system

Abstract

The system according to this embodiment aims to efficiently review and deepen understanding of the content of a meeting. [Solution] The system according to this embodiment comprises a reading unit, an analysis unit, and a providing unit. The reading unit reads the contents of a meeting. The analysis unit analyzes the contents of the meeting read by the reading unit. The providing unit provides the contents analyzed by the analysis unit in an interactive manner.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently review and deepen the understanding of the content of a meeting.

[0005] The system according to the embodiment aims to efficiently review and deepen the understanding of the content of a meeting.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reading unit, an analysis unit, and a providing unit. The reading unit reads the content of a meeting. The analysis unit analyzes the content of the meeting read by the reading unit. The providing unit provides the content analyzed by the analysis unit in an interactive manner.

Effects of the Invention

[0007] The system according to this embodiment allows for efficient review and deepening of the content of 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.

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meeting content review system according to an embodiment of the present invention is a system that allows users to review the content of a meeting interactively by inputting the meeting content. This system can identify items that the General Manager focused on, as well as positive and negative opinions. When this prompt is used, it not only reduces the cost of asking and confirming the content with people, but also leads to improved understanding. The meeting content review system is a system that allows users to review the content of a meeting interactively by inputting the meeting content. This system can identify items that the General Manager focused on, as well as positive and negative opinions. When this prompt is used, it not only reduces the cost of asking and confirming the content with people, but also leads to improved understanding. Specifically, it consists of the following steps. First, the meeting content is input into the system. Next, the system analyzes the meeting content and extracts important items and statements. For example, this may include items that the General Manager focused on, as well as positive and negative opinions. This allows users to review the meeting content interactively and quickly obtain necessary information. By using this system, the person in charge of minutes no longer needs to manually record the meeting content, significantly reducing time and effort. Furthermore, employees who did not attend the meeting can easily review the meeting content through the system, reducing the time spent clarifying and understanding unclear points. For example, if a one-hour meeting requires two hours for a minute-taker to attend and create the minutes, using the system can significantly reduce this time. In addition, the system allows for interactive review of the meeting content, leading to a deeper understanding. For instance, asking questions about specific statements or topics will result in detailed explanations from the system. This allows for a more effective grasp of the meeting content, leading to increased work efficiency. This system is particularly effective in large organizations. For example, in an organization of 300 people holding one meeting per day, the traditional method would cost 300 hours, but using the system can significantly reduce this cost. This improves the overall productivity of the organization and increases work efficiency.This allows the meeting review system to efficiently read, analyze, and provide interactive summaries of meeting content.

[0029] The meeting content review system according to this embodiment comprises a reading unit, an analysis unit, and a provision unit. The reading unit reads the content of the meeting. The reading unit can, for example, record the audio data of the meeting and convert it into text data. The reading unit can also directly read the text data of the meeting. Furthermore, the reading unit can also read the presentation materials and minutes of the meeting. For example, the reading unit can convert the audio data of the meeting into text data using speech recognition technology. The text data is analyzed using natural language processing technology. The analysis unit analyzes the content of the meeting read by the reading unit. For example, the analysis unit analyzes the content of the meeting and extracts important items and statements. The analysis unit can also identify positive and negative opinions. For example, the analysis unit analyzes the content of the meeting using natural language processing technology and extracts important keywords and phrases. The analysis unit can also identify positive and negative opinions using sentiment analysis algorithms. The provision unit provides the content analyzed by the analysis unit in an interactive manner. The provision unit can, for example, provide the content of the meeting in an interactive manner using a chatbot. Furthermore, the service provider can provide meeting content interactively using a voice assistant. In addition, the service provider can provide meeting content interactively using an interactive dashboard. For example, the service provider can use a chatbot to answer user questions about meeting content. The voice assistant provides meeting content verbally in response to user voice input. The interactive dashboard visually displays meeting content, allowing users to obtain information interactively. As a result, the meeting content review system according to this embodiment can efficiently read, analyze, and provide meeting content interactively.

[0030] The reading unit reads the content of meetings. For example, the reading unit can record meeting audio data and convert it into text data. Specifically, it uses speech recognition technology to transcribe speech during meetings in real time. This speech recognition technology uses advanced algorithms that leverage deep learning, and can effectively process the characteristics and accents of the speaker's voice and background noise. Furthermore, the reading unit can also directly read meeting text data. For example, it can digitize pre-prepared meeting minutes and notes using OCR (optical character recognition) technology and import them into the system. This makes it easy to handle handwritten notes and printed materials as digital data. The reading unit can also read meeting presentation materials and minutes. For presentation materials, it analyzes the content of the slides and extracts information such as text, images, and graphs. This allows for a detailed understanding of the content of materials used during the meeting. For example, the reading unit converts meeting audio data into text data using speech recognition technology. The text data is then analyzed using natural language processing technology. This allows for the accurate and rapid digitization of meeting content, which can then be used for subsequent analysis and provision. Furthermore, the data entry unit can integrate information from multiple data sources and manage it centrally. This provides a foundation for comprehensively understanding and efficiently analyzing meeting content.

[0031] The analysis unit analyzes the meeting content read by the reading unit. For example, the analysis unit analyzes the meeting content and extracts important items and statements. Specifically, it uses natural language processing technology to analyze text data and identify meeting topics, important keywords, and phrases. This allows for a quick grasp of the meeting's key points and the extraction of information useful for later reference. The analysis unit can also identify positive and negative opinions. For example, it uses sentiment analysis algorithms to analyze the emotions and tone of opinions of speakers and classify them into positive and negative opinions. This allows for an understanding of the meeting's atmosphere and the trends in participants' opinions. Furthermore, the analysis unit can analyze the content of each speaker's statements and identify who said what. This allows for an evaluation of each speaker's contribution and the diversity of opinions. For example, the analysis unit uses natural language processing technology to analyze the meeting content and extract important keywords and phrases. The analysis unit can also use sentiment analysis algorithms to identify positive and negative opinions. This allows the analysis unit to analyze the meeting content from multiple perspectives and extract important information. Furthermore, the analysis unit can analyze the meeting content chronologically, allowing for an understanding of the progress of discussions and the evolution of topics. This enables an understanding of the meeting's flow and the identification of important turning points and decisions. Based on these analysis results, the analysis unit can also automatically generate meeting summaries and reports. This allows for an efficient review of the meeting content, which can be used to inform future meetings and decision-making.

