system

Abstract

An object of the system according to the embodiment is to improve the efficiency of creating and reviewing conference materials, participating in a conference, and grasping main points of report contents.SOLUTION: A system according to an embodiment includes an information reading section, an information aggregation section, a personality avatar section, and a focusing section. The information reading unit automatically reads information from ZOOM, a communication tool, or a mail. The information aggregation unit aggregates the information read by the information reading unit. The personality avatar section participates in the conference based on the information aggregated by the information aggregation section, and automatically performs the discussion. The focusing part focuses the report contents of the conference body, the mail and the chat in real time.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 have had the problem that it takes a lot of time to create and review meeting materials, participate in meetings, and grasp the main points of reports.

[0005] The system according to the embodiment aims to improve the efficiency of creating and reviewing meeting materials, participating in meetings, and grasping the main points of report contents. [Means for solving the problem]

[0006] The system according to the embodiment includes an information reading unit, an information aggregation unit, a personality avatar unit, and a summary unit. The information reading unit automatically reads information from ZOOM, a communication tool, or email. The information aggregation unit aggregates the information read by the information reading unit. The personality avatar unit participates in meetings and automatically holds discussions based on the information aggregated by the information aggregation unit. The summary unit summarizes the contents of reports made in meetings, emails, or chats in real time. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of creating and reviewing meeting materials, participating in meetings, and grasping the main points of report contents. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 business efficiency improvement system according to an embodiment of the present invention is a system for improving the business efficiency of general employees and managers. This system reduces the time required to create and review meeting materials, participate in meetings, and grasp the main points of reports. As a result, the business efficiency improvement system can significantly improve the business efficiency of general employees and managers.

[0029] The business efficiency improvement system according to the embodiment includes an information reading unit, an information aggregation unit, a personality avatar unit, and a summary unit. The information reading unit automatically reads information from ZOOM, a communication tool, or email. For example, it automatically acquires ZOOM meeting recordings, chat logs, and email content. The information reading unit can also acquire information using API integration or scraping technology. For example, it can acquire information from Microsoft Teams or Slack using an API. It can extract necessary information from web pages using scraping technology. The information aggregation unit aggregates the information read by the information reading unit. For example, it stores the information in a database and classifies it. The information aggregation unit also automatically corrects errors or omissions in documents based on the read information. For example, it analyzes the content of documents, predicts frequently asked questions, and automatically generates answers. The personality avatar unit participates in meetings and automatically holds discussions based on the information aggregated by the information aggregation unit. For example, a generation AI understands the content of the meeting and makes appropriate comments and questions. The personality avatar unit also automatically creates and reports meeting minutes. The summarization unit summarises the contents of reports made in meetings, emails, and chats in real time. For example, it analyzes comments made during meetings and emails, extracts important points, and summarizes them. As a result, the business efficiency improvement system according to the embodiment can significantly improve the business efficiency of general employees and managers. For example, it can reduce the time required to create and review meeting materials, and enable quick participation in meetings and grasping the main points of reports. Furthermore, by participating in meetings using a personality avatar, managers no longer need to attend every meeting, thereby reducing their workload.

[0030] The information reading unit can analyze the audio data of a meeting and automatically tag what each speaker said. For example, the information reading unit uses a generation AI to analyze the audio data of a meeting and automatically tag what each speaker said. For example, it generates tags based on the speaker's name and position and categorizes what was said. The information reading unit also uses voice recognition technology to convert the audio data into text data and tag it. For example, voice recognition software automatically analyzes the audio and saves it as text. This allows the content of what was said in a meeting to be organized efficiently.

[0031] The information reading unit can analyze the video data of a meeting and identify important statements based on the speaker's facial expressions or gestures. For example, the information reading unit uses a generation AI to analyze the video data of a meeting and identify important statements based on the speaker's facial expressions and gestures. For example, it analyzes the speaker's facial expression changes and hand movements to extract important statements. The information reading unit also analyzes the video data using image recognition technology to identify important statements. For example, it uses face recognition technology to analyze the speaker's facial expressions and extract important statements. This allows important statements to be identified efficiently.

[0032] The information reading unit automatically translates the contents of meetings held in different languages, enabling uniform analysis of information even at international conferences. For example, the information reading unit uses a generation AI to automatically translate the contents of meetings held in different languages, enabling uniform analysis of information even at international conferences. For example, it translates meeting contents into English, Japanese, Chinese, etc. in real time. The information reading unit also automatically translates the contents of meetings using machine translation technology. For example, it uses a translation algorithm to translate the contents of meetings into multiple languages. This enables uniform analysis of information even at international conferences.

