system

Abstract

The system according to this embodiment aims to streamline the scheduling of meetings and the creation of meeting minutes. [Solution] The system according to the embodiment comprises a schedule analysis unit, a proposal unit, a meeting minutes creation unit, and a coordination unit. The schedule analysis unit analyzes the schedules of participants in real time. The proposal unit proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. The meeting minutes creation unit analyzes audio data during the meeting and creates meeting minutes and action plans. The coordination unit distributes the meeting minutes and action plans created by the meeting minutes creation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it takes a lot of time and labor to adjust the schedule of a meeting and create minutes, and there are problems in improving productivity.

[0005] The system according to the embodiment aims to streamline the schedule adjustment of a meeting and the creation of minutes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a schedule analysis unit, a proposal unit, a meeting minutes creation unit, and a coordination unit. The schedule analysis unit analyzes the schedules of participants in real time. The proposal unit proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. The meeting minutes creation unit analyzes audio data during the meeting and creates meeting minutes and action plans. The coordination unit distributes the meeting minutes and action plans created by the meeting minutes creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the scheduling of meetings and the creation of meeting minutes. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters linked by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent-based automated internal meeting scheduling and minute-sharing system according to an embodiment of the present invention is a next-generation tool that streamlines a series of meeting-related tasks and maximizes business productivity. This system uses AI to analyze participants' schedules in real time and propose the optimal date, time, and participants. Furthermore, the AI ​​automatically analyzes audio and discussions during meetings, instantly creating and distributing meeting minutes and action plans. For example, the AI ​​agent-based automated internal meeting scheduling and minute-sharing system also integrates with vendor tools. This frees employees from the burden of meeting preparation and minute-sharing, improving the speed of decision-making. For instance, the AI ​​agent-based automated internal meeting scheduling and minute-sharing system accelerates internal digital transformation while providing a more comfortable work environment. It enables efficient and transparent meeting management, supporting the strengthening of corporate competitiveness. Thus, the AI ​​agent-based automated internal meeting scheduling and minute-sharing system automates everything from meeting scheduling to minute-sharing creation and distribution, achieving increased efficiency and productivity.

[0029] The AI ​​agent-based internal meeting automatic scheduling and meeting minutes distribution system according to this embodiment comprises a schedule analysis unit, a proposal unit, a meeting minutes creation unit, and a collaboration unit. The schedule analysis unit analyzes participants' schedules in real time. For example, the schedule analysis unit obtains each participant's calendar information and proposes the optimal meeting date and time. The schedule analysis unit can use AI to analyze each participant's schedule and automatically select a date and time when everyone can attend. The proposal unit proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. For example, the proposal unit can automatically select a date and time when everyone can attend. The proposal unit can use AI to propose the optimal meeting date and time. The meeting minutes creation unit analyzes audio data during meetings and creates meeting minutes and action plans. For example, the meeting minutes creation unit analyzes meeting audio data in real time, transcribes the spoken content into text, extracts important points, and creates meeting minutes. The meeting minutes creation unit can use AI to analyze meeting audio data and create meeting minutes and action plans. The Collaboration Department distributes meeting minutes and action plans created by the Meeting Minutes Creation Department. The Collaboration Department can use AI to distribute meeting minutes and action plans. As a result, the AI ​​agent-based internal meeting automation and meeting minutes distribution system according to this embodiment can automate everything from scheduling meetings to creating and distributing meeting minutes, thereby improving operational efficiency and productivity.

[0030] The Schedule Analysis Department analyzes participants' schedules in real time. Specifically, it obtains each participant's calendar information and proposes the optimal meeting date and time. Calendar information is obtained from the schedule management tools used by each participant, including company groupware and personal calendar applications. The Schedule Analysis Department uses AI to analyze each participant's schedule and can automatically select a date and time when everyone can attend. The AI ​​uses natural language processing technology to analyze the calendar content and selects the optimal date and time considering the importance and priority of the meeting. For example, the AI ​​determines the importance of a meeting from its title and content and proposes the optimal date and time considering the balance with other appointments. Furthermore, the Schedule Analysis Department learns from participants' past schedule data and can make more accurate suggestions by considering each participant's meeting attendance patterns and preferences. As a result, the Schedule Analysis Department can efficiently and effectively adjust meeting schedules and select a date and time that all participants can attend without difficulty. In addition, even if there are changes or additions to the schedule, the Schedule Analysis Department can perform analysis in real time and propose the optimal meeting date and time again. This allows for flexible response to schedule changes and enables schedule adjustments based on the latest information at all times.

[0031] The Proposal Department suggests the optimal meeting date and time based on information analyzed by the Schedule Analysis Department. Specifically, the Proposal Department automatically selects a date and time when all participants can attend. The Proposal Department can also use AI to suggest the optimal meeting date and time. Based on data provided by the Schedule Analysis Department, the AI ​​comprehensively evaluates each participant's schedule and selects the optimal date and time. For example, the AI ​​compares the availability of each participant's schedule and identifies a time slot when everyone can attend. The AI ​​also considers the importance and priority of the meeting when selecting the optimal date and time. For example, in the case of an important meeting, it prioritizes selecting a date and time when all participants can definitely attend and adjusts other appointments accordingly. Furthermore, the Proposal Department can learn from participants' past schedule data and make more accurate suggestions by considering each participant's meeting attendance patterns and preferences. This allows the Proposal Department to efficiently and effectively adjust meeting schedules and select a date and time when all participants can attend without difficulty. In addition, even if there are changes or additions to the schedule, the Proposal Department can analyze the data in real time and re-suggest the optimal meeting date and time. This allows for flexible responses to schedule changes and enables schedule adjustments based on the latest information at all times.

[0032] The meeting minutes creation department analyzes audio data from meetings to create meeting minutes and action plans. Specifically, the department analyzes the meeting audio data in real time, transcribes the spoken content into text, extracts key points, and creates meeting minutes. The meeting minutes creation department can also use AI to analyze meeting audio data and create meeting minutes and action plans. The AI ​​uses speech recognition technology to transcribe the meeting audio data into text and natural language processing technology to extract key points. For example, the AI ​​analyzes the content of the speeches, organizes them by agenda item, and extracts important statements and decisions. The AI ​​also organizes the content of the speeches by speaker, making it clear who said what. As a result, the meeting minutes creation department can create meeting minutes efficiently and accurately, and clearly record the content of the meeting. Furthermore, the meeting minutes creation department can update the meeting minutes in real time according to the progress of the meeting and complete the meeting minutes immediately after the meeting ends. This allows for rapid sharing of the meeting content, ensuring that all participants share the same information. The meeting minutes creation department can also create action plans and provide specific instructions for each participant. This allows for translating meeting outcomes into concrete actions, leading to increased efficiency and productivity in operations.