[0032] The service provider provides the information analyzed by the analysis provider in an interactive manner. For example, the service provider can provide meeting content interactively using a chatbot. Specifically, when a user enters a question into the chatbot, the chatbot generates an appropriate answer based on the analysis results and provides it to the user. This allows the user to quickly obtain the necessary information. The service provider can also provide meeting content interactively using a voice assistant. When a user asks a question by voice, the voice assistant provides an answer by voice based on the analysis results. This allows for hands-free information acquisition and improves convenience. Furthermore, the service provider can provide meeting content interactively using an interactive dashboard. The interactive dashboard visually displays the meeting content, allowing users to obtain information interactively. For example, important keywords and phrases are highlighted, and clicking them displays detailed information. In addition, graphs and charts can be used to visually grasp the progress of the meeting and trends in opinions. This allows the service provider to provide information to users in a variety of ways and efficiently review the meeting content. For example, the service provider can use a chatbot to answer user questions about the meeting content. The voice assistant provides the meeting content verbally in response to the user's voice input. The interactive dashboard visually displays the meeting content, allowing the user to obtain information interactively. As a result, the meeting content review system according to this embodiment can efficiently read, analyze, and interactively provide the meeting content. Furthermore, the delivery unit can collect user feedback and continuously improve the accuracy and usability of the system. For example, by having the user input evaluations and comments on the provided information, the system can improve its analysis algorithm and delivery method based on that feedback. As a result, the delivery unit can continue to provide users with more appropriate and useful information.

[0033] The analysis unit includes an extraction unit that extracts important items and statements. The extraction unit can, for example, analyze the content of a meeting and extract important keywords and phrases. The extraction unit uses natural language processing technology to extract important items from the meeting content. For example, the extraction unit analyzes the meeting content and extracts frequently used keywords and phrases. Furthermore, the extraction unit can analyze the meeting content and extract important items based on the speaker's position and the importance of their statement. For example, the extraction unit analyzes the meeting content and prioritizes extracting statements made by the general manager. This allows for the efficient extraction of important items and statements.

[0034] The analysis unit includes a identifier unit that identifies positive and negative opinions. The identifier unit can, for example, analyze the content of a meeting and identify positive and negative opinions. The identifier unit uses a sentiment analysis algorithm to identify positive and negative opinions from the meeting content. For example, the identifier unit analyzes the meeting content and displays positive opinions in green and negative opinions in red. Alternatively, the identifier unit can analyze the meeting content and display positive opinions in blue and negative opinions in yellow. This allows for the efficient identification of positive and negative opinions.

[0035] The service provider includes a function for interactively reviewing meeting content. For example, the service provider can provide meeting content interactively using a chatbot. It can also provide meeting content interactively using a voice assistant. Furthermore, it can provide meeting content interactively using an interactive dashboard. For example, the service provider can use a chatbot to answer user questions about meeting content. A voice assistant provides meeting content verbally in response to user voice input. An interactive dashboard visually displays meeting content, allowing users to obtain information interactively. This enables interactive review of meeting content.

[0036] The reading unit can automatically read the contents of a meeting. For example, the reading unit can automatically record the audio data of a meeting and convert it into text data. The reading unit can also automatically read the text data of a meeting. Furthermore, the reading unit can automatically read meeting presentation materials and minutes. For example, the reading unit can use speech recognition technology to automatically convert the audio data of a meeting into text data. The text data is then analyzed using natural language processing technology. This allows the reading unit to automatically read the contents of the meeting. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the audio data of a meeting into a generating AI and have the generating AI perform the conversion from audio data to text data.

[0037] The reading unit can be equipped with the ability to process both audio and text data simultaneously when reading the contents of a meeting. For example, the reading unit can convert the meeting's audio data into text in real time and perform analysis simultaneously. The reading unit can also analyze the audio data in the background while reading the meeting's text data. Furthermore, the reading unit can synchronize the audio and text data to instantly identify important statements. For example, the reading unit can use speech recognition technology to convert the meeting's audio data into text data in real time. The text data is then analyzed using natural language processing technology. This allows for the simultaneous processing of both audio and text data. Some or all of the above-described processes in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the meeting's audio and text data into a generating AI and have the generating AI perform the synchronization process between the audio and text data.

[0038] The input unit can be enhanced to prioritize the input of specific keywords or phrases when inputting meeting content. For example, the input unit can pre-set keywords related to the meeting agenda and prioritize inputting statements containing those keywords. The input unit can also prioritize inputting phrases related to items that the general manager was paying attention to. Furthermore, the input unit can improve the accuracy of the analysis by prioritizing the input of keywords indicating positive or negative opinions. For example, the input unit can analyze the meeting content and prioritize inputting frequently occurring keywords or phrases. This allows for the priority input of specific keywords or phrases. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the meeting content into a generating AI and have the generating AI perform the priority input of specific keywords or phrases.

[0039] The data entry unit can adjust its data entry method based on the roles and expertise of the meeting participants. For example, it can prioritize reading the statements of the general manager and identify important items. It can also prioritize reading technical content based on expertise. Furthermore, it can prioritize reading statements related to decision-making based on role. For example, the data entry unit analyzes the meeting content and extracts important items based on role and expertise. This allows it to adjust the data entry method based on the roles and expertise of the meeting participants. Some or all of the above processing in the data entry unit may be performed using AI, for example, or not. For example, the data entry unit can input the meeting content into a generating AI and have the generating AI adjust the data entry method based on role and expertise.

[0040] The data entry unit can adjust its data entry method based on the meeting location and time of day when reading the meeting content. For example, if the meeting location differs, the data entry unit will prioritize reading information related to that location. The data entry unit can also prioritize reading important statements based on the meeting time of day. Furthermore, the data entry unit can prioritize reading participants' statements based on the meeting location and time of day. For example, the data entry unit can analyze the meeting content and extract important items based on the meeting location and time of day. This allows the data entry method to be adjusted based on the meeting location and time of day. Some or all of the above processing in the data entry unit may be performed using AI, for example, or not. For example, the data entry unit can input the meeting content into a generating AI and have the generating AI perform adjustments to the data entry method based on the meeting location and time of day.

[0041] The analysis unit can be equipped with the ability to analyze meeting content in real time and instantly extract important items and statements. For example, the analysis unit can analyze meeting audio data in real time and instantly extract important statements. The analysis unit can also analyze meeting text data in real time and instantly extract important items. Furthermore, the analysis unit can analyze meeting content in real time and instantly extract items that the general manager was paying attention to. For example, the analysis unit can use speech recognition technology to analyze meeting audio data in real time and extract important statements. Text data is analyzed using natural language processing technology. This allows for real-time analysis of meeting content and instant extraction of important items and statements. 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 meeting audio data and text data into a generating AI and have the generating AI perform real-time analysis.

[0042] The analysis unit can classify the meeting content into multiple categories and apply different analysis methods to each category. For example, the analysis unit can classify the meeting content into categories such as technology, management, and marketing, and apply an appropriate analysis method to each. The analysis unit can also classify the meeting content into positive and negative opinions and apply an appropriate analysis method to each. Furthermore, the analysis unit can classify the meeting content by speaker and apply an appropriate analysis method to each statement. For example, the analysis unit can analyze the meeting content using natural language processing technology and extract important items for each category. This allows the meeting content to be classified into multiple categories, and different analysis methods to be applied to each category. 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 meeting content into a generating AI and have the generating AI perform the process of applying different analysis methods to each category.

