System

Abstract

An object of the system according to the embodiment is to provide advice and suggestions in real time during a meeting to improve the quality and effect of the meeting.SOLUTION: A system according to an embodiment includes a voice recognizer, a natural language processor, an advice generator, and a task management linker. The speech recognition unit recognizes speech during a conference in real time. The natural language processor analyzes the utterance content recognized by the voice recognizer. The advice generation unit generates advice or a proposal based on the content analyzed by the natural language processing unit. The task management coordination module coordinates the advice and the proposal generated by the advice generation module with the task management tool.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies make it difficult to provide appropriate advice and suggestions in real time during meetings, and there is room for improvement in terms of improving the quality and effectiveness of meetings.

[0005] The system according to the embodiment aims to provide advice and suggestions in real time during a meeting, thereby improving the quality and effectiveness of the meeting. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a natural language processing unit, an advice generation unit, and a task management collaboration unit. The speech recognition unit recognizes utterances made during a meeting in real time. The natural language processing unit analyzes the content of the utterances recognized by the speech recognition unit. The advice generation unit generates advice or suggestions based on the content analyzed by the natural language processing unit. The task management collaboration unit collaborates with a task management tool on the advice or suggestions generated by the advice generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide advice and suggestions in real time during a meeting, thereby improving the quality and effectiveness of the meeting. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The conference support system according to an embodiment of the present invention uses AI to analyze the progress of a conference and the content of comments, and provides advice and suggestions. This allows the conference support system to improve the quality and effectiveness of the conference.

[0029] A conference support system according to an embodiment includes a speech recognition unit, a natural language processing unit, an advice generation unit, and a task management collaboration unit. The speech recognition unit recognizes speech made during a conference in real time. For example, the speech recognition unit collects speech made during a conference using a microphone and converts the speech data into text data. The speech recognition unit can also use noise canceling technology to remove background noise and accurately recognize speech content. The speech recognition unit can also distinguish between multiple speakers and recognize each speech individually. The natural language processing unit analyzes the speech content recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the grammatical structure of the speech content. The natural language processing unit can also perform semantic analysis to understand the meaning of the speech content. The natural language processing unit can also estimate the emotion of the speech content and record changes in emotion. The advice generation unit generates advice or suggestions based on the content analyzed by the natural language processing unit. For example, the advice generation unit can suggest appropriate timing for proceeding with the conference. The advice generation unit can also identify and suggest topics that require follow-up. The advice generation unit can also present questions to encourage broad participation of opinions. The task management collaboration unit collaborates with a task management tool on advice and suggestions generated by the advice generation unit. For example, the task management collaboration unit automatically registers tasks that arise during a meeting in the task management tool. The task management collaboration unit can also track the progress of tasks and send reminders as needed. The task management collaboration unit can also determine the priority of tasks using an emotion estimation function. As a result, the meeting support system according to the embodiment can provide advice and suggestions in real time to improve the progress of meetings and the quality of discussions.

[0030] The voice recognition unit can learn the characteristics of a speaker's voice in advance and create an individual voice profile. For example, to learn the characteristics of a speaker's voice in advance, the voice recognition unit registers the speaker's voice data in the voice recognition system and creates an individual voice profile. For example, the voice recognition unit learns the speaker's voice pitch, speed, accent, etc. In addition, to improve the accuracy of voice recognition, the voice recognition unit analyzes the characteristics of the speaker's voice in real time and updates the individual voice profile. For example, it responds to changes in the speaker's voice. In addition, to create an individual voice profile, the voice recognition unit collects the speaker's voice data and has the voice recognition system learn the data. For example, it collects voice data of the speaker speaking in different situations. This can improve the accuracy of voice recognition.

[0031] The speech recognition unit can translate utterances in real time to facilitate communication between participants who speak different languages. For example, the speech recognition unit incorporates a multilingual translation algorithm into the speech recognition system to translate utterances during a meeting in real time. For example, it translates from English to Japanese. The speech recognition unit also displays the translated text data in real time to facilitate communication between participants who speak different languages. For example, it displays the translation results on a conference screen. The speech recognition unit also links the speech recognition system to a cloud-based translation service to translate utterances during a meeting in real time. For example, it uses Google Translate or Microsoft Translator. This facilitates communication between participants who speak different languages.

[0032] When analyzing the content of a statement, the natural language processing unit can compare it with past meeting data and identify similar discussions and topics. For example, when analyzing the content of a statement, the natural language processing unit incorporates a database search function into the natural language processing AI to compare it with past meeting data. For example, it searches past discussion content and identifies similar topics. In addition, to identify similar discussions and topics, the natural language processing unit analyzes the content of a statement for keywords and phrases and compares it with past meeting data. For example, it extracts statements that contain the same keywords. In addition, the natural language processing unit builds a system for analyzing the content of a statement and comparing it with past meeting data. For example, it automatically generates a report based on the content of past discussions. This makes it possible to identify similar discussions and topics by comparing it with past meeting data.