[0033] The information reading unit can remove background sounds or noise from a conference and extract information based on clear audio data. For example, the information reading unit uses a generation AI to remove background sounds or noise from a conference and extract information based on the clear audio data. For example, the information reading unit cleans up the audio data using noise reduction technology. The information reading unit also removes background sounds using noise canceling technology to obtain clear audio data. For example, the information reading unit removes noise using audio filtering technology. This allows information to be extracted based on the clear audio data.

[0034] The information aggregation unit can refer to past meeting materials or reports, compare them with current materials, and automatically fill in any missing information. For example, the generation AI can refer to past meeting materials or reports, compare them with current materials, and automatically fill in any missing information. For example, it can insert additional information into current materials based on past data. The information aggregation unit can also analyze past materials such as PDF files and Word documents to fill in any missing information in current materials. For example, it can extract important information from past meeting materials and add it to current materials. This allows missing information to be automatically filled in.

[0035] The information aggregation unit can analyze the content of the document and automatically add related external data. For example, the information aggregation unit uses a generation AI to analyze the content of the document and automatically add related external data (e.g., industry reports and news articles). For example, it searches for and adds the latest industry reports related to the topic of the document. The information aggregation unit also clarifies how to obtain external data and adds information related to the document. For example, it automatically obtains news articles and reflects them in the document. This makes it possible to automatically add external data related to the document.

[0036] The information aggregation unit can analyze documents in different formats in a unified manner and aggregate information. For example, the generation AI in the information aggregation unit analyzes documents in different formats (e.g., PDF, Word, Excel) in a unified manner and aggregates information. For example, it converts data in each format into text and analyzes it in a unified manner. The information aggregation unit also standardizes data and analyzes documents in different formats. For example, it converts PDF files into text data and aggregates information. This allows documents in different formats to be analyzed in a unified manner and aggregate information.

[0037] The information aggregation unit can automatically generate visual elements of documents and create documents that are visually easy to understand. For example, the information aggregation unit uses a generation AI to automatically generate visual elements of documents (e.g., graphs and charts) and create documents that are visually easy to understand. For example, it automatically creates graphs based on data and inserts them into the documents. The information aggregation unit also generates visual elements using data visualization technology. For example, it visualizes data using a graph generation algorithm. This makes it possible to automatically create documents that are visually easy to understand.

[0038] The personality avatar unit can automatically generate optimal remarks or questions based on the meeting agenda, and the personality avatar can execute them. For example, the personality avatar unit can use a generation AI to automatically generate optimal remarks or questions based on the meeting agenda, and the personality avatar can execute them. For example, it can generate appropriate questions based on information related to the agenda. The personality avatar unit can also generate and execute remarks using natural language generation technology. For example, it can generate questions based on the content of the meeting using a question generation algorithm. This makes it possible to automatically generate and execute optimal remarks or questions based on the meeting agenda.

[0039] The personality avatar section analyzes the progress of the meeting in real time and can change the agenda or ask additional questions as needed. For example, the generation AI of the personality avatar section analyzes the progress of the meeting in real time and can change the agenda or ask additional questions as needed. For example, if progress on the agenda is behind schedule, it can propose a new agenda. The personality avatar section also analyzes the progress of the meeting using real-time data analysis and changes the agenda. For example, it can monitor the progress of the meeting using a progress management system and change the agenda at the appropriate time. This makes it possible to change the agenda or ask additional questions according to the progress of the meeting.

[0040] The personality avatar unit generates personality avatars with specialized knowledge in different industries or fields, and can hold specialized discussions. For example, the generation AI generates personality avatars with specialized knowledge in different industries or fields, and can hold specialized discussions. For example, avatars with specialized knowledge in a technical field can hold technical discussions. The personality avatar unit also generates avatars with specialized knowledge using a knowledge base system. For example, an avatar can be generated based on a database of specialized knowledge and can hold specialized discussions. This allows personality avatars with specialized knowledge in different industries or fields to be generated, and can hold specialized discussions.

