System

Abstract

An object of the system according to the embodiment is to efficiently perform a process from schedule adjustment of a meeting to progress, creation of minutes, and management of action items.SOLUTION: A system includes a reception part, a schedule adjustment part, a facilitation part, a minutes creation part, and a progress management part. The reception unit receives information for adjusting a schedule of a conference. The schedule adjusting section checks the calendar of the participant based on the information received by the receiving section, and proposes an appropriate date and time of the meeting. The facilitation section proceeds with the meeting based on the date and time proposed by the schedule adjustment section. The minutes creating section converts the contents of the conference progressed by the facilitation section into text, and extracts important points and action items. The progress managing unit is configured to manage a progress status of the action item extracted by the minutes creating unit and transmit a reminder.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, scheduling and conducting meetings, creating minutes, and managing action items required time and effort, making it difficult to carry out these tasks efficiently.

[0005] The system according to the embodiment aims to efficiently carry out tasks from scheduling meetings to proceedings, creating minutes, and managing action items. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a schedule adjustment unit, a facilitation unit, a minutes-taking unit, and a progress management unit. The reception unit receives information for adjusting the meeting schedule. The schedule adjustment unit checks the participants' calendars based on the information received by the reception unit and proposes an appropriate date and time for the meeting. The facilitation unit conducts the meeting based on the date and time proposed by the schedule adjustment unit. The minutes-taking unit converts the contents of the meeting conducted by the facilitation unit into text and extracts important points and action items. The progress management unit manages the progress of the action items extracted by the minutes-taking unit and sends reminders. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform tasks from scheduling meetings to proceedings, creating minutes, and managing action items. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A conference management system according to an embodiment of the present invention automates meeting scheduling, meeting facilitation, and post-meeting agenda and action item management. The conference management system allows users to input information for scheduling meetings, and AI analyzes the information to suggest optimal meeting dates and times. The AI ​​automatically checks participants' calendars to find dates and times when all participants are available. When the meeting begins, the AI ​​facilitates the meeting, manages the progress of the agenda, and adjusts time allocation. When the meeting ends, the AI ​​automatically creates minutes, transcribes what was said during the meeting into text, and extracts important points and action items. Furthermore, the AI ​​manages the progress of action items and sends reminders when deadlines approach. For example, a conference management system allows users to input information for scheduling meetings. For example, the conference management system checks participants' calendars in real time and suggests optimal dates and times. When the meeting begins, the conference management system monitors the progress of each agenda item based on pre-set agenda items in real time and instructs participants to move on to the next agenda item as necessary. When the meeting ends, the conference management system converts what was said during the meeting into text and extracts important points and action items. This allows the conference management system to automate meeting schedule adjustment, meeting facilitation, and post-meeting management of agenda items and action items, reducing the burden on users. This allows the conference management system to automate meeting schedule adjustment, meeting facilitation, and post-meeting management of agenda items and action items, reducing the burden on users. For example, meeting schedule adjustment is made quickly and accurately, meetings proceed smoothly, and post-meeting follow-up is made easy.

[0029] A conference management system according to an embodiment includes a reception unit, a schedule adjustment unit, a facilitation unit, a minutes creation unit, and a progress management unit. The reception unit receives information for adjusting a conference schedule. For example, the reception unit receives information such as the date and time of the conference, participants, and agenda entered by a user. The reception unit can also use AI to analyze the user's input and automatically complete the necessary information. The schedule adjustment unit checks the participants' calendars based on the information received by the reception unit and proposes the optimal conference date and time. For example, the schedule adjustment unit checks the participants' calendars in real time to find a date and time when all participants can attend. The schedule adjustment unit can also use AI to analyze the participants' calendar information and propose the optimal date and time. The facilitation unit proceeds with the conference based on the date and time proposed by the schedule adjustment unit. For example, the facilitation unit monitors the progress of each agenda item based on pre-set agenda items in real time and instructs participants to move on to the next agenda item as necessary. The facilitation unit can also use AI to manage the progress of agenda items and adjust time allocation. The minutes-taking unit converts the contents of the meeting conducted by the facilitation unit into text and extracts important points and action items. For example, the minutes-taking unit converts statements made during the meeting into text using voice recognition technology and extracts important points and action items. The minutes-taking unit can also use AI to analyze the statements and automatically extract important points and action items. The progress management unit manages the progress of the action items extracted by the minutes-taking unit and sends reminders. For example, the progress management unit monitors the progress of action items in real time and sends reminders when deadlines approach. The progress management unit can also use AI to analyze the progress of action items and automatically send necessary reminders. As a result, the conference management system according to the embodiment automates meeting schedule adjustment, meeting facilitation, and post-meeting agenda and action item management, thereby reducing the burden on users.

[0030] The schedule adjustment unit can check the participants' calendars in real time and propose an appropriate date and time. "Real time" means, for example, that the calendar information is updated immediately whenever it is updated. The schedule adjustment unit can, for example, check the participants' calendars in real time and find a date and time when everyone can attend. The schedule adjustment unit can also use AI to analyze the participants' calendar information and propose the optimal date and time. For example, the schedule adjustment unit can obtain the participants' calendar information in real time and propose the optimal meeting date and time based on everyone's free time. The schedule adjustment unit can also propose the optimal date and time by taking into account conditions such as the importance of the meeting and the time of day. This allows the optimal date and time to be quickly proposed by checking the calendars in real time.

[0031] The facilitation department monitors the progress of each agenda item in real time based on pre-set agenda items and can instruct the meeting to move on to the next agenda item as necessary. Progress refers to, for example, the degree of completion of the agenda item and the passage of time. The facilitation department monitors the progress of each agenda item in real time based on pre-set agenda items. The facilitation department can also use AI to analyze the progress of each agenda item and instruct the meeting to move on to the next agenda item as necessary. For example, the facilitation department can monitor the progress of each agenda item in real time and determine the timing to move on to the next agenda item based on the degree of completion of the agenda item and the passage of time. The facilitation department can also adjust the priority of agenda items based on the progress of each agenda item. This allows the progress of agenda items to be monitored in real time, enabling efficient meeting conduct.