[0033] The natural language processing unit can analyze the content of comments and automatically extract unresolved problems and new issues in a discussion. For example, the natural language processing unit incorporates a problem extraction algorithm into the natural language processing AI to analyze the content of comments and automatically extract unresolved problems and new issues. For example, it identifies problems from the content of comments. The natural language processing unit also builds a system that analyzes the content of comments and lists problems and issues in order to extract unresolved problems and new issues in a discussion. For example, it classifies the content of comments and extracts problems. The natural language processing unit also develops a system that analyzes the content of comments and automatically extracts unresolved problems and new issues. For example, it automatically generates a problem list based on the content of comments. This makes it possible to automatically extract unresolved problems and new issues in a discussion.

[0034] The advice generation unit can compare the content of advice or proposals with past success cases or best practices and select the optimal proposal. The advice generation unit, for example, incorporates a database search function to compare the content of advice or proposals with past success cases. For example, it searches past success cases and selects the optimal proposal for a similar situation. The advice generation unit also builds a system to evaluate the content of proposals in order to select the optimal proposal by comparing with best practices. For example, it calculates an evaluation score based on past success cases. The advice generation unit also develops a system to compare the content of advice or proposals with past success cases or best practices and select the optimal proposal. For example, it automatically evaluates the content of proposals and selects the optimal proposal. This makes it possible to select the optimal proposal by comparing with past success cases and best practices.

[0035] The advice generation unit can make customized suggestions based on the expertise and role of the participant. The advice generation unit incorporates a customization function based on profile data to take into account the expertise and role of the participant when making advice or suggestions. For example, it makes suggestions based on expertise. The advice generation unit also builds a system that provides advice based on role data to make customized suggestions based on the role of the participant. For example, it makes suggestions to demonstrate leadership. The advice generation unit also develops a system for making customized suggestions based on the expertise and role of the participant when making advice or suggestions. For example, it provides specific advice based on expertise. This makes it possible to make customized suggestions based on the expertise and role of the participant.

[0036] The task management collaboration unit works in conjunction with a task management tool to monitor task progress in real time and send reminders as necessary. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that monitors task progress in real time. For example, it displays task progress in graphs and charts. The task management collaboration unit also develops a reminder function that works in conjunction with the task management tool to send reminders as necessary. For example, it sends reminders when a task deadline is approaching. The task management collaboration unit also builds a system that monitors task progress in real time and sends reminders as necessary. For example, it automatically sends reminders according to the task progress. This makes it possible to monitor task progress in real time and send reminders as necessary.

[0037] The task management collaboration unit works in conjunction with a task management tool to automatically analyze task dependencies and allocate tasks efficiently. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that automatically analyzes task dependencies. For example, it displays the dependencies between tasks in a graph. The task management collaboration unit also develops an allocation algorithm based on task dependencies to allocate tasks efficiently. For example, it allocates tasks with strong dependencies preferentially. The task management collaboration unit also works in conjunction with a task management tool to automatically analyze task dependencies and build a system for efficient task allocation. For example, it schedules tasks taking dependencies into consideration. This makes it possible to automatically analyze task dependencies and allocate tasks efficiently.

[0038] The task management collaboration unit works in conjunction with a task management tool and can be applied to project management other than meetings and task management for daily work. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that can be applied to project management other than meetings. For example, it can centrally manage the progress of a project. The task management collaboration unit also develops a task management system that works in conjunction with a task management tool to be applied to task management for daily work. For example, it can automatically register tasks for daily work and monitor their progress. The task management collaboration unit also works in conjunction with a task management tool to build a system that can be applied to project management other than meetings and task management for daily work. For example, it can integrate and manage project tasks and daily work tasks. This makes it possible to apply it to project management other than meetings and task management for daily work.

[0039] The task management collaboration unit works in conjunction with a task management tool to visualize task progress and share it with the entire team. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that visualizes task progress. For example, it displays task progress in graphs and charts. The task management collaboration unit also develops a system that displays visualized progress in real time to share task progress with the entire team. For example, it displays the progress on a screen. The task management collaboration unit also works in conjunction with a task management tool to build a system that visualizes task progress and shares it with the entire team. For example, it provides a dashboard for sharing progress. This allows task progress to be visualized and shared with the entire team.

[0040] The task management collaboration unit can automatically generate minutes after a meeting and distribute them to participants. The task management collaboration unit, for example, builds a system that automatically generates minutes based on voice data recognized during a meeting. For example, it organizes what is said and summarizes the important points. The task management collaboration unit also uses email or cloud storage to distribute the automatically generated minutes to meeting participants. For example, it automatically sends the minutes after the meeting ends. The task management collaboration unit also uses voice recognition data to customize the format of the minutes. For example, it can color-code each speaker or highlight important comments. This allows minutes to be automatically generated after a meeting and distributed to participants.