[0043] The analysis unit can determine the priority of analysis based on the frequency and content of each participant's statements in the meeting. For example, the analysis unit may prioritize the analysis of statements made by participants who speak frequently. The analysis unit may also prioritize the analysis of statements made by participants whose statements are important. Furthermore, the analysis unit may prioritize the analysis of statements made by the general manager. For example, the analysis unit analyzes the content of the meeting and extracts important items based on the frequency and content of statements. This allows the analysis unit to determine the priority of analysis based on the frequency and content of each participant's statements. 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 content of the meeting into a generating AI and have the generating AI determine the priority of analysis based on the frequency and content of statements.

[0044] The analysis unit can improve the accuracy of its analysis by comparing the content of the meeting with the content of other relevant meetings. For example, the analysis unit can identify important items by comparing them with the content of past meetings. The analysis unit can also verify the consistency of statements by comparing them with the content of other relevant meetings. Furthermore, the analysis unit can analyze changes in positive and negative opinions by comparing them with the content of past meetings. For example, the analysis unit can analyze the content of the meeting and extract important items by comparing them with past meeting data. This improves the accuracy of the analysis by comparing the content of the meeting with the content of other relevant meetings. 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 content of the meeting into a generating AI and have the generating AI perform a comparison with the content of other relevant meetings.

[0045] The service provider can add a function to generate graphs and charts to visually display the content of a meeting. For example, the service provider can visually display the content of a meeting using graphs and charts, highlighting important items. The service provider can also display the content of a meeting chronologically, allowing for a visual understanding of the flow of discussions. Furthermore, the service provider can display the content of a meeting by category using graphs and charts, visually demonstrating the importance of each category. For example, the service provider can use data visualization technology to visually display the content of a meeting. This allows for the generation of graphs and charts to visually display the content of a meeting. 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 content of a meeting into a generating AI and have the generating AI generate graphs and charts.

[0046] The service provider can add a function to play back the meeting content in audio, allowing users to acquire information aurally. For example, the service provider can play back important statements from the meeting in audio, allowing users to acquire information aurally. The service provider can also summarize the meeting content and play it back in audio, allowing users to acquire information efficiently. Furthermore, the service provider can play back the meeting content in audio by category, aurally indicating the importance of each category. For example, the service provider can use speech synthesis technology to play back the meeting content in audio. This allows users to acquire information aurally by playing back the meeting content in audio. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the meeting content into a generating AI and have the generating AI perform the audio playback.

[0047] The service provider can add a function to display meeting content linked to other relevant materials. For example, the service provider can link meeting content to relevant materials to provide detailed information. The service provider can also link meeting content to past meeting materials to verify the consistency of statements. Furthermore, the service provider can link meeting content to relevant research materials to provide technical background. For example, the service provider can link meeting content to other relevant materials using hyperlinks. This allows for the provision of detailed information by displaying meeting content linked to other relevant materials. 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 meeting content into a generating AI and have the generating AI perform the linking to relevant materials.

[0048] The service provider can add a function to translate and display the meeting content in multiple languages. For example, the service provider can translate the meeting content into English and provide it to international participants. The service provider can also translate the meeting content into multiple languages ​​and provide multilingual information. Furthermore, the service provider can translate the meeting content in real time and provide it instantly in multiple languages. For example, the service provider can translate the meeting content into multiple languages ​​using machine translation technology. This allows for the provision of multilingual information by translating and displaying the meeting content in multiple languages. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the meeting content into a generating AI and have the generating AI perform the translation into multiple languages.

[0049] The extraction unit can be enhanced with a function to organize the meeting content along a timeline and display important items in chronological order. For example, the extraction unit can organize the meeting content along a timeline and display important statements in chronological order. The extraction unit can also organize the meeting content along a timeline and display important items for each agenda item. Furthermore, the extraction unit can organize the meeting content along a timeline and display important items for each speaker. For example, the extraction unit can organize the meeting content chronologically using a timeline display. This allows the meeting content to be organized along a timeline and important items to be displayed in chronological order. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the meeting content into a generating AI and have the generating AI perform the chronological organization and display.

[0050] The extraction unit can improve the accuracy of its extraction by comparing the content of the meeting with the content of other relevant meetings. For example, the extraction unit can identify important items by comparing them with the content of past meetings. The extraction unit can also verify the consistency of statements by comparing them with the content of other relevant meetings. Furthermore, the extraction unit can analyze changes in positive and negative opinions by comparing them with the content of past meetings. For example, the extraction unit can analyze the content of the meeting and extract important items by comparing them with past meeting data. This improves the accuracy of the extraction by comparing the content of the meeting with the content of other relevant meetings. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the content of the meeting into a generating AI and have the generating AI perform a comparison with the content of other relevant meetings.

[0051] The specific unit can add a function to emotionally analyze the content of a meeting and display positive and negative opinions in different colors. For example, the specific unit can emotionally analyze the content of a meeting and display positive opinions in green and negative opinions in red. The specific unit can also emotionally analyze the content of a meeting and display positive opinions in blue and negative opinions in yellow. Furthermore, the specific unit can emotionally analyze the content of a meeting and display positive opinions in orange and negative opinions in purple. For example, the specific unit can analyze the content of a meeting using an emotional analysis algorithm and display positive and negative opinions in different colors. This makes it possible to emotionally analyze the content of a meeting and display positive and negative opinions in different colors. Some or all of the above processing in the specific unit may be performed using AI, for example, or without using AI. For example, the specific unit can input the content of a meeting into a generating AI and have the generating AI perform the emotional analysis and color-coding display.

[0052] The identification unit can improve the accuracy of identification by comparing the content of a meeting with the content of other relevant meetings. For example, the identification unit can identify important opinions by comparing them with past meeting content. The identification unit can also verify the consistency of statements by comparing them with the content of other relevant meetings. Furthermore, the identification unit can analyze changes in positive and negative opinions by comparing them with past meeting content. For example, the identification unit can analyze the content of a meeting and extract important opinions by comparing them with past meeting data. This allows the identification unit to improve the accuracy of identification by comparing the content of a meeting with the content of other relevant meetings. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the content of a meeting into a generating AI and have the generating AI perform a comparison with the content of other relevant meetings.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The meeting review system can also include a translation unit that translates the meeting content in real time and provides it in multiple languages. For example, the translation unit can translate the meeting audio data in real time and provide it in multiple languages ​​such as English, French, and Chinese. Furthermore, the translation unit can translate the meeting text data in real time and display it in the user's chosen language. For example, the translation unit can use machine translation technology to translate the meeting content in real time. This allows the meeting content to be provided in multiple languages, accommodating international participants.

[0055] The meeting review system can also include a linking section that displays meeting content in conjunction with other relevant materials. For example, the linking section can link meeting content to related research materials or past meeting materials, providing more detailed information. Furthermore, the linking section can link meeting content to relevant technical documents or marketing materials, providing technical background and market trends. For instance, the linking section uses hyperlinks to link meeting content to other relevant materials. This allows for the display of meeting content linked to other relevant materials, thereby providing more detailed information.

[0056] The meeting review system may also include a visualization unit that generates graphs and charts to visually display the meeting content. The visualization unit can, for example, visually display the meeting content using graphs and charts, highlighting important items. Furthermore, the visualization unit can display the meeting content chronologically, allowing for a visual understanding of the flow of discussion. For example, the visualization unit can use data visualization technology to visually display the meeting content. This enables the generation of graphs and charts to visually represent the meeting content.

