System

Abstract

An object of the system according to the embodiment is to improve the efficiency of material preparation, progress management, and minutes creation for a conference.SOLUTION: A system includes a collection part, a progress management part, an interpretation part, and a minutes creation part. The collection unit collects and organizes materials of a conference. The progress management unit manages the progress of the conference based on the material collected by the collection unit. The interpretation part interprets the contents of the conference whose progress is managed by the progress management part in real time. The minutes creating section creates minutes based on the content interpreted by the interpreting section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of requiring a lot of time and effort to prepare meeting materials, manage progress, and create minutes.

[0005] The system according to the embodiment aims to improve the efficiency of preparing materials for a meeting, managing the progress of the meeting, and creating minutes. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a progress management unit, an interpretation unit, and a minutes creation unit. The collection unit collects and organizes materials for the meeting. The progress management unit manages the progress of the meeting based on the materials collected by the collection unit. The interpretation unit translates the content of the meeting whose progress is managed by the progress management unit in real time. The minutes creation unit creates minutes based on the content interpreted by the interpretation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of preparing materials for a meeting, managing the progress of the meeting, and creating minutes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A meeting support system according to an embodiment of the present invention uses AI to intelligently support meeting progress management, minutes creation, interpretation, and document organization. In the meeting support system, AI collects and organizes necessary materials on behalf of project members as pre-meeting preparation. Then, on the day of the meeting, AI manages the progress and ensures the meeting proceeds according to the schedule. AI also provides real-time interpretation, ensuring that all participants can participate without experiencing language barriers. After the meeting, AI automatically creates and distributes meeting minutes to participants. For example, the meeting support system streamlines meeting preparation by automatically collecting and organizing minutes and related documents from past meetings. Next, the meeting support system monitors the progress of the meeting in real time and adjusts the agenda to keep it moving forward within the scheduled time. Furthermore, the meeting support system provides real-time interpretation, enabling participants who speak different languages ​​to communicate smoothly. Furthermore, the meeting support system uses AI to record what is said during the meeting in real time and automatically generate minutes after the meeting, eliminating the need for participants to create meeting minutes. This allows the meeting support system to immerse all participants in the meeting, improving productivity and enabling effective collaboration. This allows the meeting support system to immerse all participants in the meeting, improving productivity and enabling effective collaboration. For example, by having AI manage the progress, the meeting will proceed smoothly and participants will be able to focus on the agenda. In addition, by having AI act as an interpreter, participants who speak different languages ​​will be able to communicate smoothly. Furthermore, by having AI automatically create meeting minutes, participants will be able to save the trouble of having to create minutes themselves.

[0029] A conference support system according to an embodiment includes a collection unit, a progress management unit, an interpretation unit, and a minutes creation unit. The collection unit collects and organizes conference materials. For example, the collection unit can collect and organize minutes of past conferences and related documents. The collection unit can also automatically collect and organize related materials using AI. For example, the collection unit retrieves minutes of past conferences from a database and organizes related documents. The progress management unit manages the progress of the conference based on the materials collected by the collection unit. For example, the progress management unit monitors the progress of the conference in real time and adjusts the agenda so that the agenda is progressed within the scheduled time. The progress management unit can use AI to ensure smooth progress of the conference. For example, the progress management unit manages the time schedule of the conference and monitors the progress of the agenda in real time. The interpretation unit interprets the content of the conference progress-managed by the progress management unit in real time. For example, the interpretation unit provides real-time interpretation so that participants who speak different languages ​​can communicate smoothly. The interpretation unit can perform interpretation in real time using AI. For example, the interpretation unit translates the contents of a meeting in real time and provides the translated content to participants. The minutes-taking unit creates minutes based on the content interpreted by the interpretation unit. For example, the minutes-taking unit records what is said during a meeting in real time and automatically generates minutes after the meeting ends. The minutes-taking unit can automatically create minutes using AI. For example, the minutes-taking unit records what is said during a meeting in real time and automatically generates minutes after the meeting ends. As a result, the meeting support system according to the embodiment allows all participants to immerse themselves in the meeting, improving productivity and enabling effective collaboration.

[0030] The collection unit can collect and organize minutes and related documents of past meetings. For example, the collection unit retrieves minutes of past meetings from a database and organizes related documents. The collection unit can also use AI to automatically collect and organize related materials. For example, the collection unit retrieves minutes of past meetings from a database and organizes related documents. In this way, collecting and organizing minutes and related documents of past meetings makes meeting preparation more efficient.

[0031] The progress management unit can monitor the progress of the meeting in real time and make adjustments so that the agenda is progressed within the allotted time. For example, the progress management unit can monitor the progress of the meeting in real time and make adjustments so that the agenda is progressed within the scheduled time. The progress management unit can use AI to ensure that the meeting proceeds smoothly. For example, the progress management unit can manage the meeting time schedule and monitor the progress of the agenda in real time. This allows the meeting to proceed smoothly by monitoring the progress of the meeting in real time and making adjustments so that the agenda is progressed within the allotted time.

[0032] The interpretation unit can provide real-time interpretation so that participants who speak different languages ​​can communicate with each other. For example, the interpretation unit provides real-time interpretation so that participants who speak different languages ​​can communicate smoothly with each other. The interpretation unit can provide real-time interpretation using AI. For example, the interpretation unit translates the contents of a meeting in real time and provides it to the participants. This allows participants who speak different languages ​​to communicate smoothly with each other.

