system

Abstract

The system according to this embodiment aims to search for and provide necessary information in real time during a meeting. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit tracks the conversation during the meeting in real time and collects information. The analysis unit analyzes the information collected by the collection unit and searches for related information. The provision unit provides the information searched by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to quickly search for and provide the information necessary during a meeting, and there is a risk of reducing the efficiency of the meeting.

[0005] The system according to the embodiment aims to search for and provide the information necessary during a meeting in real time.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit follows the conversation during the meeting in real time and collects information. The analysis unit analyzes the information collected by the collection unit and searches for relevant information. The provision unit provides the information searched by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can search for and provide necessary information in real time during a meeting. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment. <000009l>

[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). <00,00097>

[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. <0,000101> The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that provides information when a participant says, "What was that again?" during a meeting. This AI agent system can follow the flow of conversation in real time during a meeting and quickly provide the necessary information. The AI ​​agent system stores its own materials, communication tools, emails, and conversation history in a database. For example, if a participant says, "I think I heard about a similar case before, is there a history?" during a meeting, the AI ​​agent system recognizes the statement and searches the database for relevant information. Next, a large-scale language model (LLM) analyzes the search results and presents relevant cases. This allows for quick information provision even when a participant says, "What was that again?" during a meeting. Also, if a participant says, "Where is that document?" during a meeting, the AI ​​agent system searches the database for the relevant document, and the LLM presents it. For example, if a participant says, "Show me last week's meeting materials," the AI ​​agent system recognizes the statement, searches the database for last week's meeting materials, and the LLM presents them. This allows for quick acquisition of necessary materials during a meeting. Furthermore, if it becomes necessary to share meeting content in another meeting, simply asking "Please document this meeting" will allow the AI ​​agent system to automatically generate reports, approval documents, etc., by referencing the conversation history and other materials. For example, if someone says "Please summarize the contents of this meeting in a report" during a meeting, the AI ​​agent system will recognize the statement and automatically generate a report based on the conversation history and related materials. This saves the effort of creating documents and allows for efficient information sharing. This system also allows for quick information provision even if someone forgets something during a meeting. Additionally, necessary materials can be quickly retrieved, and meeting content can be shared efficiently. This improves meeting efficiency and increases work productivity. As a result, the AI ​​agent system can provide information quickly and accurately during meetings.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit tracks conversations during a meeting in real time and collects information. For example, the collection unit collects audio data during a meeting in real time. The collection unit can also collect text data during a meeting in real time. Furthermore, the collection unit can also collect materials during a meeting. For example, the collection unit collects presentation materials shared during a meeting. The analysis unit analyzes the information collected by the collection unit and searches for relevant information. For example, the analysis unit analyzes the collected audio data and extracts relevant keywords. Furthermore, the analysis unit can also analyze the collected text data and search for relevant information. Furthermore, the analysis unit can analyze the collected materials and search for relevant information. For example, the analysis unit analyzes the collected presentation materials and searches for relevant slides. The provision unit provides the information retrieved by the analysis unit. For example, the provision unit displays relevant information based on the searched keywords. Furthermore, the provision unit can display the searched materials. Furthermore, the provision unit can also provide the searched information in audio format. For example, the provisioning unit reads aloud the retrieved information using speech synthesis technology. This allows the AI ​​agent system according to the embodiment to follow the conversation during the meeting in real time and quickly provide the necessary information. Some or all of the above-described processes in the collection unit, analysis unit, and provisioning unit may be performed using AI, or not using AI. For example, the collection unit can input audio data from the meeting into the AI ​​and have the AI ​​perform the analysis of the audio data.

[0030] The data collection unit tracks conversations during meetings in real time and collects information. For example, the unit collects audio data during meetings in real time. Specifically, it collects high-precision audio data of conversations through microphones installed in the meeting room and microphones built into participants' devices. This audio data is converted into clear audio using noise reduction technology and transmitted to a central database. The data collection unit can also collect text data during meetings in real time. For example, it obtains text data from chat tools and messaging applications used during meetings and manages this data centrally. Furthermore, the data collection unit can also collect materials during meetings. For example, it automatically obtains presentation materials and documents shared during meetings and saves these materials in digital format. This allows the data collection unit to collect diverse data such as audio, text, and materials in real time, enabling it to grasp the overall picture of the meeting. The data collection unit can efficiently manage this data and collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and provisioning departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit and searches for relevant information. For example, the analysis unit analyzes collected audio data and extracts relevant keywords. Specifically, it uses speech recognition technology to convert the audio data into text and extracts important keywords and phrases from that text. This makes it possible to identify the main topics and important statements discussed during the meeting. The analysis unit can also analyze collected text data and search for relevant information. For example, it extracts relevant information from chat messages and documents used during the meeting, organizes this information, and converts it into a searchable format. Furthermore, the analysis unit can analyze collected materials and search for relevant information. For example, it analyzes collected presentation materials and identifies relevant slides and charts. This allows the analysis unit to quickly search for information related to a specific topic from the materials used during the meeting. The analysis unit uses AI to analyze this data and simulate multiple scenarios to identify the most relevant information. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past meeting data, it can predict trends in specific topics and discussions, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The information delivery unit provides information retrieved by the analysis unit. For example, the delivery unit displays relevant information based on searched keywords. Specifically, it displays information related to topics discussed during a meeting on the user's device in real time. This allows users to quickly access the information they need during the meeting. The delivery unit can also display retrieved materials. For example, it can display presentation materials and documents identified by the analysis unit on the user's device, providing the necessary information immediately. Furthermore, the delivery unit can provide retrieved information in audio format. For example, it can read the retrieved information aloud using speech synthesis technology. This allows users to grasp the information during the meeting using not only visual information but also auditory information. The delivery unit can efficiently manage this information and collaborate with other systems and departments as needed. For example, the provided information can be stored on a cloud server, making it accessible to other users and systems. The delivery unit can also collect user feedback and continuously improve the accuracy and effectiveness of the provided content. For example, it can review and improve the content based on user reactions and evaluations of the provided information. This allows the service provider to deliver information to users quickly and accurately, improving the efficiency and effectiveness of meetings.