[0033] The Liaison Department distributes meeting minutes and action plans created by the Meeting Minutes Creation Department. The Liaison Department can use AI to distribute meeting minutes and action plans. The AI ​​understands the tools and communication methods used by each participant and distributes information in the most optimal way. This allows the Liaison Department to distribute information to each participant in the most appropriate way, enabling quick and reliable information sharing. Furthermore, the Liaison Department can monitor the receipt status of distributed information and send reminders as needed. This ensures that important information is reliably communicated and that all participants share the same information. In addition, the Liaison Department can collect feedback on the distributed information and continuously improve the content of meeting minutes and action plans. This allows the Liaison Department to establish a cycle of information sharing and feedback, leading to increased efficiency and productivity.

[0034] The schedule analysis unit includes an acquisition unit that retrieves each participant's calendar information. The schedule analysis unit uses AI to acquire each participant's calendar information in real time and utilize it for schedule analysis. For example, the schedule analysis unit uses an API to acquire each participant's calendar information. Using AI, the schedule analysis unit can analyze the calendar information and propose the optimal meeting date and time. This allows the schedule analysis unit to perform more accurate schedule analysis by acquiring each participant's calendar information.

[0035] The meeting minutes creation department analyzes meeting audio data in real time, transcribes the spoken content into text, extracts key points, and creates meeting minutes. For example, the meeting minutes creation department uses speech recognition technology to convert meeting audio data into text data. The meeting minutes creation department can use AI to analyze meeting audio data in real time and transcribe the spoken content into text. For example, the meeting minutes creation department uses keyword extraction technology to extract key points and create meeting minutes. The meeting minutes creation department can use AI to extract key points and create meeting minutes. As a result, the meeting minutes creation department can analyze meeting audio data in real time and extract key points, enabling the rapid creation of accurate meeting minutes.

[0036] The Integration Department integrates with tools to instantly distribute generated meeting minutes and action plans. For example, the Integration Department synchronizes data with tools using API integration. The Integration Department can use AI to instantly distribute generated meeting minutes and action plans. For example, the Integration Department can adjust distribution timing to distribute meeting minutes and action plans immediately after the meeting ends. The Integration Department can use AI to automatically select recipients and distribute meeting minutes and action plans. This allows the Integration Department to quickly follow up after meetings by instantly distributing generated meeting minutes and action plans.

[0037] The proposal department automatically selects a date and time when everyone can attend. For example, the proposal department analyzes each participant's availability and selects a date and time when everyone can attend. The proposal department can use AI to automatically select a date and time when everyone can attend. For example, the proposal department considers the importance of the meeting and prioritizes selecting a date and time when everyone can attend for important meetings. The proposal department can use AI to suggest the optimal meeting date and time based on the importance of the meeting. In this way, the proposal department improves meeting attendance rates by automatically selecting a date and time when everyone can attend.

[0038] The Collaboration Department is equipped with a distribution function to quickly follow up after meetings. For example, the Collaboration Department can distribute action plans immediately after the meeting ends and conduct follow-up. The Collaboration Department can use AI to realize a distribution function that enables quick follow-up after meetings. For example, the Collaboration Department can regularly distribute updates on the progress of action plans and conduct follow-up. The Collaboration Department can use AI to automatically monitor the progress of action plans and conduct follow-up. In this way, the Collaboration Department can quickly follow up after meetings and promote the implementation of action plans.

[0039] The Schedule Analysis Unit analyzes each participant's past meeting attendance history and selects the optimal schedule analysis method. For example, the Schedule Analysis Unit analyzes each participant's past meeting attendance rate and prioritizes scheduling for participants with high attendance rates. The Schedule Analysis Unit can use AI to analyze each participant's past meeting attendance history and select the optimal schedule analysis method. For example, the Schedule Analysis Unit can identify tendencies for attendance on specific days or times based on each participant's past meeting attendance history and schedule meetings during those times. The Schedule Analysis Unit can use AI to send reminders to participants with low attendance rates based on each participant's past meeting attendance history. This allows the Schedule Analysis Unit to perform more appropriate schedule analysis by analyzing each participant's past meeting attendance history.

[0040] The schedule analysis unit improves the accuracy of its analysis based on participants' current projects and workloads. For example, the schedule analysis unit considers the progress of participants' current projects and sets meetings to coincide with important project milestones. The schedule analysis unit can use AI to analyze participants' current projects and workloads to improve the accuracy of its schedule analysis. For example, the schedule analysis unit analyzes participants' workloads and sets meetings to avoid periods of high workload. The schedule analysis unit can use AI to consider the priorities of participants' current projects and prioritize setting meetings related to important projects. As a result, the schedule analysis unit can perform more accurate schedule analysis by considering participants' current projects and workloads.

[0041] The schedule analysis unit considers the geographical location information of participants when performing schedule analysis. For example, the schedule analysis unit prioritizes scheduling meetings in physically close locations based on the geographical location information of participants. The schedule analysis unit can use AI to perform schedule analysis while considering the geographical location information of participants. For example, if participants are in different time zones, the schedule analysis unit will select a time slot that is convenient for everyone to attend. The schedule analysis unit can use AI to suggest the optimal combination of online and offline meetings based on the geographical location information of participants. As a result, the schedule analysis unit can prioritize scheduling meetings in physically close locations by considering the geographical location information of participants.

[0042] The schedule analysis unit analyzes participants' social media activity during schedule analysis and obtains relevant schedule information. For example, the schedule analysis unit obtains information about specific events or meetings from participants' social media activity and reflects it in the schedule. The schedule analysis unit can use AI to analyze participants' social media activity and obtain relevant schedule information. For example, the schedule analysis unit analyzes participants' social media activity and evaluates the importance and level of interest in meetings. The schedule analysis unit can use AI to obtain information related to the theme and content of meetings based on participants' social media activity and reflect it in the schedule. In this way, the schedule analysis unit improves the accuracy of schedule analysis by obtaining relevant schedule information through the analysis of participants' social media activity.

[0043] The proposal department adjusts the level of detail in proposals based on the importance of the meeting. For example, the proposal department provides detailed proposals for high-priority meetings. The proposal department can use AI to adjust the level of detail in proposals based on the importance of the meeting. For example, the proposal department provides concise proposals for low-priority meetings. The proposal department can use AI to adjust the content and format of proposals according to the importance of the meeting. This allows the proposal department to provide appropriate proposals for important meetings by adjusting the level of detail according to the importance of the meeting.