[0033] The minutes-taking unit can record what is said during a meeting in real time and generate minutes after it has ended. For example, the minutes-taking unit can record what is said during a meeting in real time and automatically generate minutes after it has ended. The minutes-taking unit can automatically create minutes using AI. For example, the minutes-taking unit can record what is said during a meeting in real time and automatically generate minutes after it has ended. This allows the work of creating minutes to be reduced by recording what is said during a meeting in real time and automatically generating minutes after it has ended.

[0034] The collection unit can analyze the minutes and related documents of past meetings and select the optimal collection method. For example, the collection unit uses AI to analyze past minutes and extract frequently used keywords to collect related documents. The collection unit can also use AI to analyze the themes of past meetings and prioritize the collection of related documents. The collection unit can also use AI to analyze the content of remarks made by participants in past meetings and collect related materials. This makes it possible to select the optimal collection method by analyzing the minutes and related documents of past meetings.

[0035] When collecting materials, the collection unit can filter them based on the theme and purpose of the meeting. For example, the collection unit uses AI to filter and collect relevant materials based on the theme of the meeting. The collection unit can also use AI to select and collect necessary materials based on the purpose of the meeting. The collection unit can also use AI to filter and collect relevant materials based on the areas of expertise of the meeting participants. This makes it possible to efficiently collect necessary materials by filtering materials based on the theme and purpose of the meeting.

[0036] When collecting materials, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user requests materials by voice, the collection unit uses AI to collect the materials using voice recognition technology. Also, if the user requests materials by text, the collection unit can use AI to collect materials using text analysis technology. Also, if the user requests materials by image, the collection unit can use AI to collect materials using image recognition technology. This allows materials to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0037] When collecting materials, the collection unit can prioritize collecting highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting materials related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting materials related to the user's current location. Furthermore, if the user is in a specific location, the collection unit can also prioritize collecting materials related to that location. In this way, by prioritizing the collection of highly relevant materials by taking into account the user's geographical location information, necessary materials can be collected efficiently.

[0038] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0039] When collecting materials, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal collection method. The collection unit can also customize the type of materials to be collected by reflecting the user's past feedback. In this way, the optimal collection method can be selected by reflecting the user's past feedback.

[0040] The progress management unit can monitor the progress of the meeting in real time and apply an algorithm to optimize the progress of the agenda. For example, the progress management unit uses AI to monitor the progress in real time and optimize the progress of the agenda. The progress management unit can also use AI to analyze the progress and propose the optimal way to proceed. The progress management unit can also use AI to monitor the progress and make adjustments so that the agenda progresses within the scheduled time. In this way, by monitoring the progress of the meeting in real time and optimizing the progress of the agenda, the meeting progresses smoothly.

[0041] The progress management unit can adjust the progress of a meeting by analyzing the frequency and content of participants' comments as the meeting progresses. For example, the progress management unit can use AI to analyze the frequency of participants' comments and adjust the progress to balance the comments. The progress management unit can also use AI to analyze the content of participants' comments and adjust the progress to prioritize comments related to the agenda. The progress management unit can also use AI to analyze the frequency and content of participants' comments and propose the optimal way to proceed. In this way, the progress can be adjusted by analyzing the frequency and content of participants' comments.

[0042] The progress management unit can improve the accuracy of progress management by referring to past meeting data as the meeting progresses. For example, the progress management unit uses AI to refer to past meeting data and improve the accuracy of progress management. The progress management unit can also use AI to analyze past meeting data and propose the optimal way to proceed. The progress management unit can also use AI to adjust the progress management algorithm based on past meeting data. In this way, the accuracy of progress management is improved by referring to past meeting data.

[0043] The progress management unit can manage the progress of a meeting by taking into account the geographical location information of participants. For example, when participants are in different locations, the progress management unit uses AI to manage the progress by taking into account the geographical location information. Furthermore, when a participant is moving, the progress management unit can also manage the progress by taking into account the geographical location information. Furthermore, when a participant is in a specific location, the progress management unit can also manage the progress by taking into account the geographical location information. In this way, optimal progress management can be performed by taking into account the geographical location information of participants.

[0044] The progress management unit can manage the progress of a meeting by referring to related external data. For example, the progress management unit uses AI to refer to market trends and manage the progress. The progress management unit can also use AI to refer to technology trends and manage the progress. The progress management unit can also use AI to refer to related external data and manage the progress. In this way, by referring to related external data, the accuracy of progress management is improved.

[0045] The progress management unit can adjust the progress of the meeting according to the expertise levels of the participants. For example, the progress management unit uses AI to analyze the expertise levels of the participants and adjust the progress. The progress management unit can also use AI to adjust the progress of the agenda according to the expertise levels of the participants. The progress management unit can also use AI to propose the optimal progress method, taking into account the expertise levels of the participants. In this way, the optimal progress can be achieved by adjusting the progress according to the expertise levels of the participants.

[0046] When interpreting, the interpretation department can analyze the content of what participants say in real time and select the most appropriate interpretation method. For example, the interpretation department can use AI to analyze the content of what participants say in real time and select the most appropriate interpretation method. The interpretation department can also use AI to analyze the content of what participants say in real time and prioritize interpretation related to the agenda. The interpretation department can also use AI to analyze the content of what participants say in real time and suggest the most appropriate interpretation method. This makes it possible to select the most appropriate interpretation method by analyzing the content of what participants say in real time.

[0047] The interpretation unit can take into consideration the nuances of different languages ​​and cultural backgrounds when interpreting. For example, the interpretation unit can use AI to interpret taking into consideration the nuances of different languages. The interpretation unit can also use AI to interpret taking into consideration the cultural backgrounds when interpreting. The interpretation unit can also use AI to interpret taking into consideration the nuances of different languages ​​and cultural backgrounds when interpreting. This allows for more appropriate interpretation by taking into consideration the nuances of different languages ​​and cultural backgrounds.