[0033] The AI ​​agent system includes a documentation unit that documents meeting content. The documentation unit can document meeting content. For example, the documentation unit creates documents based on the conversation history during the meeting. The documentation unit can also create documents based on documents shared during the meeting. Furthermore, the documentation unit can create documents based on the content of statements made during the meeting. For example, the documentation unit summarizes the content of statements made during the meeting and creates a report. This makes it easier to share information by documenting meeting content. Some or all of the above processing in the documentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the documentation unit can input the conversation history during the meeting into a generative AI and have the generative AI summarize the conversation history.

[0034] The documentation unit can automatically generate reports, proposals, and other documents by referring to conversation history and other documents. For example, the documentation unit can create reports based on conversation history. It can also create proposals based on other documents. Furthermore, the documentation unit can create documents by combining conversation history and other documents. For example, the documentation unit can summarize the conversation history and create a report that reflects the contents of other documents. This allows for the automatic generation of reports, proposals, and other documents by referring to conversation history and other documents. Some or all of the above processing in the documentation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the documentation unit can input conversation history and other documents into a generation AI and have the generation AI generate reports.

[0035] The data collection unit can evaluate the importance of statements made during a meeting in real time and prioritize the collection of important statements. For example, the data collection unit can analyze keywords in the content of statements and prioritize the collection of statements of high importance. The data collection unit can also prioritize the collection of statements of high importance based on the speaker's position and expertise. Furthermore, the data collection unit can prioritize the collection of statements related to important agenda items according to the progress of the meeting. For example, the data collection unit can analyze the content of statements made during the meeting and evaluate the importance of statements in real time. This allows for the priority collection of important statements by evaluating the importance of each statement. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of statements made during the meeting into AI and have the AI ​​perform the evaluation of the importance of the statements.

[0036] The data collection unit can collect information from relevant external databases based on the content of statements made during a meeting. For example, the data collection unit can collect technical literature related to the content of the statements from external databases. The data collection unit can also collect market data related to the content of the statements from external databases. Furthermore, the data collection unit can collect legal and regulatory information related to the content of the statements from external databases. For example, the data collection unit can analyze the content of statements made during a meeting and collect information from relevant external databases. This allows for the rapid acquisition of relevant information by collecting information from external databases based on the content of the statements. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the content of statements made during a meeting into an AI and have the AI ​​perform the collection of information from relevant external databases.

[0037] The data collection unit can automatically collect relevant past meeting records based on the content of statements made during a meeting. For example, the data collection unit can search for past meeting records related to the content of the statements and collect relevant information. The data collection unit can also automatically collect the content of discussions in past meetings based on the content of the statements. Furthermore, the data collection unit can collect decisions made in past meetings related to the content of the statements and provide them as reference information. For example, the data collection unit can analyze the content of statements made during a meeting and automatically collect relevant past meeting records. This allows for the rapid acquisition of relevant information by collecting past meeting records based on the content of the statements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of statements made during a meeting into AI and have the AI ​​perform the collection of relevant past meeting records.

[0038] The data collection unit can collect relevant social media posts based on the content of statements made during a meeting. For example, the data collection unit can search for social media posts related to the content of the statements and collect relevant information. The data collection unit can also collect opinions and comments on social media based on the content of the statements. Furthermore, the data collection unit can collect trend information related to the content of the statements from social media. For example, the data collection unit can analyze the content of statements made during a meeting and collect relevant social media posts. This allows for the rapid acquisition of relevant information by collecting social media posts based on the content of the statements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the content of statements made during a meeting into an AI and have the AI ​​perform the collection of relevant social media posts.

[0039] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the reliability of the information source and prioritize the analysis of highly reliable information. The analysis unit can also evaluate the content of the information and prioritize the analysis of highly reliable information. Furthermore, the analysis unit can evaluate the reliability of the information provider and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. In this way, by evaluating the reliability of the information, highly reliable information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the evaluation of the reliability of the information.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the collected information. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a market analysis algorithm to market information. Furthermore, it can apply a legal and regulatory analysis algorithm to legal and regulatory information. For example, the analysis unit applies different analysis algorithms depending on the category of the collected information. By applying analysis algorithms according to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​execute the application of analysis algorithms according to the category.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the collected information. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. Furthermore, the analysis unit may prioritize the analysis of information of high importance based on the submission date. For example, the analysis unit determines the priority of analysis based on the submission date of the collected information. This allows for the prioritization of analysis based on the submission date of the information, thereby prioritizing the analysis of the most recent information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the priority determination based on the submission date.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the collected information. For example, the analysis unit may prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can prioritize the analysis of information of high importance based on relevance. For example, the analysis unit adjusts the order of analysis based on the relevance of the collected information. This allows for the prioritization of important information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the adjustment of the analysis order based on relevance.

[0043] The information provider can adjust the level of detail provided based on the importance of the information being provided. For example, the provider can provide highly important information in detail. It can also provide less important information concisely. Furthermore, the provider can adjust the level of detail based on importance. For example, the provider can adjust the level of detail based on the importance of the information being provided. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the information to be provided into AI and have the AI ​​perform the adjustment of level of detail based on importance.