[0044] The proposal department applies different proposal algorithms depending on the meeting category when making proposals. For example, for a technical meeting, the proposal department will make proposals that include technical details. The proposal department can use AI to apply different proposal algorithms depending on the meeting category. For example, for a marketing meeting, the proposal department will make proposals regarding marketing strategies. The proposal department can use AI to make proposals regarding management strategies and policies for management meetings. In this way, the proposal department can make more appropriate proposals by applying different proposal algorithms depending on the meeting category.

[0045] The proposal department prioritizes proposals based on the timing of the meetings. For example, it prioritizes proposals for upcoming meetings. The proposal department can use AI to prioritize proposals based on the timing of the meetings. For example, it will postpone proposals for meetings far in the future. The proposal department can use AI to adjust the content and format of proposals according to the timing of the meetings. This allows the proposal department to prioritize proposals for upcoming meetings by prioritizing them according to the timing of the meetings.

[0046] The proposal department adjusts the order of proposals based on the relevance of the meetings. For example, the proposal department prioritizes proposals for highly relevant meetings. The proposal department can use AI to adjust the order of proposals based on the relevance of the meetings. For example, the proposal department postpones proposals for less relevant meetings. The proposal department can use AI to adjust the content and format of proposals according to the relevance of the meetings. This allows the proposal department to prioritize proposals for highly relevant meetings by adjusting the order of proposals according to the relevance of the meetings.

[0047] The meeting minutes creation department extracts key points based on the speaker's position and area of ​​expertise when creating meeting minutes. For example, if the speaker is from management, the department extracts statements related to management strategy and policies as key points. The meeting minutes creation department can use AI to extract key points based on the speaker's position and area of ​​expertise. For example, if the speaker is from the technical department, the department extracts statements related to technical details and issues as key points. The meeting minutes creation department can use AI to extract statements related to marketing strategy and measures if the speaker is from the marketing department as key points. As a result, the meeting minutes creation department can create more accurate meeting minutes by extracting key points based on the speaker's position and area of ​​expertise.

[0048] The meeting minutes creation system updates the minutes in real time according to the progress of the meeting. For example, if a new agenda item is added during the meeting, the meeting minutes creation system will reflect it in real time. The meeting minutes creation system can use AI to update the minutes in real time according to the progress of the meeting. For example, if an important decision is made during the meeting, the meeting minutes creation system will immediately record it in the minutes. The meeting minutes creation system can use AI to update the minutes in real time if the content of what was said during the meeting changes. As a result, the meeting minutes creation system updates the minutes in real time according to the progress of the meeting, so the latest information is immediately reflected.

[0049] The meeting minutes creation department prioritizes the memos based on the meeting agenda when creating meeting minutes. For example, the department prioritizes creating minutes related to important agenda items. The meeting minutes creation department can use AI to prioritize memos based on the meeting agenda. For example, the department will postpone creating minutes related to less important agenda items. The meeting minutes creation department can use AI to adjust the content and format of the meeting minutes according to the meeting agenda. As a result, the meeting minutes creation department can prioritize creating minutes related to important agenda items by prioritizing memos based on the meeting agenda.

[0050] The meeting minutes creation department improves the accuracy of meeting minutes by referring to relevant literature during the creation process. For example, the meeting minutes creation department refers to literature related to the meeting agenda and incorporates it into the meeting minutes. The meeting minutes creation department can use AI to improve the accuracy of meeting minutes by referring to relevant literature. For example, the meeting minutes creation department supplements the content of meeting minutes based on literature cited during the meeting. The meeting minutes creation department can use AI to refer to the latest research and data related to the meeting agenda and incorporate it into the meeting minutes. As a result, the accuracy of meeting minutes is improved by the meeting minutes creation department referring to relevant literature.

[0051] The integration unit selects the optimal integration method by referring to the usage history of each tool during integration. For example, the integration unit analyzes the frequency of use of each tool and prioritizes integrating with the most frequently used tool. The integration unit can use AI to select the optimal integration method by referring to the usage history of each tool. For example, the integration unit integrates tools that frequently use specific functions based on the usage history of each tool. The integration unit can use AI to integrate with the tool that the user finds easiest to use, based on the usage history of each tool. In this way, the integration unit can select the optimal integration method by referring to the usage history of each tool.

[0052] The Integration Department customizes the settings of the tools to be integrated according to the content of the meeting. For example, if the meeting content is technical, the Integration Department will prioritize integrating technical tools. The Integration Department can use AI to customize the settings of the tools to be integrated according to the content of the meeting. For example, if the meeting content is marketing, the Integration Department will integrate marketing tools. The Integration Department can use AI to customize the settings of the tools according to the content of the meeting and achieve optimal integration. As a result, the Integration Department can achieve optimal integration by customizing the settings of the tools according to the content of the meeting.

[0053] The liaison department determines the priority of collaboration based on the timing of meetings. For example, the liaison department prioritizes collaboration for upcoming meetings. The liaison department can use AI to determine the priority of collaboration based on the timing of meetings. For example, the liaison department will postpone collaboration for meetings far in the future. The liaison department can use AI to adjust the content and format of collaboration according to the timing of meetings. This allows the liaison department to prioritize collaboration for upcoming meetings by determining the priority of collaboration based on the timing of meetings.

[0054] The collaboration department improves the accuracy of collaboration by referring to relevant market data during the collaboration process. For example, the collaboration department refers to market data related to the meeting agenda and reflects it in the collaboration content. The collaboration department can use AI to improve the accuracy of collaboration by referring to relevant market data during the meeting. For example, the collaboration department supplements the collaboration content based on market data cited during the meeting. The collaboration department can use AI to refer to the latest market data related to the meeting agenda and reflect it in the collaboration content. As a result, the collaboration department improves the accuracy of collaboration by referring to relevant market data during the meeting.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The Schedule Analysis Unit can also acquire and analyze each participant's health data. For example, it can acquire participants' sleep and exercise data and suggest optimal meeting dates and times based on their health status. Using AI, the Schedule Analysis Unit can analyze health data and suggest schedules tailored to each participant's health condition. This allows the Schedule Analysis Unit to adjust schedules considering participants' health conditions, thereby supporting healthier work styles.

[0057] The integration department can also customize notification settings for integrated tools based on the content of the meeting. For example, it can send frequent notifications for important meetings and fewer notifications for less important meetings. The integration department can use AI to customize notification settings according to the content of the meeting and perform optimal integration. This allows the integration department to provide users with appropriate notifications by customizing notification settings according to the content of the meeting.

[0058] The proposal team can also tailor proposals based on the expertise and skills of the meeting participants. For example, for a technical meeting, it will provide proposals that include technical details, and for a marketing meeting, it will provide proposals related to marketing strategies. The proposal team can use AI to adjust proposals based on the participants' expertise and skills. This allows the proposal team to provide appropriate proposals according to the participants' expertise and skills.

