System

Abstract

An object of the system according to the embodiment is to efficiently create minutes of a conference, provide opinions, and deal with foreign languages.SOLUTION: A system according to an embodiment includes a voice analysis unit, a minutes generation unit, an advice providing unit, and a foreign language handling unit. The voice analysis unit analyzes voice data during a conference. The minutes creating section creates minutes based on the voice data analyzed by the voice analyzing section. The advice providing unit provides advice or an opinion on the basis of the minutes generated by the minutes generation unit. The foreign language correspondence unit translates the minutes generated by the minutes generation unit into a foreign language.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] With conventional technology, creating meeting minutes, providing opinions, and supporting foreign languages ​​required time and effort, making it difficult to do this efficiently.

[0005] The system according to the embodiment aims to efficiently create meeting minutes, provide opinions, and handle foreign language support. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice analysis unit, a minutes generation unit, an advice providing unit, and a foreign language support unit. The voice analysis unit analyzes voice data during a meeting. The minutes generation unit generates minutes based on the voice data analyzed by the voice analysis unit. The advice providing unit provides advice or opinions based on the minutes generated by the minutes generation unit. The foreign language support unit translates the minutes generated by the minutes generation unit into a foreign language. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create meeting minutes, provide opinions, and handle foreign language support. [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 meeting support system according to an embodiment of the present invention analyzes audio data during a meeting, and uses a generation AI to instantly create minutes and provide advice, opinions, and overall evaluations based on the content of comments, opinions, keywords, etc. This allows the meeting support system to improve the efficiency of meetings and ensure that conclusions are reached each time without having to be carried over to the next meeting. It also supports foreign languages, making it possible to overcome language barriers even in global meetings.

[0029] A conference support system according to an embodiment includes a voice analysis unit, a minutes generation unit, an advice providing unit, and a foreign language support unit. The voice analysis unit analyzes voice data generated during a conference. For example, the voice analysis unit converts statements made during the conference into text data using voice recognition technology. The voice analysis unit can also improve the quality of the voice data using noise reduction technology. The voice analysis unit can also analyze speaker characteristics using voice feature extraction technology. For example, the voice analysis unit converts statements made during the conference into text in real time using voice recognition technology. The noise reduction technology removes background noise and improves the quality of the voice data. The voice feature extraction technology analyzes the characteristics of the speaker's voice and identifies the speaker. The minutes generation unit generates minutes based on the voice data analyzed by the voice analysis unit. For example, the minutes generation unit uses a generation AI to summarize statements made during the conference and create minutes. The minutes generation unit can also highlight important statements made during the conference. The minutes generation unit can also classify statements made during the conference and organize the minutes. For example, the minutes generation unit uses a generation AI to summarize what is said during a meeting and create minutes. Important comments are highlighted to make them visually noticeable. Classifying comments organizes the minutes and makes them easier to refer to later. The advice provision unit provides advice and opinions based on the minutes generated by the minutes generation unit. For example, the advice provision unit uses a generation AI to analyze what is said during a meeting and generate appropriate advice. The advice provision unit can also analyze keywords used during the meeting and provide related information. The advice provision unit can also summarize opinions used during the meeting and provide an overall evaluation. For example, the advice provision unit uses a generation AI to analyze what is said during a meeting and generate appropriate advice. Keyword analysis provides related information and supports the progress of the meeting. Summarizing opinions clarifies the conclusions of the meeting and prevents them from being carried over to the next meeting. The foreign language support unit translates the minutes generated by the minutes generation unit into foreign languages. For example, the foreign language support unit uses a generation AI to translate minutes into multiple languages. The foreign language support department can also translate statements made during meetings in real time and provide them to participants speaking foreign languages.The foreign language support unit can also display comments made during a meeting in multiple languages, breaking down language barriers. For example, the foreign language support unit uses generation AI to translate meeting minutes into multiple languages. Real-time translation instantly translates comments made during a meeting and provides them to foreign-speaking participants. Multilingual display displays comments made during a meeting in multiple languages, breaking down language barriers. This allows the meeting support system according to the embodiment to improve the efficiency of meetings and reach a conclusion each time without carrying over to the next meeting. For example, the meeting support system analyzes comments made during a meeting in real time and instantly creates minutes. The advice provision unit analyzes the content of comments made during a meeting and provides appropriate advice. The foreign language support unit translates the minutes into multiple languages ​​and provides them to foreign-speaking participants. This allows meetings to proceed smoothly and time to be used more effectively.

[0030] The voice analysis unit analyzes the tone and speed of the speaker's voice and can automatically determine the importance or urgency of the statement and reflect it in the minutes. The voice analysis unit, for example, analyzes the voice data during a meeting in real time and analyzes the tone and speed of the speaker's voice. For example, if the speaker is speaking in a hurry, it can determine that the statement is of high urgency and note this in the minutes. The voice analysis unit also builds a system that automatically determines the importance of a statement based on the tone and speed of the speaker's voice. For example, if the speaker is speaking in a calm tone, it can determine that the statement is of high importance and reflect this in the minutes. The voice analysis unit also automatically classifies the urgency and importance of the statement based on the analysis results of the voice data and reflects this in the minutes. For example, it can highlight statements with a high level of urgency. This allows the importance and urgency of a statement to be automatically determined and reflected in the minutes.