[0048] The interpretation unit can improve the accuracy of interpretation by referring to past interpretation data when interpreting. For example, the interpretation unit can improve the accuracy of interpretation by using AI to refer to past interpretation data. The interpretation unit can also analyze past interpretation data and propose the optimal interpretation method. The interpretation unit can also adjust the interpretation algorithm based on past interpretation data by using AI. In this way, the accuracy of interpretation is improved by referring to past interpretation data.

[0049] The interpretation unit can take into account the geographical location information of participants when interpreting. For example, if participants are in different locations, the interpretation unit uses AI to take into account their geographical location information when interpreting. In addition, if participants are on the move, the interpretation unit can use AI to take into account their geographical location information when interpreting. In addition, if participants are in a specific location, the interpretation unit can use AI to take into account their geographical location information when interpreting. This allows for optimal interpretation by taking into account the geographical location information of participants.

[0050] The interpretation unit can improve the accuracy of the interpretation by referring to related external data during interpretation. For example, the interpretation unit can improve the accuracy of the interpretation by having AI refer to a technical dictionary. The interpretation unit can also improve the accuracy of the interpretation by having AI refer to industry terminology. The interpretation unit can also improve the accuracy of the interpretation by having AI refer to related external data. In this way, the accuracy of the interpretation is improved by referring to related external data.

[0051] The interpretation department can adjust the interpretation according to the participants' level of expertise when interpreting. For example, the interpretation department uses AI to analyze the participants' level of expertise and adjust the interpretation. The interpretation department can also adjust the content of the interpretation according to the participants' level of expertise. The interpretation department can also propose the optimal interpretation method by using AI to take into account the participants' level of expertise. This allows the optimal interpretation to be performed by adjusting the interpretation according to the participants' level of expertise.

[0052] The minutes-taking department can analyze what is said during a meeting in real time and select the optimal method for creating minutes. For example, the minutes-taking department can use AI to analyze what is said during a meeting in real time and select the optimal method for creating minutes. The minutes-taking department can also use AI to analyze what is said and create minutes by prioritizing comments related to the agenda. The minutes-taking department can also use AI to analyze what is said in real time and suggest the optimal method for creating minutes. This makes it possible to select the optimal method for creating minutes by analyzing what is said during a meeting in real time.

[0053] When creating minutes, the minutes-taking department can adjust the level of detail in the minutes based on the theme and purpose of the meeting. For example, the minutes-taking department may use AI to adjust the level of detail in the minutes based on the theme of the meeting. The minutes-taking department may also use AI to adjust the level of detail in the minutes based on the purpose of the meeting. The minutes-taking department may also use AI to adjust the level of detail in the minutes based on the areas of expertise of the meeting participants. In this way, optimal minutes are created by adjusting the level of detail in the minutes based on the theme and purpose of the meeting.

[0054] When creating minutes, the minutes-taking department can refer to past minutes data to improve the accuracy of the minutes. For example, the minutes-taking department can use AI to refer to past minutes data to improve the accuracy of the minutes. The minutes-taking department can also use AI to analyze past minutes data and propose the optimal method for creating minutes. The minutes-taking department can also use AI to adjust the algorithm for creating minutes based on past minutes data. In this way, the accuracy of the minutes can be improved by referring to past minutes data.

[0055] When creating minutes, the minutes-taking unit can create minutes taking into account the geographical location information of participants. For example, when participants are in different locations, the minutes-taking unit creates minutes by having the AI ​​take into account the geographical location information. Furthermore, when participants are on the move, the minutes-taking unit can also create minutes by having the AI ​​take into account the geographical location information. Furthermore, when participants are in a specific location, the minutes-taking unit can also create minutes by having the AI ​​take into account the geographical location information. In this way, optimal minutes are created by taking into account the geographical location information of participants.

[0056] When creating minutes, the minutes-taking department can improve the accuracy of the minutes by referring to related external data. For example, the minutes-taking department can improve the accuracy of the minutes by using AI to refer to market trends. The minutes-taking department can also improve the accuracy of the minutes by using AI to refer to technological trends. The minutes-taking department can also improve the accuracy of the minutes by using AI to refer to related external data. In this way, the accuracy of the minutes is improved by referring to related external data.

[0057] When creating minutes, the minutes-taking department can adjust the minutes according to the expertise level of the participants. For example, the minutes-taking department uses AI to analyze the expertise level of the participants and adjust the minutes. The minutes-taking department can also adjust the content of the minutes according to the expertise level of the participants. The minutes-taking department can also suggest the optimal method for creating minutes by using AI to take into account the expertise level of the participants. In this way, optimal minutes are created by adjusting the minutes according to the expertise level of the participants.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The meeting support system can further include an evaluation unit that analyzes the content of participants' comments and evaluates their importance. The evaluation unit, for example, uses AI to analyze the content of comments in real time and prioritizes recording of important comments. The evaluation unit can also use AI to analyze the frequency and content of comments and extract important comments related to the agenda. Furthermore, the evaluation unit can use AI to analyze the emotional tone of comments and identify emotionally important comments. This allows the meeting support system to efficiently record important comments and improve the quality of meeting minutes.

[0060] The meeting support system can further include an expertise adjustment unit that analyzes the expertise levels of the participants and adjusts the progress of the agenda. The expertise adjustment unit, for example, uses AI to analyze the expertise levels of the participants and adjusts the progress of the agenda. The expertise adjustment unit can also use AI to provide detailed explanations of the agenda according to the expertise levels of the participants. Furthermore, the expertise adjustment unit can also use AI to propose the optimal way to proceed, taking into account the expertise levels of the participants. In this way, the meeting support system can adjust the progress according to the expertise levels of the participants and improve the understanding of the meeting.

