system

Abstract

The system according to this embodiment aims to automate the organization of meeting content, information sharing, and the aggregation and management of feedback. [Solution] The system according to this embodiment comprises an automatic confirmation item generation unit, a survey tool linkage unit, an automatic question and answer unit, and a feedback collection and sharing unit. The automatic confirmation item generation unit analyzes meeting content in real time, extracts important items, and organizes them. The survey tool linkage unit distributes the confirmation items extracted by the automatic confirmation item generation unit to each team in a survey format and aggregates the responses. The automatic question and answer unit automatically creates a dedicated communication tool channel based on the responses aggregated by the survey tool linkage unit and supports question and answer using a bot. The feedback collection and sharing unit automatically organizes and sends the feedback collected by the automatic question and answer unit to the person in charge and reports it to everyone using the survey tool.

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 conventional technology, there is a problem that it takes time and is inefficient because the collation of confirmation items of meeting content, information sharing, and the aggregation and management of feedback are performed manually.

[0005] The system according to the embodiment aims to automate the collation of confirmation items of meeting content, information sharing, and the aggregation and management of feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an automatic confirmation item generation unit, a survey tool integration unit, an automatic question and answer unit, and a feedback collection and sharing unit. The automatic confirmation item generation unit analyzes meeting content in real time, extracts important items, and organizes them. The survey tool integration unit distributes the confirmation items extracted by the automatic confirmation item generation unit to each team in a survey format and aggregates the responses. The automatic question and answer unit automatically creates a dedicated communication tool channel based on the responses aggregated by the survey tool integration unit and supports question and answer using a bot. The feedback collection and sharing unit automatically organizes and sends the feedback collected by the automatic question and answer unit to the person in charge and reports it to everyone using the survey tool. [Effects of the Invention]

[0007] The system according to this embodiment can automate the organization of meeting contents, information sharing, and the aggregation and management of feedback. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 meeting efficiency system according to an embodiment of the present invention is a system that analyzes meeting content in real time, extracts important matters, and organizes them. This meeting efficiency system includes an automatic confirmation item generation unit that analyzes meeting content in real time, extracts important matters, and organizes them. Next, it includes a survey tool linkage unit that distributes the extracted confirmation items to each team in a survey format and aggregates the responses. Furthermore, it includes an automatic question and answer unit that automatically creates a dedicated communication tool channel and supports question and answer using a bot. Finally, it includes a feedback collection and sharing unit that automatically organizes and sends the collected feedback to the person in charge and reports it to the whole using a survey tool. For example, the meeting efficiency system analyzes the content spoken during a meeting and automatically extracts important confirmation items. For example, it can extract and list important decisions and action items discussed during the meeting. This eliminates the need to manually organize confirmation items and reduces the workload after the meeting. Next, the meeting efficiency system distributes the extracted confirmation items to each team in a survey format and aggregates the responses from the members. For example, it can distribute a survey to members of each team regarding action items decided in the meeting and collect progress and opinions. This speeds up information sharing and accelerates the feedback loop. Furthermore, the meeting efficiency system automatically creates a dedicated communication tool channel after the meeting and uses a bot to support Q&A. For example, the bot can receive and answer questions about what was discussed in the meeting. This allows for quick Q&A and smooth information sharing. Finally, the meeting efficiency system automatically organizes the feedback collected through the survey tool and sends it to the responsible person. For example, organizing the feedback collected from each team and sending it to the responsible person enables early problem resolution and rapid implementation of improvement measures. Also, reporting the collected feedback to the entire group through the survey tool speeds up information sharing. In summary, the meeting efficiency system improves meeting efficiency, speeds up information sharing, and makes feedback management easier.This allows the meeting efficiency system to improve meeting efficiency, accelerate information sharing, and facilitate feedback management.

[0029] The meeting efficiency system according to this embodiment comprises an automatic confirmation item generation unit, a survey tool integration unit, an automatic question and answer unit, and a feedback collection and sharing unit. The automatic confirmation item generation unit analyzes the meeting content in real time and extracts and organizes important items. For example, the automatic confirmation item generation unit analyzes the content spoken during the meeting and automatically extracts important confirmation items. For example, the automatic confirmation item generation unit can extract and list important decisions and action items discussed during the meeting. Some or all of the above processing in the automatic confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automatic confirmation item generation unit can transcribe the content spoken during the meeting using speech recognition technology and extract important items using natural language processing technology. The survey tool integration unit distributes the confirmation items extracted by the automatic confirmation item generation unit to each team in a survey format and aggregates the responses. For example, the survey tool integration unit distributes the extracted confirmation items to each team in a survey format and aggregates the responses from the members. The survey tool integration unit can, for example, distribute surveys to members of each team regarding action items decided at a meeting, and collect progress and opinions. Some or all of the above processing in the survey tool integration unit may be performed using AI, or not. For example, the survey tool integration unit inputs the survey questions into a generation AI, and the generation AI aggregates the answers. The automated question and answer unit automatically creates a dedicated communication tool channel based on the answers aggregated by the survey tool integration unit and supports question and answer using a bot. For example, the automated question and answer unit automatically creates a dedicated communication tool channel after a meeting and supports question and answer using a bot. For example, the automated question and answer unit can have a bot receive and answer questions about the content discussed at the meeting. Some or all of the above processing in the automated question and answer unit may, for example, be performed using AI, or not. For example, the automated question and answer unit inputs the questions into a bot, and a generation AI generates the answers.The Feedback Collection and Sharing Unit automatically organizes and sends feedback collected by the Automated Question and Answer Unit to the responsible person, and reports it to the entire team using a survey tool. The Feedback Collection and Sharing Unit, for example, automatically organizes feedback collected by the survey tool and sends it to the responsible person. The Feedback Collection and Sharing Unit, for example, organizes feedback collected from each team and sends it to the responsible person, enabling early problem resolution and rapid implementation of improvement measures. Some or all of the above processing in the Feedback Collection and Sharing Unit may be performed using AI, for example, or without AI. For example, the Feedback Collection and Sharing Unit inputs the collected feedback into a generating AI, and the generating AI organizes the feedback. As a result, the meeting efficiency system according to the embodiment can improve the efficiency of meetings, expedite information sharing, and facilitate feedback management.

[0030] The automated confirmation item generation unit analyzes meeting content in real time, extracting and organizing important points. Specifically, it uses speech recognition technology to transcribe what is said during the meeting, and then analyzes that text data using natural language processing technology. For example, speech recognition technology can transcribe what is said during the meeting with high accuracy and categorize it by speaker. Next, natural language processing technology is used to extract important keywords and phrases from the text data, and these are used to create a list of confirmation items. When using AI, machine learning algorithms can be utilized to learn from past meeting data and improve the accuracy of extracting important points. For example, by automatically extracting and listing important decisions and action items discussed during the meeting, the post-meeting confirmation process can be made significantly more efficient. Furthermore, the automated confirmation item generation unit can update the extracted confirmation items in real time according to the progress of the meeting, providing immediate feedback to participants. This prevents important points from being overlooked during the meeting and supports rapid decision-making. In addition, the generated confirmation items are automatically saved after the meeting and can be referenced later, making meeting record management easier.

[0031] The Survey Tool Integration Unit distributes the confirmation items extracted by the Automatic Confirmation Item Generation Unit to each team in a survey format and aggregates the responses. Specifically, it automatically generates a survey based on the extracted confirmation items and distributes it to the members of each team. The survey content can include opinions on the confirmation items, progress reports, and additional questions. The Survey Tool Integration Unit distributes the generated surveys via email or a dedicated survey tool and aggregates the responses from members. When using AI, the generation AI can automatically generate the survey questions and aggregate the responses automatically. For example, regarding action items decided in a meeting, a survey can be distributed to the members of each team to collect progress reports and opinions. The collected responses are automatically compiled and visualized as graphs and charts, allowing for an at-a-glance understanding of progress and opinion trends. Furthermore, the Survey Tool Integration Unit automatically generates reports based on the responses, which can be used for meeting follow-up and preparation for the next meeting. This allows the Survey Tool Integration Unit to efficiently follow up on confirmation items and facilitate communication between teams.