[0031] The audio analysis unit can analyze video footage of a meeting and create minutes taking into account the speaker's facial expressions or gestures. The audio analysis unit, for example, analyzes video footage of a meeting and builds a system that recognizes the speaker's facial expressions and gestures. For example, if a speaker is smiling while speaking, it is determined to be a positive comment and reflected in the minutes. The audio analysis unit also creates minutes taking into account the speaker's facial expressions and gestures based on the results of the video analysis. For example, if a speaker raises their hand, it is determined to be an important comment and recorded in the minutes. The audio analysis unit also combines voice recognition technology and video analysis to automatically generate minutes taking into account the speaker's facial expressions and gestures. For example, if a speaker is nodding, it is determined to be an indication of agreement and reflected in the minutes. This makes it possible to create minutes taking into account the speaker's facial expressions and gestures.

[0032] The voice analysis unit can simultaneously analyze not only the voice data during a meeting but also the text data of chat or email, and create comprehensive minutes. For example, the voice analysis unit builds a system that simultaneously analyzes the voice data during a meeting and the text data of chat or email in real time. For example, minutes are created by integrating voice and text. The voice analysis unit also simultaneously analyzes voice data and text data and automatically generates comprehensive minutes. For example, important comments made in chat are reflected in the minutes. The voice analysis unit also develops a system that integrates voice data and text data during a meeting and creates comprehensive minutes. For example, email exchanges are included in the minutes. This makes it possible to integrate voice data and text data and create comprehensive minutes.

[0033] The speech analysis unit can refer to past meeting data when creating minutes and automatically link related agendas or decisions. The speech analysis unit, for example, builds a system that references past meeting data and automatically links related agendas and decisions. For example, the decisions made in the previous meeting are reflected in the minutes. The speech analysis unit also automatically searches past meeting data when creating minutes and links related information. For example, past agendas and decisions are included in the minutes. The speech analysis unit also develops a system that automatically links related agendas and decisions based on past meeting data. For example, remarks made in the previous meeting are quoted in the minutes. This makes it possible to automatically link related agendas and decisions by referencing past meeting data.

[0034] The advice providing unit can provide more appropriate advice by taking into account the speaker's expertise and past speech history. The advice providing unit, for example, registers the speaker's expertise and past speech history in a database, and builds a system that provides advice based on that information. For example, it makes specific suggestions based on the expertise. Furthermore, when analyzing the content of a speech, the advice providing unit takes into account the speaker's past speech history and provides more appropriate advice. For example, it makes suggestions that match past speech. Furthermore, the advice providing unit develops a system that analyzes the content of a speech based on the speaker's expertise and past speech history, and provides appropriate advice. For example, it makes specific suggestions based on the expertise. This makes it possible to provide appropriate advice by taking into account the speaker's expertise and past speech history.

[0035] The advice providing unit can automatically recognize industry-specific terms or trends in keyword analysis and generate advice based thereon. The advice providing unit, for example, builds a system that automatically recognizes industry-specific terms and trends and provides advice based thereon. For example, it makes suggestions based on the latest technology trends. The advice providing unit also automatically recognizes industry-specific terms in keyword analysis and generates advice based thereon. For example, it makes suggestions including technical terms. The advice providing unit also develops a system that automatically recognizes industry-specific terms and trends and provides advice based thereon. For example, it makes suggestions based on the latest market trends. This makes it possible to recognize industry-specific terms and trends and generate advice.

[0036] The advice providing unit can integrate the analysis results of the utterance content with data from other meetings or projects, and provide advice from a broader perspective. The advice providing unit, for example, integrates the analysis results of the utterance content with data from other meetings or projects, and builds a system that provides advice from a broader perspective. For example, it references data from related projects. The advice providing unit also integrates data from other meetings and projects, and provides advice based on the analysis results of the utterance content. For example, it makes suggestions based on successful cases of past projects. The advice providing unit also integrates the analysis results of the utterance content with data from other meetings and projects, and develops a system that provides advice from a broader perspective. For example, it references data from related meetings. This allows the analysis results of the utterance content to be integrated with data from other meetings and projects, and provide advice from a broader perspective.

[0037] The advice providing unit allows the generation AI to refer to past success cases or failure cases and make specific suggestions based on them. The advice providing unit, for example, builds a system where the generation AI refers to past success cases and failure cases and makes specific suggestions based on them. For example, it makes suggestions based on success cases. The advice providing unit also registers past success cases and failure cases in a database, and the generation AI refers to them to make specific suggestions. For example, it makes improvement suggestions based on failure cases. The advice providing unit also develops a system where the generation AI analyzes past success cases and failure cases and makes specific suggestions based on them. For example, it makes suggestions based on the factors for success. This allows specific suggestions to be made by referring to past success cases and failure cases.