[0059] The meeting minutes creation system can automatically collect relevant data and materials based on the meeting agenda and incorporate them into the minutes. For example, it can automatically collect data and materials cited during the meeting and incorporate them into the minutes. The meeting minutes creation system can use AI to automatically collect data and materials related to the meeting agenda and incorporate them into the minutes. As a result, the meeting minutes creation system can create more accurate and detailed minutes by collecting relevant data and materials based on the meeting agenda.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The schedule analysis unit analyzes participants' schedules in real time. For example, it obtains each participant's calendar information and uses AI to automatically select a date and time when everyone can participate. Step 2: The proposal department proposes the optimal meeting date and time based on the information analyzed by the schedule analysis department. For example, it automatically selects a date and time when everyone can attend and uses AI to propose the optimal meeting date and time. Step 3: The meeting minutes creation team analyzes the audio data from the meeting and creates meeting minutes and action plans. For example, they analyze the meeting audio data in real time, transcribe what was said into text, extract key points, and create meeting minutes. Step 4: The Liaison Department distributes the meeting minutes and action plans created by the Meeting Minutes Preparation Department.

[0062] (Example of form 2) The AI ​​agent-based automated internal meeting scheduling and minute-sharing system according to an embodiment of the present invention is a next-generation tool that streamlines a series of meeting-related tasks and maximizes business productivity. This system uses AI to analyze participants' schedules in real time and propose the optimal date, time, and participants. Furthermore, the AI ​​automatically analyzes audio and discussions during meetings, instantly creating and distributing meeting minutes and action plans. For example, the AI ​​agent-based automated internal meeting scheduling and minute-sharing system also integrates with other tools. This frees employees from the burden of meeting preparation and minute-sharing, improving the speed of decision-making. For instance, the AI ​​agent-based automated internal meeting scheduling and minute-sharing system accelerates internal digital transformation while providing a more comfortable work environment. It enables efficient and transparent meeting management, supporting the strengthening of corporate competitiveness. Thus, the AI ​​agent-based automated internal meeting scheduling and minute-sharing system automates everything from scheduling meetings to creating and distributing minute-sharing, achieving increased efficiency and productivity.

[0063] The AI ​​agent-based internal meeting automatic scheduling and meeting minutes distribution system according to this embodiment comprises a schedule analysis unit, a proposal unit, a meeting minutes creation unit, and a collaboration unit. The schedule analysis unit analyzes participants' schedules in real time. For example, the schedule analysis unit obtains each participant's calendar information and proposes the optimal meeting date and time. The schedule analysis unit can use AI to analyze each participant's schedule and automatically select a date and time when everyone can attend. The proposal unit proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. For example, the proposal unit can automatically select a date and time when everyone can attend. The proposal unit can use AI to propose the optimal meeting date and time. The meeting minutes creation unit analyzes audio data during meetings and creates meeting minutes and action plans. For example, the meeting minutes creation unit analyzes meeting audio data in real time, transcribes the spoken content into text, extracts important points, and creates meeting minutes. The meeting minutes creation unit can use AI to analyze meeting audio data and create meeting minutes and action plans. The Collaboration Department distributes meeting minutes and action plans created by the Meeting Minutes Creation Department. The Collaboration Department can use AI to distribute meeting minutes and action plans. As a result, the AI ​​agent-based internal meeting automation and meeting minutes distribution system according to this embodiment can automate everything from scheduling meetings to creating and distributing meeting minutes, thereby improving operational efficiency and productivity.

[0064] The Schedule Analysis Department analyzes participants' schedules in real time. Specifically, it obtains each participant's calendar information and proposes the optimal meeting date and time. Calendar information is obtained from the schedule management tools used by each participant, including company groupware and personal calendar applications. The Schedule Analysis Department uses AI to analyze each participant's schedule and can automatically select a date and time when everyone can attend. The AI ​​uses natural language processing technology to analyze the calendar content and selects the optimal date and time considering the importance and priority of the meeting. For example, the AI ​​determines the importance of a meeting from its title and content and proposes the optimal date and time considering the balance with other appointments. Furthermore, the Schedule Analysis Department learns from participants' past schedule data and can make more accurate suggestions by considering each participant's meeting attendance patterns and preferences. As a result, the Schedule Analysis Department can efficiently and effectively adjust meeting schedules and select a date and time that all participants can attend without difficulty. In addition, even if there are changes or additions to the schedule, the Schedule Analysis Department can perform analysis in real time and propose the optimal meeting date and time again. This allows for flexible response to schedule changes and enables schedule adjustments based on the latest information at all times.

[0065] The Proposal Department suggests the optimal meeting date and time based on information analyzed by the Schedule Analysis Department. Specifically, the Proposal Department automatically selects a date and time when all participants can attend. The Proposal Department can also use AI to suggest the optimal meeting date and time. Based on data provided by the Schedule Analysis Department, the AI ​​comprehensively evaluates each participant's schedule and selects the optimal date and time. For example, the AI ​​compares the availability of each participant's schedule and identifies a time slot when everyone can attend. The AI ​​also considers the importance and priority of the meeting when selecting the optimal date and time. For example, in the case of an important meeting, it prioritizes selecting a date and time when all participants can definitely attend and adjusts other appointments accordingly. Furthermore, the Proposal Department can learn from participants' past schedule data and make more accurate suggestions by considering each participant's meeting attendance patterns and preferences. This allows the Proposal Department to efficiently and effectively adjust meeting schedules and select a date and time when all participants can attend without difficulty. In addition, even if there are changes or additions to the schedule, the Proposal Department can analyze the data in real time and re-suggest the optimal meeting date and time. This allows for flexible responses to schedule changes and enables schedule adjustments based on the latest information at all times.

[0066] The meeting minutes creation department analyzes audio data from meetings to create meeting minutes and action plans. Specifically, the department analyzes the meeting audio data in real time, transcribes the spoken content into text, extracts key points, and creates meeting minutes. The meeting minutes creation department can also use AI to analyze meeting audio data and create meeting minutes and action plans. The AI ​​uses speech recognition technology to transcribe the meeting audio data into text and natural language processing technology to extract key points. For example, the AI ​​analyzes the content of the speeches, organizes them by agenda item, and extracts important statements and decisions. The AI ​​also organizes the content of the speeches by speaker, making it clear who said what. As a result, the meeting minutes creation department can create meeting minutes efficiently and accurately, and clearly record the content of the meeting. Furthermore, the meeting minutes creation department can update the meeting minutes in real time according to the progress of the meeting and complete the meeting minutes immediately after the meeting ends. This allows for rapid sharing of the meeting content, ensuring that all participants share the same information. The meeting minutes creation department can also create action plans and provide specific instructions for each participant. This allows for translating meeting outcomes into concrete actions, leading to increased efficiency and productivity in operations.

