system

Abstract

The system according to this embodiment aims to facilitate communication by generating appropriate questions in real time during a meeting. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and an output unit. The analysis unit analyzes the meeting materials and audio. The generation unit generates questions based on the content analyzed by the analysis unit. The output unit outputs the questions generated by the generation unit in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to generate appropriate questions in real time during a meeting, and there is a problem that the quality of communication varies.

[0005] The system according to the embodiment aims to generate appropriate questions in real time during a meeting and smooth the communication.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and an output unit. The analysis unit analyzes the materials and voices of the meeting. The generation unit generates questions based on the content analyzed by the analysis unit. The output unit outputs the questions generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can generate appropriate questions in real time during a meeting, thereby facilitating smooth communication. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An online meeting support system according to an embodiment of the present invention is a system that analyzes meeting materials and audio, generates optimal questions based on the content, and outputs them in real time. By analyzing meeting materials and audio, generating questions based on the content, and outputting them in real time, the online meeting support system improves the quality of discussions and enables efficient decision-making. For example, the online meeting support system analyzes meeting materials and audio. For example, it analyzes presentation materials and meeting recordings to understand their content. In this process, a generation AI is used to understand the content of the materials and audio. Next, the online meeting support system generates questions based on the analyzed content. The generation AI generates appropriate questions based on the analyzed content. For example, questions such as "How much can we gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" are generated. The generated questions are output in real time. For example, they are output in real time to a spreadsheet such as Google Sheets, allowing the user to check them. The user checks the generated questions and proceeds with the discussion based on the answers to those questions. This system improves the quality of discussions and enables efficient decision-making. For example, GQT can be used in advance to identify potential questions and prepare countermeasures. It can also help identify concerns early in the discussion, supporting quick decision-making. In this way, online meeting support systems can improve the quality of discussions and support efficient decision-making.

[0029] The online meeting support system according to this embodiment comprises an analysis unit, a generation unit, and an output unit. The analysis unit analyzes the meeting materials and audio. The analysis unit analyzes, for example, presentation materials and meeting recordings and understands their content. The analysis unit uses generation AI to understand the content of the materials and audio. The generation unit generates questions based on the content analyzed by the analysis unit. The generation unit uses, for example, generation AI to generate appropriate questions based on the analyzed content. The generation unit generates questions such as, for example, "How much can we gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" The output unit outputs the questions generated by the generation unit in real time. The output unit outputs the questions in real time to, for example, Google Sheets, so that the user can check them. The user can check the generated questions and proceed with the discussion based on the answers to those questions. As a result, the online meeting support system according to this embodiment can improve the quality of discussions and support efficient decision-making.

[0030] The analysis unit analyzes meeting materials and audio. For example, the analysis unit analyzes presentation materials and meeting recordings to understand their content. Specifically, the analysis unit uses generative AI to understand the content of the materials and audio. The generative AI utilizes natural language processing technology to analyze text, graphs, and charts in presentation materials and extract important points and keywords. In addition, for meeting recordings, speech recognition technology is used to transcribe the text and analyze its content. For example, speech recognition technology converts what the speaker says into text in real time and extracts the intent of the statement and important information. Furthermore, the analysis unit can also refer to past meeting data and related documents to evaluate the relevance to the current meeting content. This allows the analysis unit to comprehensively analyze meeting materials and audio to understand the progress of the meeting and important agenda items. The analysis unit provides these analysis results to the generation unit, which uses them as basic data to generate appropriate questions.

[0031] The generation unit generates questions based on the analysis performed by the analysis unit. The generation unit uses, for example, a generation AI to generate appropriate questions based on the analysis. The generation AI uses natural language generation technology to automatically generate appropriate questions from the analysis results. Specifically, the generation AI generates questions related to the progress and agenda of the meeting based on the data provided by the analysis unit. For example, if a sales data presentation is taking place, the generation AI will generate a specific question such as, "How much year-on-year can we expect to gain from this project?" Also, if a discussion is taking place regarding project priorities, the generation AI will generate a question such as, "What is the priority of this project compared to other projects?" The generation AI can refer to past meeting data and related documents to generate more specific and useful questions. Furthermore, the generation unit can evaluate the appropriateness of the generated questions and make corrections or additions as needed. This allows the generation unit to provide appropriate questions to support the progress of the meeting and improve the quality of the discussion.

[0032] The output unit outputs the questions generated by the generation unit in real time. The output unit outputs the questions in real time to a spreadsheet, such as Google Sheets, for user review. Specifically, the output unit displays the generated questions in a visually easy-to-understand format so that users can easily review them. For example, it can automatically input questions generated during a meeting into Google Sheets in real time, making them shareable with all participants. The output unit also has a function to read the generated questions aloud, which helps to smooth the meeting. Furthermore, the output unit can collect answers to the generated questions and monitor the progress of the discussion in real time. For example, when a participant enters an answer to a question, the content is automatically recorded and used as reference information for moving on to the next agenda item. This allows the output unit to quickly and effectively output generated questions and provide them in a user-friendly format. The output unit plays a crucial role in supporting the progress of meetings and facilitating efficient decision-making.