[0038] The advice providing unit monitors the progress of a meeting in real time and can automatically suggest the timing to reach a conclusion for each agenda item. The advice providing unit, for example, builds a system that monitors the progress of a meeting in real time and automatically suggests the timing to reach a conclusion for each agenda item. For example, it may encourage a conclusion if the discussion is dragging on. The advice providing unit also analyzes the progress of the meeting and makes suggestions for reaching a conclusion at the appropriate time. For example, it may present a conclusion when the agenda item is finished. The advice providing unit also develops a system that monitors the progress of a meeting in real time and automatically suggests the timing to reach a conclusion for each agenda item. For example, it may present a conclusion when the discussion has converged. This makes it possible to monitor the progress of a meeting in real time and automatically suggest the timing to reach a conclusion for each agenda item.

[0039] The advice-providing unit enables the generating AI to automatically summarize the key points of the discussion at the end of a meeting and present options for reaching a conclusion. For example, the advice-providing unit builds a system in which the generating AI automatically summarizes the key points of the discussion at the end of a meeting and presents options for reaching a conclusion. For example, it displays the key points of the discussion in bullet points. The advice-providing unit also enables the generating AI to automatically summarize the key points of the discussion at the end of a meeting and present options for reaching a conclusion. For example, it presents multiple options and lets participants choose. The advice-providing unit also develops a system in which the generating AI automatically summarizes the key points of the discussion at the end of a meeting and presents options for reaching a conclusion. For example, it displays the key points in a graph or chart. This makes it possible to summarize the key points of the discussion at the end of a meeting and present options for reaching a conclusion.

[0040] The advice providing unit enables the generation AI to refer to relevant laws, regulations, or industry standards when reaching a meeting conclusion, and propose a legally appropriate conclusion. For example, the advice providing unit builds a system in which the generation AI automatically refers to relevant laws, regulations, and industry standards when reaching a meeting conclusion, and proposes a legally appropriate conclusion. For example, it presents a conclusion that complies with laws and regulations. The advice providing unit also develops a system in which the generation AI refers to relevant laws, regulations, and industry standards when reaching a meeting conclusion, and proposes an appropriate conclusion. For example, it presents a conclusion based on industry standards. The advice providing unit also develops a system in which the generation AI refers to relevant laws, regulations, and industry standards when reaching a meeting conclusion, and proposes a legally appropriate conclusion. For example, it presents a conclusion that avoids legal risks. This makes it possible to refer to laws, regulations, and industry standards and propose a legally appropriate conclusion.

[0041] The advice-providing unit automates the process for reaching a conclusion, and the generation AI guides the progress of the discussion, thereby enabling efficient meeting management. The advice-providing unit, for example, automates the process for reaching a conclusion, and builds a system in which the generation AI guides the progress of the discussion. For example, it monitors the progress of the discussion in real time and encourages a conclusion at the appropriate time. The advice-providing unit also develops a system in which the generation AI guides the progress of the discussion, enabling efficient meeting management. For example, it suggests the next step if the discussion is stalled. The advice-providing unit also builds a system in which the process for reaching a conclusion, and the generation AI guides the progress of the discussion, enabling efficient meeting management. For example, it summarizes the main points of the discussion in real time. This automates the process for reaching a conclusion, enabling efficient meeting management.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The conference support system can further include a participant schedule management unit. The schedule management unit can automatically adjust conference schedules and propose optimal conference times based on the schedules of all participants. For example, the schedule management unit can refer to participants' calendars in real time and automatically select a time slot when all participants can attend. The schedule management unit can also monitor the progress of the conference and issue alerts to ensure that the conference ends within the scheduled time. The schedule management unit can also automatically set the date and time of the next conference and notify participants. This makes conference schedule adjustment more efficient, allowing all participants to join the conference smoothly.

[0044] The meeting support system can further include a document sharing unit. The document sharing unit can automatically collect documents to be used during a meeting and share them with all participants. For example, the document sharing unit can collect documents related to the meeting agenda in advance and distribute them to participants before the meeting starts. The document sharing unit can also share newly submitted documents during a meeting in real time. Furthermore, the document sharing unit can collectively save all documents used after the meeting ends so that they can be referenced later. This allows for smooth document sharing during a meeting, allowing all participants to quickly obtain the information they need.

[0045] The meeting support system can further include a voting unit. The voting unit can conduct votes on agenda items during a meeting and tally the results in real time. For example, the voting unit can allow participants to vote on ideas or opinions proposed during a meeting. The voting unit can also instantly tally the vote results and announce the results during the meeting. Furthermore, the voting unit can reflect the vote results in the minutes of the meeting so that they can be referenced later. This allows for quick decision-making during the meeting and makes it easier for all participants to express their opinions.

[0046] The meeting support system can further include a reminder unit. The reminder unit can automatically remind participants of important meeting agenda items and tasks and notify them. For example, the reminder unit can set deadlines for tasks decided during a meeting and notify participants when the deadline approaches. The reminder unit can also notify participants of the agenda items for the next meeting in advance so that they can prepare. Furthermore, the reminder unit can monitor the progress of the meeting and issue an alert if an important agenda item remains unresolved. This makes meeting task management more efficient and prevents important matters from being overlooked.