[0041] The personality avatar unit can automatically summarize the contents of the meeting and provide feedback to participants in real time. For example, the personality avatar unit uses a generation AI to automatically summarize the contents of the meeting and provide feedback to participants in real time. For example, it extracts important points of the meeting and displays a summary. The personality avatar unit also uses a summarization algorithm to summarize the contents of the meeting and provide feedback. For example, it uses natural language processing technology to summarize the contents of the meeting and provide feedback in real time. This makes it possible to automatically summarize the contents of the meeting and provide feedback to participants in real time.

[0042] The gist generation unit can analyze statements made during a meeting in real time, extract important keywords, and automatically generate gist information. For example, the gist generation unit uses a generation AI to analyze statements made during a meeting in real time, extract important keywords, and automatically generate gist information. For example, the content of statements is converted into text and important keywords are extracted. The gist generation unit also analyzes the content of statements using a summarization algorithm and generates gist information. For example, natural language generation technology is used to summarize the content of statements and extract gist information. This makes it possible to analyze statements made during a meeting in real time, extract important keywords, and automatically generate gist information.

[0043] The summarization unit can prioritize and display the main points based on the agenda or purpose of the meeting. For example, the generation AI prioritizes and displays the main points based on the agenda or purpose of the meeting. For example, the generation AI ranks the main points based on the importance of the agenda. The summarization unit also displays the main points using a prioritization algorithm. For example, the importance of the main points is evaluated and prioritized for display. This makes it possible to prioritize and display the main points based on the agenda or purpose of the meeting.

[0044] The summarization unit can translate the content of a meeting held in different languages ​​in real time and display the main points in multiple languages. For example, the summarization unit uses a generation AI to translate the content of a meeting held in different languages ​​in real time and display the main points in multiple languages. For example, the summarization unit translates the content of a meeting into English, Japanese, Chinese, etc. in real time. The summarization unit also uses machine translation technology to translate the content of a meeting into multiple languages ​​and display the main points. For example, the summarization unit uses a translation algorithm to translate the content of a meeting into multiple languages ​​and display the main points. This makes it possible to translate the content of a meeting held in different languages ​​in real time and display the main points in multiple languages.

[0045] The gist extraction unit can analyze the video data of the meeting and extract key points based on visual elements. For example, the gist extraction unit uses a generation AI to analyze the video data of the meeting and extract key points based on visual elements (for example, the contents of slides or whiteboards). For example, it analyzes the text and diagrams on the slides and extracts key points. The gist extraction unit also analyzes the video data using image recognition technology and extracts key points based on visual elements. For example, it uses a video analysis algorithm to analyze the contents of slides or whiteboards and extracts key points. In this way, it is possible to analyze the video data of the meeting and extract key points based on visual elements.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The business efficiency improvement system can further include a schedule adjustment unit. The schedule adjustment unit automatically adjusts the meeting schedule and proposes the optimal meeting time taking into account the participants' free time. For example, it can analyze the participants' calendars and automatically select a time slot when everyone can attend. The schedule adjustment unit can also prioritize the schedule based on the importance and urgency of the meeting. This improves the efficiency of meeting schedule adjustment and allows participants' time to be used effectively.

[0048] The business efficiency system can further include a reminder section. The reminder section sends reminders to participants before the meeting to encourage them to prepare for the meeting. For example, a reminder can be sent 30 minutes before the start of the meeting to encourage participants to check the meeting materials. The reminder section can also notify participants of important points and agenda items of the meeting in advance. This allows participants to prepare for the meeting efficiently.

[0049] The business efficiency improvement system can further include a feedback collection unit. The feedback collection unit collects feedback from participants after the meeting and identifies areas for improvement in the meeting. For example, it automatically sends a questionnaire about the progress and content of the meeting to collect participants' opinions. The feedback collection unit can also analyze the collected feedback and reflect it in the next meeting. This can improve the quality of the meeting.

[0050] The business efficiency improvement system can further include a document sharing unit. The document sharing unit shares documents used during a meeting with all participants in real time. For example, presentation materials and documents can be automatically shared during a meeting so that all participants can view them simultaneously. The document sharing unit can also automatically save documents after the meeting ends so that they can be referenced later. This improves the efficiency of document sharing and management.