[0032] The automated Q&A unit automatically creates a dedicated communication tool channel based on the responses collected by the survey tool integration unit and supports Q&A using a bot. Specifically, it automatically creates a dedicated communication tool channel after the meeting and supports Q&A using a bot. The bot can automatically answer questions from participants based on the content discussed in the meeting and the responses to the survey. When using AI, the generating AI analyzes the content of the question and generates an appropriate answer. For example, the bot receives a question about the content discussed in the meeting, and the generating AI generates an answer based on past meeting data and related information. This allows participants to quickly resolve their doubts and streamlines post-meeting follow-up. Furthermore, the automated Q&A unit can learn from the bot's answers and improve the accuracy of future Q&A. For example, it collects feedback on the content of the bot's answers, and the generating AI improves the accuracy of the answers based on that feedback. As a result, the automated Q&A unit can quickly and accurately resolve participants' doubts and support the efficiency of meetings.

[0033] The Feedback Collection and Sharing Department automatically organizes and sends feedback collected by the Automated Question and Answer Department to the relevant personnel and reports it to the entire team using a survey tool. Specifically, it automatically organizes the feedback collected by the survey tool and sends it to the relevant personnel. For example, by organizing the feedback collected from each team and sending it to the relevant personnel, the Feedback Collection and Sharing Department can enable early problem resolution and rapid implementation of improvement measures. When using AI, the generation AI analyzes the collected feedback, extracts and organizes the important points. For example, it can classify the content of the feedback by category and prioritize it according to its importance and urgency. This allows the person in charge to efficiently review the feedback and respond quickly. Furthermore, the Feedback Collection and Sharing Department can report and share the organized feedback to the entire team through the survey tool. This allows the entire team to share the overall progress and problems and collaboratively consider solutions. The Feedback Collection and Sharing Department can also periodically analyze the collected feedback and extract and report areas for improvement and success stories in meetings. This allows the Feedback Collection and Sharing Department to improve the quality of meetings and enhance the overall team performance.

[0034] The automatic confirmation item generation unit can analyze what is said during a meeting and automatically extract important confirmation items. For example, the automatic confirmation item generation unit can analyze what is said during a meeting and automatically extract important confirmation items. For example, the automatic confirmation item generation unit can extract and list important decisions and action items discussed during a meeting. This eliminates the need for manual organization of confirmation items by automatically extracting important confirmation items during a meeting. Some or all of the above processing in the automatic confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automatic confirmation item generation unit can transcribe what is said during a meeting using speech recognition technology and extract important items using natural language processing technology.

[0035] The Survey Tool Integration Unit can distribute the extracted confirmation items to each team in survey format and aggregate the responses from the members. For example, the Survey Tool Integration Unit can distribute the extracted confirmation items to each team in survey format and aggregate the responses from the members. For example, the Survey Tool Integration Unit can distribute surveys to members of each team regarding action items decided at a meeting and collect progress and opinions. This speeds up information sharing by distributing the extracted confirmation items in survey format and aggregating the responses. Some or all of the above processing in the Survey Tool Integration Unit may be performed using AI, for example, or not using AI. For example, the Survey Tool Integration Unit inputs the content of the survey questions into a generating AI, and the generating AI aggregates the responses.

[0036] The automated Q&A unit can automatically create a dedicated communication tool channel after a meeting and support Q&A using a bot. For example, the automated Q&A unit can automatically create a dedicated communication tool channel after a meeting and support Q&A using a bot. For example, the automated Q&A unit can have a bot receive and answer questions about the content discussed in the meeting. This facilitates information sharing by supporting Q&A after the meeting. Some or all of the above processing in the automated Q&A unit may be performed using AI, for example, or not using AI. For example, the automated Q&A unit inputs the question content into a bot, and a generating AI generates the answer.

[0037] The feedback collection and sharing unit can automatically organize feedback collected through a survey tool and send it to the responsible person. For example, the feedback collection and sharing unit automatically organizes feedback collected through a survey tool and sends it to the responsible person. For example, by organizing feedback collected from each team and sending it to the responsible person, the feedback collection and sharing unit enables early problem resolution and rapid implementation of improvement measures. This allows for early problem resolution and rapid implementation of improvement measures by automatically organizing the collected feedback and sending it to the responsible person. Some or all of the above processing in the feedback collection and sharing unit may be performed using AI, or not. For example, the feedback collection and sharing unit inputs the collected feedback into a generating AI, which then organizes the feedback.

[0038] The automated confirmation item generation unit can dynamically change the frequency of extracting important items in real time according to the progress of the meeting. For example, in the initial stages of the meeting, the automated confirmation item generation unit sets the frequency of extracting important items to a low level to grasp the overall flow. For example, in the middle of the meeting, the automated confirmation item generation unit increases the frequency of extracting important items to extract specific discussion content in detail. For example, in the final stages of the meeting, the automated confirmation item generation unit sets the frequency of extracting important items to a low level again to extract final conclusions and action items. In this way, by changing the frequency of extracting important items according to the progress of the meeting, appropriate information corresponding to the content of the meeting can be extracted. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without using AI. For example, the automated confirmation item generation unit inputs meeting progress data into a generation AI, and the generation AI dynamically changes the frequency of extracting important items.

[0039] The automated confirmation item generation unit can set priorities for important items to extract based on the roles and areas of expertise of the meeting participants. For example, the automated confirmation item generation unit may prioritize extracting statements made by participants with higher roles and organize them as important items. For example, the automated confirmation item generation unit may prioritize extracting statements related to areas of expertise and organize them as important specialized items. For example, the automated confirmation item generation unit may consider both roles and areas of expertise to extract important items in a balanced manner. This allows for the prioritization of more important information by setting priorities for important items based on the roles and areas of expertise of the meeting participants. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automated confirmation item generation unit inputs participant role and area of ​​expertise data into a generating AI, and the generating AI sets the priorities for important items.

[0040] The automated confirmation item generation unit can extract important items by applying different analysis algorithms depending on the content of the meeting. For example, in a technical meeting, the automated confirmation item generation unit applies an analysis algorithm specialized in technical terminology. For example, in a business meeting, the automated confirmation item generation unit applies an analysis algorithm specialized in business terminology. For example, in a mixed-type meeting, the automated confirmation item generation unit extracts important items by combining multiple analysis algorithms. This makes it possible to extract more appropriate important items by changing the analysis algorithm according to the content of the meeting. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without using AI. For example, the automated confirmation item generation unit inputs meeting content data into a generating AI, and the generating AI applies different analysis algorithms to extract important items.

[0041] The automated confirmation item generation unit can analyze meeting recordings and extract important points using speech recognition technology. For example, the automated confirmation item generation unit can analyze meeting recordings in real time and transcribe important statements into text. For example, the automated confirmation item generation unit can extract statements containing specific keywords using speech recognition technology. For example, the automated confirmation item generation unit can analyze the recording data and extract important points based on the frequency of statements and emphasized content. In this way, important statements can be automatically extracted by analyzing meeting recordings and using speech recognition technology. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automated confirmation item generation unit inputs meeting recordings into a generation AI, and the generation AI extracts important points using speech recognition technology.

[0042] The survey tool integration unit can analyze response trends for each team and optimize the timing of survey distribution. For example, the survey tool integration unit analyzes team response trends and distributes surveys during the time slot with the highest response rate. For example, the survey tool integration unit considers team schedules and distributes surveys immediately after meetings. For example, the survey tool integration unit adjusts the frequency of survey distribution based on team response trends. This allows for the distribution of surveys at the optimal time by analyzing response trends for each team. Some or all of the above processes in the survey tool integration unit may be performed using AI, for example, or without AI. For example, the survey tool integration unit inputs team response data into a generating AI, which analyzes response trends and optimizes the timing of survey distribution.