[0057] The meeting review system can also include an audio playback unit that plays back the meeting content in audio format. For example, the audio playback unit can play back important statements from the meeting, allowing users to acquire information aurally. Furthermore, the audio playback unit can summarize the meeting content and play it back aurally, enabling users to acquire information efficiently. For instance, the audio playback unit can use speech synthesis technology to play back the meeting content. This allows users to acquire information aurally by playing back the meeting content in audio format.

[0058] The meeting content review system may also include a comparative analysis unit that improves the accuracy of the analysis by comparing the meeting content with the content of other related meetings. The comparative analysis unit can, for example, identify important items by comparing it with past meeting content. Furthermore, the comparative analysis unit can also verify the consistency of statements by comparing them with the content of other related meetings. For example, the comparative analysis unit analyzes the meeting content and extracts important items by comparing it with past meeting data. This improves the accuracy of the analysis by comparing the meeting content with the content of other related meetings.

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

[0060] Step 1: The reading unit reads the meeting content. For example, it can record meeting audio data and convert it into text data. It can also directly read meeting text data. Furthermore, it can read meeting presentation materials and minutes. It uses speech recognition technology to convert meeting audio data into text data. Step 2: The analysis unit analyzes the meeting content read by the reading unit. For example, it analyzes the meeting content and extracts important items and statements. It can also identify positive and negative opinions. It uses natural language processing technology to analyze the meeting content and extract important keywords and phrases. It can also use sentiment analysis algorithms to identify positive and negative opinions. Step 3: The delivery unit interactively provides the content analyzed by the analysis unit. For example, the content of the meeting can be provided interactively using a chatbot. The content of the meeting can also be provided interactively using a voice assistant. The content of the meeting can also be provided interactively using an interactive dashboard. The chatbot answers the user's questions with the content of the meeting, and the voice assistant provides the content of the meeting verbally in response to the user's voice input. The interactive dashboard visually displays the content of the meeting, allowing the user to obtain information interactively.

[0061] (Example of form 2)The meeting content review system according to an embodiment of the present invention is a system that allows users to review the content of a meeting interactively by inputting the meeting content. This system can identify items that the General Manager focused on, as well as positive and negative opinions. When this prompt is used, it not only reduces the cost of asking and confirming the content with people, but also leads to improved understanding. The meeting content review system is a system that allows users to review the content of a meeting interactively by inputting the meeting content. This system can identify items that the General Manager focused on, as well as positive and negative opinions. When this prompt is used, it not only reduces the cost of asking and confirming the content with people, but also leads to improved understanding. Specifically, it consists of the following steps. First, the meeting content is input into the system. Next, the system analyzes the meeting content and extracts important items and statements. For example, this may include items that the General Manager focused on, as well as positive and negative opinions. This allows users to review the meeting content interactively and quickly obtain necessary information. By using this system, the person in charge of minutes no longer needs to manually record the meeting content, significantly reducing time and effort. Furthermore, employees who did not attend the meeting can easily review the meeting content through the system, reducing the time spent clarifying and understanding unclear points. For example, if a one-hour meeting requires two hours for a minute-taker to attend and create the minutes, using the system can significantly reduce this time. In addition, the system allows for interactive review of the meeting content, leading to a deeper understanding. For instance, asking questions about specific statements or topics will result in detailed explanations from the system. This allows for a more effective grasp of the meeting content, leading to increased work efficiency. This system is particularly effective in large organizations. For example, in an organization of 300 people holding one meeting per day, the traditional method would cost 300 hours, but using the system can significantly reduce this cost. This improves the overall productivity of the organization and increases work efficiency.This allows the meeting review system to efficiently read, analyze, and provide interactive summaries of meeting content.

[0062] The meeting content review system according to this embodiment comprises a reading unit, an analysis unit, and a provision unit. The reading unit reads the content of the meeting. The reading unit can, for example, record the audio data of the meeting and convert it into text data. The reading unit can also directly read the text data of the meeting. Furthermore, the reading unit can also read the presentation materials and minutes of the meeting. For example, the reading unit can convert the audio data of the meeting into text data using speech recognition technology. The text data is analyzed using natural language processing technology. The analysis unit analyzes the content of the meeting read by the reading unit. For example, the analysis unit analyzes the content of the meeting and extracts important items and statements. The analysis unit can also identify positive and negative opinions. For example, the analysis unit analyzes the content of the meeting using natural language processing technology and extracts important keywords and phrases. The analysis unit can also identify positive and negative opinions using sentiment analysis algorithms. The provision unit provides the content analyzed by the analysis unit in an interactive manner. The provision unit can, for example, provide the content of the meeting in an interactive manner using a chatbot. Furthermore, the service provider can provide meeting content interactively using a voice assistant. In addition, the service provider can provide meeting content interactively using an interactive dashboard. For example, the service provider can use a chatbot to answer user questions about meeting content. The voice assistant provides meeting content verbally in response to user voice input. The interactive dashboard visually displays meeting content, allowing users to obtain information interactively. As a result, the meeting content review system according to this embodiment can efficiently read, analyze, and provide meeting content interactively.

[0063] The reading unit reads the content of meetings. For example, the reading unit can record meeting audio data and convert it into text data. Specifically, it uses speech recognition technology to transcribe speech during meetings in real time. This speech recognition technology uses advanced algorithms that leverage deep learning, and can effectively process the characteristics and accents of the speaker's voice and background noise. Furthermore, the reading unit can also directly read meeting text data. For example, it can digitize pre-prepared meeting minutes and notes using OCR (optical character recognition) technology and import them into the system. This makes it easy to handle handwritten notes and printed materials as digital data. The reading unit can also read meeting presentation materials and minutes. For presentation materials, it analyzes the content of the slides and extracts information such as text, images, and graphs. This allows for a detailed understanding of the content of materials used during the meeting. For example, the reading unit converts meeting audio data into text data using speech recognition technology. The text data is then analyzed using natural language processing technology. This allows for the accurate and rapid digitization of meeting content, which can then be used for subsequent analysis and provision. Furthermore, the data entry unit can integrate information from multiple data sources and manage it centrally. This provides a foundation for comprehensively understanding and efficiently analyzing meeting content.

[0064] The analysis unit analyzes the meeting content read by the reading unit. For example, the analysis unit analyzes the meeting content and extracts important items and statements. Specifically, it uses natural language processing technology to analyze text data and identify meeting topics, important keywords, and phrases. This allows for a quick grasp of the meeting's key points and the extraction of information useful for later reference. The analysis unit can also identify positive and negative opinions. For example, it uses sentiment analysis algorithms to analyze the emotions and tone of opinions of speakers and classify them into positive and negative opinions. This allows for an understanding of the meeting's atmosphere and the trends in participants' opinions. Furthermore, the analysis unit can analyze the content of each speaker's statements and identify who said what. This allows for an evaluation of each speaker's contribution and the diversity of opinions. For example, the analysis unit uses natural language processing technology to analyze the meeting content and extract important keywords and phrases. The analysis unit can also use sentiment analysis algorithms to identify positive and negative opinions. This allows the analysis unit to analyze the meeting content from multiple perspectives and extract important information. Furthermore, the analysis unit can analyze the meeting content chronologically, allowing for an understanding of the progress of discussions and the evolution of topics. This enables an understanding of the meeting's flow and the identification of important turning points and decisions. Based on these analysis results, the analysis unit can also automatically generate meeting summaries and reports. This allows for an efficient review of the meeting content, which can be used to inform future meetings and decision-making.