[0051] The business efficiency system may further include a task management unit. The task management unit automatically records tasks decided during a meeting and assigns them to responsible parties. For example, the task management unit automatically lists tasks discussed during a meeting and notifies the responsible parties. The task management unit may also track the progress of tasks and send reminders for tasks whose deadlines are approaching. This improves the efficiency of task management and progress tracking.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The information reading unit automatically reads information from ZOOM, communication tools, and emails. For example, it automatically obtains ZOOM meeting recordings, chat logs, and email content. The information reading unit can also obtain information using API integration and scraping technology. For example, it can use an API to obtain information from Microsoft Teams or Slack. It can also use scraping technology to extract the necessary information from web pages. Step 2: The information aggregation unit aggregates the information read by the information reading unit. For example, it stores the information in a database and classifies it. The information aggregation unit also automatically corrects errors or omissions in the documents based on the information it has read. For example, it analyzes the contents of the documents, predicts frequently asked questions, and automatically generates answers to them. Step 3: The Personality Avatar section participates in the meeting based on the information collected by the Information Aggregation section and automatically holds discussions. For example, the Generative AI understands the content of the meeting and makes appropriate comments and questions. The Personality Avatar section also automatically creates and reports meeting minutes. Step 4: The Summarization Department summarizes reports received in meetings, emails, and chats in real time. For example, it analyzes statements made during meetings and emails, extracts important points, and summarizes them.

[0054] (Example 2) The business efficiency improvement system according to an embodiment of the present invention is a system for improving the business efficiency of general employees and managers. This system reduces the time required to create and review meeting materials, participate in meetings, and grasp the main points of reports. As a result, the business efficiency improvement system can significantly improve the business efficiency of general employees and managers.

[0055] The business efficiency improvement system according to the embodiment includes an information reading unit, an information aggregation unit, a personality avatar unit, and a summary unit. The information reading unit automatically reads information from ZOOM, a communication tool, or email. For example, it automatically acquires ZOOM meeting recordings, chat logs, and email content. The information reading unit can also acquire information using API integration or scraping technology. For example, it can acquire information from Microsoft Teams or Slack using an API. It can extract necessary information from web pages using scraping technology. The information aggregation unit aggregates the information read by the information reading unit. For example, it stores the information in a database and classifies it. The information aggregation unit also automatically corrects errors or omissions in documents based on the read information. For example, it analyzes the content of documents, predicts frequently asked questions, and automatically generates answers. The personality avatar unit participates in meetings and automatically holds discussions based on the information aggregated by the information aggregation unit. For example, a generation AI understands the content of the meeting and makes appropriate comments and questions. The personality avatar unit also automatically creates and reports meeting minutes. The summarization unit summarises the contents of reports made in meetings, emails, and chats in real time. For example, it analyzes comments made during meetings and emails, extracts important points, and summarizes them. As a result, the business efficiency improvement system according to the embodiment can significantly improve the business efficiency of general employees and managers. For example, it can reduce the time required to create and review meeting materials, and enable quick participation in meetings and grasping the main points of reports. Furthermore, by participating in meetings using a personality avatar, managers no longer need to attend every meeting, thereby reducing their workload.

[0056] The information reading unit can analyze the audio data of a meeting and automatically tag what each speaker said. For example, the information reading unit uses a generation AI to analyze the audio data of a meeting and automatically tag what each speaker said. For example, it generates tags based on the speaker's name and position and categorizes what was said. The information reading unit also uses voice recognition technology to convert the audio data into text data and tag it. For example, voice recognition software automatically analyzes the audio and saves it as text. This allows the content of what was said in a meeting to be organized efficiently.

[0057] The information reading unit can analyze the video data of a meeting and identify important statements based on the speaker's facial expressions or gestures. For example, the information reading unit uses a generation AI to analyze the video data of a meeting and identify important statements based on the speaker's facial expressions and gestures. For example, it analyzes the speaker's facial expression changes and hand movements to extract important statements. The information reading unit also analyzes the video data using image recognition technology to identify important statements. For example, it uses face recognition technology to analyze the speaker's facial expressions and extract important statements. This allows important statements to be identified efficiently.

[0058] The information reading unit can use the emotion estimation function to analyze the emotions of speakers during a meeting and extract important information based on changes in emotions. The information reading unit, for example, uses the emotion estimation function to analyze the emotions of speakers during a meeting and extract important information based on changes in emotions. For example, the information reading unit analyzes the tone of voice and facial expressions of the speakers to detect changes in emotions. The information reading unit also analyzes changes in emotions using an emotion recognition algorithm and extracts important information. For example, the information reading unit analyzes the emotions of speakers using voice emotion analysis technology and extracts important information. This makes it possible to extract important information based on changes in emotions.