[0032] The minutes-taking department can convert what is said during a meeting into text and extract important points and action items. Text conversion refers to, for example, converting speech into text using speech recognition technology. The minutes-taking department can also use AI to analyze what is said and automatically extract important points and action items. For example, the minutes-taking department can extract important points based on the frequency of comments and the number of times keywords appear. The minutes-taking department can also extract action items based on information such as task content, deadlines, and responsible persons. This makes it possible to automatically convert what is said during a meeting into text and extract important points and action items, thereby streamlining the creation of minutes.

[0033] The progress management unit manages the progress of action items and can send reminders when deadlines approach. Progress refers to, for example, the degree of task completion and the compliance status with deadlines. The progress management unit, for example, monitors the progress of action items in real time. The progress management unit can also use AI to analyze the progress of action items and automatically send necessary reminders. For example, the progress management unit can send reminders when deadlines approach based on the progress of action items. The progress management unit can also manage progress based on the degree of task completion and the compliance status with deadlines. In this way, task progress management is made more efficient by managing the progress of action items and sending reminders when deadlines approach.

[0034] The reception unit can analyze the user's past conference history and select an appropriate reception method. Past conference history refers to information such as the date and time of the conference, participants, and agenda. The reception unit, for example, analyzes the user's past conference history and selects the optimal reception method. The reception unit can also use AI to analyze the past conference history and suggest the optimal reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past conference history. Furthermore, the reception unit can analyze the user's past conference history and select the most efficient reception method. In this way, the optimal reception method can be provided to the user by analyzing the past conference history.

[0035] The reception unit can filter the meeting information based on the user's current project and areas of interest when receiving the meeting information. The current project, for example, refers to a project that the user is currently working on. The areas of interest, for example, refer to themes or topics in which the user is interested. For example, the reception unit can preferentially receive meeting information related to the user's current project when receiving the meeting information. The reception unit can also filter and receive highly relevant meeting information based on the user's areas of interest. For example, the reception unit can receive optimal meeting information taking into account the progress of the user's project. The reception unit can also use AI to analyze the user's current project and areas of interest and automatically filter highly relevant meeting information. This makes it possible to provide highly relevant information by filtering meeting information based on the user's current project and areas of interest.

[0036] When accepting conference information, the reception unit can select an appropriate reception means according to the user's input method. Examples of input methods include voice input, text input, and image input. For example, when the user uses voice input, the reception unit accepts the conference information using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also accept the conference information using text analysis technology. For example, when the user uses image input, the reception unit accepts the conference information using image recognition technology. Furthermore, the reception unit can use AI to select the optimal reception means according to the user's input method. For example, the reception unit analyzes the user's input method and automatically selects the optimal reception means. This improves reception efficiency by selecting the optimal reception means according to the user's input method.

[0037] When adjusting a schedule, the schedule adjustment unit can adjust the level of detail of the proposal based on the importance of the participants. Importance refers to, for example, the participants' job titles and frequency of comments. For example, the schedule adjustment unit provides a detailed schedule proposal when there are many important participants. The schedule adjustment unit can also provide a concise schedule proposal when there are many participants with low importance. For example, the schedule adjustment unit adjusts the level of detail of the schedule proposal according to the importance of the participants. The schedule adjustment unit can also use AI to analyze the importance of the participants and automatically provide an optimal schedule proposal. This enables efficient schedule adjustment by adjusting the level of detail of the proposal according to the importance of the participants.

[0038] When adjusting a schedule, the schedule adjustment unit can apply different adjustment algorithms depending on the category of the meeting. A category refers to, for example, the purpose and content of the meeting, the roles of the participants, etc. For example, the schedule adjustment unit applies a detailed adjustment algorithm to an important meeting. The schedule adjustment unit can also apply a simple adjustment algorithm to a casual meeting. For example, the schedule adjustment unit applies the optimal adjustment algorithm depending on the category of the meeting. The schedule adjustment unit can also use AI to analyze the category of the meeting and automatically apply the optimal adjustment algorithm. This enables efficient schedule adjustment by applying the optimal adjustment algorithm depending on the category of the meeting.

[0039] When adjusting a schedule, the schedule adjustment unit can improve the accuracy of proposals by referring to the user's past schedule results. Past schedule results refer to, for example, meeting attendance rates and schedule change history. The schedule adjustment unit, for example, analyzes the user's past schedule results and provides an optimal schedule proposal. The schedule adjustment unit can also use AI to analyze the past schedule results and improve the accuracy of proposals. For example, the schedule adjustment unit provides an optimal schedule proposal based on the user's past schedule results. The schedule adjustment unit can also improve the accuracy of proposals by referring to the past schedule results. In this way, the accuracy of proposals is improved by referring to the past schedule results.

[0040] During facilitation, the facilitation department can adjust the level of detail in the proceedings based on the importance of the agenda item. Importance refers to, for example, the priority or impact of the agenda item. For example, the facilitation department provides a detailed proceeding method for important agenda items. The facilitation department can also provide a concise proceeding method for less important agenda items. For example, the facilitation department adjusts the level of detail in the proceedings based on the importance of the agenda item. The facilitation department can also use AI to analyze the importance of the agenda item and automatically provide the optimal proceeding method. This allows for efficient meeting progress by adjusting the level of detail in the proceedings based on the importance of the agenda item.

[0041] During facilitation, the facilitation department can apply different progress algorithms depending on the category of the agenda. A category refers to, for example, the type, content, or purpose of the agenda. For example, the facilitation department applies a detailed progress algorithm for important agenda items. The facilitation department can also apply a simple progress algorithm for casual agenda items. For example, the facilitation department applies the optimal progress algorithm depending on the agenda category. The facilitation department can also use AI to analyze the agenda category and automatically apply the optimal progress algorithm. This enables efficient meeting progress by applying the optimal progress algorithm depending on the agenda category.

