System

Abstract

An object of the system according to the embodiment is to improve the efficiency of the overall conference operation.SOLUTION: A system includes an agenda creation part, a schedule confirmation part, a facilitation part, a minutes creation part, and a mail distribution part. The agenda creation section automatically creates an agenda. The schedule confirmation unit confirms the schedule of the participant. The facilitation unit supports the progress during the conference. The minutes creating section creates minutes. The mail delivery unit delivers the minutes.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the overall operation of a meeting, including preparation, progress, and the creation of minutes, requires a lot of time and effort.

[0005] The system according to the embodiment aims to improve the efficiency of the overall management of a conference. [Means for solving the problem]

[0006] The system according to the embodiment includes an agenda creation unit, a schedule confirmation unit, a facilitation unit, a minutes creation unit, and an email distribution unit. The agenda creation unit automatically creates an agenda. The schedule confirmation unit checks the schedules of the participants. The facilitation unit supports the progress of the meeting. The minutes creation unit creates the minutes. The email distribution unit distributes the minutes. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the overall management of a conference. [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) A conference management support system according to an embodiment of the present invention is a system that improves the overall efficiency of conference management and reduces the burden on participants. This system utilizes AI technology to automatically create agendas, check participants' schedules, facilitate meetings, and create and distribute minutes. As a result, the conference management support system can improve the overall efficiency of conference management and reduce the burden on participants.

[0029] A meeting management support system according to an embodiment includes an agenda creation unit, a schedule confirmation unit, a facilitation unit, a minutes creation unit, and an email distribution unit. The agenda creation unit automatically creates an agenda. For example, the generation AI generates a draft agenda based on the purpose and theme of the meeting and information about the participants. The generation AI receives a prompt, for example, "The purpose of the next meeting is to discuss the development plan for a new product. The participants are members of the development team," and generates a draft agenda. The schedule confirmation unit checks the schedules of the participants. For example, the generation AI proposes optimal meeting date candidates based on the schedule information of the participants. For example, the generation AI receives a prompt, for example, "Participant A's schedule is free on Mondays and Wednesdays. Participant B's schedule is free on Tuesdays and Thursdays," and proposes optimal date candidates. The facilitation unit supports the progress of the meeting. For example, the generation AI provides comments, summaries, and closing content for the facilitator based on the progress and content of the meeting. The generation AI receives a prompt, for example, "The current topic is the new product development plan. All participants have expressed their opinions," and generates appropriate comments and summaries. The minutes-taking unit creates minutes. For example, the generation AI generates minutes based on the content of comments made during the meeting. For example, the generation AI receives a prompt, for example, "The content of the comments made during the meeting was as follows: Person A: Regarding the new product development plan, Person B: Regarding the market research results...," and generates the minutes. The email distribution unit distributes the minutes. For example, the generation AI creates emails based on the contents of the minutes and distributes them to participants. For example, the generation AI receives a prompt, for example, "The content of the minutes is as follows: 1. Regarding the new product development plan, 2. Regarding the market research results...," and generates the emails. As a result, the meeting management support system according to the embodiment can improve the overall efficiency of meeting management and reduce the burden on participants. For example, the automatic creation of an agenda speeds up meeting preparations, and the optimal date is suggested by checking the schedules of participants. In addition, facilitation during meetings stimulates discussion, and automatic minutes creation and email distribution ensure smooth follow-up after meetings.

[0030] The agenda creation unit can learn from past meeting data and propose the optimal agenda for a specific theme. For example, the agenda creation unit uses a generation AI to analyze past meeting data and extract agenda items and topics related to a specific theme. For example, based on past meeting data related to new product development, it can automatically propose related agenda items. This makes it possible to propose the optimal agenda based on past meeting data.

[0031] The agenda creation unit can create individually customized agendas based on the expertise and job titles of participants. For example, the agenda creation unit uses a generation AI to consider the expertise and job titles of participants and propose the optimal agenda for each participant. For example, it could include technical topics for technical personnel and topics related to market strategies for marketing personnel. This makes it possible to create agendas that are tailored to the expertise and job titles of participants.

[0032] The schedule confirmation unit learns the past schedule patterns of participants and can predict the time period when it is easiest for them to participate. For example, the schedule confirmation unit uses a generation AI to learn the past schedule patterns of participants and predict the time period when it is easiest for them to participate. For example, it suggests the optimal time period based on past meeting attendance rates. This makes it possible to predict the optimal time period based on the past schedule patterns of participants.