[0065] The service provider provides the information analyzed by the analysis provider in an interactive manner. For example, the service provider can provide meeting content interactively using a chatbot. Specifically, when a user enters a question into the chatbot, the chatbot generates an appropriate answer based on the analysis results and provides it to the user. This allows the user to quickly obtain the necessary information. The service provider can also provide meeting content interactively using a voice assistant. When a user asks a question by voice, the voice assistant provides an answer by voice based on the analysis results. This allows for hands-free information acquisition and improves convenience. Furthermore, the service provider can provide meeting content interactively using an interactive dashboard. The interactive dashboard visually displays the meeting content, allowing users to obtain information interactively. For example, important keywords and phrases are highlighted, and clicking them displays detailed information. In addition, graphs and charts can be used to visually grasp the progress of the meeting and trends in opinions. This allows the service provider to provide information to users in a variety of ways and efficiently review the meeting content. For example, the service provider can use a chatbot to answer user questions about the meeting content. The voice assistant provides the meeting content verbally in response to the user's voice input. The interactive dashboard visually displays the meeting content, allowing the user to obtain information interactively. As a result, the meeting content review system according to this embodiment can efficiently read, analyze, and interactively provide the meeting content. Furthermore, the delivery unit can collect user feedback and continuously improve the accuracy and usability of the system. For example, by having the user input evaluations and comments on the provided information, the system can improve its analysis algorithm and delivery method based on that feedback. As a result, the delivery unit can continue to provide users with more appropriate and useful information.

[0066] The analysis unit includes an extraction unit that extracts important items and statements. The extraction unit can, for example, analyze the content of a meeting and extract important keywords and phrases. The extraction unit uses natural language processing technology to extract important items from the meeting content. For example, the extraction unit analyzes the meeting content and extracts frequently used keywords and phrases. Furthermore, the extraction unit can analyze the meeting content and extract important items based on the speaker's position and the importance of their statement. For example, the extraction unit analyzes the meeting content and prioritizes extracting statements made by the general manager. This allows for the efficient extraction of important items and statements.

[0067] The analysis unit includes a identifier unit that identifies positive and negative opinions. The identifier unit can, for example, analyze the content of a meeting and identify positive and negative opinions. The identifier unit uses a sentiment analysis algorithm to identify positive and negative opinions from the meeting content. For example, the identifier unit analyzes the meeting content and displays positive opinions in green and negative opinions in red. Alternatively, the identifier unit can analyze the meeting content and display positive opinions in blue and negative opinions in yellow. This allows for the efficient identification of positive and negative opinions.

[0068] The service provider includes a function for interactively reviewing meeting content. For example, the service provider can provide meeting content interactively using a chatbot. It can also provide meeting content interactively using a voice assistant. Furthermore, it can provide meeting content interactively using an interactive dashboard. For example, the service provider can use a chatbot to answer user questions about meeting content. A voice assistant provides meeting content verbally in response to user voice input. An interactive dashboard visually displays meeting content, allowing users to obtain information interactively. This enables interactive review of meeting content.

[0069] The reading unit can automatically read the contents of a meeting. For example, the reading unit can automatically record the audio data of a meeting and convert it into text data. The reading unit can also automatically read the text data of a meeting. Furthermore, the reading unit can automatically read meeting presentation materials and minutes. For example, the reading unit can use speech recognition technology to automatically convert the audio data of a meeting into text data. The text data is then analyzed using natural language processing technology. This allows the reading unit to automatically read the contents of the meeting. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the audio data of a meeting into a generating AI and have the generating AI perform the conversion from audio data to text data.

[0070] The reading unit can estimate the user's emotions and adjust the timing of reading the meeting content based on the estimated emotions. For example, if the user is stressed, the reading unit can delay the reading of the meeting content and read it in a relaxed state. If the user is focused, the reading unit can also immediately read the meeting content and start analysis quickly. Furthermore, if the user is tired, the reading unit can divide the reading of the meeting content and read it in multiple short bursts. For example, the reading unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reading unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the reading unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the timing of reading the meeting content to be adjusted 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0071] The reading unit can be equipped with the ability to process both audio and text data simultaneously when reading the contents of a meeting. For example, the reading unit can convert the meeting's audio data into text in real time and perform analysis simultaneously. The reading unit can also analyze the audio data in the background while reading the meeting's text data. Furthermore, the reading unit can synchronize the audio and text data to instantly identify important statements. For example, the reading unit can use speech recognition technology to convert the meeting's audio data into text data in real time. The text data is then analyzed using natural language processing technology. This allows for the simultaneous processing of both audio and text data. Some or all of the above-described processes in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the meeting's audio and text data into a generating AI and have the generating AI perform the synchronization process between the audio and text data.

[0072] The input unit can be enhanced to prioritize the input of specific keywords or phrases when inputting meeting content. For example, the input unit can pre-set keywords related to the meeting agenda and prioritize inputting statements containing those keywords. The input unit can also prioritize inputting phrases related to items that the general manager was paying attention to. Furthermore, the input unit can improve the accuracy of the analysis by prioritizing the input of keywords indicating positive or negative opinions. For example, the input unit can analyze the meeting content and prioritize inputting frequently occurring keywords or phrases. This allows for the priority input of specific keywords or phrases. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the meeting content into a generating AI and have the generating AI perform the priority input of specific keywords or phrases.

[0073] The reading unit can estimate the user's emotions and prioritize the meeting content to read based on the estimated emotions. For example, if the user is excited, the reading unit will prioritize reading important topics. If the user is relaxed, the reading unit can also prioritize reading the overall flow. Furthermore, if the user is tired, the reading unit can prioritize reading content that can be understood in a short time. For example, the reading unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reading unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the reading unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to prioritize meeting content 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0074] The data entry unit can adjust its data entry method based on the roles and expertise of the meeting participants. For example, it can prioritize reading the statements of the general manager and identify important items. It can also prioritize reading technical content based on expertise. Furthermore, it can prioritize reading statements related to decision-making based on role. For example, the data entry unit analyzes the meeting content and extracts important items based on role and expertise. This allows it to adjust the data entry method based on the roles and expertise of the meeting participants. Some or all of the above processing in the data entry unit may be performed using AI, for example, or not. For example, the data entry unit can input the meeting content into a generating AI and have the generating AI adjust the data entry method based on role and expertise.