[0067] The Liaison Department distributes meeting minutes and action plans created by the Meeting Minutes Creation Department. The Liaison Department can use AI to distribute meeting minutes and action plans. The AI ​​understands the tools and communication methods used by each participant and distributes information in the most optimal way. This allows the Liaison Department to distribute information to each participant in the most appropriate way, enabling quick and reliable information sharing. Furthermore, the Liaison Department can monitor the receipt status of distributed information and send reminders as needed. This ensures that important information is reliably communicated and that all participants share the same information. In addition, the Liaison Department can collect feedback on the distributed information and continuously improve the content of meeting minutes and action plans. This allows the Liaison Department to establish a cycle of information sharing and feedback, leading to increased efficiency and productivity.

[0068] The schedule analysis unit includes an acquisition unit that retrieves each participant's calendar information. The schedule analysis unit uses AI to acquire each participant's calendar information in real time and utilize it for schedule analysis. For example, the schedule analysis unit uses an API to acquire each participant's calendar information. Using AI, the schedule analysis unit can analyze the calendar information and propose the optimal meeting date and time. This allows the schedule analysis unit to perform more accurate schedule analysis by acquiring each participant's calendar information.

[0069] The meeting minutes creation department analyzes meeting audio data in real time, transcribes the spoken content into text, extracts key points, and creates meeting minutes. For example, the meeting minutes creation department uses speech recognition technology to convert meeting audio data into text data. The meeting minutes creation department can use AI to analyze meeting audio data in real time and transcribe the spoken content into text. For example, the meeting minutes creation department uses keyword extraction technology to extract key points and create meeting minutes. The meeting minutes creation department can use AI to extract key points and create meeting minutes. As a result, the meeting minutes creation department can analyze meeting audio data in real time and extract key points, enabling the rapid creation of accurate meeting minutes.

[0070] The Integration Department integrates with tools to instantly distribute generated meeting minutes and action plans. For example, the Integration Department synchronizes data with tools using API integration. The Integration Department can use AI to instantly distribute generated meeting minutes and action plans. For example, the Integration Department can adjust distribution timing to distribute meeting minutes and action plans immediately after the meeting ends. The Integration Department can use AI to automatically select distribution destinations and distribute meeting minutes and action plans. This allows the Integration Department to quickly follow up after meetings by instantly distributing generated meeting minutes and action plans.

[0071] The proposal department automatically selects a date and time when everyone can attend. For example, the proposal department analyzes each participant's availability and selects a date and time when everyone can attend. The proposal department can use AI to automatically select a date and time when everyone can attend. For example, the proposal department considers the importance of the meeting and prioritizes selecting a date and time when everyone can attend for important meetings. The proposal department can use AI to suggest the optimal meeting date and time based on the importance of the meeting. In this way, the proposal department improves meeting attendance rates by automatically selecting a date and time when everyone can attend.

[0072] The Collaboration Department is equipped with a distribution function to quickly follow up after meetings. For example, the Collaboration Department can distribute action plans immediately after the meeting ends and conduct follow-up. The Collaboration Department can use AI to realize a distribution function that enables quick follow-up after meetings. For example, the Collaboration Department can regularly distribute updates on the progress of action plans and conduct follow-up. The Collaboration Department can use AI to automatically monitor the progress of action plans and conduct follow-up. In this way, the Collaboration Department can quickly follow up after meetings and promote the implementation of action plans.

[0073] The schedule analysis unit estimates the user's emotions and adjusts the priority of the schedule analysis based on the estimated emotions. For example, if the user is stressed, the schedule analysis unit will postpone less important meetings and prioritize more important ones. The schedule analysis unit can use AI to estimate the user's emotions and adjust the priority of the schedule analysis. For example, if the user is relaxed, the schedule analysis unit will perform a normal schedule analysis and analyze all meetings equally. The schedule analysis unit can use AI to adjust the priority of the schedule analysis based on the user's emotions. This allows the schedule analysis unit to reduce user stress and enable efficient schedule adjustment by adjusting the priority of the schedule analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The Schedule Analysis Unit analyzes each participant's past meeting attendance history and selects the optimal schedule analysis method. For example, the Schedule Analysis Unit analyzes each participant's past meeting attendance rate and prioritizes scheduling for participants with high attendance rates. The Schedule Analysis Unit can use AI to analyze each participant's past meeting attendance history and select the optimal schedule analysis method. For example, the Schedule Analysis Unit can identify tendencies for attendance on specific days or times based on each participant's past meeting attendance history and schedule meetings during those times. The Schedule Analysis Unit can use AI to send reminders to participants with low attendance rates based on each participant's past meeting attendance history. This allows the Schedule Analysis Unit to perform more appropriate schedule analysis by analyzing each participant's past meeting attendance history.

[0075] The schedule analysis unit improves the accuracy of its analysis based on participants' current projects and workloads. For example, the schedule analysis unit considers the progress of participants' current projects and sets meetings to coincide with important project milestones. The schedule analysis unit can use AI to analyze participants' current projects and workloads to improve the accuracy of its schedule analysis. For example, the schedule analysis unit analyzes participants' workloads and sets meetings to avoid periods of high workload. The schedule analysis unit can use AI to consider the priorities of participants' current projects and prioritize setting meetings related to important projects. As a result, the schedule analysis unit can perform more accurate schedule analysis by considering participants' current projects and workloads.

[0076] The schedule analysis unit estimates the user's emotions and adjusts the order in which the schedule analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the schedule analysis unit will display high-priority meetings first and lower-priority meetings later. The schedule analysis unit can use AI to estimate the user's emotions and adjust the order in which the schedule analysis results are displayed. For example, if the user is relaxed, the schedule analysis unit will display the schedule analysis results in the normal order. The schedule analysis unit can use AI to adjust the order in which the schedule analysis results are displayed based on the user's emotions. This allows the schedule analysis unit to reduce user stress and enable efficient schedule adjustment by adjusting the order in which the schedule analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The schedule analysis unit considers the geographical location information of participants when performing schedule analysis. For example, the schedule analysis unit prioritizes scheduling meetings in physically close locations based on the geographical location information of participants. The schedule analysis unit can use AI to perform schedule analysis while considering the geographical location information of participants. For example, if participants are in different time zones, the schedule analysis unit will select a time slot that is convenient for everyone to attend. The schedule analysis unit can use AI to suggest the optimal combination of online and offline meetings based on the geographical location information of participants. As a result, the schedule analysis unit can prioritize scheduling meetings in physically close locations by considering the geographical location information of participants.

