System

Abstract

An object of a system according to an embodiment is to improve the efficiency of setting a meeting and creating minutes.SOLUTION: A system according to an embodiment includes a conference setting unit, a participant recommendation unit, a schedule table analysis unit, and a minutes creation unit. The conference setting unit recommends participants based on the purpose of the conference. The participant recommendation unit analyzes the schedules of the participants recommended by the meeting setting unit. The schedule analysis unit automatically generates a candidate date and time from the schedule analyzed by the participant recommendation unit. The minutes creating section converts the voice data during the conference into text to create 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 technology has the drawback of requiring a lot of time and effort to set up meetings and create minutes.

[0005] The system according to the embodiment aims to improve the efficiency of setting up a meeting and creating minutes. [Means for solving the problem]

[0006] The system according to the embodiment includes a conference setting unit, a participant recommendation unit, a schedule analysis unit, and a minutes creation unit. The conference setting unit recommends participants based on the purpose of the conference. The participant recommendation unit analyzes the schedules of the participants recommended by the conference setting unit. The schedule analysis unit automatically generates candidate dates and times from the schedule analyzed by the participant recommendation unit. The minutes creation unit converts audio data from the conference into text and creates minutes. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of setting up a meeting and creating minutes. [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 meeting assistance system according to an embodiment of the present invention is a system that uses a generative AI to streamline a series of tasks from setting up a meeting to creating minutes. As a result, the meeting assistance system automates the setting up of a meeting and the creation of minutes, allowing participants to secure time to focus on the discussion.

[0029] A meeting assistance system according to an embodiment includes a meeting scheduling unit, a participant recommendation unit, a schedule analysis unit, and a minutes creation unit. The meeting scheduling unit recommends participants based on the purpose of the meeting. For example, for a project progress meeting, the meeting scheduling unit recommends the project manager or key personnel. The meeting scheduling unit can also select participants based on prompts including the purpose and theme of the meeting. The participant recommendation unit analyzes the schedules of the participants recommended by the meeting scheduling unit. For example, the schedule analysis unit compares the schedules of the participants and proposes candidate dates and times that are available to all participants. The schedule analysis unit automatically generates candidate dates and times from the schedule analyzed by the participant recommendation unit. For example, the schedule analysis unit proposes a specific date and time, such as "Tuesday next week, from 2:00 to 3:00 PM." The minutes creation unit converts audio data from the meeting into text and creates minutes. For example, the system converts statements made during the meeting into text in real time and saves them as minutes. This allows the meeting assistance system to streamline the entire process, from setting up the meeting to creating the minutes, ensuring time to focus on the discussion. For example, it can be effectively used in various meetings, such as project progress meetings and new product development meetings.

[0030] The meeting setup unit can analyze past meeting data and propose the optimal meeting format (online, offline, hybrid). For example, the generation AI analyzes past meeting data and proposes the optimal meeting format based on the attendance rate of participants and the outcome of the meeting. For example, if the attendance rate of online meetings is high, the system will recommend an online format for the next meeting as well. This makes it possible to propose the optimal meeting format based on past meeting data.

[0031] The meeting scheduling unit can automatically generate the necessary materials and presentations according to the purpose of the meeting and distribute them to participants in advance. For example, the generation AI in the meeting scheduling unit analyzes the purpose of the meeting and automatically generates relevant materials and presentations. For example, in a project progress meeting, progress reports and graphs are generated. This allows materials and presentations according to the purpose of the meeting to be automatically generated and distributed in advance.

[0032] The participant recommendation unit analyzes the expertise and skill sets of participants and can recommend the experts most suited to the purpose of the meeting. For example, the generative AI analyzes the expertise and skill sets of participants and recommends the experts most suited to the purpose of the meeting. For example, in a new product development meeting, technical experts are given priority in selection. This makes it possible to recommend the experts most suited to the purpose of the meeting.

