system

Abstract

The system according to this embodiment aims to reflect everyone's opinions in remote meetings and improve the sense of participation. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, an input unit, and an aggregation unit. The analysis unit analyzes the content of conversations. The generation unit generates questionnaires based on the content analyzed by the analysis unit. The input unit receives comments from participants. The aggregation unit aggregates and displays the comments received by the input unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 prior art, there was a problem that it was difficult to feel that all members were participating in a remote meeting and it was difficult to grasp the opinions of participants who did not speak.

[0005] The system according to the embodiment aims to reflect the opinions of all members and improve the sense of participation in a remote meeting.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, an input unit, and an aggregation unit. The analysis unit analyzes the content of conversations. The generation unit generates questionnaires based on the content analyzed by the analysis unit. The input unit receives comments from participants. The aggregation unit aggregates and displays the comments received by the input unit. [Effects of the Invention]

[0007] The system according to this embodiment can reflect everyone's opinions in a remote meeting and improve the sense of participation. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) The remote conferencing system according to an embodiment of the present invention is a mechanism for enabling all participants to take part. This remote conferencing system includes a function to analyze conversation content and generate questionnaires, and a function to aggregate and display participant comments. First, as a conversation content questionnaire function, the AI ​​analyzes the conversation content and creates a questionnaire. By having participants answer, the speaker can understand the reactions of the participants. Next, as a participant comment aggregation and display function, a field is provided where participants can enter comments on the conversation in progress, and the AI ​​aggregates the comments and displays them on the screen. This makes it possible to convey opinions even without speaking, and the intentions of all participants can be reflected in the meeting. For example, as a conversation content questionnaire function, the AI ​​analyzes the conversation content and generates an appropriate questionnaire. For example, a questionnaire such as "What do you think about this proposal?" can be generated during the meeting, and participants can answer in real time. Next, as a participant comment aggregation and display function, a field is provided where participants can enter comments on the conversation in progress. If a participant enters a comment such as "I would like to know more about this part," the AI ​​aggregates it and displays it to the speaker. This allows the speaker to understand the interests and questions of the participants and to enrich the content of the meeting. These features prevent situations where only those speaking participate in the meeting, ensuring that everyone's opinions are reflected. Even in remote meetings, everyone can actively participate and share their opinions, improving the quality of the meeting and leading to greater satisfaction for all participants. Thus, remote meeting systems can enable fully-participating remote meetings.

[0029] The remote conferencing system according to this embodiment comprises an analysis unit, a generation unit, an input unit, and an aggregation unit. The analysis unit analyzes the content of the conversation. For example, the analysis unit analyzes the content of the conversation and generates an appropriate questionnaire. The generation unit generates a questionnaire based on the content analyzed by the analysis unit. For example, the generation unit provides the generated questionnaire to the participants. The input unit receives comments from participants. For example, the input unit receives comments from participants in real time. The aggregation unit aggregates and displays the comments received by the input unit. For example, the aggregation unit aggregates the received comments and displays them to the speaker. As a result, the remote conferencing system can realize a remote conferencing in which everyone can participate by analyzing the content of the conversation, generating a questionnaire, and receiving, aggregating, and displaying comments.

[0030] The analytics department analyzes the content of conversations. Specifically, it analyzes conversations in real time and extracts important keywords and topics. This includes using natural language processing technology to analyze the intent and emotions of what is said. For example, it acquires conversations during meetings as text data and analyzes the content using AI. The AI ​​extracts important keywords from the conversations and grasps the flow of the conversation and the focus of the discussion. It can also evaluate the atmosphere of the meeting and the reactions of the participants by analyzing the emotions of the speakers. This allows the analytics department to analyze conversations in detail and accurately grasp the progress of the meeting and the opinions of the participants. Furthermore, based on past meeting data, the analytics department can analyze trends and patterns and propose ways to improve future meetings. For example, it can extract topics that were frequently discussed in past meetings and proposals that received positive responses from participants and use them to set the agenda for the next meeting. In this way, the analytics department can provide valuable information to improve the quality of meetings.

[0031] The generation unit generates questionnaires based on the analysis performed by the analysis unit. Specifically, it creates appropriate questions for participants based on keywords and topics extracted by the analysis unit. For example, it generates questionnaires to collect opinions on important topics discussed during a meeting. The generation unit uses AI to automatically create effective questions that elicit participants' interests and opinions. The AI ​​can leverage past questionnaire data and participant profile information to generate questions optimized for each individual participant. This allows the generation unit to efficiently collect participant opinions and maximize the outcome of the meeting. Furthermore, the generation unit also provides an interface for providing the generated questionnaires to participants. For example, it can automatically send questionnaires after the meeting to allow participants to easily answer them. It also has a function to monitor the questionnaire response status in real time and send reminders to participants who have not yet responded. This allows the generation unit to improve the questionnaire response rate and collect more opinions.

[0032] The input section receives comments from participants. Specifically, it provides an interface that allows participants to enter comments in real time during the meeting. For example, participants can freely post opinions and questions through a chat window or comment box. The input section receives these comments immediately and processes them within the system. Furthermore, the input section has the ability to analyze the content of comments and automatically classify important opinions and questions. Using AI, it analyzes the content of comments and classifies them based on relevant topics and keywords. This allows the input section to efficiently collect participants' opinions and use them to facilitate the meeting. The input section also has a comment filtering function that can automatically remove inappropriate content and spam. This allows the input section to provide an environment where participants can post comments with confidence, thereby improving the quality of the meeting.

[0033] The aggregation unit aggregates and displays comments received by the input unit. Specifically, it aggregates received comments in real time and provides an interface for displaying them to speakers and other participants. For example, it displays a list of comments posted during the meeting and highlights important opinions and questions. It also has a function to analyze the content of comments and group them by related topics. This allows the aggregation unit to efficiently organize participants' opinions and support the progress of the meeting. Furthermore, the aggregation unit also has a function to visualize the aggregated results of comments. For example, it can display the number and content of comments in graphs and charts, allowing participants to grasp the progress of the meeting and their reactions at a glance. This allows the aggregation unit to effectively share the results of the meeting and clarify areas for improvement in the next meeting. The aggregation unit also has a function to output the aggregated results of comments as a report after the meeting ends. This allows for the easy creation and sharing of meeting records with participants. This allows the aggregation unit to increase the transparency of the meeting and provide an environment where all participants can share their opinions.