[0044] The information delivery unit can apply different delivery algorithms depending on the category of information to be delivered. For example, the information delivery unit can apply a technology delivery algorithm to technology information. It can also apply a market delivery algorithm to market information. Furthermore, it can apply a legal and regulatory delivery algorithm to legal and regulatory information. For example, the information delivery unit applies different delivery algorithms depending on the category of information to be delivered. This improves the accuracy of delivery by applying a delivery algorithm according to the category of information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the information to be delivered into AI and have the AI ​​execute the application of a delivery algorithm according to the category.

[0045] The information provisioning department can determine the priority of information provision based on the submission date. For example, the department may prioritize providing the most recent information. It may also postpone providing older information. Furthermore, the department may prioritize providing information of high importance based on the submission date. For example, the department determines the priority of information provision based on the submission date. This allows for the provision of the most recent information by prioritizing information provision based on the submission date. Some or all of the above processes in the information provisioning department may be performed using AI, or not. For example, the information provisioning department can input the information to be provided into an AI and have the AI ​​perform the priority determination based on the submission date.

[0046] The information provider can adjust the order of information delivery based on the relevance of the information being provided. For example, the provider can prioritize the delivery of highly relevant information. It can also postpone the delivery of less relevant information. Furthermore, the provider can prioritize the delivery of highly important information based on its relevance. For example, the provider can adjust the order of information delivery based on the relevance of the information being provided. This allows for the priority delivery of important information by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the provider can input the information to be provided into an AI and have the AI ​​perform the adjustment of the delivery order based on relevance.

[0047] The data compilation unit can evaluate the reliability of conversation history and other materials, and prioritize the compilation of highly reliable information. For example, the data compilation unit can evaluate the reliability of conversation history and create data on highly reliable information. The data compilation unit can also evaluate the reliability of other materials and create data on highly reliable information. Furthermore, the data compilation unit can evaluate the reliability of information sources and create data on highly reliable information. For example, the data compilation unit can evaluate the reliability of conversation history and other materials and prioritize the compilation of highly reliable information. In this way, by evaluating the reliability of information, highly reliable information can be prioritized for compilation. Some or all of the above processing in the data compilation unit may be performed using AI, for example, or without using AI. For example, the data compilation unit can input conversation history and other materials into AI and have the AI ​​perform the reliability evaluation.

[0048] The documentation unit can apply different documentation algorithms depending on the category of the conversation history or other documents. For example, the documentation unit can apply a technical documentation algorithm to technical information. It can also apply a market documentation algorithm to market information. Furthermore, it can apply a legal and regulatory documentation algorithm to legal and regulatory information. For example, the documentation unit applies different documentation algorithms depending on the category of the conversation history or other documents. This improves the accuracy of documentation by applying a documentation algorithm according to the category of information. Some or all of the above processing in the documentation unit may be performed using AI, for example, or without AI. For example, the documentation unit can input the conversation history or other documents into the AI ​​and have the AI ​​execute the application of a documentation algorithm according to the category.

[0049] The documentation unit can adjust the order of documentation based on conversation history and the submission dates of other documents. For example, the documentation unit prioritizes the documentation of the most recent conversation history and other documents. The documentation unit can also postpone the documentation of older information. Furthermore, the documentation unit can prioritize the documentation of information of high importance based on the submission date. For example, the documentation unit adjusts the order of documentation based on conversation history and the submission dates of other documents. This allows for the prioritization of the most recent information by adjusting the order of documentation based on the submission date of the information. Some or all of the above processing in the documentation unit may be performed using AI, for example, or not using AI. For example, the documentation unit can input conversation history and other documents into AI and have the AI ​​perform the adjustment of the order based on the submission date.

[0050] The documentation unit can adjust the content of the documentation based on the relevance of conversation history and other materials. For example, the documentation unit can prioritize the documentation of highly relevant information. It can also postpone the documentation of less relevant information. Furthermore, the documentation unit can prioritize the documentation of highly important information based on its relevance. For example, the documentation unit adjusts the content of the documentation based on the relevance of conversation history and other materials. This allows important information to be prioritized for documentation by adjusting the content of the documentation based on the relevance of the information. Some or all of the above processing in the documentation unit may be performed using AI, for example, or not using AI. For example, the documentation unit can input conversation history and other materials into AI and have the AI ​​perform the adjustment of the documentation content based on relevance.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The AI ​​agent system can automatically collect relevant news articles based on the content of statements made during a meeting. The collection unit can, for example, search for the latest news articles related to the statements and collect relevant information. The collection unit can also automatically collect past news articles based on the statements. Furthermore, the collection unit can collect news articles on specific topics related to the statements and provide them as reference information. For example, the collection unit can analyze the content of statements made during a meeting and automatically collect relevant news articles. This allows for the rapid acquisition of relevant information by collecting news articles based on the content of statements. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of statements made during a meeting into an AI and have the AI ​​collect relevant news articles.

[0053] The AI ​​agent system can automatically collect relevant patent information based on the content of statements made during a meeting. The collection unit can, for example, search for patent information related to the content of the statements and collect relevant information. The collection unit can also automatically collect past patent information based on the content of the statements. Furthermore, the collection unit can collect patent information on specific technologies related to the content of the statements and provide it as reference information. For example, the collection unit can analyze the content of statements made during a meeting and automatically collect relevant patent information. This allows for the rapid acquisition of relevant information by collecting patent information based on the content of the statements. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the content of statements made during a meeting into the AI ​​and have the AI ​​perform the collection of relevant patent information.

[0054] The AI ​​agent system can automatically collect relevant academic papers based on the content of discussions during a meeting. The collection unit can, for example, search for the latest academic papers related to the discussion content and collect relevant information. The collection unit can also automatically collect past academic papers based on the discussion content. Furthermore, the collection unit can collect academic papers on specific research fields related to the discussion content and provide them as reference information. For example, the collection unit can analyze the content of discussions during a meeting and automatically collect relevant academic papers. This allows for the rapid acquisition of relevant information by collecting academic papers based on the content of discussions. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of discussions during a meeting into an AI and have the AI ​​collect relevant academic papers.