[0042] During facilitation, the facilitation unit can improve the accuracy of the progress by referring to the user's past facilitation results. Past facilitation results refer to, for example, the progress of the meeting and feedback from participants. The facilitation unit, for example, analyzes the user's past facilitation results and provides the optimal method of proceeding. The facilitation unit can also use AI to analyze past facilitation results and improve the accuracy of the progress. For example, the facilitation unit provides the optimal method of proceeding based on the user's past facilitation results. The facilitation unit can also improve the accuracy of the progress by referring to the past facilitation results. In this way, the accuracy of the progress is improved by referring to the past facilitation results.

[0043] When creating minutes, the minutes-taking department can adjust the level of detail in the minutes based on the importance of the remarks. Importance refers to, for example, the frequency of remarks and the importance of the content. For example, the minutes-taking department provides detailed minutes in the case of important remarks. The minutes-taking department can also provide concise minutes in the case of less important remarks. For example, the minutes-taking department adjusts the level of detail in the minutes based on the importance of the remarks. The minutes-taking department can also use AI to analyze the importance of remarks and automatically provide optimal minutes. This makes it possible to create minutes efficiently by adjusting the level of detail in the minutes based on the importance of the remarks.

[0044] When creating minutes, the minutes-taking unit can apply different minutes-taking algorithms depending on the category of remarks. A category refers to, for example, the type, content, or purpose of a remark. For example, the minutes-taking unit applies a detailed minutes-taking algorithm to important remarks. The minutes-taking unit can also apply a concise minutes-taking algorithm to casual remarks. For example, the minutes-taking unit applies the optimal minutes-taking algorithm depending on the category of remarks. The minutes-taking unit can also use AI to analyze the category of remarks and automatically apply the optimal minutes-taking algorithm. This enables efficient minutes creation by applying the optimal minutes-taking algorithm depending on the category of remarks.

[0045] When creating minutes, the minutes-taking unit can improve the accuracy of the minutes by referring to the user's past minutes-taking results. Past minutes-taking results refer to, for example, the accuracy of the minutes and feedback from participants. The minutes-taking unit, for example, analyzes the user's past minutes-taking results and provides optimal minutes. The minutes-taking unit can also use AI to analyze past minutes-taking results and improve the accuracy of the minutes. For example, the minutes-taking unit provides optimal minutes based on the user's past minutes-taking results. The minutes-taking unit can also improve the accuracy of the minutes by referring to the past minutes-taking results. In this way, the accuracy of the minutes is improved by referring to the past minutes-taking results.

[0046] The progress management unit can adjust the level of detail of management based on the importance of the action item during progress management. Importance refers to, for example, the priority or impact of the task. For example, the progress management unit provides a detailed progress management method for an important action item. The progress management unit can also provide a simple progress management method for an action item with low importance. For example, the progress management unit adjusts the level of detail of management according to the importance of the action item. The progress management unit can also use AI to analyze the importance of the action item and automatically provide the optimal progress management method. This enables efficient progress management by adjusting the level of detail of management according to the importance of the action item.

[0047] When managing progress, the progress management unit can apply different management algorithms depending on the category of the action item. A category refers to, for example, the type, content, or purpose of a task. For example, the progress management unit applies a detailed management algorithm to an important action item. The progress management unit can also apply a simple management algorithm to a casual action item. For example, the progress management unit applies the optimal management algorithm depending on the category of the action item. The progress management unit can also use AI to analyze the category of the action item and automatically apply the optimal management algorithm. This enables efficient progress management by applying the optimal management algorithm depending on the category of the action item.

[0048] The progress management unit can improve the accuracy of progress management by referring to the user's past progress management results. Past progress management results refer to, for example, the degree of task completion and deadline compliance. The progress management unit, for example, analyzes the user's past progress management results and provides an optimal management method. The progress management unit can also use AI to analyze past progress management results and improve the accuracy of management. For example, the progress management unit provides an optimal management method based on the user's past progress management results. The progress management unit can also improve the accuracy of management by referring to the past progress management results. In this way, the accuracy of management is improved by referring to the past progress management results.

[0049] During progress management, the progress management department can determine management priorities based on the submission dates of action items. The submission dates refer to, for example, the deadlines for action item submissions and the delays in submission. For example, the progress management department prioritizes action items that are submitted early. The progress management department can also postpone action items that are submitted late. For example, the progress management department determines management priorities based on the submission dates. The progress management department can also use AI to analyze the submission dates of action items and automatically determine optimal management priorities. This enables efficient progress management by determining management priorities based on the submission dates of action items.

[0050] During progress management, the progress management unit can adjust the management order based on the relevance of action items. Relevance refers to, for example, task dependencies or related projects. For example, the progress management unit prioritizes management of highly relevant action items. The progress management unit can also postpone less relevant action items. For example, the progress management unit adjusts the management order based on the relevance of action items. The progress management unit can also use AI to analyze the relevance of action items and automatically determine the optimal management order. This enables efficient progress management by adjusting the management order based on the relevance of action items.

[0051] The progress management unit can adjust the use of technical terminology during progress management according to the user's level of expertise. The level of expertise refers to, for example, the user's work experience or past projects. For example, if the user's level of expertise is high, the progress management unit can provide a progress management method that makes heavy use of technical terminology. Furthermore, if the user's level of expertise is low, the progress management unit can also provide a progress management method that avoids technical terminology. For example, the progress management unit can adjust the use of optimal technical terminology according to the user's level of expertise. Furthermore, the progress management unit can use AI to analyze the user's level of expertise and automatically provide the optimal progress management method. This makes progress management easier to understand by adjusting the use of technical terminology according to the user's level of expertise.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The conference management system may further include an expertise evaluation unit that evaluates the expertise of participants. The expertise evaluation unit may, for example, analyze participants' past comments and project history to evaluate the expertise level of each participant. For example, the expertise evaluation unit may identify the participant who is most appropriate for a particular topic and encourage that participant to speak. The expertise evaluation unit may also provide supplementary information to participants who lack expertise on the topic. This may improve the quality of the conference.