[0059] The information reading unit automatically translates the contents of meetings held in different languages, enabling uniform analysis of information even at international conferences. For example, the information reading unit uses a generation AI to automatically translate the contents of meetings held in different languages, enabling uniform analysis of information even at international conferences. For example, it translates meeting contents into English, Japanese, Chinese, etc. in real time. The information reading unit also automatically translates the contents of meetings using machine translation technology. For example, it uses a translation algorithm to translate the contents of meetings into multiple languages. This enables uniform analysis of information even at international conferences.

[0060] The information reading unit can remove background sounds or noise from a conference and extract information based on clear audio data. For example, the information reading unit uses a generation AI to remove background sounds or noise from a conference and extract information based on the clear audio data. For example, the information reading unit cleans up the audio data using noise reduction technology. The information reading unit also removes background sounds using noise canceling technology to obtain clear audio data. For example, the information reading unit removes noise using audio filtering technology. This allows information to be extracted based on the clear audio data.

[0061] The information reading unit can use the emotion estimation function to analyze the emotions of speakers during a meeting in real time and support the progress of the meeting based on changes in emotions. The information reading unit, for example, uses the emotion estimation function to analyze the emotions of speakers during a meeting in real time and support the progress of the meeting based on changes in emotions. For example, the information reading unit adjusts the progress of the meeting based on the emotion score of the speaker. The information reading unit also analyzes changes in emotions using an emotion recognition algorithm and supports the progress of the meeting. For example, the information reading unit analyzes the emotions of speakers using voice emotion analysis technology and adjusts the progress of the meeting. This makes it possible to support the progress of the meeting based on emotions.

[0062] The information aggregation unit can refer to past meeting materials or reports, compare them with current materials, and automatically fill in any missing information. For example, the generation AI can refer to past meeting materials or reports, compare them with current materials, and automatically fill in any missing information. For example, it can insert additional information into current materials based on past data. The information aggregation unit can also analyze past materials such as PDF files and Word documents to fill in any missing information in current materials. For example, it can extract important information from past meeting materials and add it to current materials. This allows missing information to be automatically filled in.

[0063] The information aggregation unit can analyze the content of the document and automatically add related external data. For example, the information aggregation unit uses a generation AI to analyze the content of the document and automatically add related external data (e.g., industry reports and news articles). For example, it searches for and adds the latest industry reports related to the topic of the document. The information aggregation unit also clarifies how to obtain external data and adds information related to the document. For example, it automatically obtains news articles and reflects them in the document. This makes it possible to automatically add external data related to the document.

[0064] The information aggregating unit can use the emotion estimation function to analyze the user's emotional response to the content of the material and modify the material to elicit a positive response. The information aggregating unit, for example, uses the emotion estimation function to analyze the user's emotional response to the content of the material and modify the material to elicit a positive response. For example, the information aggregating unit adjusts the expression of the material based on the user's emotion score. The information aggregating unit also analyzes the user's emotion using an emotion recognition algorithm and modifies the content of the material. For example, the information aggregating unit analyzes the user's emotion using voice emotion analysis technology and modifies the content of the material to elicit a positive response. This allows the content of the material to be modified to elicit a positive response.

[0065] The information aggregation unit can analyze documents in different formats in a unified manner and aggregate information. For example, the generation AI in the information aggregation unit analyzes documents in different formats (e.g., PDF, Word, Excel) in a unified manner and aggregates information. For example, it converts data in each format into text and analyzes it in a unified manner. The information aggregation unit also standardizes data and analyzes documents in different formats. For example, it converts PDF files into text data and aggregates information. This allows documents in different formats to be analyzed in a unified manner and aggregate information.

[0066] The information aggregation unit can automatically generate visual elements of documents and create documents that are visually easy to understand. For example, the information aggregation unit uses a generation AI to automatically generate visual elements of documents (e.g., graphs and charts) and create documents that are visually easy to understand. For example, it automatically creates graphs based on data and inserts them into the documents. The information aggregation unit also generates visual elements using data visualization technology. For example, it visualizes data using a graph generation algorithm. This makes it possible to automatically create documents that are visually easy to understand.