[0055] The AI ​​agent system can automatically collect relevant market research reports based on the content of discussions during a meeting. The collection unit can, for example, search for the latest market research reports related to the discussion content and collect relevant information. The collection unit can also automatically collect past market research reports based on the discussion content. Furthermore, the collection unit can collect market research reports on specific markets related to the discussion content and provide them as reference information. For example, the collection unit can analyze the content of discussions during a meeting and automatically collect relevant market research reports. This allows for the rapid acquisition of relevant information by collecting market research reports based on the content of discussions. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of discussions during a meeting into an AI and have the AI ​​collect relevant market research reports.

[0056] The AI ​​agent system can automatically collect relevant legal and regulatory information based on the content of statements made during a meeting. The collection unit can, for example, search for the latest legal and regulatory information related to the content of the statements and collect relevant information. The collection unit can also automatically collect past legal and regulatory information based on the content of the statements. Furthermore, the collection unit can collect information on specific legal and regulatory matters related to the content of the statements and provide it as reference information. For example, the collection unit can analyze the content of statements made during a meeting and automatically collect relevant legal and regulatory information. This allows for the rapid acquisition of relevant information by collecting legal and regulatory information based on the content of the statements. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of statements made during a meeting into the AI ​​and have the AI ​​perform the collection of relevant legal and regulatory information.

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

[0058] Step 1: The collection unit tracks the conversation during the meeting in real time and collects information. For example, it collects audio data, text data, and shared presentation materials during the meeting in real time. Step 2: The analysis unit analyzes the information collected by the collection unit and searches for relevant information. For example, it extracts relevant keywords from the collected audio data and searches for relevant information by analyzing text data and presentation materials. Step 3: The provisioning unit provides the information retrieved by the analysis unit. For example, it may display relevant information based on the searched keywords, display materials, or read the information aloud using speech synthesis technology.

[0059] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that provides information when a participant says, "What was that again?" during a meeting. This AI agent system can follow the flow of conversation in real time during a meeting and quickly provide the necessary information. The AI ​​agent system stores its own materials, communication tools, emails, and conversation history in a database. For example, if a participant says, "I think I heard about a similar case before, is there a history?" during a meeting, the AI ​​agent system recognizes the statement and searches the database for relevant information. Next, a large-scale language model (LLM) analyzes the search results and presents relevant cases. This allows for quick information provision even when a participant says, "What was that again?" during a meeting. Also, if a participant says, "Where is that document?" during a meeting, the AI ​​agent system searches the database for the relevant document, and the LLM presents it. For example, if a participant says, "Show me last week's meeting materials," the AI ​​agent system recognizes the statement, searches the database for last week's meeting materials, and the LLM presents them. This allows for quick acquisition of necessary materials during a meeting. Furthermore, if it becomes necessary to share meeting content in another meeting, simply asking "Please document this meeting" will allow the AI ​​agent system to automatically generate reports, approval documents, etc., by referencing the conversation history and other materials. For example, if someone says "Please summarize the contents of this meeting in a report" during a meeting, the AI ​​agent system will recognize the statement and automatically generate a report based on the conversation history and related materials. This saves the effort of creating documents and allows for efficient information sharing. This system also allows for quick information provision even if someone forgets something during a meeting. Additionally, necessary materials can be quickly retrieved, and meeting content can be shared efficiently. This improves meeting efficiency and increases work productivity. As a result, the AI ​​agent system can provide information quickly and accurately during meetings.

[0060] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit tracks conversations during a meeting in real time and collects information. For example, the collection unit collects audio data during a meeting in real time. The collection unit can also collect text data during a meeting in real time. Furthermore, the collection unit can also collect materials during a meeting. For example, the collection unit collects presentation materials shared during a meeting. The analysis unit analyzes the information collected by the collection unit and searches for relevant information. For example, the analysis unit analyzes the collected audio data and extracts relevant keywords. Furthermore, the analysis unit can also analyze the collected text data and search for relevant information. Furthermore, the analysis unit can analyze the collected materials and search for relevant information. For example, the analysis unit analyzes the collected presentation materials and searches for relevant slides. The provision unit provides the information retrieved by the analysis unit. For example, the provision unit displays relevant information based on the searched keywords. Furthermore, the provision unit can display the searched materials. Furthermore, the provision unit can also provide the searched information in audio format. For example, the provisioning unit reads aloud the retrieved information using speech synthesis technology. This allows the AI ​​agent system according to the embodiment to follow the conversation during the meeting in real time and quickly provide the necessary information. Some or all of the above-described processes in the collection unit, analysis unit, and provisioning unit may be performed using AI, or not using AI. For example, the collection unit can input audio data from the meeting into the AI ​​and have the AI ​​perform the analysis of the audio data.