[0054] The conference management system can also include an opinion aggregation unit that aggregates the opinions of participants. The opinion aggregation unit can, for example, aggregate the opinions expressed by participants during the conference in real time and visualize common and differing opinions. For example, the opinion aggregation unit can cluster the opinions of participants and extract common themes. In addition, if there are many differing opinions, it can also clarify the points of conflict. This allows conference discussions to proceed more efficiently and makes it easier to reach a consensus.

[0055] The conference management system may further include a comment evaluation unit that evaluates the content of participants' comments. The comment evaluation unit may, for example, analyze the content of participants' comments and evaluate how much those comments contribute to the agenda. For example, the comment evaluation unit may perform evaluations based on the content and frequency of comments and the reactions of other participants. The comment evaluation unit may also provide feedback based on the evaluation results to improve the quality of comments. This may improve the quality of the conference.

[0056] The conference management system may further include a summarizing unit that summarizes what participants have said. The summarizing unit may, for example, summarize what has been said during a conference in real time and extract important points. For example, the summarizing unit may summarize based on keywords and frequency of comments. The summarizing unit may also provide the summary results to participants after the conference has ended. This allows participants to efficiently understand the content of the conference.

[0057] The conference management system may further include a translation unit that translates the content of participants' remarks. For example, the translation unit may translate the remarks of participants who speak different languages ​​in real time and provide the translated content to other participants. For example, the translation unit may combine speech recognition technology and translation technology to instantly translate the remarks. The translation unit may also provide the translation results in text or audio. This facilitates communication between participants who speak different languages.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives information for arranging the meeting schedule. For example, it receives information such as the date and time of the meeting, participants, and agenda entered by the user. The reception unit can also use AI to analyze the user's input and automatically complete the necessary information. Step 2: The schedule adjustment unit checks the participants' calendars based on the information received by the reception unit and proposes the optimal date and time for the meeting. For example, it can check the participants' calendars in real time to find a date and time when everyone can attend. It can also use AI to analyze the participants' calendar information and propose the optimal date and time. Step 3: The Facilitation Department proceeds with the meeting based on the date and time proposed by the Schedule Adjustment Department. For example, based on the pre-set agenda items, it monitors the progress of each agenda item in real time and instructs participants to move on to the next agenda item as necessary. AI can also be used to manage the progress of agenda items and adjust time allocation. Step 4: The minutes-taking department transcribes the meeting contents conducted by the facilitation department and extracts important points and action items. For example, speech recognition technology can be used to transcribe what was said during the meeting and extract important points and action items. AI can also be used to analyze what was said and automatically extract important points and action items. Step 5: The Progress Management Department manages the progress of the action items extracted by the Minutes Creation Department and sends reminders. For example, it monitors the progress of action items in real time and sends reminders when the deadline approaches. It can also use AI to analyze the progress of action items and automatically send necessary reminders.

[0060] (Example 2) A conference management system according to an embodiment of the present invention automates meeting scheduling, meeting facilitation, and post-meeting agenda and action item management. The conference management system allows users to input information for scheduling meetings, and AI analyzes the information to suggest optimal meeting dates and times. The AI ​​automatically checks participants' calendars to find dates and times when all participants are available. When the meeting begins, the AI ​​facilitates the meeting, manages the progress of the agenda, and adjusts time allocation. When the meeting ends, the AI ​​automatically creates minutes, transcribes what was said during the meeting into text, and extracts important points and action items. Furthermore, the AI ​​manages the progress of action items and sends reminders when deadlines approach. For example, a conference management system allows users to input information for scheduling meetings. For example, the conference management system checks participants' calendars in real time and suggests optimal dates and times. When the meeting begins, the conference management system monitors the progress of each agenda item based on pre-set agenda items in real time and instructs participants to move on to the next agenda item as necessary. When the meeting ends, the conference management system converts what was said during the meeting into text and extracts important points and action items. This allows the conference management system to automate meeting schedule adjustment, meeting facilitation, and post-meeting management of agenda items and action items, reducing the burden on users. This allows the conference management system to automate meeting schedule adjustment, meeting facilitation, and post-meeting management of agenda items and action items, reducing the burden on users. For example, meeting schedule adjustment is made quickly and accurately, meetings proceed smoothly, and post-meeting follow-up is made easy.

[0061] A conference management system according to an embodiment includes a reception unit, a schedule adjustment unit, a facilitation unit, a minutes creation unit, and a progress management unit. The reception unit receives information for adjusting a conference schedule. For example, the reception unit receives information such as the date and time of the conference, participants, and agenda entered by a user. The reception unit can also use AI to analyze the user's input and automatically complete the necessary information. The schedule adjustment unit checks the participants' calendars based on the information received by the reception unit and proposes the optimal conference date and time. For example, the schedule adjustment unit checks the participants' calendars in real time to find a date and time when all participants can attend. The schedule adjustment unit can also use AI to analyze the participants' calendar information and propose the optimal date and time. The facilitation unit proceeds with the conference based on the date and time proposed by the schedule adjustment unit. For example, the facilitation unit monitors the progress of each agenda item based on pre-set agenda items in real time and instructs participants to move on to the next agenda item as necessary. The facilitation unit can also use AI to manage the progress of agenda items and adjust time allocation. The minutes-taking unit converts the contents of the meeting conducted by the facilitation unit into text and extracts important points and action items. For example, the minutes-taking unit converts statements made during the meeting into text using voice recognition technology and extracts important points and action items. The minutes-taking unit can also use AI to analyze the statements and automatically extract important points and action items. The progress management unit manages the progress of the action items extracted by the minutes-taking unit and sends reminders. For example, the progress management unit monitors the progress of action items in real time and sends reminders when deadlines approach. The progress management unit can also use AI to analyze the progress of action items and automatically send necessary reminders. As a result, the conference management system according to the embodiment automates meeting schedule adjustment, meeting facilitation, and post-meeting agenda and action item management, thereby reducing the burden on users.