[0033] The participant recommendation unit can analyze past meeting data and prioritize recommend participants who have had the most effective discussions. For example, the generation AI analyzes past meeting data and prioritizes recommending participants who have had the most effective discussions. For example, selection can be based on the number of comments made and the quality of the comments. This makes it possible to prioritize recommending participants who have had the most effective discussions.

[0034] The participant recommendation unit can recommend participants from different departments and teams, promoting cross-functional discussions. For example, the generative AI can recommend participants from different departments and teams, promoting cross-functional discussions. For example, it can select members from the technical and marketing departments. This can recommend participants from different departments and teams, promoting cross-functional discussions.

[0035] The participant recommendation unit can recommend external experts and consultants and incorporate opinions from an external perspective. For example, the generative AI can recommend external experts and consultants and incorporate opinions from an external perspective. For example, it can select experts in a specific technical field. This makes it possible to recommend external experts and consultants and incorporate opinions from an external perspective.

[0036] The schedule analysis unit analyzes participants' past schedule patterns and can propose the most efficient meeting time. For example, the generation AI analyzes participants' past schedule patterns and proposes the most efficient meeting time. For example, it selects the time period when participants attended the most meetings. This makes it possible to propose the most efficient meeting time.

[0037] The schedule analysis unit can analyze the individual productivity peak times of participants and propose the most effective meeting time. For example, the generation AI analyzes the individual productivity peak times of participants and proposes the most effective meeting time. For example, it selects a time slot in the morning. This makes it possible to propose the most effective meeting time.

[0038] The schedule analysis unit can propose balanced meeting times by taking into account the participants' home and personal schedules. For example, the generation AI analyzes the participants' home and personal schedules and proposes balanced meeting times. For example, it selects time slots that take into account home circumstances. This makes it possible to propose balanced meeting times by taking into account the participants' home and personal schedules.

[0039] The schedule analysis unit can analyze the schedules of different projects and propose meeting times with the least overlap. For example, the generation AI can analyze the schedules of different projects and propose meeting times with the least overlap. For example, it can select a time slot that does not overlap with meetings of other projects. This allows it to consider the schedules of different projects and propose meeting times with the least overlap.

[0040] The minutes-taking unit can convert statements made during meetings into text in real time and automatically highlight important points. For example, the minutes-taking unit uses a generation AI to convert statements made during meetings into text in real time and automatically highlight important points. For example, it highlights the main points of discussion and decisions. This allows statements made during meetings to be converted into text in real time and automatically highlight important points.

[0041] The minutes-taking unit can automatically translate minutes into different languages, making them easier to share with international teams. For example, the minutes-taking unit can use a generation AI to automatically translate minutes into different languages, making them easier to share with international teams. For example, it can translate into English, Japanese, Chinese, etc. This allows minutes to be automatically translated into different languages, making them easier to share with international teams.

[0042] The minutes-taking unit can convert minutes into visual notes or mind maps to make them easier to understand visually. For example, the minutes-taking unit uses a generation AI to convert minutes into visual notes to make them easier to understand visually. For example, important points are shown with diagrams or icons. This allows the minutes to be converted into visual notes or mind maps to make them easier to understand visually.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The meeting assistance system can also monitor the progress of the meeting in real time and automatically change the agenda if the discussion stalls. For example, it can suggest moving on to the next topic if the discussion hits an impasse. It can also analyze how frequently participants speak and encourage others to speak if a particular participant is speaking too much. This helps the meeting proceed smoothly and provides everyone with an equal opportunity to express their opinions.

[0045] The meeting assistance system can also be equipped with a function to automatically summarize the contents of a meeting, extracting important points and providing them to participants. For example, after the meeting, a summary report can be generated and sent to participants by email. It can also display summaries in real time during the meeting to support the progress of the discussion. This makes it easier for participants to grasp the overall picture of the meeting and ensures that they do not miss any important points.