[0043] The survey tool integration unit can analyze survey response data in real time and provide immediate feedback. For example, the survey tool integration unit can analyze survey response data in real time and immediately display the aggregated results. For example, the survey tool integration unit can provide immediate feedback based on the response data and suggest areas for improvement. For example, the survey tool integration unit can analyze response data in real time and immediately suggest the next action items. In this way, by analyzing survey response data in real time, immediate feedback can be provided. Some or all of the above processing in the survey tool integration unit may be performed using AI, for example, or without AI. For example, the survey tool integration unit inputs response data into a generating AI, which analyzes it in real time and provides feedback.

[0044] The automated question-and-answer unit can analyze the history of questions and answers and provide the best answer by referring to past questions and answers. For example, the automated question-and-answer unit can provide the best answer to similar questions based on the history of past questions and answers. For example, the automated question-and-answer unit can analyze the history of questions and answers and provide template answers to frequently asked questions. For example, the automated question-and-answer unit can refer to past answers and provide the best answer that includes the latest information. In this way, by analyzing the history of questions and answers, the system can provide the best answer by referring to past questions and answers. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs the question-and-answer history data into a generating AI, and the generating AI provides the best answer.

[0045] The automated question-and-answer unit can dynamically change the bot's response speed depending on the content of the question. For example, the automated question-and-answer unit will answer simple questions quickly and provide detailed answers to complex questions over time. For example, the automated question-and-answer unit will adjust the response speed according to the content of the question to meet the user's expectations. For example, the automated question-and-answer unit will dynamically change the response speed according to the urgency of the question. This allows the bot to provide answers that meet the user's expectations by changing the response speed according to the content of the question. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs question content data into a generating AI, and the generating AI dynamically changes the response speed.

[0046] The automated question-and-answer unit can analyze the content of questions and answers and automatically refer to relevant external resources to provide answers. For example, the automated question-and-answer unit can automatically search for external resources related to the question and include them in the answer. For example, the automated question-and-answer unit can automatically refer to relevant documents and materials based on the content of the questions and answers. For example, the automated question-and-answer unit can refer to external resources and provide answers that include the latest information. This allows for the provision of more accurate answers by referring to external resources related to the content of the questions and answers. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs question data into a generating AI, and the generating AI refers to relevant external resources to provide an answer.

[0047] The automated question-and-answer unit can automatically generate relevant documents and materials based on the content of the questions and answers. For example, the automated question-and-answer unit can automatically generate relevant documents based on the content of the questions and answers. For example, the automated question-and-answer unit can automatically generate relevant materials based on the content of the questions and answers. For example, the automated question-and-answer unit can automatically generate relevant reports based on the content of the questions and answers. This makes it easier to organize information by generating relevant documents and materials based on the content of the questions and answers. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs the question-and-answer content data into a generating AI, and the generating AI generates relevant documents and materials.

[0048] The feedback collection and sharing unit can analyze the content of the feedback and prioritize notifications to the responsible person according to their importance. For example, the feedback collection and sharing unit can prioritize notifying the responsible person of high-importance feedback. For example, the feedback collection and sharing unit can analyze the content of the feedback and set notification priorities according to urgency. For example, the feedback collection and sharing unit can adjust the notification method according to the importance of the feedback. This allows for a quick response to important feedback by prioritizing notifications according to the importance of the feedback. Some or all of the above processes in the feedback collection and sharing unit may be performed using AI, for example, or not using AI. For example, the feedback collection and sharing unit inputs feedback content data into a generating AI, which analyzes the importance and prioritizes notifications.

[0049] The feedback collection and sharing unit can refer to the feedback history and identify areas for improvement by comparing it with past feedback. For example, the feedback collection and sharing unit can refer to the feedback history and identify areas for improvement by comparing it with past feedback. For example, the feedback collection and sharing unit can analyze the feedback history and identify problems that have been repeatedly pointed out. For example, the feedback collection and sharing unit can identify areas for improvement and propose specific action plans based on the feedback history. This allows for the identification of areas for improvement by referring to the feedback history and comparing it with past feedback. Some or all of the above processing in the feedback collection and sharing unit may be performed using AI, for example, or without AI. For example, the feedback collection and sharing unit inputs feedback history data into a generating AI, and the generating AI identifies areas for improvement.

[0050] The feedback collection and sharing unit can automatically generate relevant action items based on the content of the feedback. For example, the feedback collection and sharing unit can analyze the content of the feedback and automatically generate specific action items. For example, the feedback collection and sharing unit can generate action items for improvement based on the content of the feedback. For example, the feedback collection and sharing unit can refer to the content of the feedback and generate high-priority action items. This allows for the rapid implementation of specific improvement measures by generating action items based on the content of the feedback. Some or all of the above processes in the feedback collection and sharing unit may be performed using AI, for example, or without AI. For example, the feedback collection and sharing unit inputs the feedback content data into a generating AI, and the generating AI generates action items.

[0051] The feedback collection and sharing unit can analyze the content of the feedback and automatically link it to relevant projects and tasks. For example, the feedback collection and sharing unit analyzes the content of the feedback and automatically links it to relevant projects. For example, the feedback collection and sharing unit automatically links it to relevant tasks based on the content of the feedback. For example, the feedback collection and sharing unit refers to the content of the feedback and automatically identifies relevant projects and tasks. This makes it easier to utilize feedback by analyzing the content of the feedback and linking it to relevant projects and tasks. Some or all of the above processing in the feedback collection and sharing unit may be performed using AI, for example, or not using AI. For example, the feedback collection and sharing unit inputs the feedback content data into a generating AI, and the generating AI links it to relevant projects and tasks.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The meeting efficiency system can dynamically change the frequency of extracting key points in real time according to the progress of the meeting. For example, in the early stages of the meeting, the frequency of extracting key points is set low to grasp the overall flow. In the middle of the meeting, the frequency of extracting key points is increased to extract specific discussion points in detail. In the final stages of the meeting, the frequency of extracting key points is set low again to extract the final conclusions and action items. In this way, by changing the frequency of extracting key points according to the progress of the meeting, appropriate information can be extracted according to the content of the meeting.

[0054] The meeting efficiency system can prioritize the key points to extract based on the roles and expertise of meeting participants. For example, it can prioritize extracting and organizing the statements of high-ranking participants as key points. It can also prioritize extracting statements related to specific areas of expertise and organizing them as specialized key points. By considering both roles and expertise, it can extract key points in a balanced way. In this way, by prioritizing key points based on the roles and expertise of meeting participants, more important information can be extracted preferentially.

[0055] The meeting efficiency system can extract key information by applying different analysis algorithms depending on the content of the meeting. For example, a technical meeting would use an analysis algorithm specialized in technical terminology. A business meeting would use an analysis algorithm specialized in business terminology. In a mixed-type meeting, multiple analysis algorithms would be combined to extract key information. This allows for the extraction of more appropriate key information by changing the analysis algorithm according to the content of the meeting.

[0056] The meeting efficiency system can analyze meeting recordings and extract important points using speech recognition technology. For example, it can analyze meeting recordings in real time and transcribe important statements into text. It can also use speech recognition technology to extract statements containing specific keywords. By analyzing the recordings, it can extract important points based on the frequency of statements and emphasized content. In this way, by analyzing meeting recordings and using speech recognition technology, important statements can be automatically extracted.

[0057] The meeting efficiency system can analyze response trends for each team and optimize the timing of survey distribution. For example, it can analyze team response trends and distribute surveys during the time slot with the highest response rate. It can also distribute surveys immediately after meetings, taking team schedules into consideration. Based on team response trends, it can adjust the frequency of survey distribution. In this way, by analyzing response trends for each team, surveys can be distributed at the optimal time.