[0034] The analysis unit can analyze the conversation content and generate an appropriate questionnaire. For example, the analysis unit can analyze the conversation content and generate an appropriate questionnaire. This allows for understanding participants' reactions by analyzing the conversation content and generating an appropriate questionnaire. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the conversation content into a generation AI and have the generation AI generate an appropriate questionnaire.

[0035] The generation unit can provide the generated questionnaire to the participants. For example, the generation unit can provide the generated questionnaire to the participants. For example, the generation unit can provide the generated questionnaire to the participants. By providing the generated questionnaire to the participants, the participants can respond in real time. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the generated questionnaire into a generation AI and have the generation AI perform the processing of providing it to the participants.

[0036] The input unit can receive comments from participants in real time. For example, the input unit can receive comments from participants in real time. For example, the input unit can receive comments from participants in real time. This allows for smooth exchange of opinions during the meeting by receiving comments from participants in real time. Some or all of the above processing in the input unit may be performed using a generation AI, for example, or without a generation AI. For example, the input unit can input participants' comments into a generation AI and have the generation AI perform the processing of receiving them in real time.

[0037] The aggregation unit can aggregate the received comments and display them to the speaker. For example, the aggregation unit can aggregate the received comments and display them to the speaker. For example, the aggregation unit can aggregate the received comments and display them to the speaker. This allows the speaker to understand the opinions of all participants by aggregating and displaying the received comments. Some or all of the above processing in the aggregation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the aggregation unit can input the received comments into a generation AI and have the generation AI perform the process of aggregating and displaying them.

[0038] The analysis unit can improve the accuracy of its analysis by referring to past meeting data when analyzing conversation content. For example, the analysis unit can refer to the content of statements made in past meetings and compare it with the current conversation content to improve the accuracy of its analysis. For example, the analysis unit can refer to the results of questionnaires from past meetings and generate questionnaires that are appropriate for the current conversation content. For example, the analysis unit can analyze the reactions of participants in past meetings and predict the reactions of participants to the current conversation content. 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, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input past meeting data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the topic of the conversation when analyzing the content of the conversation. For example, in the case of a technical topic, the analysis unit applies an analysis algorithm that includes technical terms. For example, in the case of a business topic, the analysis unit applies an analysis algorithm that takes into account business terms and market trends. For example, in the case of an educational topic, the analysis unit applies an analysis algorithm that takes into account educational terms and learning theories. By applying different analysis algorithms depending on the topic of the conversation, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input conversation topic data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0040] The analysis unit can perform analysis of conversation content while considering the attribute information of the conversation participants. For example, the analysis unit can adjust the frequency of use of technical terms according to the occupation and position of the participants. For example, the analysis unit can select words that are easy to understand according to the age group of the participants. For example, the analysis unit can select appropriate expressions according to the cultural background of the participants. By considering the attribute information of the conversation participants, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the attribute information of the participants into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0041] The analysis unit can update the analysis results in real time according to the progress of the conversation when analyzing the content of the conversation. For example, as the conversation progresses, the analysis unit reflects new information in the analysis and updates the questionnaire in real time. For example, if an important point comes up during the conversation, the analysis unit generates a questionnaire that emphasizes that point. For example, the analysis unit analyzes the participants' responses in real time according to the progress of the conversation and generates an appropriate questionnaire. In this way, the latest information is reflected by updating the analysis results in real time according to the progress of the conversation. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input conversation progress data into a generative AI and have the generative AI perform real-time updates of the analysis results.

[0042] The generation unit can adjust the level of detail in a questionnaire based on the importance of the conversation when generating the questionnaire. For example, if the topic is important, the generation unit will generate a questionnaire with detailed questions. For example, if the topic is general, the generation unit will generate a questionnaire with concise questions. For example, if the topic is urgent, the generation unit will generate a questionnaire that can be answered quickly. In this way, an appropriate questionnaire can be generated by adjusting the level of detail in the questionnaire based on the importance of the conversation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the questionnaire.

[0043] The generation unit can apply different questionnaire formats depending on the category of the conversation when generating questionnaires. For example, in the case of a technical topic, the generation unit generates a questionnaire that includes technical terms. For example, in the case of a business topic, the generation unit generates a questionnaire that takes business terms and market trends into consideration. For example, in the case of an educational topic, the generation unit generates a questionnaire that takes educational terms and learning theories into consideration. In this way, by applying different questionnaire formats depending on the category of the conversation, an appropriate questionnaire can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation category data into a generation AI and have the generation AI perform the application of different questionnaire formats.

[0044] The generation unit can determine the priority of questionnaires based on the progress of the conversation when generating questionnaires. For example, the generation unit will prioritize generating questionnaires for important points in the conversation. For example, the generation unit will generate questionnaires at appropriate times according to the progress of the conversation. For example, if an important question is asked in the middle of the conversation, the generation unit will prioritize generating a questionnaire for that question. In this way, by determining the priority of questionnaires based on the progress of the conversation, questionnaires can be generated at appropriate times. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation progress data into the generation AI and have the generation AI perform the determination of questionnaire priorities.

[0045] The generation unit can adjust the order of questions based on the relevance of the conversation when generating the questionnaire. For example, the generation unit prioritizes placing highly relevant questions in line with the flow of the conversation. For example, the generation unit places questions in an appropriate order according to the topic of the conversation. For example, the generation unit adjusts the order of questions in real time according to the progress of the conversation. In this way, by adjusting the order of questions based on the relevance of the conversation, the questionnaire can be generated in an appropriate order. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation relevance data into a generation AI and have the generation AI perform the adjustment of the questionnaire order.