[0061] The conference support system may further include a location information adjustment unit that adjusts the progress of the conference taking into account the geographical location information of the participants. For example, when participants are in different locations, the location information adjustment unit adjusts the progress of the conference by using an AI that takes into account the geographical location information. The location information adjustment unit may also adjust the progress of the conference by using an AI that takes into account the geographical location information of a participant who is moving. Furthermore, when a participant is in a specific location, the location information adjustment unit may preferentially provide information related to that location. This allows the conference support system to adjust the progress of the conference by taking into account the geographical location information of the participants, thereby improving the efficiency of the conference.

[0062] The conference support system may further include a social media analysis unit that analyzes participants' social media activities and provides related information. For example, the social media analysis unit may use AI to analyze participants' social media activities and provide related information. The social media analysis unit may also use AI to provide related information based on the activities of participants' friends on social media. Furthermore, the social media analysis unit may use AI to collect related materials based on information shared by participants on social media. This allows the conference support system to analyze participants' social media activities and provide related information efficiently.

[0063] The conference support system can further include an interpretation optimization unit that analyzes participants' remarks in real time and selects the optimal interpretation method. The interpretation optimization unit, for example, uses AI to analyze participants' remarks in real time and select the optimal interpretation method. The interpretation optimization unit can also use AI to analyze remarks and prioritize interpretation related to the agenda. Furthermore, the interpretation optimization unit can also use AI to analyze remarks in real time and suggest the optimal interpretation method. In this way, the conference support system can improve the accuracy of interpretation by analyzing participants' remarks in real time and selecting the optimal interpretation method.

[0064] The meeting support system can further include a location information minutes-taking unit that creates minutes taking into account the geographical location information of participants. For example, when participants are in different locations, the location information minutes-taking unit creates minutes by using AI taking into account geographical location information. In addition, when participants are moving, the location information minutes-taking unit can also create minutes by using AI taking into account geographical location information. Furthermore, when participants are in a specific location, the location information minutes-taking unit can also prioritize recording information related to that location in the minutes. This allows the meeting support system to create minutes taking into account the geographical location information of participants and provide more appropriate minutes.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection department collects and organizes meeting materials. For example, minutes of past meetings and related documents can be retrieved from a database and automatically collected and organized using AI. Step 2: The progress management department manages the progress of the meeting based on the materials collected by the collection department. For example, it monitors the progress of the meeting in real time and adjusts the agenda so that it proceeds within the scheduled time. Step 3: The Interpretation Department interprets the contents of the conference managed by the Conference Management Department in real time, for example, to enable smooth communication between participants who speak different languages. Step 4: The minutes-taking department creates minutes based on the content interpreted by the interpretation department. For example, it records what is said during the meeting in real time and automatically generates minutes after the meeting ends.

[0067] (Example 2) A meeting support system according to an embodiment of the present invention uses AI to intelligently support meeting progress management, minutes creation, interpretation, and document organization. In the meeting support system, AI collects and organizes necessary materials on behalf of project members as pre-meeting preparation. Then, on the day of the meeting, AI manages the progress and ensures the meeting proceeds according to the schedule. AI also provides real-time interpretation, ensuring that all participants can participate without experiencing language barriers. After the meeting, AI automatically creates and distributes meeting minutes to participants. For example, the meeting support system streamlines meeting preparation by automatically collecting and organizing minutes and related documents from past meetings. Next, the meeting support system monitors the progress of the meeting in real time and adjusts the agenda to keep it moving forward within the scheduled time. Furthermore, the meeting support system provides real-time interpretation, enabling participants who speak different languages ​​to communicate smoothly. Furthermore, the meeting support system uses AI to record what is said during the meeting in real time and automatically generate minutes after the meeting, eliminating the need for participants to create meeting minutes. This allows the meeting support system to immerse all participants in the meeting, improving productivity and enabling effective collaboration. This allows the meeting support system to immerse all participants in the meeting, improving productivity and enabling effective collaboration. For example, by having AI manage the progress, the meeting will proceed smoothly and participants will be able to focus on the agenda. In addition, by having AI act as an interpreter, participants who speak different languages ​​will be able to communicate smoothly. Furthermore, by having AI automatically create meeting minutes, participants will be able to save the trouble of having to create minutes themselves.

[0068] A conference support system according to an embodiment includes a collection unit, a progress management unit, an interpretation unit, and a minutes creation unit. The collection unit collects and organizes conference materials. For example, the collection unit can collect and organize minutes of past conferences and related documents. The collection unit can also automatically collect and organize related materials using AI. For example, the collection unit retrieves minutes of past conferences from a database and organizes related documents. The progress management unit manages the progress of the conference based on the materials collected by the collection unit. For example, the progress management unit monitors the progress of the conference in real time and adjusts the agenda so that the agenda is progressed within the scheduled time. The progress management unit can use AI to ensure smooth progress of the conference. For example, the progress management unit manages the time schedule of the conference and monitors the progress of the agenda in real time. The interpretation unit interprets the content of the conference progress-managed by the progress management unit in real time. For example, the interpretation unit provides real-time interpretation so that participants who speak different languages ​​can communicate smoothly. The interpretation unit can perform interpretation in real time using AI. For example, the interpretation unit translates the contents of a meeting in real time and provides the translated content to participants. The minutes-taking unit creates minutes based on the content interpreted by the interpretation unit. For example, the minutes-taking unit records what is said during a meeting in real time and automatically generates minutes after the meeting ends. The minutes-taking unit can automatically create minutes using AI. For example, the minutes-taking unit records what is said during a meeting in real time and automatically generates minutes after the meeting ends. As a result, the meeting support system according to the embodiment allows all participants to immerse themselves in the meeting, improving productivity and enabling effective collaboration.