[0058] The meeting efficiency system can analyze survey response data in real time and provide immediate feedback. For example, it can analyze survey response data in real time and display the aggregated results immediately. Based on the response data, it can provide immediate feedback and suggest areas for improvement. It can analyze the response data in real time and immediately suggest the next action items. In this way, by analyzing survey response data in real time, it can provide immediate feedback.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The automated confirmation item generation unit analyzes the meeting content in real time, extracts important items, and organizes them. For example, it uses speech recognition technology to transcribe what was said during the meeting and natural language processing technology to extract important confirmation items. This makes it possible to create a list of important decisions and action items discussed during the meeting. Step 2: The survey tool integration unit distributes the confirmation items extracted by the automatic confirmation item generation unit to each team in a survey format and aggregates the responses. For example, the extracted confirmation items are distributed to each team in survey format, and the responses from the members are aggregated. This makes it possible to distribute surveys to the members of each team regarding the action items decided in the meeting and collect progress and opinions. Step 3: The automated Q&A unit automatically creates a dedicated communication tool channel based on the responses collected by the survey tool integration unit and uses a bot to support Q&A. For example, it automatically creates a dedicated communication tool channel after a meeting and uses a bot to support Q&A. This allows the bot to receive and answer questions about the topics discussed in the meeting. Step 4: The Feedback Collection and Sharing Department automatically organizes and sends the feedback collected by the Automated Q&A Department to the relevant personnel and reports it to the entire team using a survey tool. For example, it automatically organizes the feedback collected using the survey tool and sends it to the relevant personnel. This allows for the early resolution of problems and the rapid implementation of improvement measures by organizing the feedback collected from each team and sending it to the relevant personnel.

[0061] (Example of form 2) The meeting efficiency system according to an embodiment of the present invention is a system that analyzes meeting content in real time, extracts important matters, and organizes them. This meeting efficiency system includes an automatic confirmation item generation unit that analyzes meeting content in real time, extracts important matters, and organizes them. Next, it includes a survey tool linkage unit that distributes the extracted confirmation items to each team in a survey format and aggregates the responses. Furthermore, it includes an automatic question and answer unit that automatically creates a dedicated communication tool channel and supports question and answer using a bot. Finally, it includes a feedback collection and sharing unit that automatically organizes and sends the collected feedback to the person in charge and reports it to the whole using a survey tool. For example, the meeting efficiency system analyzes the content spoken during a meeting and automatically extracts important confirmation items. For example, it can extract and list important decisions and action items discussed during the meeting. This eliminates the need to manually organize confirmation items and reduces the workload after the meeting. Next, the meeting efficiency system distributes the extracted confirmation items to each team in a survey format and aggregates the responses from the members. For example, it can distribute a survey to members of each team regarding action items decided in the meeting and collect progress and opinions. This speeds up information sharing and accelerates the feedback loop. Furthermore, the meeting efficiency system automatically creates a dedicated communication tool channel after the meeting and uses a bot to support Q&A. For example, the bot can receive and answer questions about what was discussed in the meeting. This allows for quick Q&A and smooth information sharing. Finally, the meeting efficiency system automatically organizes the feedback collected through the survey tool and sends it to the responsible person. For example, organizing the feedback collected from each team and sending it to the responsible person enables early problem resolution and rapid implementation of improvement measures. Also, reporting the collected feedback to the entire group through the survey tool speeds up information sharing. In summary, the meeting efficiency system improves meeting efficiency, speeds up information sharing, and makes feedback management easier.This allows the meeting efficiency system to improve meeting efficiency, accelerate information sharing, and facilitate feedback management.

[0062] The meeting efficiency system according to this embodiment comprises an automatic confirmation item generation unit, a survey tool integration unit, an automatic question and answer unit, and a feedback collection and sharing unit. The automatic confirmation item generation unit analyzes the meeting content in real time and extracts and organizes important items. For example, the automatic confirmation item generation unit analyzes the content spoken during the meeting and automatically extracts important confirmation items. For example, the automatic confirmation item generation unit can extract and list important decisions and action items discussed during the meeting. Some or all of the above processing in the automatic confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automatic confirmation item generation unit can transcribe the content spoken during the meeting using speech recognition technology and extract important items using natural language processing technology. The survey tool integration unit distributes the confirmation items extracted by the automatic confirmation item generation unit to each team in a survey format and aggregates the responses. For example, the survey tool integration unit distributes the extracted confirmation items to each team in a survey format and aggregates the responses from the members. The survey tool integration unit can, for example, distribute surveys to members of each team regarding action items decided at a meeting, and collect progress and opinions. Some or all of the above processing in the survey tool integration unit may be performed using AI, or not. For example, the survey tool integration unit inputs the survey questions into a generation AI, and the generation AI aggregates the answers. The automated question and answer unit automatically creates a dedicated communication tool channel based on the answers aggregated by the survey tool integration unit and supports question and answer using a bot. For example, the automated question and answer unit automatically creates a dedicated communication tool channel after a meeting and supports question and answer using a bot. For example, the automated question and answer unit can have a bot receive and answer questions about the content discussed at the meeting. Some or all of the above processing in the automated question and answer unit may, for example, be performed using AI, or not. For example, the automated question and answer unit inputs the questions into a bot, and a generation AI generates the answers.The Feedback Collection and Sharing Unit automatically organizes and sends feedback collected by the Automated Question and Answer Unit to the responsible person, and reports it to the entire team using a survey tool. The Feedback Collection and Sharing Unit, for example, automatically organizes feedback collected by the survey tool and sends it to the responsible person. The Feedback Collection and Sharing Unit, for example, organizes feedback collected from each team and sends it to the responsible person, enabling early problem resolution and rapid implementation of improvement measures. Some or all of the above processing in the Feedback Collection and Sharing Unit may be performed using AI, for example, or without AI. For example, the Feedback Collection and Sharing Unit inputs the collected feedback into a generating AI, and the generating AI organizes the feedback. As a result, the meeting efficiency system according to the embodiment can improve the efficiency of meetings, expedite information sharing, and facilitate feedback management.

[0063] The automated confirmation item generation unit analyzes meeting content in real time, extracting and organizing important points. Specifically, it uses speech recognition technology to transcribe what is said during the meeting, and then analyzes that text data using natural language processing technology. For example, speech recognition technology can transcribe what is said during the meeting with high accuracy and categorize it by speaker. Next, natural language processing technology is used to extract important keywords and phrases from the text data, and these are used to create a list of confirmation items. When using AI, machine learning algorithms can be utilized to learn from past meeting data and improve the accuracy of extracting important points. For example, by automatically extracting and listing important decisions and action items discussed during the meeting, the post-meeting confirmation process can be made significantly more efficient. Furthermore, the automated confirmation item generation unit can update the extracted confirmation items in real time according to the progress of the meeting, providing immediate feedback to participants. This prevents important points from being overlooked during the meeting and supports rapid decision-making. In addition, the generated confirmation items are automatically saved after the meeting and can be referenced later, making meeting record management easier.

[0064] The Survey Tool Integration Unit distributes the confirmation items extracted by the Automatic Confirmation Item Generation Unit to each team in a survey format and aggregates the responses. Specifically, it automatically generates a survey based on the extracted confirmation items and distributes it to the members of each team. The survey content can include opinions on the confirmation items, progress reports, and additional questions. The Survey Tool Integration Unit distributes the generated surveys via email or a dedicated survey tool and aggregates the responses from members. When using AI, the generation AI can automatically generate the survey questions and aggregate the responses automatically. For example, regarding action items decided in a meeting, a survey can be distributed to the members of each team to collect progress reports and opinions. The collected responses are automatically compiled and visualized as graphs and charts, allowing for an at-a-glance understanding of progress and opinion trends. Furthermore, the Survey Tool Integration Unit automatically generates reports based on the responses, which can be used for meeting follow-up and preparation for the next meeting. This allows the Survey Tool Integration Unit to efficiently follow up on confirmation items and facilitate communication between teams.