[0033] The analysis unit can analyze the materials and audio of online meetings. For example, the analysis unit can analyze the presentation materials and audio recordings of online meetings and understand their content. The analysis unit uses generative AI to understand the content of the materials and audio. This allows for an accurate understanding of the meeting content by analyzing the materials and audio of online meetings. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input the materials and audio of the online meeting into the generative AI, which can then analyze the content of the materials and audio.

[0034] The generation unit can generate questions based on the analyzed content. For example, the generation unit uses a generation AI to generate appropriate questions based on the analyzed content. The generation unit generates questions such as, "How much can we expect to gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" By generating questions based on the analyzed content, it can provide appropriate questions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the analyzed content into a generation AI, and the generation AI can generate questions.

[0035] The output unit can output the generated questions in real time to Google Sheets or other spreadsheet applications. For example, the output unit can output the generated questions to Google Sheets in real time so that the user can review them. The output unit can also output the generated questions to other spreadsheet applications (e.g., Excel or LibreOffice Calc) in real time. This allows the user to review the generated questions immediately by outputting them in real time. Some or all of the above processing in the output unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the output unit can input the generated questions into a generation AI, which can then output them in real time.

[0036] The generation unit can list anticipated questions in advance and generate questions for developing countermeasures. For example, the generation unit uses a generation AI to list anticipated questions in advance based on past meeting data and general question lists. The generation unit then generates questions for developing countermeasures based on the listed questions. This streamlines meeting preparation by identifying anticipated questions in advance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past meeting data into a generation AI, which can then list anticipated questions in advance.

[0037] The generation unit can identify concerns early in a discussion and generate questions that support quick decision-making. For example, the generation unit uses a generation AI to identify concerns early in a discussion. Based on the identified concerns, the generation unit generates questions that support quick decision-making. This enables quick decision-making by identifying concerns early. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the content of the discussion into a generation AI, which can then identify concerns and generate questions.

[0038] The analysis unit can improve the accuracy of its analysis by referring to past meeting data when analyzing meeting materials and audio. For example, the analysis unit can improve the accuracy of its analysis by extracting information on similar topics based on past meeting data. The analysis unit can refer to questions and answers from past meetings and reflect them in the analysis of the current meeting. The analysis unit can analyze the speaking patterns of participants from past meetings and utilize this in the analysis of the current meeting. In this way, the accuracy of the analysis is improved by referring to past meeting data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past meeting data into a generative AI, which can then improve the accuracy of the analysis.

[0039] The analysis unit can update its analysis content in real time according to the progress of the meeting. For example, the analysis unit's generating AI analyzes the materials and audio in real time in accordance with the progress of the meeting, providing the latest information. If new materials are added during the meeting, the generating AI immediately performs the analysis and updates the content. As the meeting progresses, the generating AI extracts important points in real time and updates the analysis content. In this way, the analysis unit can provide the latest information by updating the analysis content according to the progress of the meeting. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the progress of the meeting into the generating AI, and the generating AI can update the analysis content in real time.

[0040] The analysis unit can perform analysis while considering attribute information such as the age, position, and field of expertise of the meeting participants. For example, the analysis unit can use the generating AI to adjust the analysis content based on the participants' fields of expertise. The analysis unit can use the generating AI to highlight important information according to the participants' positions. The analysis unit can use the generating AI to customize the analysis content based on the participants' past speaking history. This allows for the provision of more appropriate analysis results by considering the attribute information of the meeting participants. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input participant attribute information into the generating AI, and the generating AI can adjust the analysis content.

[0041] The analysis unit can apply different analysis algorithms based on the meeting topic during analysis. For example, if the topic is technical, the generation AI will apply a specialized analysis algorithm. If the topic is business strategy, the generation AI will apply a strategic analysis algorithm. If the topic is human resources, the generation AI will apply a human resources-related analysis algorithm. By applying different analysis algorithms based on the meeting topic, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the analysis unit can input the meeting topic into the generation AI, and the generation AI can apply an appropriate analysis algorithm.

[0042] The generation unit can update the question content in real time according to the progress of the meeting when generating questions. For example, the generation unit's AI generates questions and updates the content in real time in accordance with the progress of the meeting. If a new agenda item is added during the meeting, the generation unit's AI immediately generates questions and updates the content. As the meeting progresses, the generation unit's AI extracts important points in real time and updates the question content. In this way, the latest information can be provided by updating the question content according to the progress of the meeting. Some or all of the above processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the progress of the meeting into the generation AI, and the generation AI can update the question content in real time.

[0043] The generation unit can generate appropriate questions by referring to past meeting data when generating questions. For example, the generation unit can generate questions on similar topics based on past meeting data. The generation unit refers to questions and answers from past meetings and reflects them in the questions for the current meeting. The generation unit analyzes the speaking patterns of participants in past meetings and utilizes this in the questions for the current meeting. In this way, the optimal questions can be generated by referring to past meeting data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past meeting data into a generation AI, and the generation AI can generate appropriate questions.