[0067] The personality avatar unit can automatically generate optimal remarks or questions based on the meeting agenda, and the personality avatar can execute them. For example, the personality avatar unit can use a generation AI to automatically generate optimal remarks or questions based on the meeting agenda, and the personality avatar can execute them. For example, it can generate appropriate questions based on information related to the agenda. The personality avatar unit can also generate and execute remarks using natural language generation technology. For example, it can generate questions based on the content of the meeting using a question generation algorithm. This makes it possible to automatically generate and execute optimal remarks or questions based on the meeting agenda.

[0068] The personality avatar section analyzes the progress of the meeting in real time and can change the agenda or ask additional questions as needed. For example, the generation AI of the personality avatar section analyzes the progress of the meeting in real time and can change the agenda or ask additional questions as needed. For example, if progress on the agenda is behind schedule, it can propose a new agenda. The personality avatar section also analyzes the progress of the meeting using real-time data analysis and changes the agenda. For example, it can monitor the progress of the meeting using a progress management system and change the agenda at the appropriate time. This makes it possible to change the agenda or ask additional questions according to the progress of the meeting.

[0069] The personality avatar unit can use an emotion estimation function to analyze the emotions of participants in a meeting and make appropriate remarks and questions based on the emotions. The personality avatar unit, for example, uses the emotion estimation function to analyze the emotions of participants in a meeting and make appropriate remarks and questions based on the emotions. For example, the content of remarks can be adjusted based on the emotion scores of the participants. The personality avatar unit can also use an emotion recognition algorithm to analyze the emotions of participants and make remarks and questions. For example, the personality avatar unit can use voice emotion analysis technology to analyze the emotions of participants and generate appropriate remarks and questions. This makes it possible to make appropriate remarks and questions based on the emotions of participants in a meeting.

[0070] The personality avatar unit generates personality avatars with specialized knowledge in different industries or fields, and can hold specialized discussions. For example, the generation AI generates personality avatars with specialized knowledge in different industries or fields, and can hold specialized discussions. For example, avatars with specialized knowledge in a technical field can hold technical discussions. The personality avatar unit also generates avatars with specialized knowledge using a knowledge base system. For example, an avatar can be generated based on a database of specialized knowledge and can hold specialized discussions. This allows personality avatars with specialized knowledge in different industries or fields to be generated, and can hold specialized discussions.

[0071] The personality avatar unit can automatically summarize the contents of the meeting and provide feedback to participants in real time. For example, the personality avatar unit uses a generation AI to automatically summarize the contents of the meeting and provide feedback to participants in real time. For example, it extracts important points of the meeting and displays a summary. The personality avatar unit also uses a summarization algorithm to summarize the contents of the meeting and provide feedback. For example, it uses natural language processing technology to summarize the contents of the meeting and provide feedback in real time. This makes it possible to automatically summarize the contents of the meeting and provide feedback to participants in real time.

[0072] The personality avatar unit can use the emotion estimation function to analyze the emotions of participants during a meeting in real time and support the progress of the meeting based on the emotions. The personality avatar unit, for example, uses the emotion estimation function to analyze the emotions of participants during a meeting in real time and support the progress of the meeting based on the emotions. For example, the progress of the meeting can be adjusted based on the emotion scores of the participants. The personality avatar unit also uses an emotion recognition algorithm to analyze the emotions of participants and support the progress of the meeting. For example, the personality avatar unit uses voice emotion analysis technology to analyze the emotions of participants and adjust the progress of the meeting. This makes it possible to support the progress of the meeting based on the emotions of participants during the meeting.

[0073] The gist generation unit can analyze statements made during a meeting in real time, extract important keywords, and automatically generate gist information. For example, the gist generation unit uses a generation AI to analyze statements made during a meeting in real time, extract important keywords, and automatically generate gist information. For example, the content of statements is converted into text and important keywords are extracted. The gist generation unit also analyzes the content of statements using a summarization algorithm and generates gist information. For example, natural language generation technology is used to summarize the content of statements and extract gist information. This makes it possible to analyze statements made during a meeting in real time, extract important keywords, and automatically generate gist information.

[0074] The summarization unit can prioritize and display the main points based on the agenda or purpose of the meeting. For example, the generation AI prioritizes and displays the main points based on the agenda or purpose of the meeting. For example, the generation AI ranks the main points based on the importance of the agenda. The summarization unit also displays the main points using a prioritization algorithm. For example, the importance of the main points is evaluated and prioritized for display. This makes it possible to prioritize and display the main points based on the agenda or purpose of the meeting.