[0062] The schedule adjustment unit can check the participants' calendars in real time and propose an appropriate date and time. "Real time" means, for example, that the calendar information is updated immediately whenever it is updated. The schedule adjustment unit can, for example, check the participants' calendars in real time and find a date and time when everyone can attend. The schedule adjustment unit can also use AI to analyze the participants' calendar information and propose the optimal date and time. For example, the schedule adjustment unit can obtain the participants' calendar information in real time and propose the optimal meeting date and time based on everyone's free time. The schedule adjustment unit can also propose the optimal date and time by taking into account conditions such as the importance of the meeting and the time of day. This allows the optimal date and time to be quickly proposed by checking the calendars in real time.

[0063] The facilitation department monitors the progress of each agenda item in real time based on pre-set agenda items and can instruct the meeting to move on to the next agenda item as necessary. Progress refers to, for example, the degree of completion of the agenda item and the passage of time. The facilitation department monitors the progress of each agenda item in real time based on pre-set agenda items. The facilitation department can also use AI to analyze the progress of each agenda item and instruct the meeting to move on to the next agenda item as necessary. For example, the facilitation department can monitor the progress of each agenda item in real time and determine the timing to move on to the next agenda item based on the degree of completion of the agenda item and the passage of time. The facilitation department can also adjust the priority of agenda items based on the progress of each agenda item. This allows the progress of agenda items to be monitored in real time, enabling efficient meeting conduct.

[0064] The minutes-taking department can convert what is said during a meeting into text and extract important points and action items. Text conversion refers to, for example, converting speech into text using speech recognition technology. The minutes-taking department can also use AI to analyze what is said and automatically extract important points and action items. For example, the minutes-taking department can extract important points based on the frequency of comments and the number of times keywords appear. The minutes-taking department can also extract action items based on information such as task content, deadlines, and responsible persons. This makes it possible to automatically convert what is said during a meeting into text and extract important points and action items, thereby streamlining the creation of minutes.

[0065] The progress management unit manages the progress of action items and can send reminders when deadlines approach. Progress refers to, for example, the degree of task completion and the compliance status with deadlines. The progress management unit, for example, monitors the progress of action items in real time. The progress management unit can also use AI to analyze the progress of action items and automatically send necessary reminders. For example, the progress management unit can send reminders when deadlines approach based on the progress of action items. The progress management unit can also manage progress based on the degree of task completion and the compliance status with deadlines. In this way, task progress management is made more efficient by managing the progress of action items and sending reminders when deadlines approach.

[0066] The reception unit can estimate the user's emotions and adjust the timing of accepting conference information based on the estimated user emotions. Emotions refer to, for example, a psychological state estimated from the user's facial expressions and voice. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, if the reception unit is stressed, it can delay the timing of accepting the conference information to provide the user with time to relax. If the user is relaxed, it can immediately accept the conference information to ensure smooth progress. Furthermore, if the user is in a hurry, it can quickly accept the conference information to prioritize schedule adjustments. This reduces the burden on the user by adjusting the timing of accepting conference information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The reception unit can analyze the user's past conference history and select an appropriate reception method. Past conference history refers to information such as the date and time of the conference, participants, and agenda. The reception unit, for example, analyzes the user's past conference history and selects the optimal reception method. The reception unit can also use AI to analyze the past conference history and suggest the optimal reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past conference history. Furthermore, the reception unit can analyze the user's past conference history and select the most efficient reception method. In this way, the optimal reception method can be provided to the user by analyzing the past conference history.

[0068] The reception unit can filter the meeting information based on the user's current project and areas of interest when receiving the meeting information. The current project, for example, refers to a project that the user is currently working on. The areas of interest, for example, refer to themes or topics in which the user is interested. For example, the reception unit can preferentially receive meeting information related to the user's current project when receiving the meeting information. The reception unit can also filter and receive highly relevant meeting information based on the user's areas of interest. For example, the reception unit can receive optimal meeting information taking into account the progress of the user's project. The reception unit can also use AI to analyze the user's current project and areas of interest and automatically filter highly relevant meeting information. This makes it possible to provide highly relevant information by filtering meeting information based on the user's current project and areas of interest.

[0069] When accepting conference information, the reception unit can select an appropriate reception means according to the user's input method. Examples of input methods include voice input, text input, and image input. For example, when the user uses voice input, the reception unit accepts the conference information using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also accept the conference information using text analysis technology. For example, when the user uses image input, the reception unit accepts the conference information using image recognition technology. Furthermore, the reception unit can use AI to select the optimal reception means according to the user's input method. For example, the reception unit analyzes the user's input method and automatically selects the optimal reception means. This improves reception efficiency by selecting the optimal reception means according to the user's input method.

[0070] The schedule adjustment unit can estimate the user's emotions and adjust the presentation of schedule suggestions based on the estimated user emotions. Emotions refer to, for example, a psychological state estimated from the user's facial expressions or voice. For example, the schedule adjustment unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The schedule adjustment unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the schedule adjustment unit can provide detailed schedule suggestions when the user is relaxed. The schedule adjustment unit can also provide concise schedule suggestions when the user is in a hurry. Furthermore, the schedule adjustment unit can provide simple, highly visible schedule suggestions when the user is stressed. This allows the presentation of schedule suggestions to be adjusted according to the user's emotions, making them easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] When adjusting a schedule, the schedule adjustment unit can adjust the level of detail of the proposal based on the importance of the participants. Importance refers to, for example, the participants' job titles and frequency of comments. For example, the schedule adjustment unit provides a detailed schedule proposal when there are many important participants. The schedule adjustment unit can also provide a concise schedule proposal when there are many participants with low importance. For example, the schedule adjustment unit adjusts the level of detail of the schedule proposal according to the importance of the participants. The schedule adjustment unit can also use AI to analyze the importance of the participants and automatically provide an optimal schedule proposal. This enables efficient schedule adjustment by adjusting the level of detail of the proposal according to the importance of the participants.

[0072] When adjusting a schedule, the schedule adjustment unit can apply different adjustment algorithms depending on the category of the meeting. A category refers to, for example, the purpose and content of the meeting, the roles of the participants, etc. For example, the schedule adjustment unit applies a detailed adjustment algorithm to an important meeting. The schedule adjustment unit can also apply a simple adjustment algorithm to a casual meeting. For example, the schedule adjustment unit applies the optimal adjustment algorithm depending on the category of the meeting. The schedule adjustment unit can also use AI to analyze the category of the meeting and automatically apply the optimal adjustment algorithm. This enables efficient schedule adjustment by applying the optimal adjustment algorithm depending on the category of the meeting.