[0046] The input unit can select the optimal input method by referring to past comment history when a comment is entered. For example, the input unit may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the input unit may suggest an input method for a specific topic based on the user's past comment history. For example, the input unit may analyze the user's past input history and suggest the most efficient input method. In this way, the optimal input method can be provided by referring to past comment history. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input past comment history data into a generative AI and have the generative AI select the optimal input method.

[0047] The input unit can adjust the timing of comment input according to the progress of the conversation. For example, the input unit prompts for comment input at the appropriate time for important points in the conversation. For example, the input unit adjusts the timing of comment input in real time according to the progress of the conversation. For example, if an important question is asked in the middle of the conversation, the input unit prioritizes prompting for comment input in response to that question. In this way, by adjusting the timing of input according to the progress of the conversation, comments can be entered at the appropriate time. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input conversation progress data into a generative AI and have the generative AI perform the adjustment of the timing of input.

[0048] The input unit can input comments while considering the attribute information of the conversation participants. For example, the input unit can suggest an appropriate comment input method according to the participant's occupation or position. For example, the input unit can suggest an easy-to-understand input method according to the participant's age group. For example, the input unit can select appropriate expressions according to the participant's cultural background. In this way, by considering the attribute information of the conversation participants, an appropriate comment input method can be provided. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input participant attribute information data into a generative AI and have the generative AI select an input method.

[0049] The input unit can change the input format depending on the conversation topic when comments are entered. For example, for technical topics, the input unit provides an input format that includes technical terms. For example, for business topics, the input unit provides an input format that takes business terms and market trends into consideration. For example, for educational topics, the input unit provides an input format that takes educational terms and learning theories into consideration. In this way, by changing the input format according to the conversation topic, an appropriate method of entering comments can be provided. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input unit can input conversation topic data into a generative AI and have the generative AI perform the change in the input format.

[0050] The aggregation unit can improve the accuracy of aggregation by referring to past comment data when aggregating comments. For example, the aggregation unit can refer to comment data from past meetings and compare it with current comments to improve the accuracy of aggregation. For example, the aggregation unit can analyze the trends of comments from past meetings and provide an aggregation method suitable for current comments. For example, the aggregation unit can analyze the reactions of participants in past meetings and predict reactions to current comments. This improves the accuracy of aggregation by referring to past comment data. Some or all of the above processing in the aggregation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the aggregation unit can input past comment data into a generative AI and have the generative AI perform the aggregation accuracy improvement.

[0051] The aggregation unit can apply different aggregation algorithms depending on the topic of the conversation when aggregating comments. For example, in the case of a technical topic, the aggregation unit prioritizes aggregating comments that include technical terms. For example, in the case of a business topic, the aggregation unit prioritizes aggregating comments that take into account business terms and market trends. For example, in the case of an educational topic, the aggregation unit prioritizes aggregating comments that take into account educational terms and learning theories. This makes it possible to aggregate comments appropriately by applying different aggregation algorithms depending on the topic of the conversation. Some or all of the above processing in the aggregation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the aggregation unit can input conversation topic data into a generative AI and have the generative AI execute the application of different aggregation algorithms.

[0052] The aggregation unit can update the aggregation results in real time according to the progress of the conversation when aggregating comments. For example, as the conversation progresses, the aggregation unit aggregates and displays new comments in real time. For example, if an important comment is made during the conversation, the aggregation unit prioritizes aggregating and displaying that comment. For example, as the conversation progresses, the aggregation unit aggregates and displays the participants' reactions in real time. This ensures that the latest information is reflected by updating the aggregation results in real time according to the progress of the conversation. Some or all of the above processing in the aggregation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the aggregation unit can input conversation progress data into a generation AI and have the generation AI perform real-time updates of the aggregation results.

[0053] The aggregation unit can adjust the order of aggregation based on the relevance of the conversation when aggregating comments. For example, the aggregation unit prioritizes aggregating comments that are highly relevant in line with the flow of the conversation. For example, the aggregation unit aggregates comments in an appropriate order according to the topic of the conversation. For example, the aggregation unit adjusts the order of comments in real time according to the progress of the conversation. In this way, comments can be aggregated in an appropriate order by adjusting the order of aggregation based on the relevance of the conversation. Some or all of the above processing in the aggregation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the aggregation unit can input conversation relevance data into a generative AI and have the generative AI perform the adjustment of the aggregation order.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Remote conferencing systems can also be equipped with a translation unit. This unit can translate conversations in real time and display them in the appropriate language for participants who speak different languages. For example, if a Japanese-speaking participant is in a meeting conducted in English, the translation unit will translate the English conversation into Japanese and display it to the participant. Similarly, if a Spanish-speaking participant is in a meeting conducted in French, the translation unit will translate the French conversation into Spanish and display it to the participant. Furthermore, if a Chinese-speaking participant is in a meeting conducted in German, the translation unit will translate the German conversation into Chinese and display it to the participant. This allows participants who speak different languages ​​to participate in the same meeting and share their opinions.

[0056] Remote conferencing systems can also be equipped with noise-canceling features. These features can remove background noise during conversations, providing clear audio. For example, if external noise is present during a meeting, the noise-canceling feature removes it, allowing participants to concentrate on the conversation. Similarly, if wind noise is picked up by a participant's microphone during a meeting, the noise-canceling feature removes the wind noise, providing clear audio. Furthermore, if keyboard typing sounds are picked up by a participant's microphone during a meeting, the noise-canceling feature removes the typing sounds, providing clear audio. This improves audio quality during meetings, allowing participants to focus on the conversation.

[0057] Remote conferencing systems can also include a virtual background feature. This feature automatically detects the participant's background and replaces it with a virtual background of their choice. For example, if a participant is joining the meeting from home, the virtual background feature can replace their home background with an office background. Similarly, if a participant is joining from a cafe, the virtual background feature can replace the cafe background with a conference room background. If a participant is joining from a park, the virtual background feature can replace the park background with a simple wallpaper. This protects participants' privacy and creates a professional impression.