[0061] The data collection unit tracks conversations during meetings in real time and collects information. For example, the unit collects audio data during meetings in real time. Specifically, it collects high-precision audio data of conversations through microphones installed in the meeting room and microphones built into participants' devices. This audio data is converted into clear audio using noise reduction technology and transmitted to a central database. The data collection unit can also collect text data during meetings in real time. For example, it obtains text data from chat tools and messaging applications used during meetings and manages this data centrally. Furthermore, the data collection unit can also collect materials during meetings. For example, it automatically obtains presentation materials and documents shared during meetings and saves these materials in digital format. This allows the data collection unit to collect diverse data such as audio, text, and materials in real time, enabling it to grasp the overall picture of the meeting. The data collection unit can efficiently manage this data and collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and provisioning departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0062] The analysis unit analyzes the information collected by the collection unit and searches for relevant information. For example, the analysis unit analyzes collected audio data and extracts relevant keywords. Specifically, it uses speech recognition technology to convert the audio data into text and extracts important keywords and phrases from that text. This makes it possible to identify the main topics and important statements discussed during the meeting. The analysis unit can also analyze collected text data and search for relevant information. For example, it extracts relevant information from chat messages and documents used during the meeting, organizes this information, and converts it into a searchable format. Furthermore, the analysis unit can analyze collected materials and search for relevant information. For example, it analyzes collected presentation materials and identifies relevant slides and charts. This allows the analysis unit to quickly search for information related to a specific topic from the materials used during the meeting. The analysis unit uses AI to analyze this data and simulate multiple scenarios to identify the most relevant information. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past meeting data, it can predict trends in specific topics and discussions, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0063] The information delivery unit provides information retrieved by the analysis unit. For example, the delivery unit displays relevant information based on searched keywords. Specifically, it displays information related to topics discussed during a meeting on the user's device in real time. This allows users to quickly access the information they need during the meeting. The delivery unit can also display retrieved materials. For example, it can display presentation materials and documents identified by the analysis unit on the user's device, providing the necessary information immediately. Furthermore, the delivery unit can provide retrieved information in audio format. For example, it can read the retrieved information aloud using speech synthesis technology. This allows users to grasp the information during the meeting using not only visual information but also auditory information. The delivery unit can efficiently manage this information and collaborate with other systems and departments as needed. For example, the provided information can be stored on a cloud server, making it accessible to other users and systems. The delivery unit can also collect user feedback and continuously improve the accuracy and effectiveness of the provided content. For example, it can review and improve the content based on user reactions and evaluations of the provided information. This allows the service provider to deliver information to users quickly and accurately, improving the efficiency and effectiveness of meetings.

[0064] The AI ​​agent system includes a documentation unit that documents meeting content. The documentation unit can document meeting content. For example, the documentation unit creates documents based on the conversation history during the meeting. The documentation unit can also create documents based on documents shared during the meeting. Furthermore, the documentation unit can create documents based on the content of statements made during the meeting. For example, the documentation unit summarizes the content of statements made during the meeting and creates a report. This makes it easier to share information by documenting meeting content. Some or all of the above processing in the documentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the documentation unit can input the conversation history during the meeting into a generative AI and have the generative AI summarize the conversation history.

[0065] The documentation unit can automatically generate reports, proposals, and other documents by referring to conversation history and other documents. For example, the documentation unit can create reports based on conversation history. It can also create proposals based on other documents. Furthermore, the documentation unit can create documents by combining conversation history and other documents. For example, the documentation unit can summarize the conversation history and create a report that reflects the contents of other documents. This allows for the automatic generation of reports, proposals, and other documents by referring to conversation history and other documents. Some or all of the above processing in the documentation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the documentation unit can input conversation history and other documents into a generation AI and have the generation AI generate reports.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of conversation collection based on the estimated emotions. For example, if the user is nervous, the data collection unit can increase the frequency of conversation collection to ensure that important information is not missed. Conversely, if the user is relaxed, the data collection unit can maintain a moderate frequency of conversation collection to ensure a natural flow of conversation. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of important statements to provide information quickly. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for the collection of important information without missing anything by adjusting the timing of conversation collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can evaluate the importance of statements made during a meeting in real time and prioritize the collection of important statements. For example, the data collection unit can analyze keywords in the content of statements and prioritize the collection of statements of high importance. The data collection unit can also prioritize the collection of statements of high importance based on the speaker's position and expertise. Furthermore, the data collection unit can prioritize the collection of statements related to important agenda items according to the progress of the meeting. For example, the data collection unit can analyze the content of statements made during the meeting and evaluate the importance of statements in real time. This allows for the priority collection of important statements by evaluating the importance of each statement. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of statements made during the meeting into AI and have the AI ​​perform the evaluation of the importance of the statements.

[0068] The data collection unit can collect information from relevant external databases based on the content of statements made during a meeting. For example, the data collection unit can collect technical literature related to the content of the statements from external databases. The data collection unit can also collect market data related to the content of the statements from external databases. Furthermore, the data collection unit can collect legal and regulatory information related to the content of the statements from external databases. For example, the data collection unit can analyze the content of statements made during a meeting and collect information from relevant external databases. This allows for the rapid acquisition of relevant information by collecting information from external databases based on the content of the statements. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the content of statements made during a meeting into an AI and have the AI ​​perform the collection of information from relevant external databases.

[0069] The data collection unit can estimate the user's emotions and determine the priority of conversations to collect based on the estimated emotions. For example, if the user is nervous, the data collection unit can prioritize collecting important conversations and provide information quickly. If the user is relaxed, the data collection unit can also collect conversations in a balanced manner. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting conversations that get straight to the point. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the system to prioritize conversations according to the user's emotions, thereby prioritizing the collection of important conversations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can automatically collect relevant past meeting records based on the content of statements made during a meeting. For example, the data collection unit can search for past meeting records related to the content of the statements and collect relevant information. The data collection unit can also automatically collect the content of discussions in past meetings based on the content of the statements. Furthermore, the data collection unit can collect decisions made in past meetings related to the content of the statements and provide them as reference information. For example, the data collection unit can analyze the content of statements made during a meeting and automatically collect relevant past meeting records. This allows for the rapid acquisition of relevant information by collecting past meeting records based on the content of the statements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of statements made during a meeting into AI and have the AI ​​perform the collection of relevant past meeting records.

[0071] The data collection unit can collect relevant social media posts based on the content of statements made during a meeting. For example, the data collection unit can search for social media posts related to the content of the statements and collect relevant information. The data collection unit can also collect opinions and comments on social media based on the content of the statements. Furthermore, the data collection unit can collect trend information related to the content of the statements from social media. For example, the data collection unit can analyze the content of statements made during a meeting and collect relevant social media posts. This allows for the rapid acquisition of relevant information by collecting social media posts based on the content of the statements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the content of statements made during a meeting into an AI and have the AI ​​perform the collection of relevant social media posts.