[0033] The schedule confirmation unit can propose the optimal meeting date by taking into account the individual priorities of the participants. For example, the generation AI can consider the individual priorities of the participants and propose the optimal meeting date. For example, the schedule can be adjusted by taking into account the progress of important tasks and projects of the participants. This makes it possible to propose the optimal meeting date by taking into account the individual priorities of the participants.

[0034] The facilitation department can analyze the progress of a meeting in real time and suggest the best timing to move on to the next agenda item. For example, the generation AI can analyze the progress of a meeting in real time and suggest the best timing to move on to the next agenda item. For example, it can suggest the best time to move on to the next agenda item when the discussion has stalled. This allows the progress of a meeting to be analyzed in real time and suggest the best timing to move on to the next agenda item.

[0035] The facilitation department can analyze the content of participants' comments and provide comments to correct the direction of the discussion. For example, the generation AI analyzes the content of participants' comments and provides comments to correct the direction of the discussion. For example, if the discussion goes off track, it generates a comment to bring the discussion back to the original topic. This makes it possible to analyze the content of participants' comments and provide comments to correct the direction of the discussion.

[0036] The minutes-taking unit can summarize what is said in real time and provide minutes immediately at the end of the meeting. For example, the minutes-taking unit uses a generation AI to summarize what is said in real time and provide minutes immediately at the end of the meeting. For example, minutes are generated by automatically extracting the main points of each statement. This allows what is said to be summarized in real time and provide minutes immediately at the end of the meeting.

[0037] The minutes-taking unit can tag the content of each speaker's remarks and create minutes in a searchable format. For example, the generation AI can tag the content of each speaker's remarks and create minutes in a searchable format. For example, the content of each speaker's remarks can be tagged to make them easier to search. This makes it possible to tag the content of each speaker's remarks and create minutes in a searchable format.

[0038] The email distribution unit can create individually customized emails based on the contents of the minutes and distribute them to each participant. For example, the generation AI creates individually customized emails based on the contents of the minutes and distributes them to each participant. For example, the emails can include content according to the role and interests of each participant. This makes it possible to create individually customized emails based on the contents of the minutes and distribute them to each participant.

[0039] The email distribution department can automatically post the contents of emails to social media and obtain widespread feedback. For example, the email distribution department will build a system in which a generative AI automatically posts to social media based on the contents of meeting minutes. For example, the main points of a meeting will be posted on Twitter and LinkedIn. This allows the contents of emails to be automatically posted to social media and obtain widespread feedback.

[0040] The email delivery unit can convert the contents of emails into audio format and deliver them as voicemails. For example, the email delivery unit can build a system in which a generation AI converts the contents of meeting minutes into audio format and delivers them as voicemails. For example, the main points of the minutes are conveyed by voice. This allows the contents of emails to be converted into audio format and delivered as voicemails.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The conference management support system can further include a health management unit that monitors the health of participants. The health management unit, for example, monitors participants' heart rates and stress levels in real time and provides advice based on their health status. For example, if the heart rate is high, it can suggest breathing techniques to help participants relax. Also, if the stress level is high, it can suggest taking a short break. This makes it possible to manage conferences while taking into account the health of participants.

[0043] The conference management support system may further include an anonymous opinion collection unit that collects participants' opinions anonymously. The anonymous opinion collection unit may, for example, provide an anonymous chat function that allows participants to freely post their opinions. This allows participants to easily express their opinions. The anonymous opinion collection unit may also automatically classify the collected opinions and use them to help the conference proceed. For example, it may be possible to compile and present opinions on a specific topic. This allows participants' opinions to be collected effectively and improve the quality of the conference.

[0044] The conference management support system can also be equipped with a translation unit that automatically translates what participants say. For example, if there are participants who speak different languages, the translation unit translates what is said in real time and provides it to the other participants. This enables smooth communication across language barriers. The translation unit can also create minutes of the conference in multiple languages. For example, minutes of a conference held in English can be translated into Japanese and French and provided. This allows even international conferences to be managed effectively.

[0045] The conference management support system may further include an anonymous opinion collection unit that collects participants' opinions anonymously. The anonymous opinion collection unit may, for example, provide an anonymous chat function that allows participants to freely post their opinions. This allows participants to easily express their opinions. The anonymous opinion collection unit may also automatically classify the collected opinions and use them to help the conference proceed. For example, it may be possible to compile and present opinions on a specific topic. This allows participants' opinions to be collected effectively and improve the quality of the conference.

