooth communication, even in international meetings. The translated content can also be automatically reflected in the minutes of the meeting. This allows discussions to proceed smoothly even between participants who speak different languages.

[0099] The facilitation support system can also be equipped with a function to analyze what participants say during a meeting and adjust the balance of speech. For example, the generation AI can analyze what is being said in real time, and if a particular participant is speaking too much, it can encourage other participants to speak up. It can also display messages encouraging participants who are not speaking much to speak at the appropriate time. This makes the meeting proceed fairly and provides an environment where everyone can easily express their opinions.

[0100] The facilitation support system can also be equipped with a function to analyze the content of comments made by participants during meetings and evaluate their quality. For example, the generation AI can analyze the content of comments in real time and evaluate their logic and concreteness. It can also suggest areas for improvement based on the evaluation results. Furthermore, it can use an emotion estimation function to analyze the emotions of speakers and provide feedback based on their emotions. This can improve the quality of comments and promote constructive discussions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The scenario creation support unit creates a scenario by inputting the agenda and items in a wizard format. For example, when a user inputs information such as the "purpose of the meeting," "topic," and "participants," the generation AI generates a meeting progress scenario based on that information. The generation AI generates the scenario using a text generation AI (e.g., LLM). The generation AI can also learn from the user's past meeting data and automatically generate a scenario that matches the user's preferences and style. Furthermore, the generation AI can also incorporate external industry news and trend information in real time and provide a scenario that reflects the latest information. Step 2: The cartoon generation support unit generates cartoons during the meeting. For example, the generation AI analyzes the content of discussions during the meeting in real time, extracts important keywords, and reflects them in the cartoon. The generation AI can also refer to past meeting data and provide illustrations based on similar discussion content. Furthermore, the generation AI can use an emotion estimation function to analyze the emotions of participants during the discussion and incorporate visual expressions corresponding to their emotions into the cartoon. Step 3: The summary support unit summarizes the content of discussions during the meeting. For example, the generation AI can individually summarize the content of each speaker's remarks during the meeting and provide a summary for each speaker. The generation AI can also take into account the purpose and goals of the meeting when generating the summary, prioritizing the summary of information necessary to achieve that purpose. Furthermore, the generation AI can use its emotion estimation function to analyze changes in emotions during the discussion and generate a summary that emphasizes emotionally important points. Step 4: The timekeeper monitors the progress of the meeting and manages time allocation. For example, the generation AI analyzes the progress of the meeting in real time and automatically makes suggestions to speed things up if the meeting is behind schedule. The generation AI can also learn from past meeting data and suggest optimal time allocation. Furthermore, the generation AI can use its emotion estimation function to analyze the emotions of participants and prompt them to proceed at an emotionally appropriate time. Step 5: The minutes creation support unit creates minutes after the meeting ends. For example, the generation AI analyzes what is said during the meeting in real time and automatically highlights important comments. The generation AI can also refer to past meeting data to provide consistent minutes. Furthermore, the generation AI can use emotion estimation functionality to reflect the emotional nuances of what is said in the minutes.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[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 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.

[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 (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).

[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] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 scenario creation support section that creates a scenario by inputting agendas and items in a wizard format; a cartoon generation support unit that generates cartoons during a meeting; a summary support unit that summarizes the contents of discussions during meetings; The timekeeper section monitors the progress of the meeting and manages the time allocation. A minutes creation support unit that creates minutes after the meeting is completed. A system characterized by:

2. The scenario creation support unit Incorporating external industry news and trend information in real time to provide scenarios that reflect the latest information 2. The system of claim 1.

3. The cartoon creation support unit Analyze the audio data during the meeting in real time, extract important keywords, and reflect them in the cartoon.

2. The system of claim 1.

4. The summary support unit When generating a summary, the purpose and goals of the meeting are taken into consideration, and information necessary to achieve the purpose is summarized first.

2. The system of claim 1.

5. The timekeeper unit Analyze the progress of the meeting in real time, and if the progress is slow, automatically make suggestions to speed up the progress.

2. The system of claim 1.

6. The minutes creation support department Reflect the emotional nuances of the statements in the minutes.

2. The system of claim 1.

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