[0072] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can increase the accuracy of the analysis to ensure that important information is not missed. Conversely, if the user is relaxed, the analysis unit can maintain a moderate level of accuracy to ensure a natural flow of conversation. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize the provision of important information. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the analysis to adjust accuracy according to the user's emotions, ensuring that important information is not missed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the reliability of the information source and prioritize the analysis of highly reliable information. The analysis unit can also evaluate the content of the information and prioritize the analysis of highly reliable information. Furthermore, the analysis unit can evaluate the reliability of the information provider and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. In this way, by evaluating the reliability of the information, highly reliable information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the evaluation of the reliability of the information.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the collected information. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a market analysis algorithm to market information. Furthermore, it can apply a legal and regulatory analysis algorithm to legal and regulatory information. For example, the analysis unit applies different analysis algorithms depending on the category of the collected information. By applying analysis algorithms according to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​execute the application of analysis algorithms according to the category.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This improves visibility by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can determine the priority of analysis based on the submission date of the collected information. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. Furthermore, the analysis unit may prioritize the analysis of information of high importance based on the submission date. For example, the analysis unit determines the priority of analysis based on the submission date of the collected information. This allows for the prioritization of analysis based on the submission date of the information, thereby prioritizing the analysis of the most recent information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the priority determination based on the submission date.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the collected information. For example, the analysis unit may prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can prioritize the analysis of information of high importance based on relevance. For example, the analysis unit adjusts the order of analysis based on the relevance of the collected information. This allows for the prioritization of important information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the adjustment of the analysis order based on relevance.

[0078] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and easily visible presentation. If the user is relaxed, the service provider can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise presentation. For example, the service provider can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This improves visibility by adjusting the way the information is presented 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The information provider can adjust the level of detail provided based on the importance of the information being provided. For example, the provider can provide highly important information in detail. It can also provide less important information concisely. Furthermore, the provider can adjust the level of detail based on importance. For example, the provider can adjust the level of detail based on the importance of the information being provided. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the information to be provided into AI and have the AI ​​perform the adjustment of level of detail based on importance.

[0080] The information delivery unit can apply different delivery algorithms depending on the category of information to be delivered. For example, the information delivery unit can apply a technology delivery algorithm to technology information. It can also apply a market delivery algorithm to market information. Furthermore, it can apply a legal and regulatory delivery algorithm to legal and regulatory information. For example, the information delivery unit applies different delivery algorithms depending on the category of information to be delivered. This improves the accuracy of delivery by applying a delivery algorithm according to the category of information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the information to be delivered into AI and have the AI ​​execute the application of a delivery algorithm according to the category.

[0081] The information provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is nervous, the provider can provide short, concise information. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is in a hurry, the provider can provide short, quick information. For example, the provider can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This improves readability by adjusting the length of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not using AI. For example, the provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The information provisioning department can determine the priority of information provision based on the submission date. For example, the department may prioritize providing the most recent information. It may also postpone providing older information. Furthermore, the department may prioritize providing information of high importance based on the submission date. For example, the department determines the priority of information provision based on the submission date. This allows for the provision of the most recent information by prioritizing information provision based on the submission date. Some or all of the above processes in the information provisioning department may be performed using AI, or not. For example, the information provisioning department can input the information to be provided into an AI and have the AI ​​perform the priority determination based on the submission date.

[0083] The information provider can adjust the order of information delivery based on the relevance of the information being provided. For example, the provider can prioritize the delivery of highly relevant information. It can also postpone the delivery of less relevant information. Furthermore, the provider can prioritize the delivery of highly important information based on its relevance. For example, the provider can adjust the order of information delivery based on the relevance of the information being provided. This allows for the priority delivery of important information by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the provider can input the information to be provided into an AI and have the AI ​​perform the adjustment of the delivery order based on relevance.

[0084] The documentation unit can estimate the user's emotions and adjust the documentation method based on the estimated emotions. For example, if the user is nervous, the documentation unit can create simple and highly visual documentation. If the user is relaxed, the documentation unit can also create documentation containing detailed information. Furthermore, if the user is in a hurry, the documentation unit can create concise documentation that gets straight to the point. For example, the documentation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This improves visual clarity by adjusting the documentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the documentation unit may be performed using AI, or not using AI. For example, the documentation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data compilation unit can evaluate the reliability of conversation history and other materials, and prioritize the compilation of highly reliable information. For example, the data compilation unit can evaluate the reliability of conversation history and create data on highly reliable information. The data compilation unit can also evaluate the reliability of other materials and create data on highly reliable information. Furthermore, the data compilation unit can evaluate the reliability of information sources and create data on highly reliable information. For example, the data compilation unit can evaluate the reliability of conversation history and other materials and prioritize the compilation of highly reliable information. In this way, by evaluating the reliability of information, highly reliable information can be prioritized for compilation. Some or all of the above processing in the data compilation unit may be performed using AI, for example, or without using AI. For example, the data compilation unit can input conversation history and other materials into AI and have the AI ​​perform the reliability evaluation.