[0069] The collection unit can collect and organize minutes and related documents of past meetings. For example, the collection unit retrieves minutes of past meetings from a database and organizes related documents. The collection unit can also use AI to automatically collect and organize related materials. For example, the collection unit retrieves minutes of past meetings from a database and organizes related documents. In this way, collecting and organizing minutes and related documents of past meetings makes meeting preparation more efficient.

[0070] The progress management unit can monitor the progress of the meeting in real time and make adjustments so that the agenda is progressed within the allotted time. For example, the progress management unit can monitor the progress of the meeting in real time and make adjustments so that the agenda is progressed within the scheduled time. The progress management unit can use AI to ensure that the meeting proceeds smoothly. For example, the progress management unit can manage the meeting time schedule and monitor the progress of the agenda in real time. This allows the meeting to proceed smoothly by monitoring the progress of the meeting in real time and making adjustments so that the agenda is progressed within the allotted time.

[0071] The interpretation unit can provide real-time interpretation so that participants who speak different languages ​​can communicate with each other. For example, the interpretation unit provides real-time interpretation so that participants who speak different languages ​​can communicate smoothly with each other. The interpretation unit can provide real-time interpretation using AI. For example, the interpretation unit translates the contents of a meeting in real time and provides it to the participants. This allows participants who speak different languages ​​to communicate smoothly with each other.

[0072] The minutes-taking unit can record what is said during a meeting in real time and generate minutes after it has ended. For example, the minutes-taking unit can record what is said during a meeting in real time and automatically generate minutes after it has ended. The minutes-taking unit can automatically create minutes using AI. For example, the minutes-taking unit can record what is said during a meeting in real time and automatically generate minutes after it has ended. This allows the work of creating minutes to be reduced by recording what is said during a meeting in real time and automatically generating minutes after it has ended.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting materials based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit has the AI ​​collect materials quickly, reducing the user's burden. Furthermore, if the user is relaxed, the collection unit can have the AI ​​collect materials slowly to collect detailed information. Furthermore, if the user is in a hurry, the collection unit can have the AI ​​prioritize collecting the most important materials. This reduces the user's burden by adjusting the timing of collecting materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The collection unit can analyze the minutes and related documents of past meetings and select the optimal collection method. For example, the collection unit uses AI to analyze past minutes and extract frequently used keywords to collect related documents. The collection unit can also use AI to analyze the themes of past meetings and prioritize the collection of related documents. The collection unit can also use AI to analyze the content of remarks made by participants in past meetings and collect related materials. This makes it possible to select the optimal collection method by analyzing the minutes and related documents of past meetings.

[0075] When collecting materials, the collection unit can filter them based on the theme and purpose of the meeting. For example, the collection unit uses AI to filter and collect relevant materials based on the theme of the meeting. The collection unit can also use AI to select and collect necessary materials based on the purpose of the meeting. The collection unit can also use AI to filter and collect relevant materials based on the areas of expertise of the meeting participants. This makes it possible to efficiently collect necessary materials by filtering materials based on the theme and purpose of the meeting.

[0076] When collecting materials, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user requests materials by voice, the collection unit uses AI to collect the materials using voice recognition technology. Also, if the user requests materials by text, the collection unit can use AI to collect materials using text analysis technology. Also, if the user requests materials by image, the collection unit can use AI to collect materials using image recognition technology. This allows materials to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0077] The collection unit can estimate the user's emotions and determine the priority of materials to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit allows the AI ​​to prioritize collecting the most important materials. Also, if the user is relaxed, the collection unit can allow the AI ​​to prioritize collecting detailed materials. Also, if the user is in a hurry, the collection unit can prioritize collecting materials that the AI ​​can collect quickly. In this way, by prioritizing materials according to the user's emotions, important materials can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] When collecting materials, the collection unit can prioritize collecting highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting materials related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting materials related to the user's current location. Furthermore, if the user is in a specific location, the collection unit can also prioritize collecting materials related to that location. In this way, by prioritizing the collection of highly relevant materials by taking into account the user's geographical location information, necessary materials can be collected efficiently.

[0079] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0080] When collecting materials, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal collection method. The collection unit can also customize the type of materials to be collected by reflecting the user's past feedback. In this way, the optimal collection method can be selected by reflecting the user's past feedback.

[0081] The progress management unit can estimate the user's emotions and adjust the progress management method based on the estimated user's emotions. For example, if the user is nervous, the progress management unit causes the AI ​​to proceed slowly. Also, if the user is relaxed, the progress management unit can cause the AI ​​to proceed smoothly. Also, if the user is in a hurry, the progress management unit can cause the AI ​​to proceed quickly. In this way, by adjusting the progress management method according to the user's emotions, the meeting progresses smoothly. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The progress management unit can monitor the progress of the meeting in real time and apply an algorithm to optimize the progress of the agenda. For example, the progress management unit uses AI to monitor the progress in real time and optimize the progress of the agenda. The progress management unit can also use AI to analyze the progress and propose the optimal way to proceed. The progress management unit can also use AI to monitor the progress and make adjustments so that the agenda progresses within the scheduled time. In this way, by monitoring the progress of the meeting in real time and optimizing the progress of the agenda, the meeting progresses smoothly.