[0065] The automated Q&A unit automatically creates a dedicated communication tool channel based on the responses collected by the survey tool integration unit and supports Q&A using a bot. Specifically, it automatically creates a dedicated communication tool channel after the meeting and supports Q&A using a bot. The bot can automatically answer questions from participants based on the content discussed in the meeting and the responses to the survey. When using AI, the generating AI analyzes the content of the question and generates an appropriate answer. For example, the bot receives a question about the content discussed in the meeting, and the generating AI generates an answer based on past meeting data and related information. This allows participants to quickly resolve their doubts and streamlines post-meeting follow-up. Furthermore, the automated Q&A unit can learn from the bot's answers and improve the accuracy of future Q&A. For example, it collects feedback on the content of the bot's answers, and the generating AI improves the accuracy of the answers based on that feedback. As a result, the automated Q&A unit can quickly and accurately resolve participants' doubts and support the efficiency of meetings.

[0066] The Feedback Collection and Sharing Department automatically organizes and sends feedback collected by the Automated Question and Answer Department to the relevant personnel and reports it to the entire team using a survey tool. Specifically, it automatically organizes the feedback collected by the survey tool and sends it to the relevant personnel. For example, by organizing the feedback collected from each team and sending it to the relevant personnel, the Feedback Collection and Sharing Department can enable early problem resolution and rapid implementation of improvement measures. When using AI, the generation AI analyzes the collected feedback, extracts and organizes the important points. For example, it can classify the content of the feedback by category and prioritize it according to its importance and urgency. This allows the person in charge to efficiently review the feedback and respond quickly. Furthermore, the Feedback Collection and Sharing Department can report and share the organized feedback to the entire team through the survey tool. This allows the entire team to share the overall progress and problems and collaboratively consider solutions. The Feedback Collection and Sharing Department can also periodically analyze the collected feedback and extract and report areas for improvement and success stories in meetings. This allows the Feedback Collection and Sharing Department to improve the quality of meetings and enhance the overall team performance.

[0067] The automatic confirmation item generation unit can analyze what is said during a meeting and automatically extract important confirmation items. For example, the automatic confirmation item generation unit can analyze what is said during a meeting and automatically extract important confirmation items. For example, the automatic confirmation item generation unit can extract and list important decisions and action items discussed during a meeting. This eliminates the need for manual organization of confirmation items by automatically extracting important confirmation items during a meeting. Some or all of the above processing in the automatic confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automatic confirmation item generation unit can transcribe what is said during a meeting using speech recognition technology and extract important items using natural language processing technology.

[0068] The Survey Tool Integration Unit can distribute the extracted confirmation items to each team in survey format and aggregate the responses from the members. For example, the Survey Tool Integration Unit can distribute the extracted confirmation items to each team in survey format and aggregate the responses from the members. For example, the Survey Tool Integration Unit can distribute surveys to members of each team regarding action items decided at a meeting and collect progress and opinions. This speeds up information sharing by distributing the extracted confirmation items in survey format and aggregating the responses. Some or all of the above processing in the Survey Tool Integration Unit may be performed using AI, for example, or not using AI. For example, the Survey Tool Integration Unit inputs the content of the survey questions into a generating AI, and the generating AI aggregates the responses.

[0069] The automated Q&A unit can automatically create a dedicated communication tool channel after a meeting and support Q&A using a bot. For example, the automated Q&A unit can automatically create a dedicated communication tool channel after a meeting and support Q&A using a bot. For example, the automated Q&A unit can have a bot receive and answer questions about the content discussed in the meeting. This facilitates information sharing by supporting Q&A after the meeting. Some or all of the above processing in the automated Q&A unit may be performed using AI, for example, or not using AI. For example, the automated Q&A unit inputs the question content into a bot, and a generating AI generates the answer.

[0070] The feedback collection and sharing unit can automatically organize feedback collected through a survey tool and send it to the responsible person. For example, the feedback collection and sharing unit automatically organizes feedback collected through a survey tool and sends it to the responsible person. For example, by organizing feedback collected from each team and sending it to the responsible person, the feedback collection and sharing unit enables early problem resolution and rapid implementation of improvement measures. This allows for early problem resolution and rapid implementation of improvement measures by automatically organizing the collected feedback and sending it to the responsible person. Some or all of the above processing in the feedback collection and sharing unit may be performed using AI, or not. For example, the feedback collection and sharing unit inputs the collected feedback into a generating AI, which then organizes the feedback.

[0071] The automated confirmation item generation unit can estimate the user's emotions and adjust the criteria for extracting important information based on the estimated user emotions. For example, if the user is stressed, the automated confirmation item generation unit will relax the criteria for extracting important information and extract more information. For example, if the user is relaxed, the automated confirmation item generation unit will tighten the criteria for extracting important information and extract only the minimum necessary information. For example, if the user is excited, the automated confirmation item generation unit will dynamically adjust the criteria for extracting important information and extract information that is relevant to the user's interests. By adjusting the criteria for extracting important information according to the user's emotions, more appropriate information can be extracted. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without using AI. For example, the automated confirmation item generation unit inputs the user's facial expression data into the generation AI, which estimates the emotions and adjusts the criteria for extracting important items.

[0072] The automated confirmation item generation unit can dynamically change the frequency of extracting important items in real time according to the progress of the meeting. For example, in the initial stages of the meeting, the automated confirmation item generation unit sets the frequency of extracting important items to a low level to grasp the overall flow. For example, in the middle of the meeting, the automated confirmation item generation unit increases the frequency of extracting important items to extract specific discussion content in detail. For example, in the final stages of the meeting, the automated confirmation item generation unit sets the frequency of extracting important items to a low level again to extract final conclusions and action items. In this way, by changing the frequency of extracting important items according to the progress of the meeting, appropriate information corresponding to the content of the meeting can be extracted. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without using AI. For example, the automated confirmation item generation unit inputs meeting progress data into a generation AI, and the generation AI dynamically changes the frequency of extracting important items.

[0073] The automated confirmation item generation unit can set priorities for important items to extract based on the roles and areas of expertise of the meeting participants. For example, the automated confirmation item generation unit may prioritize extracting statements made by participants with higher roles and organize them as important items. For example, the automated confirmation item generation unit may prioritize extracting statements related to areas of expertise and organize them as important specialized items. For example, the automated confirmation item generation unit may consider both roles and areas of expertise to extract important items in a balanced manner. This allows for the prioritization of more important information by setting priorities for important items based on the roles and areas of expertise of the meeting participants. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automated confirmation item generation unit inputs participant role and area of ​​expertise data into a generating AI, and the generating AI sets the priorities for important items.

[0074] The automatic confirmation item generation unit can estimate the user's emotions and adjust the display method of important items based on the estimated user emotions. For example, if the user is nervous, the automatic confirmation item generation unit provides a simple and highly visible display method. For example, if the user is relaxed, the automatic confirmation item generation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the automatic confirmation item generation unit provides a display method that gets straight to the point. By adjusting the display method of important items according to the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the automatic confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automatic confirmation item generation unit inputs the user's facial expression data into the generation AI, the generation AI estimates the emotions, and adjusts the display method of important items.

[0075] The automated confirmation item generation unit can extract important items by applying different analysis algorithms depending on the content of the meeting. For example, in a technical meeting, the automated confirmation item generation unit applies an analysis algorithm specialized in technical terminology. For example, in a business meeting, the automated confirmation item generation unit applies an analysis algorithm specialized in business terminology. For example, in a mixed-type meeting, the automated confirmation item generation unit extracts important items by combining multiple analysis algorithms. This makes it possible to extract more appropriate important items by changing the analysis algorithm according to the content of the meeting. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without using AI. For example, the automated confirmation item generation unit inputs meeting content data into a generating AI, and the generating AI applies different analysis algorithms to extract important items.