[0075] The data entry unit can adjust its data entry method based on the meeting location and time of day when reading the meeting content. For example, if the meeting location differs, the data entry unit will prioritize reading information related to that location. The data entry unit can also prioritize reading important statements based on the meeting time of day. Furthermore, the data entry unit can prioritize reading participants' statements based on the meeting location and time of day. For example, the data entry unit can analyze the meeting content and extract important items based on the meeting location and time of day. This allows the data entry method to be adjusted based on the meeting location and time of day. Some or all of the above processing in the data entry unit may be performed using AI, for example, or not. For example, the data entry unit can input the meeting content into a generating AI and have the generating AI perform adjustments to the data entry method based on the meeting location and time of day.

[0076] 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 relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a concise analysis and provide concise information. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the accuracy of the analysis to be adjusted 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 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0077] The analysis unit can be equipped with the ability to analyze meeting content in real time and instantly extract important items and statements. For example, the analysis unit can analyze meeting audio data in real time and instantly extract important statements. The analysis unit can also analyze meeting text data in real time and instantly extract important items. Furthermore, the analysis unit can analyze meeting content in real time and instantly extract items that the general manager was paying attention to. For example, the analysis unit can use speech recognition technology to analyze meeting audio data in real time and extract important statements. Text data is analyzed using natural language processing technology. This allows for real-time analysis of meeting content and instant extraction of important items and statements. 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 meeting audio data and text data into a generating AI and have the generating AI perform real-time analysis.

[0078] The analysis unit can classify the meeting content into multiple categories and apply different analysis methods to each category. For example, the analysis unit can classify the meeting content into categories such as technology, management, and marketing, and apply an appropriate analysis method to each. The analysis unit can also classify the meeting content into positive and negative opinions and apply an appropriate analysis method to each. Furthermore, the analysis unit can classify the meeting content by speaker and apply an appropriate analysis method to each statement. For example, the analysis unit can analyze the meeting content using natural language processing technology and extract important items for each category. This allows the meeting content to be classified into multiple categories, and different analysis methods to be applied to each category. 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 meeting content into a generating AI and have the generating AI perform the process of applying different analysis methods to each category.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated 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 expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the display method of the analysis results to be adjusted 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 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0080] The analysis unit can determine the priority of analysis based on the frequency and content of each participant's statements in the meeting. For example, the analysis unit may prioritize the analysis of statements made by participants who speak frequently. The analysis unit may also prioritize the analysis of statements made by participants whose statements are important. Furthermore, the analysis unit may prioritize the analysis of statements made by the general manager. For example, the analysis unit analyzes the content of the meeting and extracts important items based on the frequency and content of statements. This allows the analysis unit to determine the priority of analysis based on the frequency and content of each participant's statements. 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 content of the meeting into a generating AI and have the generating AI determine the priority of analysis based on the frequency and content of statements.

[0081] The analysis unit can improve the accuracy of its analysis by comparing the content of the meeting with the content of other relevant meetings. For example, the analysis unit can identify important items by comparing them with the content of past meetings. The analysis unit can also verify the consistency of statements by comparing them with the content of other relevant meetings. Furthermore, the analysis unit can analyze changes in positive and negative opinions by comparing them with the content of past meetings. For example, the analysis unit can analyze the content of the meeting and extract important items by comparing them with past meeting data. This improves the accuracy of the analysis by comparing the content of the meeting with the content of other relevant meetings. 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 content of the meeting into a generating AI and have the generating AI perform a comparison with the content of other relevant meetings.

[0082] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can also provide concise information. Furthermore, if the user is excited, the service provider can provide visually stimulating information. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to adjust the level of detail of the information provided 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 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input image data of the user captured by a camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0083] The service provider can add a function to generate graphs and charts to visually display the content of a meeting. For example, the service provider can visually display the content of a meeting using graphs and charts, highlighting important items. The service provider can also display the content of a meeting chronologically, allowing for a visual understanding of the flow of discussions. Furthermore, the service provider can display the content of a meeting by category using graphs and charts, visually demonstrating the importance of each category. For example, the service provider can use data visualization technology to visually display the content of a meeting. This allows for the generation of graphs and charts to visually display the content of a meeting. 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 content of a meeting into a generating AI and have the generating AI generate graphs and charts.

[0084] The service provider can add a function to play back the meeting content in audio, allowing users to acquire information aurally. For example, the service provider can play back important statements from the meeting in audio, allowing users to acquire information aurally. The service provider can also summarize the meeting content and play it back in audio, allowing users to acquire information efficiently. Furthermore, the service provider can play back the meeting content in audio by category, aurally indicating the importance of each category. For example, the service provider can use speech synthesis technology to play back the meeting content in audio. This allows users to acquire information aurally by playing back the meeting content in audio. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the meeting content into a generating AI and have the generating AI perform the audio playback.

[0085] The information provider can estimate the user's emotions and adjust the order of information provided based on the estimated emotions. For example, if the user is nervous, the provider can provide important information first. If the user is relaxed, the provider can also provide information emphasizing the overall flow. Furthermore, if the user is in a hurry, the provider can provide concise information first. For example, the provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The provider can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the provider to adjust the order of information provided 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 provider may be performed using AI, for example, or without AI. For example, the service provider can input image data of the user captured by a camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0086] The service provider can add a function to display meeting content linked to other relevant materials. For example, the service provider can link meeting content to relevant materials to provide detailed information. The service provider can also link meeting content to past meeting materials to verify the consistency of statements. Furthermore, the service provider can link meeting content to relevant research materials to provide technical background. For example, the service provider can link meeting content to other relevant materials using hyperlinks. This allows for the provision of detailed information by displaying meeting content linked to other relevant materials. 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 meeting content into a generating AI and have the generating AI perform the linking to relevant materials.

[0087] The service provider can add a function to translate and display the meeting content in multiple languages. For example, the service provider can translate the meeting content into English and provide it to international participants. The service provider can also translate the meeting content into multiple languages ​​and provide multilingual information. Furthermore, the service provider can translate the meeting content in real time and provide it instantly in multiple languages. For example, the service provider can translate the meeting content into multiple languages ​​using machine translation technology. This allows for the provision of multilingual information by translating and displaying the meeting content in multiple languages. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the meeting content into a generating AI and have the generating AI perform the translation into multiple languages.

[0088] The extraction unit can estimate the user's emotions and determine the priority of items to extract based on the estimated emotions. For example, if the user is excited, the extraction unit will prioritize extracting important topics. If the user is relaxed, the extraction unit can also prioritize extracting items that emphasize the overall flow. Furthermore, if the user is tired, the extraction unit can prioritize extracting items that can be understood in a short time. For example, the extraction unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The extraction unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the extraction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the extraction unit to determine the priority of items to extract 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0089] The extraction unit can be enhanced with a function to organize the meeting content along a timeline and display important items in chronological order. For example, the extraction unit can organize the meeting content along a timeline and display important statements in chronological order. The extraction unit can also organize the meeting content along a timeline and display important items for each agenda item. Furthermore, the extraction unit can organize the meeting content along a timeline and display important items for each speaker. For example, the extraction unit can organize the meeting content chronologically using a timeline display. This allows the meeting content to be organized along a timeline and important items to be displayed in chronological order. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the meeting content into a generating AI and have the generating AI perform the chronological organization and display.