[0044] The generation unit can generate questions while considering attribute information such as the age, job title, and area of ​​expertise of the meeting participants. For example, the generation unit can use the generating AI to generate specialized questions based on the participants' areas of expertise. The generation unit can use the generating AI to generate important questions according to the participants' job titles. The generation unit can use the generating AI to generate customized questions based on the participants' past speaking history. This allows for the provision of more appropriate questions by considering the attribute information of the meeting participants. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the generation unit can input participant attribute information into the generating AI, and the generating AI can generate questions.

[0045] The generation unit can apply different question generation algorithms based on the meeting theme when generating questions. For example, if the theme is technical, the generation AI will apply a specialized question generation algorithm. If the theme is business strategy, the generation AI will apply a strategic question generation algorithm. If the theme is human resources, the generation AI will apply a human resources-related question generation algorithm. By applying different question generation algorithms based on the meeting theme, more appropriate questions can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can input the meeting theme into the generation AI, and the generation AI can apply an appropriate question generation algorithm.

[0046] The output unit can update its output content in real time according to the progress of the meeting. For example, the output unit can have a generating AI output questions in real time and update the content in accordance with the progress of the meeting. If a new agenda item is added during the meeting, the generating AI will immediately output questions and update the content. As the meeting progresses, the generating AI will extract important points in real time and update the output content. In this way, the output unit can provide the latest information by updating the output content according to the progress of the meeting. Some or all of the above processing in the output unit may be performed using a generating AI or not. For example, the output unit can input the progress of the meeting into the generating AI, and the generating AI can update the output content in real time.

[0047] The output unit can select the optimal output method by referring to past meeting data during output. For example, the output unit can output questions on similar topics based on past meeting data. The output unit can refer to questions and answers from past meetings and reflect them in the questions for the current meeting. The output unit can analyze the speaking patterns of participants from past meetings and utilize this for the questions in the current meeting. In this way, the optimal output method can be selected by referring to past meeting data. Some or all of the above processing in the output unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the output unit can input past meeting data into a generation AI, and the generation AI can select the optimal output method.

[0048] The output unit can consider attribute information such as the age, position, and field of expertise of meeting participants when outputting. For example, the output unit can use a generating AI to output specialized questions based on the participants' fields of expertise. The output unit can use a generating AI to output important questions according to the participants' positions. The output unit can use a generating AI to output customized questions based on the participants' past speaking history. This allows for the provision of more appropriate information by considering the attribute information of meeting participants. Some or all of the above processing in the output unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the output unit can input participant attribute information into a generating AI, and the generating AI can output questions.

[0049] The output unit can apply different output methods based on the meeting theme when outputting. For example, if the theme is technical, the generating AI will apply a specialized question output method. If the theme is business strategy, the generating AI will apply a strategic question output method. If the theme is human resources, the generating AI will apply a human resources-related question output method. By applying different output methods based on the meeting theme, more appropriate information can be provided. Some or all of the above processing in the output unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the output unit can input the meeting theme into the generating AI, and the generating AI can apply an appropriate output method.

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

[0051] The online meeting support system can also include a translation unit that translates user comments in real time, facilitating communication between participants who speak different languages. For example, the translation unit could translate comments made in English into Japanese and display them in real time. This allows participants who speak different languages ​​to participate in the same meeting without experiencing language barriers, enabling smooth discussion. The translation unit can translate comments sequentially as the meeting progresses and provide them to participants. Furthermore, the translation unit can translate meeting materials and provide them in an easily understandable format. This is expected to facilitate the smooth running of international and multilingual meetings.

[0052] Online meeting support systems can also include a recording function that automatically records the meeting proceedings for later reference. The recording function automatically saves meeting audio and materials and provides them in a searchable format. For example, it can automatically tag important points and decisions from the meeting for easy later searching. This makes it easier to review meeting content or refer to past meetings. The recording function can update the record in real time as the meeting progresses, allowing participants to quickly obtain the information they need. The recording function can also summarize the meeting content and provide it to participants. This improves meeting efficiency and prevents important information from being missed.

[0053] Online meeting support systems can also include a timekeeping unit that automatically measures the speaking time of meeting participants, promoting balanced discussion. The timekeeping unit measures each participant's speaking time in real time and issues an alert if a certain time limit is exceeded. This prevents some participants from speaking for extended periods, ensuring everyone gets an equal chance to speak. The timekeeping unit can adjust speaking times according to the progress of the meeting, supporting balanced discussion. Furthermore, the timekeeping unit can predict the meeting's end time, enabling efficient time management. This results in smoother meeting progress and more effective use of time.

[0054] Online meeting support systems can also include a summarization function that automatically summarizes participants' comments and extracts key points. This function analyzes meeting audio and materials, automatically extracting important information. For example, it can summarize meeting decisions and action items and provide them to participants. This allows for a concise understanding of the meeting content and is convenient for later reference. The summarization function can update the summary in real time as the meeting progresses, allowing participants to quickly obtain the information they need. Furthermore, the summarization function can provide a summary after the meeting, helping participants review the meeting content. This improves meeting efficiency and prevents important information from being missed.