[0076] The automated confirmation item generation unit can analyze meeting recordings and extract important points using speech recognition technology. For example, the automated confirmation item generation unit can analyze meeting recordings in real time and transcribe important statements into text. For example, the automated confirmation item generation unit can extract statements containing specific keywords using speech recognition technology. For example, the automated confirmation item generation unit can analyze the recording data and extract important points based on the frequency of statements and emphasized content. In this way, important statements can be automatically extracted by analyzing meeting recordings and using speech recognition technology. Some or all of the above processing in the automated confirmation item generation unit may be performed using AI, for example, or without AI. For example, the automated confirmation item generation unit inputs meeting recordings into a generation AI, and the generation AI extracts important points using speech recognition technology.

[0077] The survey tool integration unit can estimate the user's emotions and adjust the survey questions based on the estimated emotions. For example, if the user is stressed, the survey tool integration unit can change the questions to simpler ones. For example, if the user is relaxed, the survey tool integration unit can provide more detailed questions. For example, if the user is excited, the survey tool integration unit can add questions related to emotions. In this way, by adjusting the survey questions according to the user's emotions, more appropriate questions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the survey tool integration unit may be performed using AI, or not using AI. For example, the survey tool integration unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the survey questions.

[0078] The survey tool integration unit can analyze response trends for each team and optimize the timing of survey distribution. For example, the survey tool integration unit analyzes team response trends and distributes surveys during the time slot with the highest response rate. For example, the survey tool integration unit considers team schedules and distributes surveys immediately after meetings. For example, the survey tool integration unit adjusts the frequency of survey distribution based on team response trends. This allows for the distribution of surveys at the optimal time by analyzing response trends for each team. Some or all of the above processes in the survey tool integration unit may be performed using AI, for example, or without AI. For example, the survey tool integration unit inputs team response data into a generating AI, which analyzes response trends and optimizes the timing of survey distribution.

[0079] The survey tool integration unit can estimate the user's emotions and adjust the survey response format based on the estimated emotions. For example, if the user is stressed, the survey tool integration unit provides a multiple-choice response format. For example, if the user is relaxed, the survey tool integration unit provides a free-response response format. For example, if the user is in a hurry, the survey tool integration unit provides a simple checkbox format. In this way, by adjusting the survey response format according to the user's emotions, a more appropriate response format can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the survey tool integration unit may be performed using AI, for example, or without AI. For example, the survey tool integration unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the survey response format.

[0080] The survey tool integration unit can analyze survey response data in real time and provide immediate feedback. For example, the survey tool integration unit can analyze survey response data in real time and immediately display the aggregated results. For example, the survey tool integration unit can provide immediate feedback based on the response data and suggest areas for improvement. For example, the survey tool integration unit can analyze response data in real time and immediately suggest the next action items. In this way, by analyzing survey response data in real time, immediate feedback can be provided. Some or all of the above processing in the survey tool integration unit may be performed using AI, for example, or without AI. For example, the survey tool integration unit inputs response data into a generating AI, which analyzes it in real time and provides feedback.

[0081] The automated question-and-answer unit can estimate the user's emotions and adjust the content of the questions and answers based on the estimated emotions. For example, if the user is nervous, the automated question-and-answer unit will provide a concise and clear answer. For example, if the user is relaxed, the automated question-and-answer unit will provide an answer that includes a detailed explanation. For example, if the user is in a hurry, the automated question-and-answer unit will provide a quick and to-the-point answer. In this way, by adjusting the content of the questions and answers according to the user's emotions, more appropriate answers can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or not using AI. For example, the automated question-and-answer unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the content of the questions and answers.

[0082] The automated question-and-answer unit can analyze the history of questions and answers and provide the best answer by referring to past questions and answers. For example, the automated question-and-answer unit can provide the best answer to similar questions based on the history of past questions and answers. For example, the automated question-and-answer unit can analyze the history of questions and answers and provide template answers to frequently asked questions. For example, the automated question-and-answer unit can refer to past answers and provide the best answer that includes the latest information. In this way, by analyzing the history of questions and answers, the system can provide the best answer by referring to past questions and answers. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs the question-and-answer history data into a generating AI, and the generating AI provides the best answer.

[0083] The automated question-and-answer unit can dynamically change the bot's response speed depending on the content of the question. For example, the automated question-and-answer unit will answer simple questions quickly and provide detailed answers to complex questions over time. For example, the automated question-and-answer unit will adjust the response speed according to the content of the question to meet the user's expectations. For example, the automated question-and-answer unit will dynamically change the response speed according to the urgency of the question. This allows the bot to provide answers that meet the user's expectations by changing the response speed according to the content of the question. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs question content data into a generating AI, and the generating AI dynamically changes the response speed.

[0084] The automated question-and-answer unit can estimate the user's emotions and determine the priority of questions based on the estimated emotions. For example, if the user is nervous, the automated question-and-answer unit will prioritize answering questions. For example, if the user is relaxed, the automated question-and-answer unit will prioritize answering other questions. For example, if the user is in a hurry, the automated question-and-answer unit will answer questions quickly. This allows for more appropriate responses by determining the priority of questions and answers according to the user's emotions. 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 automated question-and-answer unit may be performed using AI or not using AI. For example, the automated question-and-answer unit inputs the user's facial expression data into the generative AI, which estimates the emotions and determines the priority of questions and answers.

[0085] The automated question-and-answer unit can analyze the content of questions and answers and automatically refer to relevant external resources to provide answers. For example, the automated question-and-answer unit can automatically search for external resources related to the question and include them in the answer. For example, the automated question-and-answer unit can automatically refer to relevant documents and materials based on the content of the questions and answers. For example, the automated question-and-answer unit can refer to external resources and provide answers that include the latest information. This allows for the provision of more accurate answers by referring to external resources related to the content of the questions and answers. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs question data into a generating AI, and the generating AI refers to relevant external resources to provide an answer.

[0086] The automated question-and-answer unit can automatically generate relevant documents and materials based on the content of the questions and answers. For example, the automated question-and-answer unit can automatically generate relevant documents based on the content of the questions and answers. For example, the automated question-and-answer unit can automatically generate relevant materials based on the content of the questions and answers. For example, the automated question-and-answer unit can automatically generate relevant reports based on the content of the questions and answers. This makes it easier to organize information by generating relevant documents and materials based on the content of the questions and answers. Some or all of the above processing in the automated question-and-answer unit may be performed using AI, for example, or without AI. For example, the automated question-and-answer unit inputs the question-and-answer content data into a generating AI, and the generating AI generates relevant documents and materials.

[0087] The feedback collection and sharing unit can estimate the user's emotions and adjust the feedback organization method based on the estimated user emotions. For example, if the user is stressed, the feedback collection and sharing unit provides a concise feedback organization method. For example, if the user is relaxed, the feedback collection and sharing unit provides a detailed feedback organization method. For example, if the user is excited, the feedback collection and sharing unit prioritizes organizing emotion-related feedback. This allows for more appropriate feedback organization by adjusting the feedback organization method according to the user's emotions. 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 feedback collection and sharing unit may be performed using AI or not using AI. For example, the feedback collection and sharing unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the feedback organization method.

[0088] The feedback collection and sharing unit can analyze the content of the feedback and prioritize notifications to the responsible person according to their importance. For example, the feedback collection and sharing unit can prioritize notifying the responsible person of high-importance feedback. For example, the feedback collection and sharing unit can analyze the content of the feedback and set notification priorities according to urgency. For example, the feedback collection and sharing unit can adjust the notification method according to the importance of the feedback. This allows for a quick response to important feedback by prioritizing notifications according to the importance of the feedback. Some or all of the above processes in the feedback collection and sharing unit may be performed using AI, for example, or not using AI. For example, the feedback collection and sharing unit inputs feedback content data into a generating AI, which analyzes the importance and prioritizes notifications.