[0046] The conference management support system can also be equipped with a translation unit that automatically translates what participants say. For example, if there are participants who speak different languages, the translation unit translates what is said in real time and provides it to the other participants. This enables smooth communication across language barriers. The translation unit can also create minutes of the conference in multiple languages. For example, minutes of a conference held in English can be translated into Japanese and French and provided. This allows even international conferences to be managed effectively.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The agenda creation unit automatically creates an agenda. For example, the generation AI generates a draft agenda based on the purpose, theme, and participant information of the meeting. For example, the generation AI receives a prompt such as, "The purpose of the next meeting is to discuss the development plan for a new product. The participants are members of the development team." and generates a draft agenda. Step 2: The schedule confirmation unit checks the schedules of the participants. For example, the generation AI proposes optimal meeting date candidates based on the participants' schedule information. For example, the generation AI receives a prompt such as, "Participant A's schedule is open on Mondays and Wednesdays. Participant B's schedule is open on Tuesdays and Thursdays." and proposes optimal date candidates. Step 3: The Facilitation Department supports the progress of the meeting. For example, the Generation AI provides comments, summaries, and closing statements for the facilitator based on the progress of the meeting and the content of comments. For example, the Generation AI receives a prompt such as, "The current topic is the development plan for a new product. All participants have expressed their opinions," and generates appropriate comments and summaries. Step 4: The minutes-taking unit creates the minutes. For example, the generation AI generates minutes based on what was said during the meeting. For example, the generation AI receives a prompt such as, "The contents of what was said during the meeting were as follows: Person A: Regarding the development plan for the new product; Person B: Regarding the results of the market research..." and generates the minutes. Step 5: The email distribution unit distributes the minutes. For example, the generation AI creates emails based on the contents of the minutes and distributes them to participants. For example, the generation AI receives a prompt such as, "The contents of the minutes are as follows: 1. Regarding the development plan for the new product, 2. Regarding the results of the market research..." and generates an email.

[0049] (Example 2) A conference management support system according to an embodiment of the present invention is a system that improves the overall efficiency of conference management and reduces the burden on participants. This system utilizes AI technology to automatically create agendas, check participants' schedules, facilitate meetings, and create and distribute minutes. As a result, the conference management support system can improve the overall efficiency of conference management and reduce the burden on participants.

[0050] A meeting management support system according to an embodiment includes an agenda creation unit, a schedule confirmation unit, a facilitation unit, a minutes creation unit, and an email distribution unit. The agenda creation unit automatically creates an agenda. For example, the generation AI generates a draft agenda based on the purpose and theme of the meeting and information about the participants. The generation AI receives a prompt, for example, "The purpose of the next meeting is to discuss the development plan for a new product. The participants are members of the development team," and generates a draft agenda. The schedule confirmation unit checks the schedules of the participants. For example, the generation AI proposes optimal meeting date candidates based on the schedule information of the participants. For example, the generation AI receives a prompt, for example, "Participant A's schedule is free on Mondays and Wednesdays. Participant B's schedule is free on Tuesdays and Thursdays," and proposes optimal date candidates. The facilitation unit supports the progress of the meeting. For example, the generation AI provides comments, summaries, and closing content for the facilitator based on the progress and content of the meeting. The generation AI receives a prompt, for example, "The current topic is the new product development plan. All participants have expressed their opinions," and generates appropriate comments and summaries. The minutes-taking unit creates minutes. For example, the generation AI generates minutes based on the content of comments made during the meeting. For example, the generation AI receives a prompt, for example, "The content of the comments made during the meeting was as follows: Person A: Regarding the new product development plan, Person B: Regarding the market research results...," and generates the minutes. The email distribution unit distributes the minutes. For example, the generation AI creates emails based on the contents of the minutes and distributes them to participants. For example, the generation AI receives a prompt, for example, "The content of the minutes is as follows: 1. Regarding the new product development plan, 2. Regarding the market research results...," and generates the emails. As a result, the meeting management support system according to the embodiment can improve the overall efficiency of meeting management and reduce the burden on participants. For example, the automatic creation of an agenda speeds up meeting preparations, and the optimal date is suggested by checking the schedules of participants. In addition, facilitation during meetings stimulates discussion, and automatic minutes creation and email distribution ensure smooth follow-up after meetings.