[0047] The conference support system can further include a feedback collection unit. The feedback collection unit collects feedback from participants after the conference ends and can use the collected feedback to improve the conference. For example, the feedback collection unit automatically sends out a questionnaire after the conference ends to collect opinions and impressions from participants. The feedback collection unit can also analyze the collected feedback and suggest improvements for the next conference. Furthermore, the feedback collection unit can evaluate the conference based on the participants' feedback and share the results with the participants. This improves the quality of the conference and enables conference management that satisfies all participants.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The voice analysis unit analyzes the voice data during the meeting. For example, the voice analysis unit uses voice recognition technology to convert what is said during the meeting into text data. It can also use noise reduction technology to improve the quality of the voice data. It can also use voice feature extraction technology to analyze the characteristics of the speaker. Step 2: The minutes generator generates minutes based on the audio data analyzed by the audio analyzer. For example, it can use the generation AI to summarize what was said during the meeting and create minutes. It can also highlight important comments and categorize them to organize the minutes. Step 3: The advice provider provides advice and opinions based on the minutes generated by the minutes generator. For example, the generator can use AI to analyze what was said during the meeting and generate appropriate advice. It can also analyze keywords, provide related information, and summarize opinions to provide an overall assessment. Step 4: The foreign language support unit translates the minutes generated by the minutes generation unit into a foreign language. For example, the minutes can be translated into multiple languages ​​using generation AI. It can also translate comments made during the meeting in real time and provide them to participants who speak a foreign language. It can also display comments made during the meeting in multiple languages, breaking down language barriers.

[0050] (Example 2) The meeting support system according to an embodiment of the present invention analyzes audio data during a meeting, and uses a generation AI to instantly create minutes and provide advice, opinions, and overall evaluations based on the content of comments, opinions, keywords, etc. This allows the meeting support system to improve the efficiency of meetings and ensure that conclusions are reached each time without having to be carried over to the next meeting. It also supports foreign languages, making it possible to overcome language barriers even in global meetings.

[0051] A conference support system according to an embodiment includes a voice analysis unit, a minutes generation unit, an advice providing unit, and a foreign language support unit. The voice analysis unit analyzes voice data generated during a conference. For example, the voice analysis unit converts statements made during the conference into text data using voice recognition technology. The voice analysis unit can also improve the quality of the voice data using noise reduction technology. The voice analysis unit can also analyze speaker characteristics using voice feature extraction technology. For example, the voice analysis unit converts statements made during the conference into text in real time using voice recognition technology. The noise reduction technology removes background noise and improves the quality of the voice data. The voice feature extraction technology analyzes the characteristics of the speaker's voice and identifies the speaker. The minutes generation unit generates minutes based on the voice data analyzed by the voice analysis unit. For example, the minutes generation unit uses a generation AI to summarize statements made during the conference and create minutes. The minutes generation unit can also highlight important statements made during the conference. The minutes generation unit can also classify statements made during the conference and organize the minutes. For example, the minutes generation unit uses a generation AI to summarize what is said during a meeting and create minutes. Important comments are highlighted to make them visually noticeable. Classifying comments organizes the minutes and makes them easier to refer to later. The advice provision unit provides advice and opinions based on the minutes generated by the minutes generation unit. For example, the advice provision unit uses a generation AI to analyze what is said during a meeting and generate appropriate advice. The advice provision unit can also analyze keywords used during the meeting and provide related information. The advice provision unit can also summarize opinions used during the meeting and provide an overall evaluation. For example, the advice provision unit uses a generation AI to analyze what is said during a meeting and generate appropriate advice. Keyword analysis provides related information and supports the progress of the meeting. Summarizing opinions clarifies the conclusions of the meeting and prevents them from being carried over to the next meeting. The foreign language support unit translates the minutes generated by the minutes generation unit into foreign languages. For example, the foreign language support unit uses a generation AI to translate minutes into multiple languages. The foreign language support department can also translate statements made during meetings in real time and provide them to participants speaking foreign languages.The foreign language support unit can also display comments made during a meeting in multiple languages, breaking down language barriers. For example, the foreign language support unit uses generation AI to translate meeting minutes into multiple languages. Real-time translation instantly translates comments made during a meeting and provides them to foreign-speaking participants. Multilingual display displays comments made during a meeting in multiple languages, breaking down language barriers. This allows the meeting support system according to the embodiment to improve the efficiency of meetings and reach a conclusion each time without carrying over to the next meeting. For example, the meeting support system analyzes comments made during a meeting in real time and instantly creates minutes. The advice provision unit analyzes the content of comments made during a meeting and provides appropriate advice. The foreign language support unit translates the minutes into multiple languages ​​and provides them to foreign-speaking participants. This allows meetings to proceed smoothly and time to be used more effectively.