[0058] Remote conferencing systems can also include a scheduling unit. This unit can automatically manage meeting schedules and send reminders to participants. For example, as the meeting start time approaches, the scheduling unit can send a reminder to participants to encourage them to prepare. Similarly, as the meeting end time approaches, the scheduling unit can send a notification to participants to encourage them to summarize the meeting. If the meeting schedule changes, the scheduling unit can notify participants and share the new schedule. This streamlines meeting scheduling and allows participants to join meetings smoothly.

[0059] Remote conferencing systems can also include a document sharing section. This section allows participants to share, view, and edit documents in real time during a meeting. For example, presentation materials can be shared during a meeting, and participants can add comments in real time. Similarly, spreadsheets can be shared during a meeting, and participants can input and edit data in real time. Documents can also be shared during a meeting, and participants can make revisions and suggestions in real time. This facilitates smooth document sharing during meetings, enabling participants to efficiently share and edit information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis department analyzes the conversation content. For example, they analyze the conversation content and extract information to generate an appropriate questionnaire. Step 2: The generation unit generates a questionnaire based on the analysis performed by the analysis unit. For example, the generated questionnaire is provided to the participants. Step 3: The input section accepts comments from participants. For example, it accepts comments from participants in real time. Step 4: The aggregation unit aggregates and displays the comments received by the input unit. For example, it aggregates the received comments and displays them to the speaker.

[0062] (Example of form 2) The remote conferencing system according to an embodiment of the present invention is a mechanism for enabling all participants to take part. This remote conferencing system includes a function to analyze conversation content and generate questionnaires, and a function to aggregate and display participant comments. First, as a conversation content questionnaire function, the AI ​​analyzes the conversation content and creates a questionnaire. By having participants answer, the speaker can understand the reactions of the participants. Next, as a participant comment aggregation and display function, a field is provided where participants can enter comments on the conversation in progress, and the AI ​​aggregates the comments and displays them on the screen. This makes it possible to convey opinions even without speaking, and the intentions of all participants can be reflected in the meeting. For example, as a conversation content questionnaire function, the AI ​​analyzes the conversation content and generates an appropriate questionnaire. For example, a questionnaire such as "What do you think about this proposal?" can be generated during the meeting, and participants can answer in real time. Next, as a participant comment aggregation and display function, a field is provided where participants can enter comments on the conversation in progress. If a participant enters a comment such as "I would like to know more about this part," the AI ​​aggregates it and displays it to the speaker. This allows the speaker to understand the interests and questions of the participants and to enrich the content of the meeting. These features prevent situations where only those speaking participate in the meeting, ensuring that everyone's opinions are reflected. Even in remote meetings, everyone can actively participate and share their opinions, improving the quality of the meeting and leading to greater satisfaction for all participants. Thus, remote meeting systems can enable fully-participating remote meetings.

[0063] The remote conferencing system according to this embodiment comprises an analysis unit, a generation unit, an input unit, and an aggregation unit. The analysis unit analyzes the content of the conversation. For example, the analysis unit analyzes the content of the conversation and generates an appropriate questionnaire. The generation unit generates a questionnaire based on the content analyzed by the analysis unit. For example, the generation unit provides the generated questionnaire to the participants. The input unit receives comments from participants. For example, the input unit receives comments from participants in real time. The aggregation unit aggregates and displays the comments received by the input unit. For example, the aggregation unit aggregates the received comments and displays them to the speaker. As a result, the remote conferencing system can realize a remote conferencing in which everyone can participate by analyzing the content of the conversation, generating a questionnaire, and receiving, aggregating, and displaying comments.

[0064] The analytics department analyzes the content of conversations. Specifically, it analyzes conversations in real time and extracts important keywords and topics. This includes using natural language processing technology to analyze the intent and emotions of what is said. For example, it acquires conversations during meetings as text data and analyzes the content using AI. The AI ​​extracts important keywords from the conversations and grasps the flow of the conversation and the focus of the discussion. It can also evaluate the atmosphere of the meeting and the reactions of the participants by analyzing the emotions of the speakers. This allows the analytics department to analyze conversations in detail and accurately grasp the progress of the meeting and the opinions of the participants. Furthermore, based on past meeting data, the analytics department can analyze trends and patterns and propose ways to improve future meetings. For example, it can extract topics that were frequently discussed in past meetings and proposals that received positive responses from participants and use them to set the agenda for the next meeting. In this way, the analytics department can provide valuable information to improve the quality of meetings.

[0065] The generation unit generates questionnaires based on the analysis performed by the analysis unit. Specifically, it creates appropriate questions for participants based on keywords and topics extracted by the analysis unit. For example, it generates questionnaires to collect opinions on important topics discussed during a meeting. The generation unit uses AI to automatically create effective questions that elicit participants' interests and opinions. The AI ​​can leverage past questionnaire data and participant profile information to generate questions optimized for each individual participant. This allows the generation unit to efficiently collect participant opinions and maximize the outcome of the meeting. Furthermore, the generation unit also provides an interface for providing the generated questionnaires to participants. For example, it can automatically send questionnaires after the meeting to allow participants to easily answer them. It also has a function to monitor the questionnaire response status in real time and send reminders to participants who have not yet responded. This allows the generation unit to improve the questionnaire response rate and collect more opinions.

[0066] The input section receives comments from participants. Specifically, it provides an interface that allows participants to enter comments in real time during the meeting. For example, participants can freely post opinions and questions through a chat window or comment box. The input section receives these comments immediately and processes them within the system. Furthermore, the input section has the ability to analyze the content of comments and automatically classify important opinions and questions. Using AI, it analyzes the content of comments and classifies them based on relevant topics and keywords. This allows the input section to efficiently collect participants' opinions and use them to facilitate the meeting. The input section also has a comment filtering function that can automatically remove inappropriate content and spam. This allows the input section to provide an environment where participants can post comments with confidence, thereby improving the quality of the meeting.