[0086] The documentation unit can apply different documentation algorithms depending on the category of the conversation history or other documents. For example, the documentation unit can apply a technical documentation algorithm to technical information. It can also apply a market documentation algorithm to market information. Furthermore, it can apply a legal and regulatory documentation algorithm to legal and regulatory information. For example, the documentation unit applies different documentation algorithms depending on the category of the conversation history or other documents. This improves the accuracy of documentation by applying a documentation algorithm according to the category of information. Some or all of the above processing in the documentation unit may be performed using AI, for example, or without AI. For example, the documentation unit can input the conversation history or other documents into the AI ​​and have the AI ​​execute the application of a documentation algorithm according to the category.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data collection based on the estimated emotions. For example, if the user is nervous, the data collection unit will prioritize the collection of important information. If the user is relaxed, the data collection unit can also prioritize the collection of information considering the overall balance. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of information that gets straight to the point. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the data collection unit to prioritize important information by determining the priority of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The documentation unit can adjust the order of documentation based on conversation history and the submission dates of other documents. For example, the documentation unit prioritizes the documentation of the most recent conversation history and other documents. The documentation unit can also postpone the documentation of older information. Furthermore, the documentation unit can prioritize the documentation of information of high importance based on the submission date. For example, the documentation unit adjusts the order of documentation based on conversation history and the submission dates of other documents. This allows for the prioritization of the most recent information by adjusting the order of documentation based on the submission date of the information. Some or all of the above processing in the documentation unit may be performed using AI, for example, or not using AI. For example, the documentation unit can input conversation history and other documents into AI and have the AI ​​perform the adjustment of the order based on the submission date.

[0089] The documentation unit can adjust the content of the documentation based on the relevance of conversation history and other materials. For example, the documentation unit can prioritize the documentation of highly relevant information. It can also postpone the documentation of less relevant information. Furthermore, the documentation unit can prioritize the documentation of highly important information based on its relevance. For example, the documentation unit adjusts the content of the documentation based on the relevance of conversation history and other materials. This allows important information to be prioritized for documentation by adjusting the content of the documentation based on the relevance of the information. Some or all of the above processing in the documentation unit may be performed using AI, for example, or not using AI. For example, the documentation unit can input conversation history and other materials into AI and have the AI ​​perform the adjustment of the documentation content based on relevance.

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

[0091] The AI ​​agent system can automatically collect relevant news articles based on the content of statements made during a meeting. The collection unit can, for example, search for the latest news articles related to the statements and collect relevant information. The collection unit can also automatically collect past news articles based on the statements. Furthermore, the collection unit can collect news articles on specific topics related to the statements and provide them as reference information. For example, the collection unit can analyze the content of statements made during a meeting and automatically collect relevant news articles. This allows for the rapid acquisition of relevant information by collecting news articles based on the content of statements. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of statements made during a meeting into an AI and have the AI ​​collect relevant news articles.

[0092] The AI ​​agent system can automatically collect relevant patent information based on the content of statements made during a meeting. The collection unit can, for example, search for patent information related to the content of the statements and collect relevant information. The collection unit can also automatically collect past patent information based on the content of the statements. Furthermore, the collection unit can collect patent information on specific technologies related to the content of the statements and provide it as reference information. For example, the collection unit can analyze the content of statements made during a meeting and automatically collect relevant patent information. This allows for the rapid acquisition of relevant information by collecting patent information based on the content of the statements. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the content of statements made during a meeting into the AI ​​and have the AI ​​perform the collection of relevant patent information.

[0093] The AI ​​agent system can automatically collect relevant academic papers based on the content of discussions during a meeting. The collection unit can, for example, search for the latest academic papers related to the discussion content and collect relevant information. The collection unit can also automatically collect past academic papers based on the discussion content. Furthermore, the collection unit can collect academic papers on specific research fields related to the discussion content and provide them as reference information. For example, the collection unit can analyze the content of discussions during a meeting and automatically collect relevant academic papers. This allows for the rapid acquisition of relevant information by collecting academic papers based on the content of discussions. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of discussions during a meeting into an AI and have the AI ​​collect relevant academic papers.

[0094] The AI ​​agent system can automatically collect relevant market research reports based on the content of discussions during a meeting. The collection unit can, for example, search for the latest market research reports related to the discussion content and collect relevant information. The collection unit can also automatically collect past market research reports based on the discussion content. Furthermore, the collection unit can collect market research reports on specific markets related to the discussion content and provide them as reference information. For example, the collection unit can analyze the content of discussions during a meeting and automatically collect relevant market research reports. This allows for the rapid acquisition of relevant information by collecting market research reports based on the content of discussions. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of discussions during a meeting into an AI and have the AI ​​collect relevant market research reports.

[0095] The AI ​​agent system can automatically collect relevant legal and regulatory information based on the content of statements made during a meeting. The collection unit can, for example, search for the latest legal and regulatory information related to the content of the statements and collect relevant information. The collection unit can also automatically collect past legal and regulatory information based on the content of the statements. Furthermore, the collection unit can collect information on specific legal and regulatory matters related to the content of the statements and provide it as reference information. For example, the collection unit can analyze the content of statements made during a meeting and automatically collect relevant legal and regulatory information. This allows for the rapid acquisition of relevant information by collecting legal and regulatory information based on the content of the statements. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the content of statements made during a meeting into the AI ​​and have the AI ​​perform the collection of relevant legal and regulatory information.