[0083] The progress management unit can adjust the progress of a meeting by analyzing the frequency and content of participants' comments as the meeting progresses. For example, the progress management unit can use AI to analyze the frequency of participants' comments and adjust the progress to balance the comments. The progress management unit can also use AI to analyze the content of participants' comments and adjust the progress to prioritize comments related to the agenda. The progress management unit can also use AI to analyze the frequency and content of participants' comments and propose the optimal way to proceed. In this way, the progress can be adjusted by analyzing the frequency and content of participants' comments.

[0084] The progress management unit can improve the accuracy of progress management by referring to past meeting data as the meeting progresses. For example, the progress management unit uses AI to refer to past meeting data and improve the accuracy of progress management. The progress management unit can also use AI to analyze past meeting data and propose the optimal way to proceed. The progress management unit can also use AI to adjust the progress management algorithm based on past meeting data. In this way, the accuracy of progress management is improved by referring to past meeting data.

[0085] The progress management unit can estimate the user's emotions and determine the priority of progress management based on the estimated user emotions. For example, if the user is feeling stressed, the progress management unit allows the AI ​​to prioritize important agenda items. Also, if the user is relaxed, the progress management unit can also allow the AI ​​to prioritize detailed agenda items. Also, if the user is in a hurry, the progress management unit can prioritize agenda items that the AI ​​can progress quickly. In this way, by determining the priority of progress management according to the user's emotions, important agenda items can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The progress management unit can manage the progress of a meeting by taking into account the geographical location information of participants. For example, when participants are in different locations, the progress management unit uses AI to manage the progress by taking into account the geographical location information. Furthermore, when a participant is moving, the progress management unit can also manage the progress by taking into account the geographical location information. Furthermore, when a participant is in a specific location, the progress management unit can also manage the progress by taking into account the geographical location information. In this way, optimal progress management can be performed by taking into account the geographical location information of participants.

[0087] The progress management unit can manage the progress of a meeting by referring to related external data. For example, the progress management unit uses AI to refer to market trends and manage the progress. The progress management unit can also use AI to refer to technology trends and manage the progress. The progress management unit can also use AI to refer to related external data and manage the progress. In this way, by referring to related external data, the accuracy of progress management is improved.

[0088] The progress management unit can adjust the progress of the meeting according to the expertise levels of the participants. For example, the progress management unit uses AI to analyze the expertise levels of the participants and adjust the progress. The progress management unit can also use AI to adjust the progress of the agenda according to the expertise levels of the participants. The progress management unit can also use AI to propose the optimal progress method, taking into account the expertise levels of the participants. In this way, the optimal progress can be achieved by adjusting the progress according to the expertise levels of the participants.

[0089] The interpretation unit can estimate the user's emotions and adjust the interpretation's expression method based on the estimated user's emotions. For example, if the user is nervous, the AI ​​can interpret using a calm expression method. Also, if the user is relaxed, the AI ​​can interpret using a cheerful expression method. Also, if the user is in a hurry, the AI ​​can interpret using a quick and concise expression method. This allows for more appropriate interpretation by adjusting the interpretation's expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] When interpreting, the interpretation department can analyze the content of what participants say in real time and select the most appropriate interpretation method. For example, the interpretation department can use AI to analyze the content of what participants say in real time and select the most appropriate interpretation method. The interpretation department can also use AI to analyze the content of what participants say in real time and prioritize interpretation related to the agenda. The interpretation department can also use AI to analyze the content of what participants say in real time and suggest the most appropriate interpretation method. This makes it possible to select the most appropriate interpretation method by analyzing the content of what participants say in real time.

[0091] The interpretation unit can take into consideration the nuances of different languages ​​and cultural backgrounds when interpreting. For example, the interpretation unit can use AI to interpret taking into consideration the nuances of different languages. The interpretation unit can also use AI to interpret taking into consideration the cultural backgrounds when interpreting. The interpretation unit can also use AI to interpret taking into consideration the nuances of different languages ​​and cultural backgrounds when interpreting. This allows for more appropriate interpretation by taking into consideration the nuances of different languages ​​and cultural backgrounds.

[0092] The interpretation unit can improve the accuracy of interpretation by referring to past interpretation data when interpreting. For example, the interpretation unit can improve the accuracy of interpretation by using AI to refer to past interpretation data. The interpretation unit can also analyze past interpretation data and propose the optimal interpretation method. The interpretation unit can also adjust the interpretation algorithm based on past interpretation data by using AI. In this way, the accuracy of interpretation is improved by referring to past interpretation data.

[0093] The interpretation unit can estimate the user's emotions and determine the priority of interpretation based on the estimated user emotions. For example, if the user is feeling stressed, the interpretation unit allows the AI ​​to prioritize interpreting important statements. Also, if the user is relaxed, the interpretation unit can prioritize interpreting detailed statements. Also, if the user is in a hurry, the interpretation unit can prioritize interpreting statements that the AI ​​can interpret quickly. In this way, by determining the priority of interpretation according to the user's emotions, important statements can be interpreted preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The interpretation unit can take into account the geographical location information of participants when interpreting. For example, if participants are in different locations, the interpretation unit uses AI to take into account their geographical location information when interpreting. In addition, if participants are on the move, the interpretation unit can use AI to take into account their geographical location information when interpreting. In addition, if participants are in a specific location, the interpretation unit can use AI to take into account their geographical location information when interpreting. This allows for optimal interpretation by taking into account the geographical location information of participants.