[0052] The voice analysis unit analyzes the tone and speed of the speaker's voice and can automatically determine the importance or urgency of the statement and reflect it in the minutes. The voice analysis unit, for example, analyzes the voice data during a meeting in real time and analyzes the tone and speed of the speaker's voice. For example, if the speaker is speaking in a hurry, it can determine that the statement is of high urgency and note this in the minutes. The voice analysis unit also builds a system that automatically determines the importance of a statement based on the tone and speed of the speaker's voice. For example, if the speaker is speaking in a calm tone, it can determine that the statement is of high importance and reflect this in the minutes. The voice analysis unit also automatically classifies the urgency and importance of the statement based on the analysis results of the voice data and reflects this in the minutes. For example, it can highlight statements with a high level of urgency. This allows the importance and urgency of a statement to be automatically determined and reflected in the minutes.

[0053] The audio analysis unit can analyze video footage of a meeting and create minutes taking into account the speaker's facial expressions or gestures. The audio analysis unit, for example, analyzes video footage of a meeting and builds a system that recognizes the speaker's facial expressions and gestures. For example, if a speaker is smiling while speaking, it is determined to be a positive comment and reflected in the minutes. The audio analysis unit also creates minutes taking into account the speaker's facial expressions and gestures based on the results of the video analysis. For example, if a speaker raises their hand, it is determined to be an important comment and recorded in the minutes. The audio analysis unit also combines voice recognition technology and video analysis to automatically generate minutes taking into account the speaker's facial expressions and gestures. For example, if a speaker is nodding, it is determined to be an indication of agreement and reflected in the minutes. This makes it possible to create minutes taking into account the speaker's facial expressions and gestures.

[0054] The voice analysis unit uses the emotion estimation function to estimate the emotional state of the speaker and can record emotionally important statements in the minutes in a way that emphasizes them. The voice analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the speaker in real time. For example, if the speaker is angry, those statements are highlighted. The voice analysis unit also builds a system that automatically emphasizes emotionally important statements based on the speaker's emotional state. For example, if the speaker is excited, those statements are highlighted in the minutes. The voice analysis unit also uses the emotion estimation function to estimate the speaker's emotional state and reflect emotionally important statements in the minutes. For example, if the speaker is sad, those statements are specially noted. This allows emotionally important statements to be highlighted in the minutes.

[0055] The voice analysis unit can simultaneously analyze not only the voice data during a meeting but also the text data of chat or email, and create comprehensive minutes. For example, the voice analysis unit builds a system that simultaneously analyzes the voice data during a meeting and the text data of chat or email in real time. For example, minutes are created by integrating voice and text. The voice analysis unit also simultaneously analyzes voice data and text data and automatically generates comprehensive minutes. For example, important comments made in chat are reflected in the minutes. The voice analysis unit also develops a system that integrates voice data and text data during a meeting and creates comprehensive minutes. For example, email exchanges are included in the minutes. This makes it possible to integrate voice data and text data and create comprehensive minutes.

[0056] The speech analysis unit can refer to past meeting data when creating minutes and automatically link related agendas or decisions. The speech analysis unit, for example, builds a system that references past meeting data and automatically links related agendas and decisions. For example, the decisions made in the previous meeting are reflected in the minutes. The speech analysis unit also automatically searches past meeting data when creating minutes and links related information. For example, past agendas and decisions are included in the minutes. The speech analysis unit also develops a system that automatically links related agendas and decisions based on past meeting data. For example, remarks made in the previous meeting are quoted in the minutes. This makes it possible to automatically link related agendas and decisions by referencing past meeting data.

[0057] The voice analysis unit uses the emotion estimation function to monitor the emotional states of all participants in a meeting in real time and reflect them in the minutes. The voice analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotional states of all participants in a meeting in real time. For example, the emotion scores of the participants are recorded in the minutes. The voice analysis unit also analyzes the emotional states of all participants in a meeting in real time and reflects the results in the minutes. For example, emotionally important statements are highlighted. The voice analysis unit also uses the emotion estimation function to develop a system that monitors the emotional states of all participants in a meeting and reflects them in the minutes. For example, statements with high emotion scores are recorded in the minutes. This makes it possible to monitor the emotional states of all participants in a meeting in real time and reflect them in the minutes.

[0058] The advice providing unit can provide more appropriate advice by taking into account the speaker's expertise and past speech history. The advice providing unit, for example, registers the speaker's expertise and past speech history in a database, and builds a system that provides advice based on that information. For example, it makes specific suggestions based on the expertise. Furthermore, when analyzing the content of a speech, the advice providing unit takes into account the speaker's past speech history and provides more appropriate advice. For example, it makes suggestions that match past speech. Furthermore, the advice providing unit develops a system that analyzes the content of a speech based on the speaker's expertise and past speech history, and provides appropriate advice. For example, it makes specific suggestions based on the expertise. This makes it possible to provide appropriate advice by taking into account the speaker's expertise and past speech history.