[0067] The aggregation unit aggregates and displays comments received by the input unit. Specifically, it aggregates received comments in real time and provides an interface for displaying them to speakers and other participants. For example, it displays a list of comments posted during the meeting and highlights important opinions and questions. It also has a function to analyze the content of comments and group them by related topics. This allows the aggregation unit to efficiently organize participants' opinions and support the progress of the meeting. Furthermore, the aggregation unit also has a function to visualize the aggregated results of comments. For example, it can display the number and content of comments in graphs and charts, allowing participants to grasp the progress of the meeting and their reactions at a glance. This allows the aggregation unit to effectively share the results of the meeting and clarify areas for improvement in the next meeting. The aggregation unit also has a function to output the aggregated results of comments as a report after the meeting ends. This allows for the easy creation and sharing of meeting records with participants. This allows the aggregation unit to increase the transparency of the meeting and provide an environment where all participants can share their opinions.

[0068] The analysis unit can analyze the conversation content and generate an appropriate questionnaire. For example, the analysis unit can analyze the conversation content and generate an appropriate questionnaire. This allows for understanding participants' reactions by analyzing the conversation content and generating an appropriate questionnaire. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the conversation content into a generation AI and have the generation AI generate an appropriate questionnaire.

[0069] The generation unit can provide the generated questionnaire to the participants. For example, the generation unit can provide the generated questionnaire to the participants. For example, the generation unit can provide the generated questionnaire to the participants. By providing the generated questionnaire to the participants, the participants can respond in real time. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the generated questionnaire into a generation AI and have the generation AI perform the processing of providing it to the participants.

[0070] The input unit can receive comments from participants in real time. For example, the input unit can receive comments from participants in real time. For example, the input unit can receive comments from participants in real time. This allows for smooth exchange of opinions during the meeting by receiving comments from participants in real time. Some or all of the above processing in the input unit may be performed using a generation AI, for example, or without a generation AI. For example, the input unit can input participants' comments into a generation AI and have the generation AI perform the processing of receiving them in real time.

[0071] The aggregation unit can aggregate the received comments and display them to the speaker. For example, the aggregation unit can aggregate the received comments and display them to the speaker. For example, the aggregation unit can aggregate the received comments and display them to the speaker. This allows the speaker to understand the opinions of all participants by aggregating and displaying the received comments. Some or all of the above processing in the aggregation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the aggregation unit can input the received comments into a generation AI and have the generation AI perform the process of aggregating and displaying them.

[0072] The analysis unit can estimate the user's emotions and adjust the conversation analysis method based on the estimated user emotions. For example, if the user is excited, the analysis unit can quickly analyze the conversation and immediately generate a questionnaire. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and generate a questionnaire with deeper insights. For example, if the user is anxious, the analysis unit can perform a careful analysis and generate a questionnaire that enhances the user's sense of security. In this way, by adjusting the conversation analysis method based on the user's emotions, a more appropriate questionnaire can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative 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 analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the conversation analysis method.

[0073] The analysis unit can improve the accuracy of its analysis by referring to past meeting data when analyzing conversation content. For example, the analysis unit can refer to the content of statements made in past meetings and compare it with the current conversation content to improve the accuracy of its analysis. For example, the analysis unit can refer to the results of questionnaires from past meetings and generate questionnaires that are appropriate for the current conversation content. For example, the analysis unit can analyze the reactions of participants in past meetings and predict the reactions of participants to the current conversation content. 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, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input past meeting data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the topic of the conversation when analyzing the content of the conversation. For example, in the case of a technical topic, the analysis unit applies an analysis algorithm that includes technical terms. For example, in the case of a business topic, the analysis unit applies an analysis algorithm that takes into account business terms and market trends. For example, in the case of an educational topic, the analysis unit applies an analysis algorithm that takes into account educational terms and learning theories. By applying different analysis algorithms depending on the topic of the conversation, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input conversation topic data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0075] 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 analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0076] The analysis unit can perform analysis of conversation content while considering the attribute information of the conversation participants. For example, the analysis unit can adjust the frequency of use of technical terms according to the occupation and position of the participants. For example, the analysis unit can select words that are easy to understand according to the age group of the participants. For example, the analysis unit can select appropriate expressions according to the cultural background of the participants. By considering the attribute information of the conversation participants, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the attribute information of the participants into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0077] The analysis unit can update the analysis results in real time according to the progress of the conversation when analyzing the content of the conversation. For example, as the conversation progresses, the analysis unit reflects new information in the analysis and updates the questionnaire in real time. For example, if an important point comes up during the conversation, the analysis unit generates a questionnaire that emphasizes that point. For example, the analysis unit analyzes the participants' responses in real time according to the progress of the conversation and generates an appropriate questionnaire. In this way, the latest information is reflected by updating the analysis results in real time according to the progress of the conversation. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input conversation progress data into a generative AI and have the generative AI perform real-time updates of the analysis results.

[0078] The generation unit can estimate the user's emotions and adjust the content of the questionnaire based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a questionnaire with detailed questions. For example, if the user is in a hurry, the generation unit will generate a questionnaire with concise questions. For example, if the user is excited, the generation unit will generate a questionnaire with a visually stimulating design. This allows for the generation of more appropriate questionnaires by adjusting the content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the content of the questionnaire.

[0079] The generation unit can adjust the level of detail in a questionnaire based on the importance of the conversation when generating the questionnaire. For example, if the topic is important, the generation unit will generate a questionnaire with detailed questions. For example, if the topic is general, the generation unit will generate a questionnaire with concise questions. For example, if the topic is urgent, the generation unit will generate a questionnaire that can be answered quickly. In this way, an appropriate questionnaire can be generated by adjusting the level of detail in the questionnaire based on the importance of the conversation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the questionnaire.

[0080] The generation unit can apply different questionnaire formats depending on the category of the conversation when generating questionnaires. For example, in the case of a technical topic, the generation unit generates a questionnaire that includes technical terms. For example, in the case of a business topic, the generation unit generates a questionnaire that takes business terms and market trends into consideration. For example, in the case of an educational topic, the generation unit generates a questionnaire that takes educational terms and learning theories into consideration. In this way, by applying different questionnaire formats depending on the category of the conversation, an appropriate questionnaire can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation category data into a generation AI and have the generation AI perform the application of different questionnaire formats.