[0041] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0042] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0043] The task management collaboration unit can automatically generate minutes after a meeting and distribute them to participants. The task management collaboration unit, for example, builds a system that automatically generates minutes based on voice data recognized during a meeting. For example, it organizes what is said and summarizes the important points. The task management collaboration unit also uses email or cloud storage to distribute the automatically generated minutes to meeting participants. For example, it automatically sends the minutes after the meeting ends. The task management collaboration unit also uses voice recognition data to customize the format of the minutes. For example, it can color-code each speaker or highlight important comments. This allows minutes to be automatically generated after a meeting and distributed to participants.

[0044] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0045] The task management collaboration unit can automatically generate minutes after a meeting and distribute them to participants. The task management collaboration unit, for example, builds a system that automatically generates minutes based on voice data recognized during a meeting. For example, it organizes what is said and summarizes the important points. The task management collaboration unit also uses email or cloud storage to distribute the automatically generated minutes to meeting participants. For example, it automatically sends the minutes after the meeting ends. The task management collaboration unit also uses voice recognition data to customize the format of the minutes. For example, it can color-code each speaker or highlight important comments. This allows minutes to be automatically generated after a meeting and distributed to participants.

[0046] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The meeting support system can also monitor the frequency of participants' speech and provide a function to encourage participants who speak less to speak. For example, the system can automatically pose questions to participants who speak less. It can also visualize speech frequency in graphs and charts and present them to the moderator. It can also make suggestions for balancing the discussion based on speech frequency. This allows all meeting participants to actively participate in the discussion.

[0049] Meeting support systems can also be equipped with a function that automatically summarizes what is said during a meeting and provides it to participants in real time. For example, long statements can be summarized in a short form, extracting only the important points. The summarized content can also be displayed on a screen so that all participants can see it. Furthermore, the summarized content can be saved as text data for later reference. This can improve the efficiency of meetings.

[0050] Meeting support systems can also be equipped with a function to automatically categorize what is said during a meeting and organize it by topic. For example, they can analyze what is said using keywords and phrases and categorize it into related topics. They can also display the progress of the discussion for each categorized topic, allowing participants to understand where they are in the discussion. They can also summarize the main points of the discussion for each topic and make them available for later reference. This helps meetings proceed more smoothly.

[0051] Meeting support systems can also be equipped with a function that automatically translates what is said during a meeting, facilitating communication between participants who speak different languages. For example, the system can translate what is said in real time and display the translation results on the screen. It can also save the translated content as text data for later reference. It can also play back the translation results aloud, allowing participants who speak different languages ​​to communicate directly. This enables smooth communication even in international meetings.

[0052] Meeting support systems can also be equipped with a function that automatically records what is said during a meeting and allows it to be searched later. For example, the contents of comments can be saved as text data and a keyword search function can be provided. The contents of comments can also be saved as audio data and a voice search function can be provided. Furthermore, it is possible to organize the contents of comments along a chronological axis, making it easy to search for comments made during a specific time period. This makes it easier to share information and review the meeting after it has finished.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The speech recognition unit recognizes what is being said during the meeting in real time. For example, the speech recognition unit collects what is being said during the meeting using a microphone and converts the audio data into text data. The speech recognition unit can also use noise canceling technology to remove background noise and accurately recognize what is being said. Furthermore, the speech recognition unit can distinguish between multiple speakers and recognize each of their utterances individually. Step 2: The natural language processing unit analyzes the utterance content recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the grammatical structure of the utterance content. The natural language processing unit can also perform semantic analysis to understand the meaning of the utterance content. Furthermore, the natural language processing unit can estimate the emotion of the utterance content and record changes in emotion. Step 3: The advice generator generates advice and suggestions based on the content analyzed by the natural language processor. For example, the advice generator may suggest appropriate timing for proceedings. The advice generator may also identify and suggest topics that require follow-up. Furthermore, the advice generator may pose questions to encourage broad participation of opinions. Step 4: The task management collaboration unit collaborates with the task management tool based on the advice and suggestions generated by the advice generation unit. For example, the task management collaboration unit automatically registers tasks that arise during a meeting in the task management tool. The task management collaboration unit can also track the progress of tasks and send reminders as needed. Furthermore, the task management collaboration unit can determine task priorities using emotion estimation functionality.

[0055] (Example 2) The conference support system according to an embodiment of the present invention uses AI to analyze the progress of a conference and the content of comments, and provides advice and suggestions. This allows the conference support system to improve the quality and effectiveness of the conference.