[0059] The advice providing unit can automatically recognize industry-specific terms or trends in keyword analysis and generate advice based thereon. The advice providing unit, for example, builds a system that automatically recognizes industry-specific terms and trends and provides advice based thereon. For example, it makes suggestions based on the latest technology trends. The advice providing unit also automatically recognizes industry-specific terms in keyword analysis and generates advice based thereon. For example, it makes suggestions including technical terms. The advice providing unit also develops a system that automatically recognizes industry-specific terms and trends and provides advice based thereon. For example, it makes suggestions based on the latest market trends. This makes it possible to recognize industry-specific terms and trends and generate advice.

[0060] The advice providing unit uses the emotion estimation function to provide advice that takes into account the emotional state of the speaker, and can make suggestions that are emotionally easy to accept. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the speaker in real time and provide advice based on the results. For example, if the speaker is relaxed, it makes a gentle suggestion. The advice providing unit also builds a system that takes into account the emotional state of the speaker and provides emotionally easy to accept advice. For example, if the speaker is nervous, it makes an encouraging suggestion. The advice providing unit also uses the emotion estimation function to develop a system that analyzes the emotional state of the speaker and makes emotionally easy to accept suggestions. For example, if the speaker is excited, it makes a calm suggestion. In this way, it is possible to make emotionally easy to accept suggestions that take into account the emotional state of the speaker.

[0061] The advice providing unit can integrate the analysis results of the utterance content with data from other meetings or projects, and provide advice from a broader perspective. The advice providing unit, for example, integrates the analysis results of the utterance content with data from other meetings or projects, and builds a system that provides advice from a broader perspective. For example, it references data from related projects. The advice providing unit also integrates data from other meetings and projects, and provides advice based on the analysis results of the utterance content. For example, it makes suggestions based on successful cases of past projects. The advice providing unit also integrates the analysis results of the utterance content with data from other meetings and projects, and develops a system that provides advice from a broader perspective. For example, it references data from related meetings. This allows the analysis results of the utterance content to be integrated with data from other meetings and projects, and provide advice from a broader perspective.

[0062] The advice providing unit allows the generation AI to refer to past success cases or failure cases and make specific suggestions based on them. The advice providing unit, for example, builds a system where the generation AI refers to past success cases and failure cases and makes specific suggestions based on them. For example, it makes suggestions based on success cases. The advice providing unit also registers past success cases and failure cases in a database, and the generation AI refers to them to make specific suggestions. For example, it makes improvement suggestions based on failure cases. The advice providing unit also develops a system where the generation AI analyzes past success cases and failure cases and makes specific suggestions based on them. For example, it makes suggestions based on the factors for success. This allows specific suggestions to be made by referring to past success cases and failure cases.

[0063] The advice providing unit can use the emotion estimation function to analyze the emotional tone of the entire meeting and provide advice for maintaining a positive atmosphere. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional tone of the entire meeting in real time and provide advice for maintaining a positive atmosphere. For example, if the meeting is tense, it makes a suggestion to relax. The advice providing unit also builds a system that analyzes the emotional tone of the entire meeting and provides advice for maintaining a positive atmosphere. For example, if the meeting is depressing, it makes an encouraging suggestion. The advice providing unit also uses the emotion estimation function to develop a system that analyzes the emotional tone of the entire meeting and provides advice for maintaining a positive atmosphere. For example, if the meeting is lively, it makes a suggestion to maintain that atmosphere. In this way, it is possible to analyze the emotional tone of the entire meeting and provide advice for maintaining a positive atmosphere.

[0064] The advice providing unit monitors the progress of a meeting in real time and can automatically suggest the timing to reach a conclusion for each agenda item. The advice providing unit, for example, builds a system that monitors the progress of a meeting in real time and automatically suggests the timing to reach a conclusion for each agenda item. For example, it may encourage a conclusion if the discussion is dragging on. The advice providing unit also analyzes the progress of the meeting and makes suggestions for reaching a conclusion at the appropriate time. For example, it may present a conclusion when the agenda item is finished. The advice providing unit also develops a system that monitors the progress of a meeting in real time and automatically suggests the timing to reach a conclusion for each agenda item. For example, it may present a conclusion when the discussion has converged. This makes it possible to monitor the progress of a meeting in real time and automatically suggest the timing to reach a conclusion for each agenda item.

[0065] The advice-providing unit enables the generating AI to automatically summarize the key points of the discussion at the end of a meeting and present options for reaching a conclusion. For example, the advice-providing unit builds a system in which the generating AI automatically summarizes the key points of the discussion at the end of a meeting and presents options for reaching a conclusion. For example, it displays the key points of the discussion in bullet points. The advice-providing unit also enables the generating AI to automatically summarize the key points of the discussion at the end of a meeting and present options for reaching a conclusion. For example, it presents multiple options and lets participants choose. The advice-providing unit also develops a system in which the generating AI automatically summarizes the key points of the discussion at the end of a meeting and presents options for reaching a conclusion. For example, it displays the key points in a graph or chart. This makes it possible to summarize the key points of the discussion at the end of a meeting and present options for reaching a conclusion.