[0081] The generation unit can estimate the user's emotions and adjust the length of the questionnaire based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, to-the-point questionnaire. For example, if the user is relaxed, the generation unit will generate a longer questionnaire with detailed explanations. For example, if the user is excited, the generation unit will generate a questionnaire with visually stimulating effects. In this way, an appropriate questionnaire can be generated by adjusting the length of the questionnaire based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the questionnaire.

[0082] The generation unit can determine the priority of questionnaires based on the progress of the conversation when generating questionnaires. For example, the generation unit will prioritize generating questionnaires for important points in the conversation. For example, the generation unit will generate questionnaires at appropriate times according to the progress of the conversation. For example, if an important question is asked in the middle of the conversation, the generation unit will prioritize generating a questionnaire for that question. In this way, by determining the priority of questionnaires based on the progress of the conversation, questionnaires can be generated at appropriate times. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation progress data into the generation AI and have the generation AI perform the determination of questionnaire priorities.

[0083] The generation unit can adjust the order of questions based on the relevance of the conversation when generating the questionnaire. For example, the generation unit prioritizes placing highly relevant questions in line with the flow of the conversation. For example, the generation unit places questions in an appropriate order according to the topic of the conversation. For example, the generation unit adjusts the order of questions in real time according to the progress of the conversation. In this way, by adjusting the order of questions based on the relevance of the conversation, the questionnaire can be generated in an appropriate order. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation relevance data into a generation AI and have the generation AI perform the adjustment of the questionnaire order.

[0084] The input unit can estimate the user's emotions and adjust the comment input interface based on the estimated emotions. For example, if the user is stressed, the input unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the input unit can prioritize voice input to allow for quick comment entry. This allows for the provision of an appropriate input method by adjusting the comment input interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative 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 input unit may be performed using or without a generative AI. For example, the input unit can input user emotion data into a generative AI and have the generative AI adjust the comment input interface.

[0085] The input unit can select the optimal input method by referring to past comment history when a comment is entered. For example, the input unit may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the input unit may suggest an input method for a specific topic based on the user's past comment history. For example, the input unit may analyze the user's past input history and suggest the most efficient input method. In this way, the optimal input method can be provided by referring to past comment history. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input past comment history data into a generative AI and have the generative AI select the optimal input method.

[0086] The input unit can adjust the timing of comment input according to the progress of the conversation. For example, the input unit prompts for comment input at the appropriate time for important points in the conversation. For example, the input unit adjusts the timing of comment input in real time according to the progress of the conversation. For example, if an important question is asked in the middle of the conversation, the input unit prioritizes prompting for comment input in response to that question. In this way, by adjusting the timing of input according to the progress of the conversation, comments can be entered at the appropriate time. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input conversation progress data into a generative AI and have the generative AI perform the adjustment of the timing of input.

[0087] The input unit can estimate the user's emotions and determine the priority of comment input based on the estimated emotions. For example, if the user is excited, the input unit may prompt the user to prioritize important comments. For example, if the user is relaxed, the input unit may prompt the user to enter detailed comments. For example, if the user is in a hurry, the input unit may prompt the user to prioritize concise comments. This ensures that important comments are prioritized by determining the priority of comment input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using a generative AI, or not. For example, the input unit can input user emotion data into a generative AI and have the generative AI determine the priority of comment input.

[0088] The input unit can input comments while considering the attribute information of the conversation participants. For example, the input unit can suggest an appropriate comment input method according to the participant's occupation or position. For example, the input unit can suggest an easy-to-understand input method according to the participant's age group. For example, the input unit can select appropriate expressions according to the participant's cultural background. In this way, by considering the attribute information of the conversation participants, an appropriate comment input method can be provided. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input participant attribute information data into a generative AI and have the generative AI select an input method.

[0089] The input unit can change the input format depending on the conversation topic when comments are entered. For example, for technical topics, the input unit provides an input format that includes technical terms. For example, for business topics, the input unit provides an input format that takes business terms and market trends into consideration. For example, for educational topics, the input unit provides an input format that takes educational terms and learning theories into consideration. In this way, by changing the input format according to the conversation topic, an appropriate method of entering comments can be provided. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input unit can input conversation topic data into a generative AI and have the generative AI perform the change in the input format.

[0090] The aggregation unit can estimate the user's emotions and adjust the comment aggregation method based on the estimated user emotions. For example, if the user is excited, the aggregation unit will prioritize aggregating important comments. For example, if the user is relaxed, the aggregation unit will provide an aggregation method that includes detailed comments. For example, if the user is in a hurry, the aggregation unit will prioritize aggregating concise comments. This allows for the prioritization of important comments by adjusting the comment aggregation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative 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 aggregation unit may be performed using a generative AI, or not. For example, the aggregation unit can input user emotion data into a generative AI and have the generative AI adjust the comment aggregation method.

[0091] The aggregation unit can improve the accuracy of aggregation by referring to past comment data when aggregating comments. For example, the aggregation unit can refer to comment data from past meetings and compare it with current comments to improve the accuracy of aggregation. For example, the aggregation unit can analyze the trends of comments from past meetings and provide an aggregation method suitable for current comments. For example, the aggregation unit can analyze the reactions of participants in past meetings and predict reactions to current comments. This improves the accuracy of aggregation by referring to past comment data. Some or all of the above processing in the aggregation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the aggregation unit can input past comment data into a generative AI and have the generative AI perform the aggregation accuracy improvement.

[0092] The aggregation unit can apply different aggregation algorithms depending on the topic of the conversation when aggregating comments. For example, in the case of a technical topic, the aggregation unit prioritizes aggregating comments that include technical terms. For example, in the case of a business topic, the aggregation unit prioritizes aggregating comments that take into account business terms and market trends. For example, in the case of an educational topic, the aggregation unit prioritizes aggregating comments that take into account educational terms and learning theories. This makes it possible to aggregate comments appropriately by applying different aggregation algorithms depending on the topic of the conversation. Some or all of the above processing in the aggregation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the aggregation unit can input conversation topic data into a generative AI and have the generative AI execute the application of different aggregation algorithms.