[0078] The schedule analysis unit analyzes participants' social media activity during schedule analysis and obtains relevant schedule information. For example, the schedule analysis unit obtains information about specific events or meetings from participants' social media activity and reflects it in the schedule. The schedule analysis unit can use AI to analyze participants' social media activity and obtain relevant schedule information. For example, the schedule analysis unit analyzes participants' social media activity and evaluates the importance and level of interest in meetings. The schedule analysis unit can use AI to obtain information related to the theme and content of meetings based on participants' social media activity and reflect it in the schedule. In this way, the schedule analysis unit improves the accuracy of schedule analysis by obtaining relevant schedule information through the analysis of participants' social media activity.

[0079] The suggestion function estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and easy-to-understand suggestions. The suggestion function can use AI to estimate the user's emotions and adjust the way it presents suggestions. For example, if the user is relaxed, the suggestion function will present suggestions that include detailed information. The suggestion function can use AI to adjust the way it presents suggestions based on the user's emotions. This allows the suggestion function to provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The proposal department adjusts the level of detail in proposals based on the importance of the meeting. For example, the proposal department provides detailed proposals for high-priority meetings. The proposal department can use AI to adjust the level of detail in proposals based on the importance of the meeting. For example, the proposal department provides concise proposals for low-priority meetings. The proposal department can use AI to adjust the content and format of proposals according to the importance of the meeting. This allows the proposal department to provide appropriate proposals for important meetings by adjusting the level of detail according to the importance of the meeting.

[0081] The proposal department applies different proposal algorithms depending on the meeting category when making proposals. For example, for a technical meeting, the proposal department will make proposals that include technical details. The proposal department can use AI to apply different proposal algorithms depending on the meeting category. For example, for a marketing meeting, the proposal department will make proposals regarding marketing strategies. The proposal department can use AI to make proposals regarding management strategies and policies for management meetings. In this way, the proposal department can make more appropriate proposals by applying different proposal algorithms depending on the meeting category.

[0082] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will provide short, concise suggestions. The suggestion unit can use AI to estimate the user's emotions and adjust the length of the suggestions. For example, if the user is relaxed, the suggestion unit will provide longer suggestions that include more detailed information. The suggestion unit can use AI to adjust the length of the suggestions based on the user's emotions. This allows the suggestion unit to provide suggestions that are easier for the user to understand by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The proposal department prioritizes proposals based on the timing of the meetings. For example, it prioritizes proposals for upcoming meetings. The proposal department can use AI to prioritize proposals based on the timing of the meetings. For example, it will postpone proposals for meetings far in the future. The proposal department can use AI to adjust the content and format of proposals according to the timing of the meetings. This allows the proposal department to prioritize proposals for upcoming meetings by prioritizing them according to the timing of the meetings.

[0084] The proposal department adjusts the order of proposals based on the relevance of the meetings. For example, the proposal department prioritizes proposals for highly relevant meetings. The proposal department can use AI to adjust the order of proposals based on the relevance of the meetings. For example, the proposal department postpones proposals for less relevant meetings. The proposal department can use AI to adjust the content and format of proposals according to the relevance of the meetings. This allows the proposal department to prioritize proposals for highly relevant meetings by adjusting the order of proposals according to the relevance of the meetings.

[0085] The meeting minutes creation unit estimates the user's emotions and adjusts the presentation of the meeting minutes based on the estimated emotions. For example, if the user is stressed, the meeting minutes creation unit will create simple and easy-to-understand meeting minutes. The meeting minutes creation unit can use AI to estimate the user's emotions and adjust the presentation of the meeting minutes. For example, if the user is relaxed, the meeting minutes creation unit will create meeting minutes that include detailed information. The meeting minutes creation unit can use AI to adjust the presentation of the meeting minutes based on the user's emotions. As a result, the meeting minutes creation unit can create meeting minutes that are easy for the user to understand by adjusting the presentation of the meeting minutes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The meeting minutes creation department extracts key points based on the speaker's position and area of ​​expertise when creating meeting minutes. For example, if the speaker is from management, the department extracts statements related to management strategy and policies as key points. The meeting minutes creation department can use AI to extract key points based on the speaker's position and area of ​​expertise. For example, if the speaker is from the technical department, the department extracts statements related to technical details and issues as key points. The meeting minutes creation department can use AI to extract statements related to marketing strategy and measures if the speaker is from the marketing department as key points. As a result, the meeting minutes creation department can create more accurate meeting minutes by extracting key points based on the speaker's position and area of ​​expertise.

[0087] The meeting minutes creation system updates the minutes in real time according to the progress of the meeting. For example, if a new agenda item is added during the meeting, the meeting minutes creation system will reflect it in real time. The meeting minutes creation system can use AI to update the minutes in real time according to the progress of the meeting. For example, if an important decision is made during the meeting, the meeting minutes creation system will immediately record it in the minutes. The meeting minutes creation system can use AI to update the minutes in real time if the content of what was said during the meeting changes. As a result, the meeting minutes creation system updates the minutes in real time according to the progress of the meeting, so the latest information is immediately reflected.

[0088] The meeting minutes creation unit estimates the user's emotions and adjusts the length of the minutes based on the estimated emotions. For example, if the user is stressed, the meeting minutes creation unit will create short, concise minutes. The meeting minutes creation unit can use AI to estimate the user's emotions and adjust the length of the minutes. For example, if the user is relaxed, the meeting minutes creation unit will create longer minutes containing detailed information. The meeting minutes creation unit can use AI to adjust the length of the minutes based on the user's emotions. This allows the meeting minutes creation unit to create minutes that are easy for the user to understand by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The meeting minutes creation department prioritizes the memos based on the meeting agenda when creating meeting minutes. For example, the department prioritizes creating minutes related to important agenda items. The meeting minutes creation department can use AI to prioritize memos based on the meeting agenda. For example, the department will postpone creating minutes related to less important agenda items. The meeting minutes creation department can use AI to adjust the content and format of the meeting minutes according to the meeting agenda. As a result, the meeting minutes creation department can prioritize creating minutes related to important agenda items by prioritizing memos based on the meeting agenda.

[0090] The meeting minutes creation department improves the accuracy of meeting minutes by referring to relevant literature during the creation process. For example, the meeting minutes creation department refers to literature related to the meeting agenda and incorporates it into the meeting minutes. The meeting minutes creation department can use AI to improve the accuracy of meeting minutes by referring to relevant literature. For example, the meeting minutes creation department supplements the content of meeting minutes based on literature cited during the meeting. The meeting minutes creation department can use AI to refer to the latest research and data related to the meeting agenda and incorporate it into the meeting minutes. As a result, the accuracy of meeting minutes is improved by the meeting minutes creation department referring to relevant literature.