[0051] The agenda creation unit can learn from past meeting data and propose the optimal agenda for a specific theme. For example, the agenda creation unit uses a generation AI to analyze past meeting data and extract agenda items and topics related to a specific theme. For example, based on past meeting data related to new product development, it can automatically propose related agenda items. This makes it possible to propose the optimal agenda based on past meeting data.

[0052] The agenda creation unit can create individually customized agendas based on the expertise and job titles of participants. For example, the agenda creation unit uses a generation AI to consider the expertise and job titles of participants and propose the optimal agenda for each participant. For example, it could include technical topics for technical personnel and topics related to market strategies for marketing personnel. This makes it possible to create agendas that are tailored to the expertise and job titles of participants.

[0053] The agenda creation unit can use the emotion estimation function to analyze participants' emotional responses in past meetings and generate an agenda that elicits positive responses. The agenda creation unit, for example, uses the emotion estimation function to analyze participants' emotional responses in past meetings and generate an agenda that elicits positive responses. For example, it prioritizes including topics that have been well-received in the past. This makes it possible to generate a positive agenda based on participants' emotional responses.

[0054] The schedule confirmation unit learns the past schedule patterns of participants and can predict the time period when it is easiest for them to participate. For example, the schedule confirmation unit uses a generation AI to learn the past schedule patterns of participants and predict the time period when it is easiest for them to participate. For example, it suggests the optimal time period based on past meeting attendance rates. This makes it possible to predict the optimal time period based on the past schedule patterns of participants.

[0055] The schedule confirmation unit can propose the optimal meeting date by taking into account the individual priorities of the participants. For example, the generation AI can consider the individual priorities of the participants and propose the optimal meeting date. For example, the schedule can be adjusted by taking into account the progress of important tasks and projects of the participants. This makes it possible to propose the optimal meeting date by taking into account the individual priorities of the participants.

[0056] The schedule confirmation unit can use the emotion estimation function to analyze participants' emotional reactions to the schedule and propose a less stressful schedule. The schedule confirmation unit can, for example, use the emotion estimation function to analyze participants' emotional reactions to the schedule and propose a less stressful schedule. For example, it can select a time period when participants can easily relax. This makes it possible to propose a less stressful schedule based on participants' emotional reactions.

[0057] The facilitation department can analyze the progress of a meeting in real time and suggest the best timing to move on to the next agenda item. For example, the generation AI can analyze the progress of a meeting in real time and suggest the best timing to move on to the next agenda item. For example, it can suggest the best time to move on to the next agenda item when the discussion has stalled. This allows the progress of a meeting to be analyzed in real time and suggest the best timing to move on to the next agenda item.

[0058] The facilitation department can analyze the content of participants' comments and provide comments to correct the direction of the discussion. For example, the generation AI analyzes the content of participants' comments and provides comments to correct the direction of the discussion. For example, if the discussion goes off track, it generates a comment to bring the discussion back to the original topic. This makes it possible to analyze the content of participants' comments and provide comments to correct the direction of the discussion.

[0059] The facilitation unit can use the emotion estimation function to monitor the emotional state of participants in real time and suggest refreshments at appropriate times. The facilitation unit can, for example, use the emotion estimation function to monitor the emotional state of participants in real time and suggest refreshments at appropriate times. For example, suggesting a break when a participant becomes tired. This makes it possible to monitor the emotional state of participants in real time and suggest refreshments at appropriate times.

[0060] The minutes-taking unit can summarize what is said in real time and provide minutes immediately at the end of the meeting. For example, the minutes-taking unit uses a generation AI to summarize what is said in real time and provide minutes immediately at the end of the meeting. For example, minutes are generated by automatically extracting the main points of each statement. This allows what is said to be summarized in real time and provide minutes immediately at the end of the meeting.

[0061] The minutes-taking unit can tag the content of each speaker's remarks and create minutes in a searchable format. For example, the generation AI can tag the content of each speaker's remarks and create minutes in a searchable format. For example, the content of each speaker's remarks can be tagged to make them easier to search. This makes it possible to tag the content of each speaker's remarks and create minutes in a searchable format.

[0062] The minutes creation unit can use the emotion estimation function to create minutes that reflect the emotional nuances of the remarks. The minutes creation unit, for example, uses the emotion estimation function to create minutes that reflect the emotional nuances of the remarks. For example, minutes that reflect the emotional state of the speaker are generated. This makes it possible to create minutes that reflect the emotional nuances of the remarks.