[0093] The aggregation unit can estimate the user's emotions and adjust the display method of the aggregation results based on the estimated user emotions. For example, if the user is tense, the aggregation unit provides a simple and highly visible display method. For example, if the user is relaxed, the aggregation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the aggregation unit provides a display method that gets straight to the point. This allows for appropriate display by adjusting the display method of the aggregation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 aggregation unit may be performed using a generative AI, or not using a generative AI. For example, the aggregation unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the aggregation results.

[0094] The aggregation unit can update the aggregation results in real time according to the progress of the conversation when aggregating comments. For example, as the conversation progresses, the aggregation unit aggregates and displays new comments in real time. For example, if an important comment is made during the conversation, the aggregation unit prioritizes aggregating and displaying that comment. For example, as the conversation progresses, the aggregation unit aggregates and displays the participants' reactions in real time. This ensures that the latest information is reflected by updating the aggregation results in real time according to the progress of the conversation. Some or all of the above processing in the aggregation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the aggregation unit can input conversation progress data into a generation AI and have the generation AI perform real-time updates of the aggregation results.

[0095] The aggregation unit can adjust the order of aggregation based on the relevance of the conversation when aggregating comments. For example, the aggregation unit prioritizes aggregating comments that are highly relevant in line with the flow of the conversation. For example, the aggregation unit aggregates comments in an appropriate order according to the topic of the conversation. For example, the aggregation unit adjusts the order of comments in real time according to the progress of the conversation. In this way, comments can be aggregated in an appropriate order by adjusting the order of aggregation based on the relevance of the conversation. Some or all of the above processing in the aggregation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the aggregation unit can input conversation relevance data into a generative AI and have the generative AI perform the adjustment of the aggregation order.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Remote conferencing systems can also be equipped with a translation unit. This unit can translate conversations in real time and display them in the appropriate language for participants who speak different languages. For example, if a Japanese-speaking participant is in a meeting conducted in English, the translation unit will translate the English conversation into Japanese and display it to the participant. Similarly, if a Spanish-speaking participant is in a meeting conducted in French, the translation unit will translate the French conversation into Spanish and display it to the participant. Furthermore, if a Chinese-speaking participant is in a meeting conducted in German, the translation unit will translate the German conversation into Chinese and display it to the participant. This allows participants who speak different languages ​​to participate in the same meeting and share their opinions.

[0098] Remote conferencing systems can also be equipped with noise-canceling features. These features can remove background noise during conversations, providing clear audio. For example, if external noise is present during a meeting, the noise-canceling feature removes it, allowing participants to concentrate on the conversation. Similarly, if wind noise is picked up by a participant's microphone during a meeting, the noise-canceling feature removes the wind noise, providing clear audio. Furthermore, if keyboard typing sounds are picked up by a participant's microphone during a meeting, the noise-canceling feature removes the typing sounds, providing clear audio. This improves audio quality during meetings, allowing participants to focus on the conversation.

[0099] Remote conferencing systems can also include a virtual background feature. This feature automatically detects the participant's background and replaces it with a virtual background of their choice. For example, if a participant is joining the meeting from home, the virtual background feature can replace their home background with an office background. Similarly, if a participant is joining from a cafe, the virtual background feature can replace the cafe background with a conference room background. If a participant is joining from a park, the virtual background feature can replace the park background with a simple wallpaper. This protects participants' privacy and creates a professional impression.

[0100] Remote conferencing systems can also include a scheduling unit. This unit can automatically manage meeting schedules and send reminders to participants. For example, as the meeting start time approaches, the scheduling unit can send a reminder to participants to encourage them to prepare. Similarly, as the meeting end time approaches, the scheduling unit can send a notification to participants to encourage them to summarize the meeting. If the meeting schedule changes, the scheduling unit can notify participants and share the new schedule. This streamlines meeting scheduling and allows participants to join meetings smoothly.

[0101] Remote conferencing systems can also include a document sharing section. This section allows participants to share, view, and edit documents in real time during a meeting. For example, presentation materials can be shared during a meeting, and participants can add comments in real time. Similarly, spreadsheets can be shared during a meeting, and participants can input and edit data in real time. Documents can also be shared during a meeting, and participants can make revisions and suggestions in real time. This facilitates smooth document sharing during meetings, enabling participants to efficiently share and edit information.

[0102] Remote conferencing systems can also be equipped with an emotion estimation unit. This unit can analyze participants' facial expressions and tone of voice to estimate their emotions. For example, if a participant is smiling while speaking, the emotion estimation unit estimates that the participant is enjoying themselves. For example, if a participant is frowning, the emotion estimation unit estimates that the participant is confused. For example, if a participant is speaking in a raised tone, the emotion estimation unit estimates that the participant is excited. This allows for real-time understanding of participants' emotions and adjustments to the meeting's progress.

[0103] Remote conferencing systems can also be equipped with an emotional feedback unit. This unit can provide real-time feedback on participants' emotions to support the progress of the meeting. For example, if a participant is excited, the emotional feedback unit will inform the speaker so that they can respond appropriately. For example, if a participant is bored, the emotional feedback unit will inform the speaker so that they can adjust the content of the meeting. For example, if a participant is feeling anxious, the emotional feedback unit will inform the speaker so that they can help increase the participant's sense of security. This allows for adjusting the progress of the meeting based on participants' emotions, resulting in more effective meetings.

[0104] Remote conferencing systems can also be equipped with an emotional history function. This function records participants' emotional data from past meetings and can be used to improve future meetings. For example, if a participant was excited in a past meeting, the emotional history function records this data and can be used as a reference if a similar situation occurs in a future meeting. For example, if a participant was bored in a past meeting, the emotional history function records this data and can be used to improve the content of the next meeting. For example, if a participant felt anxious in a past meeting, the emotional history function records this data and can be used to increase participants' sense of security in the next meeting. In this way, past emotional data can be used to conduct future meetings more effectively.