[0091] The integration unit estimates the user's emotions and selects tools to integrate with based on the estimated emotions. For example, if the user is stressed, the integration unit prioritizes integrating with simple and easy-to-use tools. The integration unit can use AI to estimate the user's emotions and select tools to integrate with. For example, if the user is relaxed, the integration unit integrates with tools that have detailed functions. The integration unit can use AI to select tools to integrate with based on the user's emotions. As a result, the integration unit can prioritize integrating with tools that are easy for the user to use by selecting tools according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The integration unit selects the optimal integration method by referring to the usage history of each tool during integration. For example, the integration unit analyzes the frequency of use of each tool and prioritizes integrating with the most frequently used tool. The integration unit can use AI to select the optimal integration method by referring to the usage history of each tool. For example, the integration unit integrates tools that frequently use specific functions based on the usage history of each tool. The integration unit can use AI to integrate with the tool that the user finds easiest to use, based on the usage history of each tool. In this way, the integration unit can select the optimal integration method by referring to the usage history of each tool.

[0093] The Integration Department customizes the settings of the tools to be integrated according to the content of the meeting. For example, if the meeting content is technical, the Integration Department will prioritize integrating technical tools. The Integration Department can use AI to customize the settings of the tools to be integrated according to the content of the meeting. For example, if the meeting content is marketing, the Integration Department will integrate marketing tools. The Integration Department can use AI to customize the settings of the tools according to the content of the meeting and achieve optimal integration. As a result, the Integration Department can achieve optimal integration by customizing the settings of the tools according to the content of the meeting.

[0094] The communication unit estimates the user's emotions and adjusts the frequency of communication based on the estimated emotions. For example, if the user is stressed, the communication unit reduces the frequency of communication and communicates only important information. The communication unit can use AI to estimate the user's emotions and adjust the frequency of communication. For example, if the user is relaxed, the communication unit maintains the normal frequency of communication. The communication unit can use AI to adjust the frequency of communication based on the user's emotions. This allows the communication unit to communicate information at an appropriate frequency for the user by adjusting the frequency of communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The liaison department determines the priority of collaboration based on the timing of meetings. For example, the liaison department prioritizes collaboration for upcoming meetings. The liaison department can use AI to determine the priority of collaboration based on the timing of meetings. For example, the liaison department will postpone collaboration for meetings far in the future. The liaison department can use AI to adjust the content and format of collaboration according to the timing of meetings. This allows the liaison department to prioritize collaboration for upcoming meetings by determining the priority of collaboration based on the timing of meetings.

[0096] The collaboration department improves the accuracy of collaboration by referring to relevant market data during the collaboration process. For example, the collaboration department refers to market data related to the meeting agenda and reflects it in the collaboration content. The collaboration department can use AI to improve the accuracy of collaboration by referring to relevant market data during the meeting. For example, the collaboration department supplements the collaboration content based on market data cited during the meeting. The collaboration department can use AI to refer to the latest market data related to the meeting agenda and reflect it in the collaboration content. As a result, the collaboration department improves the accuracy of collaboration by referring to relevant market data during the meeting.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The suggestion function can also estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion can be delayed; if the user is relaxed, the suggestion can be made immediately. Similarly, if the user feels busy, the suggestion can be postponed; if the user has free time, the suggestion can be prioritized. In this way, the suggestion function can adjust the timing of suggestions according to the user's emotions, allowing it to deliver suggestions at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The schedule analysis unit can also estimate the user's emotions and adjust the format in which the schedule analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the schedule analysis results can be displayed in a simple and easy-to-understand format, while if the user is relaxed, they can be displayed in a format that includes detailed information. Furthermore, if the user feels busy, only important information can be highlighted, while if the user has ample time, all information can be displayed equally. In this way, the schedule analysis unit can display the results in a format that is easy for the user to understand by adjusting the format in which the schedule analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The meeting minutes creation unit can also estimate the user's emotions and adjust the timing of meeting minutes delivery based on the estimated emotions. For example, if the user is feeling stressed, the delivery of the meeting minutes can be delayed, while if the user is relaxed, the meeting minutes can be delivered immediately. Similarly, if the user feels busy, the delivery of the meeting minutes can be postponed, while if the user has free time, the meeting minutes can be delivered preferentially. In this way, the meeting minutes creation unit can adjust the timing of meeting minutes delivery according to the user's emotions, delivering the meeting minutes at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The integration unit can also estimate the user's emotions and customize the functions of the integration tool based on the estimated emotions. For example, if the user is stressed, it can provide only simple and easy-to-use functions, while if the user is relaxed, it can provide detailed functions. Similarly, if the user feels busy, it can highlight and provide only essential functions, while if the user has ample time, it can provide all functions equally. In this way, the integration unit can provide a user-friendly tool by customizing the functions of the integration tool according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is stressed, it can provide simple, to-the-point suggestions; if the user is relaxed, it can provide suggestions with more detailed information. Similarly, if the user feels busy, it can highlight only the most important information; if the user has ample time, it can present all information equally. This allows the suggestion function to provide suggestions that are easy for the user to understand by adjusting the content according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The Schedule Analysis Unit can also acquire and analyze each participant's health data. For example, it can acquire participants' sleep and exercise data and suggest optimal meeting dates and times based on their health status. Using AI, the Schedule Analysis Unit can analyze health data and suggest schedules tailored to each participant's health condition. This allows the Schedule Analysis Unit to adjust schedules considering participants' health conditions, thereby supporting healthier work styles.

[0104] The meeting minutes creation department can analyze the facial expressions and tone of voice of speakers during a meeting and adjust the content of the minutes based on their emotions. For example, it can reflect points that speakers emphasize as important items in the minutes, and if a speaker is relaxed, it can create minutes that include more detailed information. In this way, the meeting minutes creation department can create more accurate and easy-to-understand minutes by adjusting the content based on the speakers' emotions.

[0105] The integration department can also customize notification settings for integrated tools based on the content of the meeting. For example, it can send frequent notifications for important meetings and fewer notifications for less important meetings. The integration department can use AI to customize notification settings according to the content of the meeting and perform optimal integration. This allows the integration department to provide users with appropriate notifications by customizing notification settings according to the content of the meeting.

[0106] The proposal team can also tailor proposals based on the expertise and skills of the meeting participants. For example, for a technical meeting, it will provide proposals that include technical details, and for a marketing meeting, it will provide proposals related to marketing strategies. The proposal team can use AI to adjust proposals based on the participants' expertise and skills. This allows the proposal team to provide appropriate proposals according to the participants' expertise and skills.