[0063] The email distribution unit can create individually customized emails based on the contents of the minutes and distribute them to each participant. For example, the generation AI creates individually customized emails based on the contents of the minutes and distributes them to each participant. For example, the emails can include content according to the role and interests of each participant. This makes it possible to create individually customized emails based on the contents of the minutes and distribute them to each participant.

[0064] The email distribution department can automatically post the contents of emails to social media and obtain widespread feedback. For example, the email distribution department will build a system in which a generative AI automatically posts to social media based on the contents of meeting minutes. For example, the main points of a meeting will be posted on Twitter and LinkedIn. This allows the contents of emails to be automatically posted to social media and obtain widespread feedback.

[0065] The email delivery unit can convert the contents of emails into audio format and deliver them as voicemails. For example, the email delivery unit can build a system in which a generation AI converts the contents of meeting minutes into audio format and delivers them as voicemails. For example, the main points of the minutes are conveyed by voice. This allows the contents of emails to be converted into audio format and delivered as voicemails.

[0066] The email delivery unit can use the emotion estimation function to monitor the recipient's emotional reaction to the email content in real time and automatically generate a follow-up email as needed. The email delivery unit can, for example, use the emotion estimation function to monitor the recipient's emotional reaction to the email content in real time and automatically generate a follow-up email as needed. For example, a follow-up email is sent if the recipient has a negative reaction. This makes it possible to monitor the recipient's emotional reaction to the email content in real time and automatically generate a follow-up email as needed.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The conference management support system can further include a health management unit that monitors the health of participants. The health management unit, for example, monitors participants' heart rates and stress levels in real time and provides advice based on their health status. For example, if the heart rate is high, it can suggest breathing techniques to help participants relax. Also, if the stress level is high, it can suggest taking a short break. This makes it possible to manage conferences while taking into account the health of participants.

[0069] The conference management support system can further include an emotion adjustment unit that estimates the emotions of the participants and adjusts the progress of the conference based on the emotions. The emotion adjustment unit, for example, analyzes the facial expressions and tone of voice of the participants to estimate their emotions. The emotion adjustment unit adjusts the progress of the conference based on the estimated emotions. For example, if a participant appears tired, it can suggest taking a break. Also, if a participant appears excited, it can suggest continuing the discussion. This enables flexible conference management according to the emotions of the participants.

[0070] The conference management support system may further include an anonymous opinion collection unit that collects participants' opinions anonymously. The anonymous opinion collection unit may, for example, provide an anonymous chat function that allows participants to freely post their opinions. This allows participants to easily express their opinions. The anonymous opinion collection unit may also automatically classify the collected opinions and use them to help the conference proceed. For example, it may be possible to compile and present opinions on a specific topic. This allows participants' opinions to be collected effectively and improve the quality of the conference.

[0071] The meeting management support system may further include an emotion-reflecting minutes module that estimates the emotions of participants and customizes minutes based on their emotions. The emotion-reflecting minutes module, for example, analyzes the content and emotions of participants' remarks and creates minutes that reflect emotional nuances. For example, it may record the points emphasized by the speaker or the parts to which the speaker responded emotionally, with particular emphasis. The emotion-reflecting minutes module may also adjust the format of the minutes based on their emotions. This allows minutes that reflect emotional nuances to be provided, more accurately conveying the content of the meeting.

[0072] The conference management support system can also be equipped with a translation unit that automatically translates what participants say. For example, if there are participants who speak different languages, the translation unit translates what is said in real time and provides it to the other participants. This enables smooth communication across language barriers. The translation unit can also create minutes of the conference in multiple languages. For example, minutes of a conference held in English can be translated into Japanese and French and provided. This allows even international conferences to be managed effectively.

[0073] The conference management support system can further include an emotion adjustment unit that estimates the emotions of the participants and adjusts the progress of the conference based on the emotions. The emotion adjustment unit, for example, analyzes the facial expressions and tone of voice of the participants to estimate their emotions. The emotion adjustment unit adjusts the progress of the conference based on the estimated emotions. For example, if a participant appears tired, it can suggest taking a break. Also, if a participant appears excited, it can suggest continuing the discussion. This enables flexible conference management according to the emotions of the participants.