[0055] The online meeting support system can also include a classification unit that automatically categorizes and organizes participants' comments by topic. This classification unit analyzes the meeting audio and materials, automatically classifying the comments. For example, it organizes comments by topic, providing them in a format that is easy for participants to refer to later. This efficiently organizes the meeting content, making it easy to find necessary information later. The classification unit can classify comments in real time as the meeting progresses, allowing participants to quickly obtain the information they need. Furthermore, the classification unit provides the classification results after the meeting, helping participants review the meeting content. This improves meeting efficiency and prevents important information from being overlooked.

[0056] Online meeting support systems can also include an evaluation unit that automatically assesses and provides feedback on participants' contributions. This evaluation unit analyzes meeting audio and materials to automatically evaluate the content of contributions. For example, it can assess the logic and specificity of contributions and provide feedback to participants. This allows participants to improve their contributions and conduct more effective discussions. The evaluation unit can evaluate contributions in real time as the meeting progresses, ensuring participants receive necessary feedback immediately. Furthermore, the evaluation unit can provide evaluation results after the meeting, helping participants reflect on their contributions. This is expected to improve the quality of meetings and enhance participants' skills.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis unit analyzes the meeting materials and audio. The analysis unit analyzes, for example, presentation materials and meeting recordings to understand their content. The analysis unit uses generative AI to understand the content of the materials and audio. Step 2: The generation unit generates questions based on the analysis performed by the analysis unit. The generation unit generates appropriate questions based on the analysis, for example, using a generation AI. The generation unit generates questions such as, "How much can we expect to gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" Step 3: The output unit outputs the questions generated by the generation unit in real time. The output unit outputs the questions in real time to, for example, Google Sheets, so that users can review them. Users can review the generated questions and use the answers to advance the discussion.

[0059] (Example of form 2) An online meeting support system according to an embodiment of the present invention is a system that analyzes meeting materials and audio, generates optimal questions based on the content, and outputs them in real time. By analyzing meeting materials and audio, generating questions based on the content, and outputting them in real time, the online meeting support system improves the quality of discussions and enables efficient decision-making. For example, the online meeting support system analyzes meeting materials and audio. For example, it analyzes presentation materials and meeting recordings to understand their content. In this process, a generation AI is used to understand the content of the materials and audio. Next, the online meeting support system generates questions based on the analyzed content. The generation AI generates appropriate questions based on the analyzed content. For example, questions such as "How much can we expect to gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" are generated. The generated questions are output in real time. For example, they are output in real time to a Google Spreadsheet or similar, allowing the user to review them. The user reviews the generated questions and proceeds with the discussion based on the answers to those questions. This system improves the quality of discussions and enables efficient decision-making. For example, GQT can be used in advance to identify potential questions and prepare countermeasures. It can also help identify concerns early in the discussion, supporting quick decision-making. In this way, online meeting support systems can improve the quality of discussions and support efficient decision-making.

[0060] The online meeting support system according to this embodiment comprises an analysis unit, a generation unit, and an output unit. The analysis unit analyzes the meeting materials and audio. The analysis unit analyzes, for example, presentation materials and meeting recordings and understands their content. The analysis unit uses generation AI to understand the content of the materials and audio. The generation unit generates questions based on the content analyzed by the analysis unit. The generation unit uses, for example, generation AI to generate appropriate questions based on the analyzed content. The generation unit generates questions such as, for example, "How much can we gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" The output unit outputs the questions generated by the generation unit in real time. The output unit outputs the questions in real time to, for example, Google Sheets, so that the user can check them. The user can check the generated questions and proceed with the discussion based on the answers to those questions. As a result, the online meeting support system according to this embodiment can improve the quality of discussions and support efficient decision-making.

[0061] The analysis unit analyzes meeting materials and audio. For example, the analysis unit analyzes presentation materials and meeting recordings to understand their content. Specifically, the analysis unit uses generative AI to understand the content of the materials and audio. The generative AI utilizes natural language processing technology to analyze text, graphs, and charts in presentation materials and extract important points and keywords. In addition, for meeting recordings, speech recognition technology is used to transcribe the text and analyze its content. For example, speech recognition technology converts what the speaker says into text in real time and extracts the intent of the statement and important information. Furthermore, the analysis unit can also refer to past meeting data and related documents to evaluate the relevance to the current meeting content. This allows the analysis unit to comprehensively analyze meeting materials and audio to understand the progress of the meeting and important agenda items. The analysis unit provides these analysis results to the generation unit, which uses them as basic data to generate appropriate questions.

[0062] The generation unit generates questions based on the analysis performed by the analysis unit. The generation unit uses, for example, a generation AI to generate appropriate questions based on the analysis. The generation AI uses natural language generation technology to automatically generate appropriate questions from the analysis results. Specifically, the generation AI generates questions related to the progress and agenda of the meeting based on the data provided by the analysis unit. For example, if a sales data presentation is taking place, the generation AI will generate a specific question such as, "How much year-on-year can we expect to gain from this project?" Also, if a discussion is taking place regarding project priorities, the generation AI will generate a question such as, "What is the priority of this project compared to other projects?" The generation AI can refer to past meeting data and related documents to generate more specific and useful questions. Furthermore, the generation unit can evaluate the appropriateness of the generated questions and make corrections or additions as needed. This allows the generation unit to provide appropriate questions to support the progress of the meeting and improve the quality of the discussion.