[0075] The summarization unit can use the emotion estimation function to analyze the emotions of speakers during a meeting and extract important key points based on changes in their emotions. The summarization unit, for example, uses the emotion estimation function to analyze the emotions of speakers during a meeting and extract important key points based on changes in their emotions. For example, the summarization unit extracts key points based on the emotion scores of speakers. The summarization unit also analyzes the emotions of speakers using an emotion recognition algorithm and extracts key points. For example, the speech emotion analysis technology is used to analyze the emotions of speakers and extract important key points. This makes it possible to extract important key points based on changes in the emotions of speakers during a meeting.

[0076] The summarization unit can translate the content of a meeting held in different languages ​​in real time and display the main points in multiple languages. For example, the summarization unit uses a generation AI to translate the content of a meeting held in different languages ​​in real time and display the main points in multiple languages. For example, the summarization unit translates the content of a meeting into English, Japanese, Chinese, etc. in real time. The summarization unit also uses machine translation technology to translate the content of a meeting into multiple languages ​​and display the main points. For example, the summarization unit uses a translation algorithm to translate the content of a meeting into multiple languages ​​and display the main points. This makes it possible to translate the content of a meeting held in different languages ​​in real time and display the main points in multiple languages.

[0077] The gist extraction unit can analyze the video data of the meeting and extract key points based on visual elements. For example, the gist extraction unit uses a generation AI to analyze the video data of the meeting and extract key points based on visual elements (for example, the contents of slides or whiteboards). For example, it analyzes the text and diagrams on the slides and extracts key points. The gist extraction unit also analyzes the video data using image recognition technology and extracts key points based on visual elements. For example, it uses a video analysis algorithm to analyze the contents of slides or whiteboards and extracts key points. In this way, it is possible to analyze the video data of the meeting and extract key points based on visual elements.

[0078] The summarization unit can use the emotion estimation function to analyze the emotions of speakers during a meeting in real time and extract key points based on the emotions. The summarization unit, for example, uses the emotion estimation function to analyze the emotions of speakers during a meeting in real time and extract key points based on the emotions. For example, the summarization unit extracts key points based on the emotion scores of speakers. The summarization unit also analyzes the emotions of speakers using an emotion recognition algorithm and extracts key points. For example, the speech emotion analysis technology is used to analyze the emotions of speakers and extract important key points. This makes it possible to extract key points based on the emotions of speakers during a meeting.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The business efficiency improvement system can further include a schedule adjustment unit. The schedule adjustment unit automatically adjusts the meeting schedule and proposes the optimal meeting time taking into account the participants' free time. For example, it can analyze the participants' calendars and automatically select a time slot when everyone can attend. The schedule adjustment unit can also prioritize the schedule based on the importance and urgency of the meeting. This improves the efficiency of meeting schedule adjustment and allows participants' time to be used effectively.

[0081] The business efficiency system can further include a reminder section. The reminder section sends reminders to participants before the meeting to encourage them to prepare for the meeting. For example, a reminder can be sent 30 minutes before the start of the meeting to encourage participants to check the meeting materials. The reminder section can also notify participants of important points and agenda items of the meeting in advance. This allows participants to prepare for the meeting efficiently.

[0082] The business efficiency improvement system can further include a feedback collection unit. The feedback collection unit collects feedback from participants after the meeting and identifies areas for improvement in the meeting. For example, it automatically sends a questionnaire about the progress and content of the meeting to collect participants' opinions. The feedback collection unit can also analyze the collected feedback and reflect it in the next meeting. This can improve the quality of the meeting.

[0083] The business efficiency improvement system can further include a document sharing unit. The document sharing unit shares documents used during a meeting with all participants in real time. For example, presentation materials and documents can be automatically shared during a meeting so that all participants can view them simultaneously. The document sharing unit can also automatically save documents after the meeting ends so that they can be referenced later. This improves the efficiency of document sharing and management.

[0084] The business efficiency system may further include a task management unit. The task management unit automatically records tasks decided during a meeting and assigns them to responsible parties. For example, the task management unit automatically lists tasks discussed during a meeting and notifies the responsible parties. The task management unit may also track the progress of tasks and send reminders for tasks whose deadlines are approaching. This improves the efficiency of task management and progress tracking.