[0089] The feedback collection and sharing unit can refer to the feedback history and identify areas for improvement by comparing it with past feedback. For example, the feedback collection and sharing unit can refer to the feedback history and identify areas for improvement by comparing it with past feedback. For example, the feedback collection and sharing unit can analyze the feedback history and identify problems that have been repeatedly pointed out. For example, the feedback collection and sharing unit can identify areas for improvement and propose specific action plans based on the feedback history. This allows for the identification of areas for improvement by referring to the feedback history and comparing it with past feedback. Some or all of the above processing in the feedback collection and sharing unit may be performed using AI, for example, or without AI. For example, the feedback collection and sharing unit inputs feedback history data into a generating AI, and the generating AI identifies areas for improvement.

[0090] The feedback collection and sharing unit can estimate the user's emotions and adjust the way feedback is displayed based on the estimated emotions. For example, if the user is nervous, the feedback collection and sharing unit provides a simple and highly visible display method. For example, if the user is relaxed, the feedback collection and sharing unit provides a display method that includes detailed information. For example, if the user is in a hurry, the feedback collection and sharing unit provides a display method that gets straight to the point. By adjusting the way feedback is displayed according to the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback collection and sharing unit may be performed using AI, for example, or without AI. For example, the feedback collection and sharing unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the way feedback is displayed.

[0091] The feedback collection and sharing unit can automatically generate relevant action items based on the content of the feedback. For example, the feedback collection and sharing unit can analyze the content of the feedback and automatically generate specific action items. For example, the feedback collection and sharing unit can generate action items for improvement based on the content of the feedback. For example, the feedback collection and sharing unit can refer to the content of the feedback and generate high-priority action items. This allows for the rapid implementation of specific improvement measures by generating action items based on the content of the feedback. Some or all of the above processes in the feedback collection and sharing unit may be performed using AI, for example, or without AI. For example, the feedback collection and sharing unit inputs the feedback content data into a generating AI, and the generating AI generates action items.

[0092] The feedback collection and sharing unit can analyze the content of the feedback and automatically link it to relevant projects and tasks. For example, the feedback collection and sharing unit analyzes the content of the feedback and automatically links it to relevant projects. For example, the feedback collection and sharing unit automatically links it to relevant tasks based on the content of the feedback. For example, the feedback collection and sharing unit refers to the content of the feedback and automatically identifies relevant projects and tasks. This makes it easier to utilize feedback by analyzing the content of the feedback and linking it to relevant projects and tasks. Some or all of the above processing in the feedback collection and sharing unit may be performed using AI, for example, or not using AI. For example, the feedback collection and sharing unit inputs the feedback content data into a generating AI, and the generating AI links it to relevant projects and tasks.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The meeting efficiency system can dynamically change the frequency of extracting key points in real time according to the progress of the meeting. For example, in the early stages of the meeting, the frequency of extracting key points is set low to grasp the overall flow. In the middle of the meeting, the frequency of extracting key points is increased to extract specific discussion points in detail. In the final stages of the meeting, the frequency of extracting key points is set low again to extract the final conclusions and action items. In this way, by changing the frequency of extracting key points according to the progress of the meeting, appropriate information can be extracted according to the content of the meeting.

[0095] The meeting efficiency system can prioritize the key points to extract based on the roles and expertise of meeting participants. For example, it can prioritize extracting and organizing the statements of high-ranking participants as key points. It can also prioritize extracting statements related to specific areas of expertise and organizing them as specialized key points. By considering both roles and expertise, it can extract key points in a balanced way. In this way, by prioritizing key points based on the roles and expertise of meeting participants, more important information can be extracted preferentially.

[0096] The meeting efficiency system can estimate the user's emotions and adjust how important information is displayed based on those emotions. For example, if the user is nervous, it provides a simple and highly visible display. If the user is relaxed, it provides a display that includes detailed information. If the user is in a hurry, it provides a display that gets straight to the point. By adjusting how important information is displayed according to the user's emotions, it enables more appropriate information presentation.

[0097] The meeting efficiency system can extract key information by applying different analysis algorithms depending on the content of the meeting. For example, a technical meeting would use an analysis algorithm specialized in technical terminology. A business meeting would use an analysis algorithm specialized in business terminology. In a mixed-type meeting, multiple analysis algorithms would be combined to extract key information. This allows for the extraction of more appropriate key information by changing the analysis algorithm according to the content of the meeting.

[0098] The meeting efficiency system can analyze meeting recordings and extract important points using speech recognition technology. For example, it can analyze meeting recordings in real time and transcribe important statements into text. It can also use speech recognition technology to extract statements containing specific keywords. By analyzing the recordings, it can extract important points based on the frequency of statements and emphasized content. In this way, by analyzing meeting recordings and using speech recognition technology, important statements can be automatically extracted.

[0099] The meeting efficiency system can estimate the user's emotions and adjust the content of survey questions based on those estimates. For example, if a user is stressed, the questions can be simplified. If a user is relaxed, more detailed questions can be provided. If a user is agitated, questions related to their emotions can be added. This allows for more appropriate questions to be provided by adjusting the survey questions according to the user's emotions.

[0100] The meeting efficiency system can analyze response trends for each team and optimize the timing of survey distribution. For example, it can analyze team response trends and distribute surveys during the time slot with the highest response rate. It can also distribute surveys immediately after meetings, taking team schedules into consideration. Based on team response trends, it can adjust the frequency of survey distribution. In this way, by analyzing response trends for each team, surveys can be distributed at the optimal time.

[0101] The meeting efficiency system can estimate user emotions and adjust the questionnaire response format based on those emotions. For example, if a user is stressed, it can provide a multiple-choice response format. If a user is relaxed, it can provide a free-response response format. If a user is in a hurry, it can provide a simple checkbox format. This allows for more appropriate responses by adjusting the questionnaire format according to the user's emotions.

[0102] The meeting efficiency system can analyze survey response data in real time and provide immediate feedback. For example, it can analyze survey response data in real time and display the aggregated results immediately. Based on the response data, it can provide immediate feedback and suggest areas for improvement. It can analyze the response data in real time and immediately suggest the next action items. In this way, by analyzing survey response data in real time, it can provide immediate feedback.