[0063] The output unit outputs the questions generated by the generation unit in real time. The output unit outputs the questions in real time to a spreadsheet, such as Google Sheets, for user review. Specifically, the output unit displays the generated questions in a visually easy-to-understand format so that users can easily review them. For example, it can automatically input questions generated during a meeting into Google Sheets in real time, making them shareable with all participants. The output unit also has a function to read the generated questions aloud, which helps to smooth the meeting. Furthermore, the output unit can collect answers to the generated questions and monitor the progress of the discussion in real time. For example, when a participant enters an answer to a question, the content is automatically recorded and used as reference information for moving on to the next agenda item. This allows the output unit to quickly and effectively output generated questions and provide them in a user-friendly format. The output unit plays a crucial role in supporting the progress of meetings and facilitating efficient decision-making.

[0064] The analysis unit can analyze the materials and audio of online meetings. For example, the analysis unit can analyze the presentation materials and audio recordings of online meetings and understand their content. The analysis unit uses generative AI to understand the content of the materials and audio. This allows for an accurate understanding of the meeting content by analyzing the materials and audio of online meetings. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input the materials and audio of the online meeting into the generative AI, which can then analyze the content of the materials and audio.

[0065] The generation unit can generate questions based on the analyzed content. For example, the generation unit uses a generation AI to generate appropriate questions based on the analyzed content. The generation unit generates questions such as, "How much can we expect to gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" By generating questions based on the analyzed content, it can provide appropriate questions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the analyzed content into a generation AI, and the generation AI can generate questions.

[0066] The output unit can output the generated questions in real time to Google Sheets or other spreadsheet applications. For example, the output unit can output the generated questions to Google Sheets in real time so that the user can review them. The output unit can also output the generated questions to other spreadsheet applications (e.g., Excel or LibreOffice Calc) in real time. This allows the user to review the generated questions immediately by outputting them in real time. Some or all of the above processing in the output unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the output unit can input the generated questions into a generation AI, which can then output them in real time.

[0067] The generation unit can list anticipated questions in advance and generate questions for developing countermeasures. For example, the generation unit uses a generation AI to list anticipated questions in advance based on past meeting data and general question lists. The generation unit then generates questions for developing countermeasures based on the listed questions. This streamlines meeting preparation by identifying anticipated questions in advance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past meeting data into a generation AI, which can then list anticipated questions in advance.

[0068] The generation unit can identify concerns early in a discussion and generate questions that support quick decision-making. For example, the generation unit uses a generation AI to identify concerns early in a discussion. Based on the identified concerns, the generation unit generates questions that support quick decision-making. This enables quick decision-making by identifying concerns early. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the content of the discussion into a generation AI, which can then identify concerns and generate questions.

[0069] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is tense, the generation AI will improve the accuracy of the analysis and provide more detailed information. If the user is relaxed, the generation AI will adjust the accuracy of the analysis and provide only the minimum necessary information. If the user is in a hurry, the generation AI will prioritize the speed of the analysis and provide results quickly. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using the generation AI or not. For example, the analysis unit can input user emotion data into the generation AI, which will estimate the emotions and adjust the accuracy of the analysis.

[0070] The analysis unit can improve the accuracy of its analysis by referring to past meeting data when analyzing meeting materials and audio. For example, the analysis unit can improve the accuracy of its analysis by extracting information on similar topics based on past meeting data. The analysis unit can refer to questions and answers from past meetings and reflect them in the analysis of the current meeting. The analysis unit can analyze the speaking patterns of participants from past meetings and utilize this in the analysis of the current meeting. In this way, the accuracy of the analysis is improved by referring to past meeting data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past meeting data into a generative AI, which can then improve the accuracy of the analysis.

[0071] The analysis unit can update its analysis content in real time according to the progress of the meeting. For example, the analysis unit's generating AI analyzes the materials and audio in real time in accordance with the progress of the meeting, providing the latest information. If new materials are added during the meeting, the generating AI immediately performs the analysis and updates the content. As the meeting progresses, the generating AI extracts important points in real time and updates the analysis content. In this way, the analysis unit can provide the latest information by updating the analysis content according to the progress of the meeting. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the progress of the meeting into the generating AI, and the generating AI can update the analysis content in real time.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the generation AI provides a simple and highly visible display method. If the user is relaxed, the generation AI provides a display method that includes detailed information. If the user is in a hurry, the generation AI provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generation AI or not. For example, the analysis unit can input user emotion data into the generation AI, the generation AI can estimate the emotions, and the display method of the analysis results can be adjusted.

[0073] The analysis unit can perform analysis while considering attribute information such as the age, position, and field of expertise of the meeting participants. For example, the analysis unit can use the generating AI to adjust the analysis content based on the participants' fields of expertise. The analysis unit can use the generating AI to highlight important information according to the participants' positions. The analysis unit can use the generating AI to customize the analysis content based on the participants' past speaking history. This allows for the provision of more appropriate analysis results by considering the attribute information of the meeting participants. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input participant attribute information into the generating AI, and the generating AI can adjust the analysis content.