[0073] When adjusting a schedule, the schedule adjustment unit can improve the accuracy of proposals by referring to the user's past schedule results. Past schedule results refer to, for example, meeting attendance rates and schedule change history. The schedule adjustment unit, for example, analyzes the user's past schedule results and provides an optimal schedule proposal. The schedule adjustment unit can also use AI to analyze the past schedule results and improve the accuracy of proposals. For example, the schedule adjustment unit provides an optimal schedule proposal based on the user's past schedule results. The schedule adjustment unit can also improve the accuracy of proposals by referring to the past schedule results. In this way, the accuracy of proposals is improved by referring to the past schedule results.

[0074] The facilitation unit can estimate a user's emotions and adjust the facilitation method based on the estimated user emotions. Emotions refer to, for example, a psychological state estimated from a user's facial expressions and voice. For example, the facilitation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The facilitation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the facilitation unit can provide a relaxed method of proceeding if the user is relaxed. Furthermore, the facilitation unit can provide a fast method of proceeding if the user is in a hurry. Furthermore, the facilitation unit can provide a simple, highly visible method of proceeding if the user is stressed. This allows for more effective meeting progress by adjusting the facilitation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] During facilitation, the facilitation department can adjust the level of detail in the proceedings based on the importance of the agenda item. Importance refers to, for example, the priority or impact of the agenda item. For example, the facilitation department provides a detailed proceeding method for important agenda items. The facilitation department can also provide a concise proceeding method for less important agenda items. For example, the facilitation department adjusts the level of detail in the proceedings based on the importance of the agenda item. The facilitation department can also use AI to analyze the importance of the agenda item and automatically provide the optimal proceeding method. This allows for efficient meeting progress by adjusting the level of detail in the proceedings based on the importance of the agenda item.

[0076] During facilitation, the facilitation department can apply different progress algorithms depending on the category of the agenda. A category refers to, for example, the type, content, or purpose of the agenda. For example, the facilitation department applies a detailed progress algorithm for important agenda items. The facilitation department can also apply a simple progress algorithm for casual agenda items. For example, the facilitation department applies the optimal progress algorithm depending on the agenda category. The facilitation department can also use AI to analyze the agenda category and automatically apply the optimal progress algorithm. This enables efficient meeting progress by applying the optimal progress algorithm depending on the agenda category.

[0077] During facilitation, the facilitation unit can improve the accuracy of the progress by referring to the user's past facilitation results. Past facilitation results refer to, for example, the progress of the meeting and feedback from participants. The facilitation unit, for example, analyzes the user's past facilitation results and provides the optimal method of proceeding. The facilitation unit can also use AI to analyze past facilitation results and improve the accuracy of the progress. For example, the facilitation unit provides the optimal method of proceeding based on the user's past facilitation results. The facilitation unit can also improve the accuracy of the progress by referring to the past facilitation results. In this way, the accuracy of the progress is improved by referring to the past facilitation results.

[0078] The minutes-taking unit can estimate the user's emotions and adjust the presentation style of the minutes based on the estimated user emotions. Emotions refer to, for example, a psychological state estimated from the user's facial expressions and voice. For example, the minutes-taking unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The minutes-taking unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the minutes-taking unit can provide detailed minutes if the user is relaxed. The minutes-taking unit can also provide concise minutes if the user is in a hurry. Furthermore, the minutes-taking unit can provide simple, highly visible minutes if the user is stressed. By adjusting the presentation style of the minutes according to the user's emotions, minutes that are easy for the user to understand are provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] When creating minutes, the minutes-taking department can adjust the level of detail in the minutes based on the importance of the remarks. Importance refers to, for example, the frequency of remarks and the importance of the content. For example, the minutes-taking department provides detailed minutes in the case of important remarks. The minutes-taking department can also provide concise minutes in the case of less important remarks. For example, the minutes-taking department adjusts the level of detail in the minutes based on the importance of the remarks. The minutes-taking department can also use AI to analyze the importance of remarks and automatically provide optimal minutes. This makes it possible to create minutes efficiently by adjusting the level of detail in the minutes based on the importance of the remarks.

[0080] When creating minutes, the minutes-taking unit can apply different minutes-taking algorithms depending on the category of remarks. A category refers to, for example, the type, content, or purpose of a remark. For example, the minutes-taking unit applies a detailed minutes-taking algorithm to important remarks. The minutes-taking unit can also apply a concise minutes-taking algorithm to casual remarks. For example, the minutes-taking unit applies the optimal minutes-taking algorithm depending on the category of remarks. The minutes-taking unit can also use AI to analyze the category of remarks and automatically apply the optimal minutes-taking algorithm. This enables efficient minutes creation by applying the optimal minutes-taking algorithm depending on the category of remarks.

[0081] When creating minutes, the minutes-taking unit can improve the accuracy of the minutes by referring to the user's past minutes-taking results. Past minutes-taking results refer to, for example, the accuracy of the minutes and feedback from participants. The minutes-taking unit, for example, analyzes the user's past minutes-taking results and provides optimal minutes. The minutes-taking unit can also use AI to analyze past minutes-taking results and improve the accuracy of the minutes. For example, the minutes-taking unit provides optimal minutes based on the user's past minutes-taking results. The minutes-taking unit can also improve the accuracy of the minutes by referring to the past minutes-taking results. In this way, the accuracy of the minutes is improved by referring to the past minutes-taking results.