[0046] The meeting assistance system can also analyze comments made during meetings and automatically search for and provide relevant materials and information based on the content of those comments. For example, when a specific technology or topic is discussed, related papers and articles can be displayed in real time. It can also automatically retrieve past meeting records and related project materials. This allows participants to quickly obtain the information they need and deepen their discussions.

[0047] The meeting assistance system can also be equipped with a function to visualize the progress of the meeting, displaying the timeline and progress in real time. For example, it can display the progress of each agenda item in graphs and charts to support time management. It can also issue an alert if the discussion does not reach a conclusion within the scheduled time. This allows for efficient management of the meeting progress and effective use of time.

[0048] The meeting assistance system can also collect feedback from participants after the meeting and provide a function to suggest improvements for the next meeting. For example, it can collect feedback in the form of a questionnaire and analyze participant satisfaction and areas for improvement. It can also adjust the agenda and progress of the next meeting based on the feedback. This allows for continuous improvement of the quality of meetings.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The meeting scheduling module recommends participants based on the purpose of the meeting. For example, if it's a project status meeting, it will recommend the project manager and key personnel. The meeting scheduling module can also select participants based on prompts that include the purpose and topic of the meeting. Step 2: The participant recommendation unit analyzes the schedules of the participants recommended by the conference planning unit. For example, it compares the schedules of the participants and proposes candidate dates and times when all participants can attend. Step 3: The schedule analysis unit automatically generates candidate dates and times from the schedule analyzed by the participant recommendation unit. For example, it suggests a specific date and time such as "next Tuesday from 2:00 to 3:00 PM." Step 4: The minutes-taking section converts the audio data from the meeting into text and creates minutes. For example, it converts statements made during the meeting into text in real time and saves them as minutes.

[0051] (Example 2) A meeting assistance system according to an embodiment of the present invention is a system that uses a generative AI to streamline a series of tasks from setting up a meeting to creating minutes. As a result, the meeting assistance system automates the setting up of a meeting and the creation of minutes, allowing participants to secure time to focus on the discussion.

[0052] A meeting assistance system according to an embodiment includes a meeting scheduling unit, a participant recommendation unit, a schedule analysis unit, and a minutes creation unit. The meeting scheduling unit recommends participants based on the purpose of the meeting. For example, for a project progress meeting, the meeting scheduling unit recommends the project manager or key personnel. The meeting scheduling unit can also select participants based on prompts including the purpose and theme of the meeting. The participant recommendation unit analyzes the schedules of the participants recommended by the meeting scheduling unit. For example, the schedule analysis unit compares the schedules of the participants and proposes candidate dates and times that are available to all participants. The schedule analysis unit automatically generates candidate dates and times from the schedule analyzed by the participant recommendation unit. For example, the schedule analysis unit proposes a specific date and time, such as "Tuesday next week, from 2:00 to 3:00 PM." The minutes creation unit converts audio data from the meeting into text and creates minutes. For example, the system converts statements made during the meeting into text in real time and saves them as minutes. This allows the meeting assistance system to streamline the entire process, from setting up the meeting to creating the minutes, ensuring time to focus on the discussion. For example, it can be effectively used in various meetings, such as project progress meetings and new product development meetings.

[0053] The meeting setup unit can analyze past meeting data and propose the optimal meeting format (online, offline, hybrid). For example, the generation AI analyzes past meeting data and proposes the optimal meeting format based on the attendance rate of participants and the outcome of the meeting. For example, if the attendance rate of online meetings is high, the system will recommend an online format for the next meeting as well. This makes it possible to propose the optimal meeting format based on past meeting data.

[0054] The meeting scheduling unit can automatically generate the necessary materials and presentations according to the purpose of the meeting and distribute them to participants in advance. For example, the generation AI in the meeting scheduling unit analyzes the purpose of the meeting and automatically generates relevant materials and presentations. For example, in a project progress meeting, progress reports and graphs are generated. This allows materials and presentations according to the purpose of the meeting to be automatically generated and distributed in advance.