[0095] The interpretation unit can improve the accuracy of the interpretation by referring to related external data during interpretation. For example, the interpretation unit can improve the accuracy of the interpretation by having AI refer to a technical dictionary. The interpretation unit can also improve the accuracy of the interpretation by having AI refer to industry terminology. The interpretation unit can also improve the accuracy of the interpretation by having AI refer to related external data. In this way, the accuracy of the interpretation is improved by referring to related external data.

[0096] The interpretation department can adjust the interpretation according to the participants' level of expertise when interpreting. For example, the interpretation department uses AI to analyze the participants' level of expertise and adjust the interpretation. The interpretation department can also adjust the content of the interpretation according to the participants' level of expertise. The interpretation department can also propose the optimal interpretation method by using AI to take into account the participants' level of expertise. This allows the optimal interpretation to be performed by adjusting the interpretation according to the participants' level of expertise.

[0097] The minutes-taking unit can estimate the user's emotions and adjust the way the minutes are expressed based on the estimated user's emotions. For example, if the user is nervous, the minutes-taking unit allows the AI ​​to create minutes using concise and clear expressions. Also, if the user is relaxed, the minutes-taking unit can create minutes using expressions that include detailed information. Also, if the user is in a hurry, the minutes-taking unit can create minutes using expressions that quickly focus on the main points. In this way, by adjusting the way the minutes are expressed based on the user's emotions, more appropriate minutes are created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The minutes-taking department can analyze what is said during a meeting in real time and select the optimal method for creating minutes. For example, the minutes-taking department can use AI to analyze what is said during a meeting in real time and select the optimal method for creating minutes. The minutes-taking department can also use AI to analyze what is said and create minutes by prioritizing comments related to the agenda. The minutes-taking department can also use AI to analyze what is said in real time and suggest the optimal method for creating minutes. This makes it possible to select the optimal method for creating minutes by analyzing what is said during a meeting in real time.

[0099] When creating minutes, the minutes-taking department can adjust the level of detail in the minutes based on the theme and purpose of the meeting. For example, the minutes-taking department may use AI to adjust the level of detail in the minutes based on the theme of the meeting. The minutes-taking department may also use AI to adjust the level of detail in the minutes based on the purpose of the meeting. The minutes-taking department may also use AI to adjust the level of detail in the minutes based on the areas of expertise of the meeting participants. In this way, optimal minutes are created by adjusting the level of detail in the minutes based on the theme and purpose of the meeting.

[0100] When creating minutes, the minutes-taking department can refer to past minutes data to improve the accuracy of the minutes. For example, the minutes-taking department can use AI to refer to past minutes data to improve the accuracy of the minutes. The minutes-taking department can also use AI to analyze past minutes data and propose the optimal method for creating minutes. The minutes-taking department can also use AI to adjust the algorithm for creating minutes based on past minutes data. In this way, the accuracy of the minutes can be improved by referring to past minutes data.

[0101] The minutes-taking unit can estimate the user's emotions and determine the priority of the minutes based on the estimated user's emotions. For example, when the user is feeling stressed, the minutes-taking unit allows the AI ​​to prioritize recording important remarks in the minutes. Furthermore, when the user is relaxed, the minutes-taking unit can also allow the AI ​​to prioritize recording detailed remarks in the minutes. Furthermore, when the user is in a hurry, the minutes-taking unit can also prioritize recording remarks that quickly capture the main points in the minutes. In this way, by determining the priority of the minutes according to the user's emotions, important remarks can be prioritized and recorded in the minutes. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When creating minutes, the minutes-taking unit can create minutes taking into account the geographical location information of participants. For example, when participants are in different locations, the minutes-taking unit creates minutes by having the AI ​​take into account the geographical location information. Furthermore, when participants are on the move, the minutes-taking unit can also create minutes by having the AI ​​take into account the geographical location information. Furthermore, when participants are in a specific location, the minutes-taking unit can also create minutes by having the AI ​​take into account the geographical location information. In this way, optimal minutes are created by taking into account the geographical location information of participants.

[0103] When creating minutes, the minutes-taking department can improve the accuracy of the minutes by referring to related external data. For example, the minutes-taking department can improve the accuracy of the minutes by using AI to refer to market trends. The minutes-taking department can also improve the accuracy of the minutes by using AI to refer to technological trends. The minutes-taking department can also improve the accuracy of the minutes by using AI to refer to related external data. In this way, the accuracy of the minutes is improved by referring to related external data.

[0104] When creating minutes, the minutes-taking department can adjust the minutes according to the expertise level of the participants. For example, the minutes-taking department uses AI to analyze the expertise level of the participants and adjust the minutes. The minutes-taking department can also adjust the content of the minutes according to the expertise level of the participants. The minutes-taking department can also suggest the optimal method for creating minutes by using AI to take into account the expertise level of the participants. In this way, optimal minutes are created by adjusting the minutes according to the expertise level of the participants. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, progress management unit, interpretation unit, and minutes creation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects and organizes minutes and related documents from past meetings. The progress management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the progress of the meeting in real time and adjusts the agenda so that it proceeds within the scheduled time. The interpretation unit is realized, for example, by the control unit 46A of the smart device 14 and provides real-time interpretation to enable participants who speak different languages ​​to communicate smoothly. The minutes creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records what is said during the meeting in real time and automatically generates minutes after the meeting ends. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, progress management unit, interpretation unit, and minutes creation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects and organizes minutes and related documents from past meetings. The progress management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the progress of the meeting in real time and adjusts the agenda so that it proceeds within the scheduled time. The interpretation unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides real-time interpretation to enable participants who speak different languages ​​to communicate smoothly. The minutes creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records what is said during the meeting in real time and automatically generates minutes after the meeting ends. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, progress management unit, interpretation unit, and minutes creation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects and organizes minutes and related documents from past meetings. The progress management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and monitors the progress of the meeting in real time and adjusts the agenda so that it proceeds within the scheduled time. The interpretation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides real-time interpretation to enable participants who speak different languages ​​to communicate smoothly. The minutes creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and records what is said during the meeting in real time and automatically generates minutes after the meeting ends. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, progress management unit, interpretation unit, and minutes creation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects and organizes minutes and related documents from past meetings. The progress management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the progress of the meeting in real time and adjusts the agenda so that it proceeds within the scheduled time. The interpretation unit is realized, for example, by the control unit 46A of the robot 414 and provides real-time interpretation to enable participants who speak different languages ​​to communicate smoothly. The minutes creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records what is said during the meeting in real time and automatically generates minutes after the meeting ends.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The meeting support system can further include an evaluation unit that analyzes the content of participants' comments and evaluates their importance. The evaluation unit, for example, uses AI to analyze the content of comments in real time and prioritizes recording of important comments. The evaluation unit can also use AI to analyze the frequency and content of comments and extract important comments related to the agenda. Furthermore, the evaluation unit can use AI to analyze the emotional tone of comments and identify emotionally important comments. This allows the meeting support system to efficiently record important comments and improve the quality of meeting minutes.