[0056] A conference support system according to an embodiment includes a speech recognition unit, a natural language processing unit, an advice generation unit, and a task management collaboration unit. The speech recognition unit recognizes speech made during a conference in real time. For example, the speech recognition unit collects speech made during a conference using a microphone and converts the speech data into text data. The speech recognition unit can also use noise canceling technology to remove background noise and accurately recognize speech content. The speech recognition unit can also distinguish between multiple speakers and recognize each speech individually. The natural language processing unit analyzes the speech content recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the grammatical structure of the speech content. The natural language processing unit can also perform semantic analysis to understand the meaning of the speech content. The natural language processing unit can also estimate the emotion of the speech content and record changes in emotion. The advice generation unit generates advice or suggestions based on the content analyzed by the natural language processing unit. For example, the advice generation unit can suggest appropriate timing for proceeding with the conference. The advice generation unit can also identify and suggest topics that require follow-up. The advice generation unit can also present questions to encourage broad participation of opinions. The task management collaboration unit collaborates with a task management tool on advice and suggestions generated by the advice generation unit. For example, the task management collaboration unit automatically registers tasks that arise during a meeting in the task management tool. The task management collaboration unit can also track the progress of tasks and send reminders as needed. The task management collaboration unit can also determine the priority of tasks using an emotion estimation function. As a result, the meeting support system according to the embodiment can provide advice and suggestions in real time to improve the progress of meetings and the quality of discussions.

[0057] The speech recognition unit can estimate the speaker's emotions and record changes in emotions. For example, the speech recognition unit recognizes speech during a meeting in real time and, when converting the speech into text data, analyzes the speaker's tone and speed to estimate emotions. For example, when a speaker is excited, their voice tone often becomes higher. To estimate the speaker's emotions, the speech recognition unit incorporates an emotion analysis algorithm into the speech recognition system and records changes in emotion along with the content of speech. For example, when a speaker is angry, the strength and speed of their voice change. To record changes in emotion, the speech recognition unit analyzes the speaker's voice characteristics in real time and visualizes the changes in emotion in graphs and charts. For example, it shows how the speaker's emotions change as the meeting progresses. This allows the speaker's emotions to be understood in real time and can be used to help progress the meeting.

[0058] The voice recognition unit can learn the characteristics of a speaker's voice in advance and create an individual voice profile. For example, to learn the characteristics of a speaker's voice in advance, the voice recognition unit registers the speaker's voice data in the voice recognition system and creates an individual voice profile. For example, the voice recognition unit learns the speaker's voice pitch, speed, accent, etc. In addition, to improve the accuracy of voice recognition, the voice recognition unit analyzes the characteristics of the speaker's voice in real time and updates the individual voice profile. For example, it responds to changes in the speaker's voice. In addition, to create an individual voice profile, the voice recognition unit collects the speaker's voice data and has the voice recognition system learn the data. For example, it collects voice data of the speaker speaking in different situations. This can improve the accuracy of voice recognition.

[0059] The speech recognition unit can translate utterances in real time to facilitate communication between participants who speak different languages. For example, the speech recognition unit incorporates a multilingual translation algorithm into the speech recognition system to translate utterances during a meeting in real time. For example, it translates from English to Japanese. The speech recognition unit also displays the translated text data in real time to facilitate communication between participants who speak different languages. For example, it displays the translation results on a conference screen. The speech recognition unit also links the speech recognition system to a cloud-based translation service to translate utterances during a meeting in real time. For example, it uses Google Translate or Microsoft Translator. This facilitates communication between participants who speak different languages.

[0060] The natural language processing unit can estimate the speaker's emotions when analyzing the content of comments and adjust the direction of the discussion based on changes in emotions. For example, the natural language processing unit incorporates an emotion analysis algorithm into the natural language processing AI to estimate the speaker's emotions when analyzing the content of comments. For example, it calculates an emotion score from the content of comments. The natural language processing unit also analyzes the speaker's emotion data in real time and provides feedback to the moderator in order to adjust the direction of the discussion based on changes in emotion. For example, it suggests a calm response when the discussion becomes heated. The natural language processing unit also builds a system to estimate the speaker's emotions and adjust the direction of the discussion based on changes in emotion. For example, it prioritizes comments with high emotion scores. This makes it possible to adjust the direction of the discussion based on emotions.

[0061] When analyzing the content of a statement, the natural language processing unit can compare it with past meeting data and identify similar discussions and topics. For example, when analyzing the content of a statement, the natural language processing unit incorporates a database search function into the natural language processing AI to compare it with past meeting data. For example, it searches past discussion content and identifies similar topics. In addition, to identify similar discussions and topics, the natural language processing unit analyzes the content of a statement for keywords and phrases and compares it with past meeting data. For example, it extracts statements that contain the same keywords. In addition, the natural language processing unit builds a system for analyzing the content of a statement and comparing it with past meeting data. For example, it automatically generates a report based on the content of past discussions. This makes it possible to identify similar discussions and topics by comparing it with past meeting data.

[0062] The natural language processing unit can analyze the content of comments and automatically extract unresolved problems and new issues in a discussion. For example, the natural language processing unit incorporates a problem extraction algorithm into the natural language processing AI to analyze the content of comments and automatically extract unresolved problems and new issues. For example, it identifies problems from the content of comments. The natural language processing unit also builds a system that analyzes the content of comments and lists problems and issues in order to extract unresolved problems and new issues in a discussion. For example, it classifies the content of comments and extracts problems. The natural language processing unit also develops a system that analyzes the content of comments and automatically extracts unresolved problems and new issues. For example, it automatically generates a problem list based on the content of comments. This makes it possible to automatically extract unresolved problems and new issues in a discussion.