[0096] An AI agent system can estimate a user's emotions and support the progress of a meeting based on those emotions. For example, if a user is nervous, the AI ​​agent system can offer advice to help them relax. If a user is relaxed, the AI ​​agent system can offer suggestions to ensure the meeting proceeds smoothly. Furthermore, if a user is in a hurry, the AI ​​agent system can help prioritize important agenda items. For example, the AI ​​agent system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the system to improve meeting efficiency by supporting the progress of the meeting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the AI ​​agent system may be performed using AI or not. For example, the AI ​​agent system can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] An AI agent system can estimate a user's emotions and provide feedback on their statements during a meeting based on those emotions. For example, if a user is nervous, the AI ​​agent system can provide positive feedback to boost their confidence. If a user is relaxed, the AI ​​agent system can provide constructive feedback to improve the quality of the meeting. Furthermore, if a user is in a hurry, the AI ​​agent system can provide concise and to-the-point feedback. For example, the AI ​​agent system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the system to improve the effectiveness of the meeting by providing feedback on statements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the AI ​​agent system may be performed using AI or not. For example, the AI ​​agent system can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0098] An AI agent system can estimate a user's emotions and adjust the tone of speech during a meeting based on the estimated emotions. For example, if a user is nervous, the AI ​​agent system can adjust its tone to be calm. If a user is relaxed, the AI ​​agent system can adjust its tone to be lively. Furthermore, if a user is in a hurry, the AI ​​agent system can adjust its tone to be quick and to the point. For example, the AI ​​agent system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the system to adjust the tone of speech according to the user's emotions, thereby improving the effectiveness of the meeting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AI ​​agent system may be performed using AI or not. For example, the AI ​​agent system can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0099] An AI agent system can estimate a user's emotions and adjust the content of their statements during a meeting based on those emotions. For example, if a user is nervous, the AI ​​agent system can provide concise and easy-to-understand content. If a user is relaxed, the AI ​​agent system can provide detailed information. Furthermore, if a user is in a hurry, the AI ​​agent system can provide concise and to-the-point content. For example, the AI ​​agent system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the system to adjust the content of its statements according to the user's emotions, thereby improving the effectiveness of the meeting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the AI ​​agent system may be performed using AI or not using AI. For example, the AI ​​agent system can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] An AI agent system can estimate a user's emotions and adjust the order of speech during a meeting based on the estimated emotions. For example, if a user is nervous, the AI ​​agent system can postpone important speeches. Conversely, if a user is relaxed, the AI ​​agent system can prioritize important speeches. Furthermore, if a user is in a hurry, the AI ​​agent system can prioritize concise speeches. For example, the AI ​​agent system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for adjustment of the order of speeches according to the user's emotions, thereby improving the effectiveness of the meeting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AI ​​agent system may be performed using AI or not. For example, the AI ​​agent system can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The collection unit tracks the conversation during the meeting in real time and collects information. For example, it collects audio data, text data, and shared presentation materials during the meeting in real time. Step 2: The analysis unit analyzes the information collected by the collection unit and searches for relevant information. For example, it extracts relevant keywords from the collected audio data and searches for relevant information by analyzing text data and presentation materials. Step 3: The provisioning unit provides the information retrieved by the analysis unit. For example, it may display relevant information based on the searched keywords, display materials, or read the information aloud using speech synthesis technology.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and documentation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects audio and text data during a meeting in real time using the microphone 38B and camera 42 of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and retrieves relevant information. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, which provides the retrieved information by display or voice. The documentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which documents the meeting content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and documentation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects audio and text data during a meeting in real time using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and retrieves relevant information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides the retrieved information by displaying or voice. The documentation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which documents the meeting content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and documentation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects audio and text data during a meeting in real time using the microphone 238 and camera 42 of the headset terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and retrieves relevant information. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides the retrieved information by displaying or voice. The documentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which documents the meeting content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and documentation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects audio and text data during a meeting in real time using the microphone 238 and camera 42 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and retrieves relevant information. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the retrieved information by display or voice. The documentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which documents the meeting content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) The collection unit tracks the conversation during the meeting in real time and collects information, An analysis unit analyzes the information collected by the aforementioned collection unit and searches for related information, The system comprises a providing unit that provides the information retrieved by the analysis unit. A system characterized by the following features. (Note 2) It has a documentation department that documents meeting content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data preparation unit, Automatically generate reports, approval documents, etc., by referring to conversation history and other materials. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The importance of comments made during a meeting is evaluated in real time, and important comments are collected preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Based on the content of the discussion during the meeting, information is collected from relevant external databases. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and determines the priority of conversations to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Based on what was said during the meeting, relevant past meeting records are automatically collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Based on the content of the discussion during the meeting, we will collect relevant social media posts. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The reliability of the collected information is evaluated, and the most reliable information is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of information collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Prioritize analysis based on when the collected information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The order of analysis is adjusted based on the relevance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, Adjust the level of detail provided based on the importance of the information being offered. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, Apply different information delivery algorithms depending on the category of information being provided. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Prioritizing the provision of information based on when it is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The order in which information is provided is adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned data preparation unit, We estimate the user's emotions and adjust the documentation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned data preparation unit, We evaluate the reliability of conversation history and other materials, and prioritize creating documentation based on the most reliable information. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned data preparation unit, Apply different documentation algorithms depending on the conversation history and the category of other documents. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned data preparation unit, We estimate user emotions and determine the priority of documentation based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned data preparation unit, The order of document creation will be adjusted based on the conversation history and the timing of submission of other documents. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned data preparation unit, Adjust the content of the document based on the conversation history and the relevance of other materials. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit tracks the conversation during the meeting in real time and collects information, An analysis unit analyzes the information collected by the aforementioned collection unit and searches for related information, The system comprises a providing unit that provides the information retrieved by the analysis unit. A system characterized by the following features.

2. It has a documentation department that documents meeting content. The system according to feature 1.

3. The aforementioned data preparation unit, Automatically generate reports, approval documents, etc., by referring to conversation history and other materials. The system according to feature 2.

4. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation collection based on the estimated user emotions. The system according to feature 1.

5. The aforementioned collection unit is The importance of comments made during a meeting is evaluated in real time, and important comments are collected preferentially. The system according to feature 1.

6. The aforementioned collection unit is Based on the content of the discussion during the meeting, information is collected from relevant external databases. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and determines the priority of conversations to collect based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Based on what was said during the meeting, relevant past meeting records are automatically collected. The system according to feature 1.

9. The aforementioned collection unit is Based on the content of the discussion during the meeting, we will collect relevant social media posts. The system according to feature 1.

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