[0085] The business efficiency system can further use the emotion estimation function to analyze the emotions of speakers during a meeting and adjust the progress of the meeting based on changes in emotion. For example, the system can adjust the progress of the meeting based on the speaker's emotion score and change the agenda if emotions are rising. The emotion estimation function can also be used to analyze the emotions of participants in real time and provide appropriate feedback based on their emotions. This allows the progress of the meeting to be flexibly adjusted based on emotions.

[0086] The business efficiency improvement system can further use an emotion estimation function to analyze the emotions of speakers during a meeting and extract important information based on changes in their emotions. For example, it can analyze the speaker's tone of voice and facial expression to detect changes in their emotions. The emotion estimation function can also be used to extract important information based on the speaker's emotion score and automatically generate key points from the meeting. This allows important information to be efficiently extracted based on changes in emotions.

[0087] The business efficiency system can also use an emotion estimation function to analyze the emotions of participants during a meeting in real time and support the progress of the meeting based on their emotions. For example, it can adjust the progress of the meeting based on the participants' emotion scores and suggest a break if emotions are low. The emotion estimation function can also be used to analyze participants' emotions and make appropriate comments or ask questions based on their emotions. This allows the progress of the meeting to be supported based on emotions.

[0088] The business efficiency improvement system can further use an emotion estimation function to analyze the emotions of speakers during meetings and extract key points from meetings based on changes in emotions. For example, key points can be extracted based on the speaker's emotion score and parts where emotions are high can be emphasized. The emotion estimation function can also be used to analyze the speaker's emotions and automatically generate important key points based on their emotions. This allows for efficient extraction of key points from meetings based on changes in emotions.

[0089] The business efficiency system can also use an emotion estimation function to analyze the emotions of participants during a meeting in real time and support the progress of the meeting based on their emotions. For example, the system can adjust the progress of the meeting based on the emotion scores of participants and change the agenda if emotions are running high. The emotion estimation function can also be used to analyze participants' emotions and provide appropriate feedback based on their emotions. This allows the progress of the meeting to be flexibly adjusted based on their emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The information reading unit automatically reads information from ZOOM, communication tools, and emails. For example, it automatically obtains ZOOM meeting recordings, chat logs, and email content. The information reading unit can also obtain information using API integration and scraping technology. For example, it can use an API to obtain information from Microsoft Teams or Slack. It can also use scraping technology to extract the necessary information from web pages. Step 2: The information aggregation unit aggregates the information read by the information reading unit. For example, it stores the information in a database and classifies it. The information aggregation unit also automatically corrects errors or omissions in the documents based on the information it has read. For example, it analyzes the contents of the documents, predicts frequently asked questions, and automatically generates answers to them. Step 3: The Personality Avatar section participates in the meeting based on the information collected by the Information Aggregation section and automatically holds discussions. For example, the Generative AI understands the content of the meeting and makes appropriate comments and questions. The Personality Avatar section also automatically creates and reports meeting minutes. Step 4: The Summarization Department summarizes reports received in meetings, emails, and chats in real time. For example, it analyzes statements made during meetings and emails, extracts important points, and summarizes them.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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, in order to avoid confusion and to 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.

[0158] 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]

[0159] 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. An information reading section that automatically reads information from ZOOM or communication tools, emails, and an information aggregation unit that aggregates the information read by the information reading unit; a personality avatar unit that participates in a conference and automatically holds a discussion based on the information collected by the information collecting unit; It also has a summarization section that summarises the contents of reports received in meetings, emails, and chats in real time. A system characterized by:

2. The information reading unit Analyzing video data from the meeting and identifying important statements based on the speaker's facial expressions or gestures 2. The system of claim 1.

3. The information reading unit Automatically translate the contents of the conference held in different languages ​​and analyze information uniformly even at international conferences.

2. The system of claim 1.

4. The information aggregation unit Refer to past meeting materials or reports, compare them with current materials, and automatically complete the missing information.

2. The system of claim 1.

5. The personality avatar unit Based on the agenda of the meeting, optimal statements or questions are automatically generated and executed by the personality avatar.

2. The system of claim 1.

6. The gist generation unit Analyze comments made during the meeting in real time, extract important keywords, and automatically generate key points.

2. The system of claim 1.

7. The information reading unit Analyzing the emotions of speakers during the meeting and extracting important information based on changes in emotions 2. The system of claim 1.

8. The gist generation unit Analyzing the emotions of the speakers during the meeting and extracting important points based on the changes in the emotions.

2. The system of claim 1.

Images