[0066] The advice providing unit can use the emotion estimation function to consider the emotional state of the participants and provide support for reaching a conclusion that everyone can agree on. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the participants in real time and provide support for reaching a conclusion that everyone can agree on. For example, if the emotion score is low, it encourages re-discussion. The advice providing unit also considers the emotional state of the participants and builds a system that provides support for reaching a conclusion that everyone can agree on. For example, if the emotion score is high, it presents a conclusion. The advice providing unit also uses the emotion estimation function to analyze the emotional state of the participants and develops a system that provides support for reaching a conclusion that everyone can agree on. For example, if the emotion score is low, it provides additional explanation. In this way, it is possible to consider the emotional state of the participants and provide support for reaching a conclusion that everyone can agree on.

[0067] The advice providing unit enables the generation AI to refer to relevant laws, regulations, or industry standards when reaching a meeting conclusion, and propose a legally appropriate conclusion. For example, the advice providing unit builds a system in which the generation AI automatically refers to relevant laws, regulations, and industry standards when reaching a meeting conclusion, and proposes a legally appropriate conclusion. For example, it presents a conclusion that complies with laws and regulations. The advice providing unit also develops a system in which the generation AI refers to relevant laws, regulations, and industry standards when reaching a meeting conclusion, and proposes an appropriate conclusion. For example, it presents a conclusion based on industry standards. The advice providing unit also develops a system in which the generation AI refers to relevant laws, regulations, and industry standards when reaching a meeting conclusion, and proposes a legally appropriate conclusion. For example, it presents a conclusion that avoids legal risks. This makes it possible to refer to laws, regulations, and industry standards and propose a legally appropriate conclusion.

[0068] The advice-providing unit automates the process for reaching a conclusion, and the generation AI guides the progress of the discussion, thereby enabling efficient meeting management. The advice-providing unit, for example, automates the process for reaching a conclusion, and builds a system in which the generation AI guides the progress of the discussion. For example, it monitors the progress of the discussion in real time and encourages a conclusion at the appropriate time. The advice-providing unit also develops a system in which the generation AI guides the progress of the discussion, enabling efficient meeting management. For example, it suggests the next step if the discussion is stalled. The advice-providing unit also builds a system in which the process for reaching a conclusion, and the generation AI guides the progress of the discussion, enabling efficient meeting management. For example, it summarizes the main points of the discussion in real time. This automates the process for reaching a conclusion, enabling efficient meeting management.

[0069] The advice providing unit uses the emotion estimation function to provide feedback on the participants' emotional reactions to the conclusion in real time, thereby enabling the optimal conclusion to be reached. The advice providing unit, for example, uses the emotion estimation function to build a system that provides feedback on the participants' emotional reactions to the conclusion in real time. For example, if the emotion score is low, the advice providing unit encourages re-discussion. The advice providing unit also analyzes the participants' emotional reactions in real time and provides feedback to lead to the optimal conclusion. For example, if the emotion score is high, the advice providing unit presents the conclusion. The advice providing unit also uses the emotion estimation function to develop a system that provides feedback on the participants' emotional reactions to the conclusion and leads to the optimal conclusion. For example, if the emotion score is low, additional explanation is provided. In this way, the participants' emotional reactions to the conclusion can be fed back in real time, enabling the optimal conclusion to be reached.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The conference support system can further include a participant schedule management unit. The schedule management unit can automatically adjust conference schedules and propose optimal conference times based on the schedules of all participants. For example, the schedule management unit can refer to participants' calendars in real time and automatically select a time slot when all participants can attend. The schedule management unit can also monitor the progress of the conference and issue alerts to ensure that the conference ends within the scheduled time. The schedule management unit can also automatically set the date and time of the next conference and notify participants. This makes conference schedule adjustment more efficient, allowing all participants to join the conference smoothly.

[0072] The meeting support system can further include a document sharing unit. The document sharing unit can automatically collect documents to be used during a meeting and share them with all participants. For example, the document sharing unit can collect documents related to the meeting agenda in advance and distribute them to participants before the meeting starts. The document sharing unit can also share newly submitted documents during a meeting in real time. Furthermore, the document sharing unit can collectively save all documents used after the meeting ends so that they can be referenced later. This allows for smooth document sharing during a meeting, allowing all participants to quickly obtain the information they need.

[0073] The meeting support system can further include a voting unit. The voting unit can conduct votes on agenda items during a meeting and tally the results in real time. For example, the voting unit can allow participants to vote on ideas or opinions proposed during a meeting. The voting unit can also instantly tally the vote results and announce the results during the meeting. Furthermore, the voting unit can reflect the vote results in the minutes of the meeting so that they can be referenced later. This allows for quick decision-making during the meeting and makes it easier for all participants to express their opinions.

[0074] The meeting support system can further include a reminder unit. The reminder unit can automatically remind participants of important meeting agenda items and tasks and notify them. For example, the reminder unit can set deadlines for tasks decided during a meeting and notify participants when the deadline approaches. The reminder unit can also notify participants of the agenda items for the next meeting in advance so that they can prepare. Furthermore, the reminder unit can monitor the progress of the meeting and issue an alert if an important agenda item remains unresolved. This makes meeting task management more efficient and prevents important matters from being overlooked.