[0063] The advice generation unit can estimate the speaker's emotions and provide appropriate advice or suggestions according to the emotions. For example, when providing advice or suggestions, the advice generation unit incorporates an emotion analysis algorithm to estimate the speaker's emotions. For example, if the speaker is feeling anxious, the advice generation unit provides advice to reassure the speaker. The advice generation unit also builds a system that provides advice based on emotion scores to provide appropriate advice or suggestions according to the speaker's emotions. For example, if the speaker is excited, the advice generation unit makes a suggestion to stay calm. The advice generation unit also develops a system that analyzes the speaker's emotions in real time when providing advice or suggestions, and responds according to the emotions. For example, if the speaker is angry, the advice generation unit suggests a way to respond calmly. This makes it possible to provide appropriate advice or suggestions according to the speaker's emotions.

[0064] The advice generation unit can compare the content of advice or proposals with past success cases or best practices and select the optimal proposal. The advice generation unit, for example, incorporates a database search function to compare the content of advice or proposals with past success cases. For example, it searches past success cases and selects the optimal proposal for a similar situation. The advice generation unit also builds a system to evaluate the content of proposals in order to select the optimal proposal by comparing with best practices. For example, it calculates an evaluation score based on past success cases. The advice generation unit also develops a system to compare the content of advice or proposals with past success cases or best practices and select the optimal proposal. For example, it automatically evaluates the content of proposals and selects the optimal proposal. This makes it possible to select the optimal proposal by comparing with past success cases and best practices.

[0065] The advice generation unit can make customized suggestions based on the expertise and role of the participant. The advice generation unit incorporates a customization function based on profile data to take into account the expertise and role of the participant when making advice or suggestions. For example, it makes suggestions based on expertise. The advice generation unit also builds a system that provides advice based on role data to make customized suggestions based on the role of the participant. For example, it makes suggestions to demonstrate leadership. The advice generation unit also develops a system for making customized suggestions based on the expertise and role of the participant when making advice or suggestions. For example, it provides specific advice based on expertise. This makes it possible to make customized suggestions based on the expertise and role of the participant.

[0066] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and determine the priority of the tasks using an emotion estimation function. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a prioritization system based on emotion scores to determine the priority of tasks using the emotion estimation function. For example, it gives priority to tasks with high emotion scores. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and determines the priority of tasks using the emotion estimation function. For example, it evaluates the importance of tasks based on the emotion scores. This makes it possible to determine the priority of tasks using the emotion estimation function.

[0067] The task management collaboration unit works in conjunction with a task management tool to monitor task progress in real time and send reminders as necessary. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that monitors task progress in real time. For example, it displays task progress in graphs and charts. The task management collaboration unit also develops a reminder function that works in conjunction with the task management tool to send reminders as necessary. For example, it sends reminders when a task deadline is approaching. The task management collaboration unit also builds a system that monitors task progress in real time and sends reminders as necessary. For example, it automatically sends reminders according to the task progress. This makes it possible to monitor task progress in real time and send reminders as necessary.

[0068] The task management collaboration unit works in conjunction with a task management tool to automatically analyze task dependencies and allocate tasks efficiently. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that automatically analyzes task dependencies. For example, it displays the dependencies between tasks in a graph. The task management collaboration unit also develops an allocation algorithm based on task dependencies to allocate tasks efficiently. For example, it allocates tasks with strong dependencies preferentially. The task management collaboration unit also works in conjunction with a task management tool to automatically analyze task dependencies and build a system for efficient task allocation. For example, it schedules tasks taking dependencies into consideration. This makes it possible to automatically analyze task dependencies and allocate tasks efficiently.

[0069] The task management collaboration unit works in conjunction with a task management tool and can be applied to project management other than meetings and task management for daily work. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that can be applied to project management other than meetings. For example, it can centrally manage the progress of a project. The task management collaboration unit also develops a task management system that works in conjunction with a task management tool to be applied to task management for daily work. For example, it can automatically register tasks for daily work and monitor their progress. The task management collaboration unit also works in conjunction with a task management tool to build a system that can be applied to project management other than meetings and task management for daily work. For example, it can integrate and manage project tasks and daily work tasks. This makes it possible to apply it to project management other than meetings and task management for daily work.

[0070] The task management collaboration unit works in conjunction with a task management tool to visualize task progress and share it with the entire team. The task management collaboration unit, for example, works in conjunction with a task management tool to build a system that visualizes task progress. For example, it displays task progress in graphs and charts. The task management collaboration unit also develops a system that displays visualized progress in real time to share task progress with the entire team. For example, it displays the progress on a screen. The task management collaboration unit also works in conjunction with a task management tool to build a system that visualizes task progress and shares it with the entire team. For example, it provides a dashboard for sharing progress. This allows task progress to be visualized and shared with the entire team.