[0082] The progress management unit can estimate the user's emotions and adjust the progress management method based on the estimated user emotions. Emotions refer to, for example, a psychological state estimated from the user's facial expressions and voice. For example, the progress management unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The progress management unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the progress management unit can provide a detailed progress management method when the user is relaxed. The progress management unit can also provide a concise progress management method when the user is in a hurry. Furthermore, the progress management unit can provide a simple, highly visible progress management method when the user is stressed. This enables more effective progress management by adjusting the progress management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The progress management unit can adjust the level of detail of management based on the importance of the action item during progress management. Importance refers to, for example, the priority or impact of the task. For example, the progress management unit provides a detailed progress management method for an important action item. The progress management unit can also provide a simple progress management method for an action item with low importance. For example, the progress management unit adjusts the level of detail of management according to the importance of the action item. The progress management unit can also use AI to analyze the importance of the action item and automatically provide the optimal progress management method. This enables efficient progress management by adjusting the level of detail of management according to the importance of the action item.

[0084] When managing progress, the progress management unit can apply different management algorithms depending on the category of the action item. A category refers to, for example, the type, content, or purpose of a task. For example, the progress management unit applies a detailed management algorithm to an important action item. The progress management unit can also apply a simple management algorithm to a casual action item. For example, the progress management unit applies the optimal management algorithm depending on the category of the action item. The progress management unit can also use AI to analyze the category of the action item and automatically apply the optimal management algorithm. This enables efficient progress management by applying the optimal management algorithm depending on the category of the action item.

[0085] The progress management unit can improve the accuracy of progress management by referring to the user's past progress management results. Past progress management results refer to, for example, the degree of task completion and deadline compliance. The progress management unit, for example, analyzes the user's past progress management results and provides an optimal management method. The progress management unit can also use AI to analyze past progress management results and improve the accuracy of management. For example, the progress management unit provides an optimal management method based on the user's past progress management results. The progress management unit can also improve the accuracy of management by referring to the past progress management results. In this way, the accuracy of management is improved by referring to the past progress management results.

[0086] The progress management unit can estimate the user's emotions and determine the priority of progress management based on the estimated user emotions. Emotions refer to, for example, a psychological state estimated from the user's facial expressions and voice. For example, the progress management unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The progress management unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, if the user is relaxed, the progress management unit can prioritize action items with high importance. If the user is in a hurry, the progress management unit can prioritize action items with high urgency. Furthermore, if the user is stressed, the progress management unit can postpone action items with low importance. This enables more effective progress management by determining the priority of progress management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] During progress management, the progress management department can determine management priorities based on the submission dates of action items. The submission dates refer to, for example, the deadlines for action item submissions and the delays in submission. For example, the progress management department prioritizes action items that are submitted early. The progress management department can also postpone action items that are submitted late. For example, the progress management department determines management priorities based on the submission dates. The progress management department can also use AI to analyze the submission dates of action items and automatically determine optimal management priorities. This enables efficient progress management by determining management priorities based on the submission dates of action items.

[0088] During progress management, the progress management unit can adjust the management order based on the relevance of action items. Relevance refers to, for example, task dependencies or related projects. For example, the progress management unit prioritizes management of highly relevant action items. The progress management unit can also postpone less relevant action items. For example, the progress management unit adjusts the management order based on the relevance of action items. The progress management unit can also use AI to analyze the relevance of action items and automatically determine the optimal management order. This enables efficient progress management by adjusting the management order based on the relevance of action items.

[0089] The progress management unit can adjust the use of technical terminology during progress management according to the user's level of expertise. The level of expertise refers to, for example, the user's work experience or past projects. For example, if the user's level of expertise is high, the progress management unit can provide a progress management method that makes heavy use of technical terminology. Furthermore, if the user's level of expertise is low, the progress management unit can also provide a progress management method that avoids technical terminology. For example, the progress management unit can adjust the use of optimal technical terminology according to the user's level of expertise. Furthermore, the progress management unit can use AI to analyze the user's level of expertise and automatically provide the optimal progress management method. This makes progress management easier to understand by adjusting the use of technical terminology according to the user's level of expertise. === Hard Collateral 1-1 === For example, each of the multiple elements including the reception unit, schedule adjustment unit, facilitation unit, minutes creation unit, and progress management unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The schedule adjustment unit is realized by the specific processing unit 290 of the data processing device 12. The facilitation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The minutes creation unit is realized by the specific processing unit 290 of the data processing device 12. The progress management unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === For example, each of a plurality of elements including a reception unit, a schedule adjustment unit, a facilitation unit, a minutes creation unit, and a progress management unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The schedule adjustment unit is realized by the specific processing unit 290 of the data processing device 12. The facilitation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The minutes creation unit is realized by the specific processing unit 290 of the data processing device 12. The progress management unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === For example, each of the multiple elements including the reception unit, schedule adjustment unit, facilitation unit, minutes creation unit, and progress management unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The schedule adjustment unit is realized by the specific processing unit 290 of the data processing device 12. The facilitation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The minutes creation unit is realized by the specific processing unit 290 of the data processing device 12. The progress management unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === For example, each of a plurality of elements including a reception unit, a schedule adjustment unit, a facilitation unit, a minutes creation unit, and a progress management unit is realized by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The schedule adjustment unit is realized by the specific processing unit 290 of the data processing device 12. The facilitation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The minutes creation unit is realized by the specific processing unit 290 of the data processing device 12. The progress management unit is realized by the specific processing unit 290 of the data processing device 12.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The conference management system can also include a health management unit that monitors the health of participants. The health management unit can, for example, monitor participants' heart rates and stress levels in real time and adjust the progress of the conference based on their health status. For example, if a participant's heart rate is high, the conference can be temporarily slowed down. Also, if a participant's stress level is high, it can suggest a break to help them relax. This makes it possible to proceed with the conference while taking into account the participants' health status, providing a more comfortable conference environment.

[0092] The conference management system may further include an expertise evaluation unit that evaluates the expertise of participants. The expertise evaluation unit may, for example, analyze participants' past comments and project history to evaluate the expertise level of each participant. For example, the expertise evaluation unit may identify the participant who is most appropriate for a particular topic and encourage that participant to speak. The expertise evaluation unit may also provide supplementary information to participants who lack expertise on the topic. This may improve the quality of the conference.