[0090] The extraction unit can estimate the user's emotions and adjust the display method of the extracted items based on the estimated user emotions. For example, if the user is tense, the extraction unit can provide a simple and highly visible display method. If the user is relaxed, the extraction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a concise display method. For example, the extraction unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The extraction unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the extraction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the display method of the extracted items to be adjusted 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-described processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0091] The extraction unit can improve the accuracy of its extraction by comparing the content of the meeting with the content of other relevant meetings. For example, the extraction unit can identify important items by comparing them with the content of past meetings. The extraction unit can also verify the consistency of statements by comparing them with the content of other relevant meetings. Furthermore, the extraction unit can analyze changes in positive and negative opinions by comparing them with the content of past meetings. For example, the extraction unit can analyze the content of the meeting and extract important items by comparing them with past meeting data. This improves the accuracy of the extraction by comparing the content of the meeting with the content of other relevant meetings. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the content of the meeting into a generating AI and have the generating AI perform a comparison with the content of other relevant meetings.

[0092] The identification unit can estimate the user's emotions and determine the priority of opinions to identify based on the estimated emotions. For example, if the user is excited, the identification unit will prioritize identifying important opinions. If the user is relaxed, the identification unit can also prioritize identifying opinions that emphasize the overall flow. Furthermore, if the user is tired, the identification unit can also prioritize identifying opinions that can be understood quickly. For example, the identification unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The identification unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the identification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the unit to determine the priority of opinions to identify 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0093] The specific unit can add a function to emotionally analyze the content of a meeting and display positive and negative opinions in different colors. For example, the specific unit can emotionally analyze the content of a meeting and display positive opinions in green and negative opinions in red. The specific unit can also emotionally analyze the content of a meeting and display positive opinions in blue and negative opinions in yellow. Furthermore, the specific unit can emotionally analyze the content of a meeting and display positive opinions in orange and negative opinions in purple. For example, the specific unit can analyze the content of a meeting using an emotional analysis algorithm and display positive and negative opinions in different colors. This makes it possible to emotionally analyze the content of a meeting and display positive and negative opinions in different colors. Some or all of the above processing in the specific unit may be performed using AI, for example, or without using AI. For example, the specific unit can input the content of a meeting into a generating AI and have the generating AI perform the emotional analysis and color-coding display.

[0094] The identification unit can estimate the user's emotions and adjust the display method of identified opinions based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visible display method. If the user is relaxed, the identification unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the identification unit can provide a concise display method. For example, the identification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The identification unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the identification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. This allows the display method of identified opinions to be adjusted 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-described processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The identification unit can improve the accuracy of identification by comparing the content of a meeting with the content of other relevant meetings. For example, the identification unit can identify important opinions by comparing them with past meeting content. The identification unit can also verify the consistency of statements by comparing them with the content of other relevant meetings. Furthermore, the identification unit can analyze changes in positive and negative opinions by comparing them with past meeting content. For example, the identification unit can analyze the content of a meeting and extract important opinions by comparing them with past meeting data. This allows the identification unit to improve the accuracy of identification by comparing the content of a meeting with the content of other relevant meetings. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the content of a meeting into a generating AI and have the generating AI perform a comparison with the content of other relevant meetings.

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

[0097] The meeting review system may also include a summarization unit that estimates the user's emotions and provides a summary of the meeting content based on those emotions. For example, the summarization unit can provide a concise summary if the user is stressed, and a detailed summary if the user is relaxed. Furthermore, if the user is in a hurry, the summarization unit can extract only the important points and provide a summary. For example, the summarization unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The summarization unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the summarization unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide a meeting summary tailored to the user's emotions.

[0098] The meeting review system can also include a translation unit that translates the meeting content in real time and provides it in multiple languages. For example, the translation unit can translate the meeting audio data in real time and provide it in multiple languages ​​such as English, French, and Chinese. Furthermore, the translation unit can translate the meeting text data in real time and display it in the user's chosen language. For example, the translation unit can use machine translation technology to translate the meeting content in real time. This allows the meeting content to be provided in multiple languages, accommodating international participants.

[0099] The meeting review system may also include a visualization unit that estimates the user's emotions and adjusts the visual display of the meeting content based on the estimated emotions. For example, the visualization unit can provide simple, highly visible graphs and charts when the user is tense, and provide a visual display containing detailed information when the user is relaxed. Furthermore, the visualization unit can provide a concise visual display when the user is in a hurry. For example, the visualization unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The visualization unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the visualization unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the visual display of the meeting content to be adjusted according to the user's emotions.

[0100] The meeting review system can also include a linking section that displays meeting content in conjunction with other relevant materials. For example, the linking section can link meeting content to related research materials or past meeting materials, providing more detailed information. Furthermore, the linking section can link meeting content to relevant technical documents or marketing materials, providing technical background and market trends. For instance, the linking section uses hyperlinks to link meeting content to other relevant materials. This allows for the display of meeting content linked to other relevant materials, thereby providing more detailed information.

[0101] The meeting content review system may also include a sequence adjustment unit that estimates the user's emotions and adjusts the order in which meeting content is presented based on the estimated emotions. For example, if the user is nervous, the sequence adjustment unit can provide important information first, and if the user is relaxed, it can provide information emphasizing the overall flow. Furthermore, if the user is in a hurry, the sequence adjustment unit can provide concise information first. For example, the sequence adjustment unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The sequence adjustment unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the sequence adjustment unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the order in which meeting content is presented to be adjusted according to the user's emotions.

[0102] The meeting review system may also include a visualization unit that generates graphs and charts to visually display the meeting content. The visualization unit can, for example, visually display the meeting content using graphs and charts, highlighting important items. Furthermore, the visualization unit can display the meeting content chronologically, allowing for a visual understanding of the flow of discussion. For example, the visualization unit can use data visualization technology to visually display the meeting content. This enables the generation of graphs and charts to visually represent the meeting content.

[0103] The meeting content review system can also include an analysis unit that estimates the user's emotions and adjusts the accuracy of the meeting content analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. Furthermore, if the user is in a hurry, the analysis unit can perform a concise analysis and provide information that gets straight to the point. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the accuracy of the meeting content analysis to be adjusted according to the user's emotions.

[0104] The meeting review system can also include an audio playback unit that plays back the meeting content in audio format. For example, the audio playback unit can play back important statements from the meeting, allowing users to acquire information aurally. Furthermore, the audio playback unit can summarize the meeting content and play it back aurally, enabling users to acquire information efficiently. For instance, the audio playback unit can use speech synthesis technology to play back the meeting content. This allows users to acquire information aurally by playing back the meeting content in audio format.

[0105] The meeting content review system can further include an extraction unit that estimates the user's emotions and adjusts the extracted meeting content based on the estimated emotions. For example, if the user is excited, the extraction unit can prioritize extracting important topics, and if the user is relaxed, it can prioritize extracting the overall flow. Furthermore, if the user is tired, the extraction unit can prioritize extracting items that can be understood in a short time. For example, the extraction unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The extraction unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the extraction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the extracted meeting content to be adjusted according to the user's emotions.