[0075] The conference support system can further include a feedback collection unit. The feedback collection unit collects feedback from participants after the conference ends and can use the collected feedback to improve the conference. For example, the feedback collection unit automatically sends out a questionnaire after the conference ends to collect opinions and impressions from participants. The feedback collection unit can also analyze the collected feedback and suggest improvements for the next conference. Furthermore, the feedback collection unit can evaluate the conference based on the participants' feedback and share the results with the participants. This improves the quality of the conference and enables conference management that satisfies all participants.

[0076] The meeting support system can also use the emotion estimation function to monitor the emotional state of speakers during a meeting in real time and highlight emotionally significant statements. For example, if a speaker is excited, those statements can be highlighted in the minutes. Also, if a speaker is sad, those statements can be specifically noted. Furthermore, the emotion estimation function can be used to monitor the emotional state of all participants during a meeting and reflect emotionally significant statements in the minutes. This highlights emotionally significant statements, making meeting minutes more meaningful.

[0077] The meeting support system can also use the emotion estimation function to monitor the emotional states of all participants in a meeting in real time and reflect them in the minutes. For example, the emotion scores of participants can be recorded in the minutes. It can also highlight emotionally significant statements. The emotion estimation function can also be used to monitor the emotional states of all participants in a meeting and record statements with high emotion scores in the minutes. This allows the emotional states of all participants in a meeting to be monitored in real time and reflected in the minutes.

[0078] The meeting support system can further use its emotion estimation function to provide advice that takes into account the speaker's emotional state and make suggestions that are emotionally acceptable. For example, if the speaker is relaxed, a gentle suggestion can be made. If the speaker is nervous, an encouraging suggestion can be made. Furthermore, if the speaker is excited, a calm suggestion can be made. In this way, it is possible to make suggestions that are emotionally acceptable by taking into account the speaker's emotional state.

[0079] The meeting support system can further use its emotion estimation function to analyze the emotional tone of the entire meeting and provide advice to maintain a positive atmosphere. For example, if the meeting is tense, it can make suggestions to relax. If the meeting is depressing, it can make suggestions to encourage people. Furthermore, if the meeting is lively, it can make suggestions to maintain that atmosphere. In this way, it is possible to analyze the emotional tone of the entire meeting and provide advice to maintain a positive atmosphere.

[0080] The meeting support system also uses an emotion estimation function to provide real-time feedback on participants' emotional reactions to the conclusion, allowing for optimal conclusions to be reached. For example, if the emotion score is low, the system can encourage further discussion. Alternatively, if the emotion score is high, the system can present a conclusion. Furthermore, if the emotion score is low, the system can provide additional explanation. This allows for real-time feedback on participants' emotional reactions to the conclusion, allowing for optimal conclusions to be reached.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The voice analysis unit analyzes the voice data during the meeting. For example, the voice analysis unit uses voice recognition technology to convert what is said during the meeting into text data. It can also use noise reduction technology to improve the quality of the voice data. It can also use voice feature extraction technology to analyze the characteristics of the speaker. Step 2: The minutes generator generates minutes based on the audio data analyzed by the audio analyzer. For example, it can use the generation AI to summarize what was said during the meeting and create minutes. It can also highlight important comments and categorize them to organize the minutes. Step 3: The advice provider provides advice and opinions based on the minutes generated by the minutes generator. For example, the generator can use AI to analyze what was said during the meeting and generate appropriate advice. It can also analyze keywords, provide related information, and summarize opinions to provide an overall assessment. Step 4: The foreign language support unit translates the minutes generated by the minutes generation unit into a foreign language. For example, the minutes can be translated into multiple languages ​​using generation AI. It can also translate comments made during the meeting in real time and provide them to participants who speak a foreign language. It can also display comments made during the meeting in multiple languages, breaking down language barriers.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice analysis unit that analyzes voice data during a conference; a minutes generation unit that generates minutes based on the voice data analyzed by the voice analysis unit; an advice providing unit that provides advice or opinions based on the minutes generated by the minutes generating unit; a foreign language translation unit that translates the minutes generated by the minutes generation unit into a foreign language. A system characterized by:

2. The voice analysis unit Analyzing video footage of a meeting and creating minutes of the meeting taking into account the facial expressions or gestures of the speaker 2. The system of claim 1.

3. The voice analysis unit Reference past meeting data when creating meeting minutes and automatically link related topics or decisions 2. The system of claim 1.

4. The advice providing unit Automatically recognize industry-specific terms or trends in keyword analysis and generate recommendations based on them 2. The system of claim 1.

5. The advice providing unit Monitor meeting progress in real time and automatically suggest when to reach a conclusion for each agenda item 2. The system of claim 1.

6. The voice analysis unit The emotional state of the speaker is estimated and recorded in the minutes in a manner that emphasizes emotionally significant statements.

2. The system of claim 1.

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