[0071] The task management collaboration unit uses the emotion estimation function to analyze team members' emotional reactions to the task progress status and propose measures to improve their motivation. The task management collaboration unit, for example, uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status in real time. For example, it displays an emotion score for the task progress. The task management collaboration unit also develops a system that analyzes team members' emotional reactions and provides advice based on emotion data to propose measures to improve their motivation. For example, it makes suggestions to increase motivation when the emotion score is low. The task management collaboration unit also uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status and proposes measures to improve motivation. For example, it provides positive feedback when the emotion score is high. This makes it possible to analyze team members' emotional reactions to the task progress status and propose measures to improve motivation.

[0072] The task management collaboration unit can automatically generate minutes after a meeting and distribute them to participants. The task management collaboration unit, for example, builds a system that automatically generates minutes based on voice data recognized during a meeting. For example, it organizes what is said and summarizes the important points. The task management collaboration unit also uses email or cloud storage to distribute the automatically generated minutes to meeting participants. For example, it automatically sends the minutes after the meeting ends. The task management collaboration unit also uses voice recognition data to customize the format of the minutes. For example, it can color-code each speaker or highlight important comments. This allows minutes to be automatically generated after a meeting and distributed to participants.

[0073] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0074] The task management collaboration unit uses the emotion estimation function to display the emotions of speakers during a meeting in real time, allowing the moderator to respond according to changes in emotion. For example, the task management collaboration unit uses the emotion estimation function to build a system that analyzes the emotions of speakers during a meeting in real time and displays them to the moderator. For example, if a speaker is nervous, the system suggests actions to help the speaker relax. The task management collaboration unit also visualizes emotion scores in graphs and charts to display the speaker's emotions in real time. For example, the system displays changes in the speaker's emotions over time. The task management collaboration unit also develops a system that provides advice based on emotion estimation data to the moderator to respond according to changes in emotion. For example, if a speaker is angry, the system suggests ways to respond calmly. This allows the emotions of speakers during a meeting to be displayed in real time, allowing the moderator to respond according to changes in emotion.

[0075] The task management collaboration unit uses the emotion estimation function to analyze team members' emotional reactions to the task progress status and propose measures to improve their motivation. The task management collaboration unit, for example, uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status in real time. For example, it displays an emotion score for the task progress. The task management collaboration unit also develops a system that analyzes team members' emotional reactions and provides advice based on emotion data to propose measures to improve their motivation. For example, it makes suggestions to increase motivation when the emotion score is low. The task management collaboration unit also uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status and proposes measures to improve motivation. For example, it provides positive feedback when the emotion score is high. This makes it possible to analyze team members' emotional reactions to the task progress status and propose measures to improve motivation.

[0076] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0077] The task management collaboration unit uses the emotion estimation function to display the emotions of speakers during a meeting in real time, allowing the moderator to respond according to changes in emotion. For example, the task management collaboration unit uses the emotion estimation function to build a system that analyzes the emotions of speakers during a meeting in real time and displays them to the moderator. For example, if a speaker is nervous, the system suggests actions to help the speaker relax. The task management collaboration unit also visualizes emotion scores in graphs and charts to display the speaker's emotions in real time. For example, the system displays changes in the speaker's emotions over time. The task management collaboration unit also develops a system that provides advice based on emotion estimation data to the moderator to respond according to changes in emotion. For example, if a speaker is angry, the system suggests ways to respond calmly. This allows the emotions of speakers during a meeting to be displayed in real time, allowing the moderator to respond according to changes in emotion.

[0078] The task management collaboration unit uses the emotion estimation function to analyze team members' emotional reactions to the task progress status and propose measures to improve their motivation. The task management collaboration unit, for example, uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status in real time. For example, it displays an emotion score for the task progress. The task management collaboration unit also develops a system that analyzes team members' emotional reactions and provides advice based on emotion data to propose measures to improve their motivation. For example, it makes suggestions to increase motivation when the emotion score is low. The task management collaboration unit also uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status and proposes measures to improve motivation. For example, it provides positive feedback when the emotion score is high. This makes it possible to analyze team members' emotional reactions to the task progress status and propose measures to improve motivation.

[0079] The task management collaboration unit can automatically generate minutes after a meeting and distribute them to participants. The task management collaboration unit, for example, builds a system that automatically generates minutes based on voice data recognized during a meeting. For example, it organizes what is said and summarizes the important points. The task management collaboration unit also uses email or cloud storage to distribute the automatically generated minutes to meeting participants. For example, it automatically sends the minutes after the meeting ends. The task management collaboration unit also uses voice recognition data to customize the format of the minutes. For example, it can color-code each speaker or highlight important comments. This allows minutes to be automatically generated after a meeting and distributed to participants.