[0074] The analysis unit can apply different analysis algorithms based on the meeting topic during analysis. For example, if the topic is technical, the generation AI will apply a specialized analysis algorithm. If the topic is business strategy, the generation AI will apply a strategic analysis algorithm. If the topic is human resources, the generation AI will apply a human resources-related analysis algorithm. By applying different analysis algorithms based on the meeting topic, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the analysis unit can input the meeting topic into the generation AI, and the generation AI can apply an appropriate analysis algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the wording of the questions it generates based on the estimated emotions. For example, if the user is nervous, the generation AI will generate simple and clear questions. If the user is relaxed, the generation AI will generate detailed questions. If the user is in a hurry, the generation AI will generate questions that can be answered quickly. This allows for the provision of more appropriate questions by adjusting the wording of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and adjust the wording of the questions.

[0076] The generation unit can update the question content in real time according to the progress of the meeting when generating questions. For example, the generation unit's AI generates questions and updates the content in real time in accordance with the progress of the meeting. If a new agenda item is added during the meeting, the generation unit's AI immediately generates questions and updates the content. As the meeting progresses, the generation unit's AI extracts important points in real time and updates the question content. In this way, the latest information can be provided by updating the question content according to the progress of the meeting. Some or all of the above processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the progress of the meeting into the generation AI, and the generation AI can update the question content in real time.

[0077] The generation unit can generate appropriate questions by referring to past meeting data when generating questions. For example, the generation unit can generate questions on similar topics based on past meeting data. The generation unit refers to questions and answers from past meetings and reflects them in the questions for the current meeting. The generation unit analyzes the speaking patterns of participants in past meetings and utilizes this in the questions for the current meeting. In this way, the optimal questions can be generated by referring to past meeting data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past meeting data into a generation AI, and the generation AI can generate appropriate questions.

[0078] The generation unit can estimate the user's emotions and determine the priority of questions to generate based on the estimated emotions. For example, if the user is nervous, the generation AI will prioritize generating important questions. If the user is relaxed, the generation AI will prioritize generating detailed questions. If the user is in a hurry, the generation AI will prioritize generating questions that can be answered quickly. This allows for the provision of more appropriate questions by prioritizing questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and determine the priority of questions.

[0079] The generation unit can generate questions while considering attribute information such as the age, job title, and area of ​​expertise of the meeting participants. For example, the generation unit can use the generating AI to generate specialized questions based on the participants' areas of expertise. The generation unit can use the generating AI to generate important questions according to the participants' job titles. The generation unit can use the generating AI to generate customized questions based on the participants' past speaking history. This allows for the provision of more appropriate questions by considering the attribute information of the meeting participants. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the generation unit can input participant attribute information into the generating AI, and the generating AI can generate questions.

[0080] The generation unit can apply different question generation algorithms based on the meeting theme when generating questions. For example, if the theme is technical, the generation AI will apply a specialized question generation algorithm. If the theme is business strategy, the generation AI will apply a strategic question generation algorithm. If the theme is human resources, the generation AI will apply a human resources-related question generation algorithm. By applying different question generation algorithms based on the meeting theme, more appropriate questions can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can input the meeting theme into the generation AI, and the generation AI can apply an appropriate question generation algorithm.

[0081] The output unit can estimate the user's emotions and adjust how the questions are displayed based on the estimated emotions. For example, if the user is nervous, the generating AI provides a simple and highly visible display method. If the user is relaxed, the generating AI provides a display method that includes detailed information. If the user is in a hurry, the generating AI provides a display method that gets straight to the point. By adjusting how the questions are displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the output unit may be performed using the generating AI or not. For example, the output unit can input user emotion data into the generating AI, which can estimate the emotions and adjust how the questions are displayed.

[0082] The output unit can update its output content in real time according to the progress of the meeting. For example, the output unit can have a generating AI output questions in real time and update the content in accordance with the progress of the meeting. If a new agenda item is added during the meeting, the generating AI will immediately output questions and update the content. As the meeting progresses, the generating AI will extract important points in real time and update the output content. In this way, the output unit can provide the latest information by updating the output content according to the progress of the meeting. Some or all of the above processing in the output unit may be performed using a generating AI or not. For example, the output unit can input the progress of the meeting into the generating AI, and the generating AI can update the output content in real time.

[0083] The output unit can select the optimal output method by referring to past meeting data during output. For example, the output unit can output questions on similar topics based on past meeting data. The output unit can refer to questions and answers from past meetings and reflect them in the questions for the current meeting. The output unit can analyze the speaking patterns of participants from past meetings and utilize this for the questions in the current meeting. In this way, the optimal output method can be selected by referring to past meeting data. Some or all of the above processing in the output unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the output unit can input past meeting data into a generation AI, and the generation AI can select the optimal output method.