[0106] The meeting content review system may also include a comparative analysis unit that improves the accuracy of the analysis by comparing the meeting content with the content of other related meetings. The comparative analysis unit can, for example, identify important items by comparing it with past meeting content. Furthermore, the comparative analysis unit can also verify the consistency of statements by comparing them with the content of other related meetings. For example, the comparative analysis unit analyzes the meeting content and extracts important items by comparing it with past meeting data. This improves the accuracy of the analysis by comparing the meeting content with the content of other related meetings.

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

[0108] Step 1: The reading unit reads the meeting content. For example, it can record meeting audio data and convert it into text data. It can also directly read meeting text data. Furthermore, it can read meeting presentation materials and minutes. It uses speech recognition technology to convert meeting audio data into text data. Step 2: The analysis unit analyzes the meeting content read by the reading unit. For example, it analyzes the meeting content and extracts important items and statements. It can also identify positive and negative opinions. It uses natural language processing technology to analyze the meeting content and extract important keywords and phrases. It can also use sentiment analysis algorithms to identify positive and negative opinions. Step 3: The delivery unit interactively provides the content analyzed by the analysis unit. For example, the content of the meeting can be provided interactively using a chatbot. The content of the meeting can also be provided interactively using a voice assistant. The content of the meeting can also be provided interactively using an interactive dashboard. The chatbot answers the user's questions with the content of the meeting, and the voice assistant provides the content of the meeting verbally in response to the user's voice input. The interactive dashboard visually displays the content of the meeting, allowing the user to obtain information interactively.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reading unit, analysis unit, provision unit, extraction unit, and identification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reading unit is implemented by the computer 36 of the smart device 14, which records the audio data of the meeting and converts it into text data. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the content of the meeting and extracts important items and statements. The provision unit is implemented by the control unit 46A of the smart device 14, which provides the content of the meeting interactively. The extraction unit is implemented by the identification processing unit 290 of the data processing device 12, which extracts important keywords and phrases. The identification unit is implemented by the identification processing unit 290 of the data processing device 12, which identifies positive and negative opinions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reading unit, analysis unit, provision unit, extraction unit, and identification unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit is implemented by the computer 36 of the smart glasses 214, which records the audio data of the meeting and converts it into text data. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the content of the meeting and extracts important items and statements. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides the content of the meeting interactively. The extraction unit is implemented by the identification processing unit 290 of the data processing device 12, which extracts important keywords and phrases. The identification unit is implemented by the identification processing unit 290 of the data processing device 12, which identifies positive and negative opinions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reading unit, analysis unit, provision unit, extraction unit, and identification unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the reading unit is implemented by the computer 36 of the headset terminal 314, which records the audio data of the meeting and converts it into text data. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the content of the meeting and extracts important items and statements. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides the content of the meeting interactively. The extraction unit is implemented by the identification processing unit 290 of the data processing device 12, which extracts important keywords and phrases. The identification unit is implemented by the identification processing unit 290 of the data processing device 12, which identifies positive and negative opinions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reading unit, analysis unit, provision unit, extraction unit, and identification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the robot 414, which records the audio data of the meeting and converts it into text data. The analysis unit is implemented by the identification unit 290 of the data processing unit 12, which analyzes the content of the meeting and extracts important items and statements. The provision unit is implemented by the control unit 46A of the robot 414, which provides the content of the meeting interactively. The extraction unit is implemented by the identification unit 290 of the data processing unit 12, which extracts important keywords and phrases. The identification unit is implemented by the identification unit 290 of the data processing unit 12, which identifies positive and negative opinions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reading unit that reads the contents of the meeting, An analysis unit analyzes the contents of the meeting read by the aforementioned reading unit, A providing unit that interactively provides the content analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes an extraction unit that extracts important items and statements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes a section for identifying positive and negative opinions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It features an interactive function for reviewing the content of meetings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reading unit, Automatically load meeting content The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reading unit, It estimates the user's emotions and adjusts the timing of loading meeting content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reading unit, Add a feature to process both audio and text data simultaneously when reading meeting content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reading unit, Add a feature to prioritize the loading of meeting content based on specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reading unit, It estimates the user's emotions and prioritizes the meeting content to read based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reading unit, When reviewing meeting content, adjust the review method based on the roles and areas of expertise of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reading unit, When importing meeting content, the import method is adjusted based on the meeting's location and time. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned analysis unit, We will add a feature that analyzes meeting content in real time and instantly extracts important items and statements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The meeting content is categorized into multiple categories, and a different analytical method is applied to each category. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, Prioritize analysis based on the frequency and content of participants' contributions to the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Improve the accuracy of the analysis by comparing the content of the meeting with the content of other related meetings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the level of detail of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, Add a function to generate graphs and charts to visually display the content of meetings. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We've added a feature that plays back the meeting content as audio, allowing users to acquire information aurally. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order of information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Add a feature to display meeting content linked to other related documents. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Add a feature to translate and display meeting content in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is It estimates the user's emotions and determines the priority of items to extract based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The extraction unit is Add a feature to organize meeting content chronologically and display important items in order. The system described in Appendix 2, characterized by the features described herein. (Note 26) The extraction unit is We estimate the user's emotions and adjust how the items extracted based on those estimated emotions are displayed. The system described in Appendix 2, characterized by the features described herein. (Note 27) The extraction unit is Improve the accuracy of extraction by comparing the content of the meeting with the content of other related meetings. The system described in Appendix 2, characterized by the features described herein. (Note 28) The specified part is, It estimates the user's emotions and determines the priority of the opinions to identify based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The specified part is, Add a feature that analyzes the sentiment of meeting content and displays positive and negative opinions in different colors. The system described in Appendix 3, characterized by the features described herein. (Note 30) The specified part is, We estimate the user's sentiment and adjust how specific opinions are displayed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 31) The specified part is, By comparing the content of this meeting with the content of other related meetings, we can improve specific accuracy. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reading unit that reads the contents of the meeting, An analysis unit analyzes the contents of the meeting read by the aforementioned reading unit, A providing unit that interactively provides the content analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, It includes an extraction unit that extracts important items and statements. The system according to feature 1.

3. The aforementioned analysis unit, It includes a special section for identifying positive and negative opinions. The system according to feature 1.

4. The aforementioned supply unit is, It features an interactive function for reviewing the content of meetings. The system according to feature 1.

5. The aforementioned reading unit, Automatically load meeting content The system according to feature 1.

6. The aforementioned reading unit, It estimates the user's emotions and adjusts the timing of loading meeting content based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reading unit, Add a feature to process both audio and text data simultaneously when reading meeting content. The system according to feature 1.

8. The aforementioned reading unit, Add a feature to prioritize the loading of meeting content based on specific keywords or phrases. The system according to feature 1.

9. The aforementioned reading unit, It estimates the user's emotions and prioritizes the meeting content to read based on those estimated emotions. The system according to feature 1.

10. The aforementioned reading unit, When reviewing meeting content, adjust the review method based on the roles and areas of expertise of the meeting participants. The system according to feature 1.

Images