[0093] The conference management system can also include an opinion aggregation unit that aggregates the opinions of participants. The opinion aggregation unit can, for example, aggregate the opinions expressed by participants during the conference in real time and visualize common and differing opinions. For example, the opinion aggregation unit can cluster the opinions of participants and extract common themes. In addition, if there are many differing opinions, it can also clarify the points of conflict. This allows conference discussions to proceed more efficiently and makes it easier to reach a consensus.

[0094] The conference management system may further include a comment evaluation unit that evaluates the content of participants' comments. The comment evaluation unit may, for example, analyze the content of participants' comments and evaluate how much those comments contribute to the agenda. For example, the comment evaluation unit may perform evaluations based on the content and frequency of comments and the reactions of other participants. The comment evaluation unit may also provide feedback based on the evaluation results to improve the quality of comments. This may improve the quality of the conference.

[0095] The conference management system may further include an emotion adjustment unit that estimates the emotions of the participants and adjusts the progress of the conference based on the estimated emotions. The emotion adjustment unit can estimate the emotions from the participants' facial expressions and voices, for example, and adjust the progress of the conference according to the emotions. For example, if a participant is feeling stressed, the pace of the conference can be temporarily slowed down. Also, if a participant is relaxed, the conference can proceed smoothly. This allows the conference to proceed flexibly according to the emotions of the participants.

[0096] The conference management system may further include a summarizing unit that summarizes what participants have said. The summarizing unit may, for example, summarize what has been said during a conference in real time and extract important points. For example, the summarizing unit may summarize based on keywords and frequency of comments. The summarizing unit may also provide the summary results to participants after the conference has ended. This allows participants to efficiently understand the content of the conference.

[0097] The conference management system may further include an emotion expression unit that estimates the emotions of the participants and adjusts the presentation method of the minutes based on the estimated emotions. The emotion expression unit can estimate the emotions from the participants' facial expressions and voices, for example, and adjust the presentation method of the minutes according to the emotions. For example, if the participants are relaxed, detailed minutes can be provided. On the other hand, if the participants are in a hurry, brief minutes can be provided. This makes it possible to flexibly create minutes according to the emotions of the participants.

[0098] The conference management system may further include a translation unit that translates the content of participants' remarks. For example, the translation unit may translate the remarks of participants who speak different languages ​​in real time and provide the translated content to other participants. For example, the translation unit may combine speech recognition technology and translation technology to instantly translate the remarks. The translation unit may also provide the translation results in text or audio. This facilitates communication between participants who speak different languages.

[0099] The conference management system may further include an emotion progress unit that estimates the emotions of the participants and adjusts the progress management method based on the estimated emotions. The emotion progress unit can estimate the emotions from the participants' facial expressions and voices, for example, and adjust the progress management method according to the emotions. For example, if a participant is relaxed, a detailed progress management method can be provided. Alternatively, if a participant is in a hurry, a simple progress management method can be provided. This enables flexible progress management according to the emotions of the participants.

[0100] The conference management system may further include a tone evaluation unit that analyzes the content of participants' remarks and evaluates the tone and emotion of the remarks. The tone evaluation unit may, for example, analyze the content of participants' remarks and the tone of their voices and evaluate whether the remarks are positive or negative. For example, the tone evaluation unit may make an evaluation based on the content of the remarks and the intonation of their voices. The tone evaluation unit may also adjust the way the conference is conducted based on the evaluation results. This makes it possible to provide feedback to improve the atmosphere of the conference.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives information for arranging the meeting schedule. For example, it receives information such as the date and time of the meeting, participants, and agenda entered by the user. The reception unit can also use AI to analyze the user's input and automatically complete the necessary information. Step 2: The schedule adjustment unit checks the participants' calendars based on the information received by the reception unit and proposes the optimal date and time for the meeting. For example, it can check the participants' calendars in real time to find a date and time when everyone can attend. It can also use AI to analyze the participants' calendar information and propose the optimal date and time. Step 3: The Facilitation Department proceeds with the meeting based on the date and time proposed by the Schedule Adjustment Department. For example, based on the pre-set agenda items, it monitors the progress of each agenda item in real time and instructs participants to move on to the next agenda item as necessary. AI can also be used to manage the progress of agenda items and adjust time allocation. Step 4: The minutes-taking department transcribes the meeting contents conducted by the facilitation department and extracts important points and action items. For example, speech recognition technology can be used to transcribe what was said during the meeting and extract important points and action items. AI can also be used to analyze what was said and automatically extract important points and action items. Step 5: The Progress Management Department manages the progress of the action items extracted by the Minutes Creation Department and sends reminders. For example, it monitors the progress of action items in real time and sends reminders when the deadline approaches. It can also use AI to analyze the progress of action items and automatically send necessary reminders.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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 AI 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 AI 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 reception unit that receives information for adjusting a meeting schedule; a schedule adjustment unit that checks the calendars of participants based on the information received by the reception unit and proposes an appropriate date and time for the meeting; a facilitation unit that conducts the conference based on the date and time proposed by the schedule adjustment unit; a minutes-taking section that converts the contents of the meeting conducted by the facilitation section into text and extracts important points and action items; a progress management unit that manages the progress of the action items extracted by the minutes creation unit and sends reminders. A system characterized by:

2. The schedule adjustment unit Check attendee calendars in real time and suggest suitable dates and times 2. The system of claim 1.

3. The facilitation department Based on pre-defined agenda items, monitor the progress of each item in real time and prompt the next item as needed 2. The system of claim 1.

4. The minutes preparation department Transcribe what is said during a meeting and extract key points and action items 2. The system of claim 1.

5. The progress management unit Track progress on action items and receive reminders when deadlines approach 2. The system of claim 1.

6. The reception unit To estimate a user's emotion and adjust the timing of accepting conference information based on the estimated user's emotion.

2. The system of claim 1.

7. The reception unit Analyze the user's past meeting history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit Filtering meeting information as it arrives based on the user's current projects and interests 2. The system of claim 1.

9. The reception unit When accepting conference information, an appropriate acceptance method is selected according to the user's input method.

2. The system of claim 1.

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