[0084] The output unit can estimate the user's emotions and determine the priority of questions to output based on the estimated emotions. For example, if the user is nervous, the output unit's generating AI will prioritize outputting important questions. If the user is relaxed, the output unit's generating AI will prioritize outputting detailed questions. If the user is in a hurry, the output unit's generating AI will prioritize outputting questions that can be answered quickly. This allows for the provision of more appropriate information by prioritizing questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using or without a generating AI. For example, the output unit can input user emotion data into a generating AI, which can estimate the emotions and determine the priority of questions.

[0085] The output unit can consider attribute information such as the age, position, and field of expertise of meeting participants when outputting. For example, the output unit can use a generating AI to output specialized questions based on the participants' fields of expertise. The output unit can use a generating AI to output important questions according to the participants' positions. The output unit can use a generating AI to output customized questions based on the participants' past speaking history. This allows for the provision of more appropriate information by considering the attribute information of meeting participants. Some or all of the above processing in the output unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the output unit can input participant attribute information into a generating AI, and the generating AI can output questions.

[0086] The output unit can apply different output methods based on the meeting theme when outputting. For example, if the theme is technical, the generating AI will apply a specialized question output method. If the theme is business strategy, the generating AI will apply a strategic question output method. If the theme is human resources, the generating AI will apply a human resources-related question output method. By applying different output methods based on the meeting theme, more appropriate information can be provided. Some or all of the above processing in the output unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the output unit can input the meeting theme into the generating AI, and the generating AI can apply an appropriate output method.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The online meeting support system can also include a translation unit that translates user comments in real time, facilitating communication between participants who speak different languages. For example, the translation unit could translate comments made in English into Japanese and display them in real time. This allows participants who speak different languages ​​to participate in the same meeting without experiencing language barriers, enabling smooth discussion. The translation unit can translate comments sequentially as the meeting progresses and provide them to participants. Furthermore, the translation unit can translate meeting materials and provide them in an easily understandable format. This is expected to facilitate the smooth running of international and multilingual meetings.

[0089] Online meeting support systems can also include a recording function that automatically records the meeting proceedings for later reference. The recording function automatically saves meeting audio and materials and provides them in a searchable format. For example, it can automatically tag important points and decisions from the meeting for easy later searching. This makes it easier to review meeting content or refer to past meetings. The recording function can update the record in real time as the meeting progresses, allowing participants to quickly obtain the information they need. The recording function can also summarize the meeting content and provide it to participants. This improves meeting efficiency and prevents important information from being missed.

[0090] Online meeting support systems can also include a timekeeping unit that automatically measures the speaking time of meeting participants, promoting balanced discussion. The timekeeping unit measures each participant's speaking time in real time and issues an alert if a certain time limit is exceeded. This prevents some participants from speaking for extended periods, ensuring everyone gets an equal chance to speak. The timekeeping unit can adjust speaking times according to the progress of the meeting, supporting balanced discussion. Furthermore, the timekeeping unit can predict the meeting's end time, enabling efficient time management. This results in smoother meeting progress and more effective use of time.

[0091] The online meeting support system can also be equipped with an emotion analysis unit that analyzes the facial expressions and tone of voice of meeting participants to estimate their emotions. For example, if a participant is nervous or excited, the emotion analysis unit can estimate their emotions in real time and notify other participants. This allows for understanding the emotional state of participants during the meeting and taking appropriate action. The emotion analysis unit can continuously analyze participants' emotions as the meeting progresses and provide feedback as needed. Furthermore, the emotion analysis unit can analyze changes in emotions after the meeting to evaluate its effectiveness. This is expected to improve the quality of meetings and increase participant satisfaction.

[0092] Online meeting support systems can also include a summarization function that automatically summarizes participants' comments and extracts key points. This function analyzes meeting audio and materials, automatically extracting important information. For example, it can summarize meeting decisions and action items and provide them to participants. This allows for a concise understanding of the meeting content and is convenient for later reference. The summarization function can update the summary in real time as the meeting progresses, allowing participants to quickly obtain the information they need. Furthermore, the summarization function can provide a summary after the meeting, helping participants review the meeting content. This improves meeting efficiency and prevents important information from being missed.

[0093] The online meeting support system can also include a progress adjustment unit that estimates the emotions of meeting participants and adjusts the meeting's progress based on those estimates. For example, the progress adjustment unit might suggest a break if a participant is tired, or suggest ways to calm the discussion if a participant is agitated. This can lead to a smoother meeting and reduce participant stress. The progress adjustment unit can continuously analyze participants' emotions as the meeting progresses and adjust the process as needed. Furthermore, the progress adjustment unit can analyze changes in emotions after the meeting and incorporate those findings into the next meeting. This is expected to improve the quality of meetings and increase participant satisfaction.

[0094] The online meeting support system can also include a classification unit that automatically categorizes and organizes participants' comments by topic. This classification unit analyzes the meeting audio and materials, automatically classifying the comments. For example, it organizes comments by topic, providing them in a format that is easy for participants to refer to later. This efficiently organizes the meeting content, making it easy to find necessary information later. The classification unit can classify comments in real time as the meeting progresses, allowing participants to quickly obtain the information they need. Furthermore, the classification unit provides the classification results after the meeting, helping participants review the meeting content. This improves meeting efficiency and prevents important information from being overlooked.