[0055] The conference planning unit can use the emotion estimation function to consider the emotional state of the participants and propose the most relaxing conference environment (location, time period). For example, the conference planning unit can use the emotion estimation function to analyze the emotional state of the participants in real time and propose the most relaxing conference environment. For example, it can select a time period with the least stress. This allows the conference planning unit to consider the emotional state of the participants and propose the most relaxing conference environment.

[0056] The participant recommendation unit analyzes the expertise and skill sets of participants and can recommend the experts most suited to the purpose of the meeting. For example, the generative AI analyzes the expertise and skill sets of participants and recommends the experts most suited to the purpose of the meeting. For example, in a new product development meeting, technical experts are given priority in selection. This makes it possible to recommend the experts most suited to the purpose of the meeting.

[0057] The participant recommendation unit can analyze past meeting data and prioritize recommend participants who have had the most effective discussions. For example, the generation AI analyzes past meeting data and prioritizes recommending participants who have had the most effective discussions. For example, selection can be based on the number of comments made and the quality of the comments. This makes it possible to prioritize recommending participants who have had the most effective discussions.

[0058] The participant recommendation unit can use the emotion estimation function to consider the emotional state of participants and recommend participants who will have the most positive influence. The participant recommendation unit, for example, uses the emotion estimation function to analyze the emotional state of participants and recommends participants who will have the most positive influence. For example, it selects participants who had positive emotions in past meetings. This makes it possible to recommend participants who will have the most positive influence.

[0059] The participant recommendation unit can recommend participants from different departments and teams, promoting cross-functional discussions. For example, the generative AI can recommend participants from different departments and teams, promoting cross-functional discussions. For example, it can select members from the technical and marketing departments. This can recommend participants from different departments and teams, promoting cross-functional discussions.

[0060] The participant recommendation unit can recommend external experts and consultants and incorporate opinions from an external perspective. For example, the generative AI can recommend external experts and consultants and incorporate opinions from an external perspective. For example, it can select experts in a specific technical field. This makes it possible to recommend external experts and consultants and incorporate opinions from an external perspective.

[0061] The participant recommendation unit uses the emotion estimation function to monitor the emotional states of participants in real time and invite additional participants at appropriate times during the meeting. The participant recommendation unit, for example, uses the emotion estimation function to monitor the emotional states of participants in real time and invite additional participants at appropriate times. For example, an expert may be invited when the discussion reaches a halt. This allows additional participants to be invited at appropriate times during the meeting.

[0062] The schedule analysis unit analyzes participants' past schedule patterns and can propose the most efficient meeting time. For example, the generation AI analyzes participants' past schedule patterns and proposes the most efficient meeting time. For example, it selects the time period when participants attended the most meetings. This makes it possible to propose the most efficient meeting time.

[0063] The schedule analysis unit can analyze the individual productivity peak times of participants and propose the most effective meeting time. For example, the generation AI analyzes the individual productivity peak times of participants and proposes the most effective meeting time. For example, it selects a time slot in the morning. This makes it possible to propose the most effective meeting time.

[0064] The schedule analysis unit can use the emotion estimation function to consider the emotional states of the participants and propose the least stressful meeting time. The schedule analysis unit, for example, uses the emotion estimation function to analyze the emotional states of the participants and propose the least stressful meeting time. For example, it selects a time period that is least stressful. This makes it possible to propose the least stressful meeting time.

[0065] The schedule analysis unit can propose balanced meeting times by taking into account the participants' home and personal schedules. For example, the generation AI analyzes the participants' home and personal schedules and proposes balanced meeting times. For example, it selects time slots that take into account home circumstances. This makes it possible to propose balanced meeting times by taking into account the participants' home and personal schedules.

[0066] The schedule analysis unit can analyze the schedules of different projects and propose meeting times with the least overlap. For example, the generation AI can analyze the schedules of different projects and propose meeting times with the least overlap. For example, it can select a time slot that does not overlap with meetings of other projects. This allows it to consider the schedules of different projects and propose meeting times with the least overlap.