[0107] The meeting minutes creation system can automatically collect relevant data and materials based on the meeting agenda and incorporate them into the minutes. For example, it can automatically collect data and materials cited during the meeting and incorporate them into the minutes. The meeting minutes creation system can use AI to automatically collect data and materials related to the meeting agenda and incorporate them into the minutes. As a result, the meeting minutes creation system can create more accurate and detailed minutes by collecting relevant data and materials based on the meeting agenda.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The schedule analysis unit analyzes participants' schedules in real time. For example, it obtains each participant's calendar information and uses AI to automatically select a date and time when everyone can participate. Step 2: The proposal department proposes the optimal meeting date and time based on the information analyzed by the schedule analysis department. For example, it automatically selects a date and time when everyone can attend and uses AI to propose the optimal meeting date and time. Step 3: The meeting minutes creation team analyzes the audio data from the meeting and creates meeting minutes and action plans. For example, they analyze the meeting audio data in real time, transcribe what was said into text, extract key points, and create meeting minutes. Step 4: The Liaison Department distributes the meeting minutes and action plans created by the Meeting Minutes Preparation Department.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the schedule analysis unit, proposal unit, meeting minutes creation unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the schedule analysis unit is implemented by the control unit 46A of the smart device 14, which acquires calendar information for each participant and proposes the optimal meeting date and time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. The meeting minutes creation unit is implemented by the control unit 46A of the smart device 14, which analyzes audio data during the meeting and creates meeting minutes and action plans. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which distributes the generated meeting minutes and action plans in conjunction with a tool. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the schedule analysis unit, proposal unit, meeting minutes creation unit, and linkage unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the schedule analysis unit is implemented by the control unit 46A of the smart glasses 214, which acquires calendar information for each participant and proposes the optimal meeting date and time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. The meeting minutes creation unit is implemented by the control unit 46A of the smart glasses 214, which analyzes audio data during the meeting and creates meeting minutes and action plans. The linkage unit is implemented by the specific processing unit 290 of the data processing unit 12, which distributes the generated meeting minutes and action plans in conjunction with a tool. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the schedule analysis unit, proposal unit, meeting minutes creation unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the schedule analysis unit is implemented by the control unit 46A of the headset terminal 314, which acquires calendar information for each participant and proposes the optimal meeting date and time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. The meeting minutes creation unit is implemented by the control unit 46A of the headset terminal 314, which analyzes audio data during the meeting and creates meeting minutes and action plans. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which distributes the generated meeting minutes and action plans in conjunction with a tool. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the schedule analysis unit, proposal unit, meeting minutes creation unit, and coordination unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the schedule analysis unit is implemented by the control unit 46A of the robot 414, which acquires calendar information for each participant and proposes the optimal meeting date and time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the optimal meeting date and time based on the information analyzed by the schedule analysis unit. The meeting minutes creation unit is implemented by, for example, the control unit 46A of the robot 414, which analyzes audio data during the meeting and creates meeting minutes and action plans. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which distributes the generated meeting minutes and action plans in coordination with a tool. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The schedule analysis unit analyzes participants' schedules in real time, Based on the information analyzed by the aforementioned schedule analysis unit, a proposal unit proposes the optimal meeting date and time. The meeting minutes creation department analyzes audio data from meetings and creates meeting minutes and action plans, The system includes a coordination unit that distributes meeting minutes and action plans created by the aforementioned meeting minutes creation unit. A system characterized by the following features. (Note 2) The aforementioned schedule analysis unit, It includes an acquisition unit that retrieves calendar information for each participant. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned meeting minutes preparation department, The system analyzes meeting audio data in real time, transcribes the spoken content into text, extracts key points, and creates meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, It integrates with tools to instantly distribute generated meeting minutes and action plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Automatically select a date and time when everyone can participate. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, It includes a distribution function to quickly follow up after meetings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned schedule analysis unit, It estimates the user's emotions and adjusts the priority of the schedule analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned schedule analysis unit, Analyze each participant's past meeting attendance history to select the optimal scheduling method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned schedule analysis unit, When analyzing schedules, improve the accuracy of the analysis based on participants' current projects and workloads. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned schedule analysis unit, It estimates the user's emotions and adjusts the order in which the schedule analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned schedule analysis unit, When analyzing the schedule, the analysis will take into account the geographical location information of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned schedule analysis unit, During schedule analysis, we analyze participants' social media activity and obtain relevant schedule information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the meeting will be held. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned meeting minutes preparation department, The system estimates the user's emotions and adjusts the way meeting minutes are written based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned meeting minutes preparation department, When creating meeting minutes, extract key points based on the speaker's position and area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned meeting minutes preparation department, When creating meeting minutes, the content of the minutes is updated in real time according to the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned meeting minutes preparation department, Estimate the user's emotions and adjust the length of the meeting minutes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned meeting minutes preparation department, When creating meeting minutes, prioritize the notes based on the meeting agenda. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned meeting minutes preparation department, When creating meeting minutes, refer to relevant literature to improve the accuracy of the notes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, The system estimates the user's emotions and selects tools to collaborate with based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, During integration, the optimal integration method is selected by referring to the usage history of each tool. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, When integrating, customize the settings of the tools to be integrated according to the content of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the frequency of interaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, When collaborating, prioritize collaborations based on the timing of meetings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, When collaborating, we improve the accuracy of the collaboration by referring to relevant market data from the meeting. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The schedule analysis unit analyzes participants' schedules in real time, Based on the information analyzed by the aforementioned schedule analysis unit, a proposal unit proposes the optimal meeting date and time. The meeting minutes creation department analyzes audio data from meetings and creates meeting minutes and action plans, The system includes a coordination unit that distributes meeting minutes and action plans created by the aforementioned meeting minutes creation unit. A system characterized by the following features.

2. The aforementioned schedule analysis unit, It includes an acquisition unit that retrieves calendar information for each participant. The system according to feature 1.

3. The aforementioned meeting minutes preparation department, The system analyzes meeting audio data in real time, transcribes the spoken content into text, extracts key points, and creates meeting minutes. The system according to feature 1.

4. The aforementioned proposal section is, Automatically select a date and time when everyone can participate. The system according to feature 1.

5. The aforementioned linkage unit is, It includes a distribution function to quickly follow up after meetings. The system according to feature 1.

6. The aforementioned schedule analysis unit, It estimates the user's emotions and adjusts the priority of the schedule analysis based on the estimated user emotions. The system according to feature 1.

7. The aforementioned schedule analysis unit, Analyze each participant's past meeting attendance history to select the optimal scheduling method. The system according to feature 1.

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