[0103] The meeting efficiency system can estimate the user's emotions and adjust the content of the Q&A based on those emotions. For example, if the user is nervous, it provides concise and clear answers. If the user is relaxed, it provides answers that include detailed explanations. If the user is in a hurry, it provides quick and to-the-point answers. In this way, by adjusting the content of the Q&A according to the user's emotions, more appropriate answers can be provided.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The automated confirmation item generation unit analyzes the meeting content in real time, extracts important items, and organizes them. For example, it uses speech recognition technology to transcribe what was said during the meeting and natural language processing technology to extract important confirmation items. This makes it possible to create a list of important decisions and action items discussed during the meeting. Step 2: The survey tool integration unit distributes the confirmation items extracted by the automatic confirmation item generation unit to each team in a survey format and aggregates the responses. For example, the extracted confirmation items are distributed to each team in survey format, and the responses from the members are aggregated. This makes it possible to distribute surveys to the members of each team regarding the action items decided in the meeting and collect progress and opinions. Step 3: The automated Q&A unit automatically creates a dedicated communication tool channel based on the responses collected by the survey tool integration unit and uses a bot to support Q&A. For example, it automatically creates a dedicated communication tool channel after a meeting and uses a bot to support Q&A. This allows the bot to receive and answer questions about the topics discussed in the meeting. Step 4: The Feedback Collection and Sharing Department automatically organizes and sends the feedback collected by the Automated Q&A Department to the relevant personnel and reports it to the entire team using a survey tool. For example, it automatically organizes the feedback collected using the survey tool and sends it to the relevant personnel. This allows for the early resolution of problems and the rapid implementation of improvement measures by organizing the feedback collected from each team and sending it to the relevant personnel.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the automatic confirmation item generation unit, the questionnaire tool linkage unit, the automatic question and answer unit, and the feedback collection and sharing unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the automatic confirmation item generation unit is implemented by the processor 46 of the smart device 14, which transcribes the content of speech during a meeting into text using speech recognition technology and extracts important points using natural language processing technology. The questionnaire tool linkage unit is implemented by the specific processing unit 290 of the data processing device 12, which distributes the extracted confirmation items to each team in questionnaire format and aggregates the responses from the members. The automatic question and answer unit is implemented by the control unit 46A of the smart device 14, which automatically creates a dedicated communication tool channel after the meeting and supports question and answer using a bot. The feedback collection and sharing unit is implemented by the specific processing unit 290 of the data processing device 12, which automatically organizes the feedback collected by the questionnaire tool and sends it to the person in charge. 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.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the automatic confirmation item generation unit, the questionnaire tool linkage unit, the automatic question and answer unit, and the feedback collection and sharing unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the automatic confirmation item generation unit is implemented by the processor 46 of the smart glasses 214, which transcribes the content of speech during a meeting into text using speech recognition technology and extracts important points using natural language processing technology. The questionnaire tool linkage unit is implemented by the specific processing unit 290 of the data processing device 12, which distributes the extracted confirmation items to each team in questionnaire format and aggregates the responses from the members. The automatic question and answer unit is implemented by the control unit 46A of the smart glasses 214, which automatically creates a dedicated communication tool channel after the meeting and supports question and answer using a bot. The feedback collection and sharing unit is implemented by the specific processing unit 290 of the data processing device 12, which automatically organizes the feedback collected by the questionnaire tool and sends it to the person in charge. 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.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the automatic confirmation item generation unit, the questionnaire tool linkage unit, the automatic question and answer unit, and the feedback collection and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the automatic confirmation item generation unit is implemented by the processor 46 of the headset terminal 314, which transcribes the content of speech during a meeting into text using speech recognition technology and extracts important points using natural language processing technology. The questionnaire tool linkage unit is implemented by the specific processing unit 290 of the data processing unit 12, which distributes the extracted confirmation items to each team in questionnaire format and aggregates the responses from members. The automatic question and answer unit is implemented by the control unit 46A of the headset terminal 314, which automatically creates a dedicated communication tool channel after the meeting and supports question and answer using a bot. The feedback collection and sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically organizes the feedback collected by the questionnaire tool and sends it to the person in charge. 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.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the automatic confirmation item generation unit, the questionnaire tool linkage unit, the automatic question and answer unit, and the feedback collection and sharing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the automatic confirmation item generation unit is implemented by the processor 46 of the robot 414, which transcribes the content of speech during a meeting into text using speech recognition technology and extracts important points using natural language processing technology. The questionnaire tool linkage unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which distributes the extracted confirmation items to each team in questionnaire format and aggregates the responses from the members. The automatic question and answer unit is implemented by, for example, the control unit 46A of the robot 414, which automatically creates a dedicated communication tool channel after the meeting and supports question and answer using a bot. The feedback collection and sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically organizes the feedback collected by the questionnaire tool and sends it to the person in charge. 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) An automated confirmation item generation unit analyzes meeting content in real time, extracts and organizes important points, The aforementioned automated confirmation item generation unit distributes the confirmation items extracted by the unit to each team in a questionnaire format, and the questionnaire tool integration unit aggregates the responses. Based on the responses collected by the aforementioned survey tool integration unit, an automated question and answer unit automatically creates a dedicated communication tool channel and uses a bot to support question and answer sessions. The system includes a feedback collection and sharing unit that automatically organizes and sends feedback collected by the aforementioned automated question-and-answer unit to the person in charge, and reports it to everyone using a survey tool. A system characterized by the following features. (Note 2) The aforementioned automatic confirmation item generation unit, The system analyzes what was said during the meeting and automatically extracts important points to confirm. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned survey tool integration unit is: The extracted confirmation items will be distributed to each team in the form of a questionnaire, and the responses from the members will be collected. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned automated question and answer unit, A dedicated communication channel is automatically created after the meeting, and a bot is used to support the Q&A session. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback collection and sharing unit is: The survey tool automatically organizes the collected feedback and sends it to the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned automatic confirmation item generation unit, We estimate the user's emotions and adjust the criteria for extracting important information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned automatic confirmation item generation unit, The frequency of extracting important information is dynamically changed in real time according to the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned automatic confirmation item generation unit, Prioritize the key points to be extracted based on the roles and areas of expertise of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned automatic confirmation item generation unit, It estimates the user's emotions and adjusts how important information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned automatic confirmation item generation unit, Depending on the content of the meeting, different analysis algorithms are applied to extract key points. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned automatic confirmation item generation unit, The system analyzes meeting recordings and uses speech recognition technology to extract key information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned survey tool integration unit is: The system estimates the user's emotions and adjusts the survey questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned survey tool integration unit is: We analyze response trends for each team and optimize the timing of survey distribution. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned survey tool integration unit is: The system estimates the user's emotions and adjusts the survey response format based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned survey tool integration unit is: We analyze survey response data in real time and provide immediate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned automated question and answer unit, The system estimates the user's emotions and adjusts the content of the Q&A session based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned automated question and answer unit, We analyze the history of questions and answers and refer to past questions and answers to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned automated question and answer unit, The bot's response speed will be dynamically adjusted based on the content of the Q&A. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned automated question and answer unit, The system estimates the user's emotions and determines the priority of questions and answers based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automated question and answer unit, The system analyzes the content of the Q&A session and automatically references relevant external resources to provide answers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automated question and answer unit, Based on the content of the Q&A session, related documents and materials will be automatically generated. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback collection and sharing unit is: We estimate the user's emotions and adjust how feedback is processed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback collection and sharing unit is: We analyze the content of the feedback and prioritize notifications to the relevant personnel based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback collection and sharing unit is: Refer to the feedback history and identify areas for improvement by comparing it with past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback collection and sharing unit is: It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback collection and sharing unit is: Based on the feedback, relevant action items will be automatically generated. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback collection and sharing unit is: The system analyzes the feedback and automatically links it to relevant projects and tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0178] 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. An automated confirmation item generation unit analyzes meeting content in real time, extracts and organizes important points, The aforementioned automated confirmation item generation unit distributes the confirmation items extracted by the unit to each team in a questionnaire format, and the questionnaire tool integration unit aggregates the responses. Based on the responses collected by the aforementioned survey tool integration unit, an automated question and answer unit automatically creates a dedicated communication tool channel and uses a bot to support question and answer sessions. The system includes a feedback collection and sharing unit that automatically organizes and sends feedback collected by the aforementioned automated question-and-answer unit to the person in charge, and reports it to everyone using a survey tool. A system characterized by the following features.

2. The aforementioned automatic confirmation item generation unit, The system analyzes what was said during the meeting and automatically extracts important points to confirm. The system according to feature 1.

3. The aforementioned survey tool integration unit is: The extracted confirmation items will be distributed to each team in the form of a questionnaire, and the responses from the members will be collected. The system according to feature 1.

4. The aforementioned automated question and answer unit, A dedicated communication channel is automatically created after the meeting, and a bot is used to support the Q&A session. The system according to feature 1.

5. The aforementioned feedback collection and sharing unit is: The survey tool automatically organizes the collected feedback and sends it to the person in charge. The system according to feature 1.

6. The aforementioned automatic confirmation item generation unit, We estimate the user's emotions and adjust the criteria for extracting important information based on the estimated user emotions. The system according to feature 1.

7. The aforementioned automatic confirmation item generation unit, The frequency of extracting important information is dynamically changed in real time according to the progress of the meeting. The system according to feature 1.

8. The aforementioned automatic confirmation item generation unit, Prioritize the key points to be extracted based on the roles and areas of expertise of the meeting participants. The system according to feature 1.

Images