[0067] The schedule analysis unit uses the emotion estimation function to monitor the emotional states of participants in real time and can propose schedule changes at appropriate times during the meeting. The schedule analysis unit, for example, uses the emotion estimation function to monitor the emotional states of participants in real time and can propose schedule changes at appropriate times. For example, the schedule can be changed when stress increases. This makes it possible to propose schedule changes at appropriate times during the meeting.

[0068] The minutes-taking unit can convert statements made during meetings into text in real time and automatically highlight important points. For example, the minutes-taking unit uses a generation AI to convert statements made during meetings into text in real time and automatically highlight important points. For example, it highlights the main points of discussion and decisions. This allows statements made during meetings to be converted into text in real time and automatically highlight important points.

[0069] The minutes-taking unit can use the emotion estimation function to analyze the emotional tone of comments made during a meeting and add emotional nuances to the minutes. The minutes-taking unit can, for example, use the emotion estimation function to analyze the emotional tone of comments made during a meeting and add emotional nuances to the minutes. For example, the emotional state of the speaker can be recorded. This allows the emotional tone of comments made during a meeting to be analyzed and emotional nuances to be added to the minutes.

[0070] The minutes-taking unit can automatically translate minutes into different languages, making them easier to share with international teams. For example, the minutes-taking unit can use a generation AI to automatically translate minutes into different languages, making them easier to share with international teams. For example, it can translate into English, Japanese, Chinese, etc. This allows minutes to be automatically translated into different languages, making them easier to share with international teams.

[0071] The minutes-taking unit can convert minutes into visual notes or mind maps to make them easier to understand visually. For example, the minutes-taking unit uses a generation AI to convert minutes into visual notes to make them easier to understand visually. For example, important points are shown with diagrams or icons. This allows the minutes to be converted into visual notes or mind maps to make them easier to understand visually.

[0072] The minutes-taking unit can use the emotion estimation function to collect participants' emotional reactions to the minutes and improve the content of the minutes. The minutes-taking unit can, for example, use the emotion estimation function to collect participants' emotional reactions to the minutes and improve the content of the minutes. For example, it can emphasize parts with many positive reactions. This allows participants' emotional reactions to the minutes to be collected and the content of the minutes to be improved.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The meeting assistance system can also monitor the progress of the meeting in real time and automatically change the agenda if the discussion stalls. For example, it can suggest moving on to the next topic if the discussion hits an impasse. It can also analyze how frequently participants speak and encourage others to speak if a particular participant is speaking too much. This helps the meeting proceed smoothly and provides everyone with an equal opportunity to express their opinions.

[0075] The meeting assistance system can also be equipped with a function to automatically summarize the contents of a meeting, extracting important points and providing them to participants. For example, after the meeting, a summary report can be generated and sent to participants by email. It can also display summaries in real time during the meeting to support the progress of the discussion. This makes it easier for participants to grasp the overall picture of the meeting and ensures that they do not miss any important points.

[0076] The meeting assistance system can also analyze comments made during meetings and automatically search for and provide relevant materials and information based on the content of those comments. For example, when a specific technology or topic is discussed, related papers and articles can be displayed in real time. It can also automatically retrieve past meeting records and related project materials. This allows participants to quickly obtain the information they need and deepen their discussions.

[0077] The meeting assistance system can also be equipped with a function to visualize the progress of the meeting, displaying the timeline and progress in real time. For example, it can display the progress of each agenda item in graphs and charts to support time management. It can also issue an alert if the discussion does not reach a conclusion within the scheduled time. This allows for efficient management of the meeting progress and effective use of time.

[0078] The meeting assistance system can also collect feedback from participants after the meeting and provide a function to suggest improvements for the next meeting. For example, it can collect feedback in the form of a questionnaire and analyze participant satisfaction and areas for improvement. It can also adjust the agenda and progress of the next meeting based on the feedback. This allows for continuous improvement of the quality of meetings.