[0105] Remote conferencing systems can also be equipped with an emotion analysis unit. This unit can analyze participants' emotional data during a meeting and evaluate its effectiveness. For example, if participants exhibit many positive emotions during a meeting, the emotion analysis unit will evaluate the meeting as successful. Conversely, if participants exhibit many negative emotions, the emotion analysis unit will evaluate the meeting as having room for improvement. Similarly, if participants experience significant emotional fluctuations during a meeting, the emotion analysis unit will evaluate the meeting as stressful for them. This allows for an evaluation of meeting effectiveness based on emotional data, which can then be used to improve future meetings.

[0106] Remote conferencing systems can also be equipped with an emotion prediction unit. This unit can predict participants' emotions in the next meeting based on their past emotional data. For example, if a participant showed positive emotions in a previous meeting, the emotion prediction unit will predict similar emotions in the next meeting. For example, if a participant showed negative emotions in a previous meeting, the emotion prediction unit will predict similar emotions in the next meeting and take appropriate measures. For example, if a participant experienced significant emotional fluctuations in a previous meeting, the emotion prediction unit will predict a similar situation in the next meeting and adjust the meeting's progress accordingly. This allows for more effective meetings by predicting the next meeting based on participants' past emotional data.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis department analyzes the conversation content. For example, they analyze the conversation content and extract information to generate an appropriate questionnaire. Step 2: The generation unit generates a questionnaire based on the analysis performed by the analysis unit. For example, the generated questionnaire is provided to the participants. Step 3: The input section accepts comments from participants. For example, it accepts comments from participants in real time. Step 4: The aggregation unit aggregates and displays the comments received by the input unit. For example, it aggregates the received comments and displays them to the speaker.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the analysis unit, generation unit, input unit, and aggregation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a questionnaire based on the analyzed content. The input unit is implemented by the control unit 46A of the smart device 14 and receives comments from participants in real time. The aggregation unit is implemented by the specific processing unit 290 of the data processing device 12 and aggregates and displays the received comments. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, generation unit, input unit, and aggregation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a questionnaire based on the analyzed content. The input unit is implemented, for example, by the control unit 46A of the smart glasses 214 and receives comments from participants in real time. The aggregation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and aggregates and displays the received comments. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, generation unit, input unit, and aggregation unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a questionnaire based on the analyzed content. The input unit is implemented by the control unit 46A of the headset terminal 314 and receives comments from participants in real time. The aggregation unit is implemented by the specific processing unit 290 of the data processing device 12 and aggregates and displays the received comments. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the analysis unit, generation unit, input unit, and aggregation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the conversation. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a questionnaire based on the analyzed content. The input unit is implemented, for example, by the control unit 46A of the robot 414 and receives comments from participants in real time. The aggregation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and aggregates and displays the received comments. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The analysis department analyzes the content of conversations, A generation unit generates a questionnaire based on the content analyzed by the aforementioned analysis unit, An input section for receiving comments from participants, An aggregation unit that aggregates and displays the comments received by the input unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze the conversation content and generate appropriate questionnaires. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Provide the generated questionnaire to the participants. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned input unit is We accept participant comments in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned aggregation unit is The received comments are compiled and displayed to the speaker. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It estimates the user's emotions and adjusts the conversation analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is When analyzing conversation content, past meeting data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is When analyzing conversation content, different analysis algorithms are applied depending on the topic of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is 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 10) The aforementioned analysis unit is When analyzing conversation content, the analysis takes into account the attribute information of the conversation participants. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During conversation analysis, the analysis results are updated in real time according to the progress of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the user's emotions and adjusts the survey content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a survey, adjust the level of detail based on the importance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a survey, apply different survey formats depending on the conversation category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the length of the survey based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a survey, prioritize the survey based on the progress of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating questionnaires, adjust the order of the questions based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned input unit is It estimates the user's emotions and adjusts the comment input interface based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned input unit is When entering a comment, the system will refer to past comment history to select the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned input unit is When entering comments, adjust the timing of your input according to the progress of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned input unit is The system estimates the user's sentiment and prioritizes comment input based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned input unit is When entering comments, the system takes into account the attribute information of the conversation participants. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned input unit is When entering comments, the input format changes depending on the topic of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned aggregation unit is We estimate the user's sentiment and adjust the comment aggregation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned aggregation unit is When aggregating comments, we refer to past comment data to improve the accuracy of the aggregation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned aggregation unit is When aggregating comments, different aggregation algorithms are applied depending on the topic of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned aggregation unit is It estimates the user's emotions and adjusts how the aggregated results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned aggregation unit is When aggregating comments, the aggregation results are updated in real time according to the progress of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned aggregation unit is When aggregating comments, adjust the aggregation order based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 department analyzes the content of conversations, A generation unit generates a questionnaire based on the content analyzed by the aforementioned analysis unit, An input section for receiving comments from participants, An aggregation unit that aggregates and displays the comments received by the input unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit is Analyze the conversation content and generate appropriate questionnaires. The system according to feature 1.

3. The generating unit is Provide the generated questionnaire to the participants. The system according to feature 1.

4. The aforementioned input unit is We accept participant comments in real time. The system according to feature 1.

5. The aforementioned aggregation unit is The received comments are compiled and displayed to the speaker. The system according to feature 1.

6. The aforementioned analysis unit is It estimates the user's emotions and adjusts the conversation analysis method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit is When analyzing conversation content, past meeting data is referenced to improve the accuracy of the analysis. The system according to feature 1.

8. The aforementioned analysis unit is When analyzing conversation content, different analysis algorithms are applied depending on the topic of the conversation. The system according to feature 1.

9. The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit is When analyzing conversation content, the analysis takes into account the attribute information of the conversation participants. The system according to feature 1.

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