[0080] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0081] The task management collaboration unit uses the emotion estimation function to display the emotions of speakers during a meeting in real time, allowing the moderator to respond according to changes in emotion. For example, the task management collaboration unit uses the emotion estimation function to build a system that analyzes the emotions of speakers during a meeting in real time and displays them to the moderator. For example, if a speaker is nervous, the system suggests actions to help the speaker relax. The task management collaboration unit also visualizes emotion scores in graphs and charts to display the speaker's emotions in real time. For example, the system displays changes in the speaker's emotions over time. The task management collaboration unit also develops a system that provides advice based on emotion estimation data to the moderator to respond according to changes in emotion. For example, if a speaker is angry, the system suggests ways to respond calmly. This allows the emotions of speakers during a meeting to be displayed in real time, allowing the moderator to respond according to changes in emotion.

[0082] The task management collaboration unit uses the emotion estimation function to analyze team members' emotional reactions to the task progress status and propose measures to improve their motivation. The task management collaboration unit, for example, uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status in real time. For example, it displays an emotion score for the task progress. The task management collaboration unit also develops a system that analyzes team members' emotional reactions and provides advice based on emotion data to propose measures to improve their motivation. For example, it makes suggestions to increase motivation when the emotion score is low. The task management collaboration unit also uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status and proposes measures to improve motivation. For example, it provides positive feedback when the emotion score is high. This makes it possible to analyze team members' emotional reactions to the task progress status and propose measures to improve motivation.

[0083] The task management collaboration unit can automatically generate minutes after a meeting and distribute them to participants. The task management collaboration unit, for example, builds a system that automatically generates minutes based on voice data recognized during a meeting. For example, it organizes what is said and summarizes the important points. The task management collaboration unit also uses email or cloud storage to distribute the automatically generated minutes to meeting participants. For example, it automatically sends the minutes after the meeting ends. The task management collaboration unit also uses voice recognition data to customize the format of the minutes. For example, it can color-code each speaker or highlight important comments. This allows minutes to be automatically generated after a meeting and distributed to participants.

[0084] The task management collaboration unit can automatically register tasks that arise during a meeting in a task management tool and track their progress. The task management collaboration unit, for example, builds a system that automatically registers tasks that arise during a meeting in a task management tool. For example, it extracts tasks from the content of comments and registers them in the task management tool. The task management collaboration unit also develops a progress management system that works with the task management tool to track the progress of tasks. For example, it displays the progress of tasks in real time. The task management collaboration unit also builds a system that automatically registers tasks that arise during a meeting in a task management tool and tracks their progress. For example, it sends reminders according to the progress of the tasks. This makes it possible to automatically register tasks that arise during a meeting in a task management tool and track their progress.

[0085] The task management collaboration unit uses the emotion estimation function to display the emotions of speakers during a meeting in real time, allowing the moderator to respond according to changes in emotion. For example, the task management collaboration unit uses the emotion estimation function to build a system that analyzes the emotions of speakers during a meeting in real time and displays them to the moderator. For example, if a speaker is nervous, the system suggests actions to help the speaker relax. The task management collaboration unit also visualizes emotion scores in graphs and charts to display the speaker's emotions in real time. For example, the system displays changes in the speaker's emotions over time. The task management collaboration unit also develops a system that provides advice based on emotion estimation data to the moderator to respond according to changes in emotion. For example, if a speaker is angry, the system suggests ways to respond calmly. This allows the emotions of speakers during a meeting to be displayed in real time, allowing the moderator to respond according to changes in emotion.

[0086] The task management collaboration unit uses the emotion estimation function to analyze team members' emotional reactions to the task progress status and propose measures to improve their motivation. The task management collaboration unit, for example, uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status in real time. For example, it displays an emotion score for the task progress. The task management collaboration unit also develops a system that analyzes team members' emotional reactions and provides advice based on emotion data to propose measures to improve their motivation. For example, it makes suggestions to increase motivation when the emotion score is low. The task management collaboration unit also uses the emotion estimation function to build a system that analyzes team members' emotional reactions to the task progress status and proposes measures to improve motivation. For example, it provides positive feedback when the emotion score is high. This makes it possible to analyze team members' emotional reactions to the task progress status and propose measures to improve motivation.

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

[0088] The meeting support system can also monitor the frequency of participants' speech and provide a function to encourage participants who speak less to speak. For example, the system can automatically pose questions to participants who speak less. It can also visualize speech frequency in graphs and charts and present them to the moderator. It can also make suggestions for balancing the discussion based on speech frequency. This allows all meeting participants to actively participate in the discussion.

[0089] Meeting support systems can also be equipped with a function that automatically summarizes what is said during a meeting and provides it to participants in real time. For example, long statements can be summarized in a short form, extracting only the important points. The summarized content can also be displayed on a screen so that all participants can see it. Furthermore, the summarized content can be saved as text data for later reference. This can improve the efficiency of meetings.

[0090] Meeting support systems can also be equipped with a function to automatically categorize what is said during a meeting and organize it by topic. For example, they can analyze what is said using keywords and phrases and categorize it into related topics. They can also display the progress of the discussion for each categorized topic, allowing participants to understand where they are in the discussion. They can also summarize the main points of the discussion for each topic and make them available for later reference. This helps meetings proceed more smoothly.