[0079] The meeting assistance system can also use its emotion estimation function to analyze participants' emotional states and suggest appropriate break times as the meeting progresses. For example, it can suggest breaks when participants' stress levels rise. It can also suggest refreshing activities based on participants' emotional states. This helps participants maintain their concentration and maximize the effectiveness of the meeting.

[0080] The meeting assistance system can also use emotion estimation to analyze participants' emotional states and make suggestions to adjust the tone of speech during the meeting. For example, if a discussion becomes heated, it can encourage a calmer tone. It can also provide a neutral perspective if there are many emotional comments. This can promote constructive discussion and avoid emotional conflicts.

[0081] The meeting assistance system can also use emotion estimation to analyze participants' emotional states and provide emotional feedback after the meeting. For example, it can display a graph of changes in emotions during the meeting and encourage participants to self-evaluate. It can also suggest improvements for the next meeting based on the participants' emotional responses. This allows participants to objectively understand their own emotional state and prepare for the next meeting.

[0082] The meeting assistance system can also use emotion estimation to analyze participants' emotional states and display the emotional tone of comments made during a meeting in real time. For example, it can color-code the speaker's emotional state to notify other participants of changes in emotion. It can also make suggestions to adjust the direction of the discussion based on the emotional tone. This allows participants to understand the emotional state of other participants and communicate more effectively.

[0083] The meeting assistance system can also use emotion estimation to analyze participants' emotional states and provide emotional feedback after the meeting. For example, it can display a graph of changes in emotions during the meeting and encourage participants to self-evaluate. It can also suggest improvements for the next meeting based on the participants' emotional responses. This allows participants to objectively understand their own emotional state and prepare for the next meeting.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The meeting scheduling module recommends participants based on the purpose of the meeting. For example, if it's a project status meeting, it will recommend the project manager and key personnel. The meeting scheduling module can also select participants based on prompts that include the purpose and topic of the meeting. Step 2: The participant recommendation unit analyzes the schedules of the participants recommended by the conference planning unit. For example, it compares the schedules of the participants and proposes candidate dates and times when all participants can attend. Step 3: The schedule analysis unit automatically generates candidate dates and times from the schedule analyzed by the participant recommendation unit. For example, it suggests a specific date and time such as "next Tuesday from 2:00 to 3:00 PM." Step 4: The minutes-taking section converts the audio data from the meeting into text and creates minutes. For example, it converts statements made during the meeting into text in real time and saves them as minutes.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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 meeting planning unit that recommends participants based on the purpose of the meeting; an attendee recommendation unit that analyzes schedules of attendees recommended by the conference setting unit; a schedule analysis unit that automatically generates candidate dates and times from the schedule analyzed by the participant recommendation unit; A minutes creation unit converts voice data during a meeting into text and creates minutes of the meeting. A system characterized by:

2. The conference setting unit Automatically generate necessary materials and presentations according to the purpose of the meeting and distribute them to the participants in advance.

2. The system of claim 1.

3. The participant recommendation unit Analyzing the expertise and skill sets of the participants and recommending the experts best suited to the purpose of the meeting 2. The system of claim 1.

4. The schedule analysis unit Analyze the participants' individual peak productivity times and suggest the most effective meeting times 2. The system of claim 1.

5. The minutes-taking department Analyzes what is said during meetings and automatically extracts action items and follow-up tasks 2. The system of claim 1.

6. The conference setting unit Consider the emotional state of the participants and suggest the most relaxing meeting environment (location, time) 2. The system of claim 1.

7. The participant recommendation unit Considering the emotional state of the participants, recommending participants who will have the most positive impact 2. The system of claim 1.

8. The schedule analysis unit Consider the emotional state of the participants and suggest the least stressful meeting time 2. The system of claim 1.

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