[0095] The online meeting support system may also include an order adjustment unit that estimates the emotions of meeting participants and adjusts the order of speaking based on those emotions. For example, the order adjustment unit might postpone a participant's speaking time if they are nervous, or encourage them to speak if they are relaxed. This can lead to a smoother meeting and provide an environment where participants feel comfortable speaking. The order adjustment unit can continuously analyze participants' emotions as the meeting progresses and adjust the speaking order as needed. Furthermore, the order adjustment unit can analyze changes in emotions after the meeting and reflect these changes in the flow of the next meeting. This is expected to improve the quality of meetings and increase participant satisfaction.

[0096] Online meeting support systems can also include an evaluation unit that automatically assesses and provides feedback on participants' contributions. This evaluation unit analyzes meeting audio and materials to automatically evaluate the content of contributions. For example, it can assess the logic and specificity of contributions and provide feedback to participants. This allows participants to improve their contributions and conduct more effective discussions. The evaluation unit can evaluate contributions in real time as the meeting progresses, ensuring participants receive necessary feedback immediately. Furthermore, the evaluation unit can provide evaluation results after the meeting, helping participants reflect on their contributions. This is expected to improve the quality of meetings and enhance participants' skills.

[0097] The online meeting support system may also include an end-of-meeting adjustment unit that estimates the emotions of meeting participants and adjusts the meeting's end time based on the estimated emotions. For example, the end-of-meeting adjustment unit might end the meeting early if participants are tired, or extend the meeting to continue the discussion if participants are excited. This can lead to smoother meeting progress and reduced stress for participants. The end-of-meeting adjustment unit can continuously analyze participants' emotions as the meeting progresses and adjust the end time as needed. Furthermore, the end-of-meeting adjustment unit can analyze changes in emotions after the meeting and reflect these changes in the progress of the next meeting. This is expected to improve the quality of meetings and increase participant satisfaction.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The analysis unit analyzes the meeting materials and audio. The analysis unit analyzes, for example, presentation materials and meeting recordings to understand their content. The analysis unit uses generative AI to understand the content of the materials and audio. Step 2: The generation unit generates questions based on the analysis performed by the analysis unit. The generation unit generates appropriate questions based on the analysis, for example, using a generation AI. The generation unit generates questions such as, "How much can we expect to gain from this project compared to the previous year?" or "What is the priority of this project compared to other projects?" Step 3: The output unit outputs the questions generated by the generation unit in real time. The output unit outputs the questions in real time to, for example, Google Sheets, so that users can review them. Users can review the generated questions and use the answers to advance the discussion.

[0100] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the smart device 14. The output unit is implemented by the output device 40 of the smart device 14. For example, it can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the smart glasses 214. The output unit is implemented by the speaker 240 of the smart glasses 214. For example, it can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the headset terminal 314. The output unit is implemented by the display 343 of the headset terminal 314. For example, it can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it can also be implemented by the control unit 46A of the robot 414. The output unit is implemented by the speaker 240 of the robot 414. For example, it can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The analysis unit analyzes the meeting materials and audio, A generation unit that generates questions based on the content analyzed by the analysis unit, An output unit that outputs the questions generated by the generation unit in real time, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the materials and audio from online meetings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate questions based on the analyzed content. The system described in Appendix 1, characterized by the features described herein. (Note 4) The output unit is, Output the generated questions to Google Sheets or another spreadsheet application in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is List anticipated questions in advance and generate questions to prepare countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Identify concerns early in the discussion and generate questions that support quick decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing meeting materials and audio, we improve the accuracy of the analysis by referring to past meeting data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the analysis results are updated in real time according to the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, attribute information such as the age, job title, and area of ​​expertise of the meeting participants will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied based on the meeting's theme. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the wording of questions generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating questions, the content of the questions is updated in real time according to the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating questions, the system references past meeting data to generate appropriate questions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines the priority of questions to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating questions, the system takes into account attribute information of meeting participants, such as age, job title, and area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating questions, apply different question generation algorithms based on the meeting theme. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, We estimate the user's emotions and adjust how questions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, During output, the output content is updated in real time according to the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When outputting data, the system will refer to past meeting data to select the most suitable output method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates the user's emotions and determines the priority of questions to output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When outputting data, attribute information such as the age, job title, and area of ​​expertise of the meeting participants is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, When outputting, apply different output methods based on the meeting theme. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes the meeting materials and audio, A generation unit that generates questions based on the content analyzed by the analysis unit, An output unit that outputs the questions generated by the generation unit in real time, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the materials and audio from online meetings. The system according to feature 1.

3. The generating unit is Generate questions based on the analyzed content. The system according to feature 1.

4. The output unit is, The generated questions are output to a spreadsheet application in real time. The system according to feature 1.

5. The generating unit is List anticipated questions in advance and generate questions to prepare countermeasures. The system according to feature 1.

6. The generating unit is Identify concerns early in the discussion and generate questions that support quick decision-making. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing meeting materials and audio, we improve the accuracy of the analysis by referring to past meeting data. The system according to feature 1.

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