[0074] The conference management support system may further include an anonymous opinion collection unit that collects participants' opinions anonymously. The anonymous opinion collection unit may, for example, provide an anonymous chat function that allows participants to freely post their opinions. This allows participants to easily express their opinions. The anonymous opinion collection unit may also automatically classify the collected opinions and use them to help the conference proceed. For example, it may be possible to compile and present opinions on a specific topic. This allows participants' opinions to be collected effectively and improve the quality of the conference.

[0075] The meeting management support system may further include an emotion-reflecting minutes module that estimates the emotions of participants and customizes minutes based on their emotions. The emotion-reflecting minutes module, for example, analyzes the content and emotions of participants' remarks and creates minutes that reflect emotional nuances. For example, it may record the points emphasized by the speaker or the parts to which the speaker responded emotionally, with particular emphasis. The emotion-reflecting minutes module may also adjust the format of the minutes based on their emotions. This allows minutes that reflect emotional nuances to be provided, more accurately conveying the content of the meeting.

[0076] The conference management support system can also be equipped with a translation unit that automatically translates what participants say. For example, if there are participants who speak different languages, the translation unit translates what is said in real time and provides it to the other participants. This enables smooth communication across language barriers. The translation unit can also create minutes of the conference in multiple languages. For example, minutes of a conference held in English can be translated into Japanese and French and provided. This allows even international conferences to be managed effectively.

[0077] The conference management support system can further include an emotion adjustment unit that estimates the emotions of the participants and adjusts the progress of the conference based on the emotions. The emotion adjustment unit, for example, analyzes the facial expressions and tone of voice of the participants to estimate their emotions. The emotion adjustment unit adjusts the progress of the conference based on the estimated emotions. For example, if a participant appears tired, it can suggest taking a break. Also, if a participant appears excited, it can suggest continuing the discussion. This enables flexible conference management according to the emotions of the participants.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The agenda creation unit automatically creates an agenda. For example, the generation AI generates a draft agenda based on the purpose, theme, and participant information of the meeting. For example, the generation AI receives a prompt such as, "The purpose of the next meeting is to discuss the development plan for a new product. The participants are members of the development team." and generates a draft agenda. Step 2: The schedule confirmation unit checks the schedules of the participants. For example, the generation AI proposes optimal meeting date candidates based on the participants' schedule information. For example, the generation AI receives a prompt such as, "Participant A's schedule is open on Mondays and Wednesdays. Participant B's schedule is open on Tuesdays and Thursdays." and proposes optimal date candidates. Step 3: The Facilitation Department supports the progress of the meeting. For example, the Generation AI provides comments, summaries, and closing statements for the facilitator based on the progress of the meeting and the content of comments. For example, the Generation AI receives a prompt such as, "The current topic is the development plan for a new product. All participants have expressed their opinions," and generates appropriate comments and summaries. Step 4: The minutes-taking unit creates the minutes. For example, the generation AI generates minutes based on what was said during the meeting. For example, the generation AI receives a prompt such as, "The contents of what was said during the meeting were as follows: Person A: Regarding the development plan for the new product; Person B: Regarding the results of the market research..." and generates the minutes. Step 5: The email distribution unit distributes the minutes. For example, the generation AI creates emails based on the contents of the minutes and distributes them to participants. For example, the generation AI receives a prompt such as, "The contents of the minutes are as follows: 1. Regarding the development plan for the new product, 2. Regarding the results of the market research..." and generates an email.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an agenda creation unit that automatically creates an agenda; A schedule confirmation section that checks the schedules of participants; The facilitation department supports the progress of the meeting, A minutes-taking department that takes minutes of meetings; an email distribution unit that distributes the minutes; A system characterized by:

2. The agenda creation unit Learn from past meeting data and suggest the best agenda for a specific topic 2. The system of claim 1.

3. The schedule confirmation unit Learn participants' past schedule patterns and predict the most convenient time to attend 2. The system of claim 1.

4. The facilitation department Analyze meeting progress in real time and suggest the best time to move on to the next topic 2. The system of claim 1.

5. The minutes preparation department Summarize what is said in real time and provide meeting minutes immediately at the end of the meeting 2. The system of claim 1.

6. The email delivery unit Based on the contents of the minutes, individually customized emails are created and sent to each of the participants.

2. The system of claim 1.

7. The agenda creation unit Analyze participants' emotional reactions to past meetings and generate agendas that elicit positive responses 2. The system of claim 1.

8. The schedule confirmation unit Analyze participants' emotional reactions to schedules and suggest less stressful schedules 2. The system of claim 1.

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