[0091] Meeting support systems can also be equipped with a function that automatically translates what is said during a meeting, facilitating communication between participants who speak different languages. For example, the system can translate what is said in real time and display the translation results on the screen. It can also save the translated content as text data for later reference. It can also play back the translation results aloud, allowing participants who speak different languages ​​to communicate directly. This enables smooth communication even in international meetings.

[0092] Meeting support systems can also be equipped with a function that automatically records what is said during a meeting and allows it to be searched later. For example, the contents of comments can be saved as text data and a keyword search function can be provided. The contents of comments can also be saved as audio data and a voice search function can be provided. Furthermore, it is possible to organize the contents of comments along a chronological axis, making it easy to search for comments made during a specific time period. This makes it easier to share information and review the meeting after it has finished.

[0093] The meeting support system can also be equipped with a function to estimate the emotions of participants and adjust the progress of the meeting based on their emotions. For example, if a participant is tired, it can suggest a break. If a participant is excited, it can also suggest ways to calm down. It can also visualize changes in emotions in graphs and charts and present them to the moderator. This allows the progress of the meeting to be adjusted according to the emotions of the participants.

[0094] The meeting support system can also be equipped with a function to estimate the emotions of participants and adjust the order of speech based on their emotions. For example, if a participant is nervous, the order of speech can be changed to help them relax. Also, if a participant is angry, it can make suggestions to help them calm down. Furthermore, it is possible to analyze changes in emotions in real time and automatically adjust the order of speech. This can help meetings proceed smoothly.

[0095] The meeting support system can also be equipped with a function to estimate the emotions of participants and adjust the direction of the discussion based on their emotions. For example, if a participant is feeling anxious, the system can make suggestions to reassure them. If a participant is excited, the system can also make suggestions to calm them down. Furthermore, it is possible to analyze changes in emotions in real time and automatically adjust the direction of the discussion. This can improve the quality of discussions.

[0096] The meeting support system can also be equipped with a function to estimate the emotions of participants and provide advice and suggestions based on their emotions. For example, if a participant is feeling anxious, it can provide advice to reassure them. If a participant is excited, it can also provide suggestions to help them calm down. It can also analyze changes in emotions in real time and automatically provide advice and suggestions based on emotions. This makes it possible to provide appropriate advice and suggestions based on the emotions of participants.

[0097] The meeting support system can also be equipped with a function to estimate participants' emotions and determine task priorities based on their emotions. For example, it can prioritize tasks that participants feel are important. It can also postpone tasks that participants feel anxious about. It can also analyze changes in emotions in real time and automatically adjust task priorities. This allows for efficient task management based on participants' emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The speech recognition unit recognizes what is being said during the meeting in real time. For example, the speech recognition unit collects what is being said during the meeting using a microphone and converts the audio data into text data. The speech recognition unit can also use noise canceling technology to remove background noise and accurately recognize what is being said. Furthermore, the speech recognition unit can distinguish between multiple speakers and recognize each of their utterances individually. Step 2: The natural language processing unit analyzes the utterance content recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the grammatical structure of the utterance content. The natural language processing unit can also perform semantic analysis to understand the meaning of the utterance content. Furthermore, the natural language processing unit can estimate the emotion of the utterance content and record changes in emotion. Step 3: The advice generator generates advice and suggestions based on the content analyzed by the natural language processor. For example, the advice generator may suggest appropriate timing for proceedings. The advice generator may also identify and suggest topics that require follow-up. Furthermore, the advice generator may pose questions to encourage broad participation of opinions. Step 4: The task management collaboration unit collaborates with the task management tool based on the advice and suggestions generated by the advice generation unit. For example, the task management collaboration unit automatically registers tasks that arise during a meeting in the task management tool. The task management collaboration unit can also track the progress of tasks and send reminders as needed. Furthermore, the task management collaboration unit can determine task priorities using emotion estimation functionality.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A speech recognition unit that recognizes statements made during a meeting in real time; a natural language processing unit that analyzes the content of speech recognized by the speech recognition unit; an advice generation unit that generates advice or suggestions based on the content analyzed by the natural language processing unit; a task management linking unit that links the advice and suggestions generated by the advice generating unit to a task management tool; A system characterized by:

2. The voice recognition unit Estimate the speaker's emotions and record changes in emotions 2. The system of claim 1.

3. The voice recognition unit Pre-learns the speaker's voice characteristics to create an individual voice profile 2. The system of claim 1.

4. The voice recognition unit Translates the speech in real time to facilitate communication between participants who speak different languages 2. The system of claim 1.

5. The natural language processing unit In analyzing the content of the above statements, the speaker's emotions are estimated and the direction of the discussion is adjusted based on changes in emotions.

2. The system of claim 1.

6. The natural language processing unit When analyzing the content of the comments, compare it with past meeting data to identify similar discussions and topics.

2. The system of claim 1.

Images