[0107] The meeting support system can further include an emotion adjustment unit that estimates the emotions of the participants and adjusts the progress of the meeting based on the estimated emotions. For example, if a participant is nervous, the emotion adjustment unit causes the AI ​​to proceed slowly. Alternatively, if a participant is relaxed, the emotion adjustment unit can cause the AI ​​to proceed smoothly. Furthermore, if a participant is in a hurry, the emotion adjustment unit can adjust the AI ​​to proceed quickly. In this way, the meeting support system can adjust the progress according to the emotions of the participants and improve the efficiency of the meeting.

[0108] The meeting support system can further include an expertise adjustment unit that analyzes the expertise levels of the participants and adjusts the progress of the agenda. The expertise adjustment unit, for example, uses AI to analyze the expertise levels of the participants and adjusts the progress of the agenda. The expertise adjustment unit can also use AI to provide detailed explanations of the agenda according to the expertise levels of the participants. Furthermore, the expertise adjustment unit can also use AI to propose the optimal way to proceed, taking into account the expertise levels of the participants. In this way, the meeting support system can adjust the progress according to the expertise levels of the participants and improve the understanding of the meeting.

[0109] The conference support system may further include a location information adjustment unit that adjusts the progress of the conference taking into account the geographical location information of the participants. For example, when participants are in different locations, the location information adjustment unit adjusts the progress of the conference by using an AI that takes into account the geographical location information. The location information adjustment unit may also adjust the progress of the conference by using an AI that takes into account the geographical location information of a participant who is moving. Furthermore, when a participant is in a specific location, the location information adjustment unit may preferentially provide information related to that location. This allows the conference support system to adjust the progress of the conference by taking into account the geographical location information of the participants, thereby improving the efficiency of the conference.

[0110] The conference support system may further include a social media analysis unit that analyzes participants' social media activities and provides related information. For example, the social media analysis unit may use AI to analyze participants' social media activities and provide related information. The social media analysis unit may also use AI to provide related information based on the activities of participants' friends on social media. Furthermore, the social media analysis unit may use AI to collect related materials based on information shared by participants on social media. This allows the conference support system to analyze participants' social media activities and provide related information efficiently.

[0111] The conference support system can further include an emotion interpretation unit that estimates the emotions of the participants and adjusts the interpretation's expression method based on the estimated emotions. For example, if a participant is nervous, the emotion interpretation unit can use an AI to interpret using a calm expression method. Also, if a participant is relaxed, the emotion interpretation unit can use an AI to interpret using a cheerful expression method. Furthermore, if a participant is in a hurry, the emotion interpretation unit can use an AI to interpret using a quick and concise expression method. This allows the conference support system to adjust the interpretation's expression method according to the participants' emotions and provide more appropriate interpretation.

[0112] The conference support system can further include an interpretation optimization unit that analyzes participants' remarks in real time and selects the optimal interpretation method. The interpretation optimization unit, for example, uses AI to analyze participants' remarks in real time and select the optimal interpretation method. The interpretation optimization unit can also use AI to analyze remarks and prioritize interpretation related to the agenda. Furthermore, the interpretation optimization unit can also use AI to analyze remarks in real time and suggest the optimal interpretation method. In this way, the conference support system can improve the accuracy of interpretation by analyzing participants' remarks in real time and selecting the optimal interpretation method.

[0113] The meeting support system can further include an emotional minutes section that estimates the emotions of the participants and adjusts the way the minutes are expressed based on the estimated emotions. For example, if the participants are nervous, the emotional minutes section can create minutes using a concise and clear expression method. Also, if the participants are relaxed, the emotional minutes section can create minutes using an expression method that includes detailed information. Furthermore, if the participants are in a hurry, the emotional minutes section can create minutes using an expression method that quickly focuses on the main points. This allows the meeting support system to adjust the way the minutes are expressed based on the emotions of the participants and create more appropriate minutes.

[0114] The meeting support system can further include a location information minutes-taking unit that creates minutes taking into account the geographical location information of participants. For example, when participants are in different locations, the location information minutes-taking unit creates minutes by using AI taking into account geographical location information. In addition, when participants are moving, the location information minutes-taking unit can also create minutes by using AI taking into account geographical location information. Furthermore, when participants are in a specific location, the location information minutes-taking unit can also prioritize recording information related to that location in the minutes. This allows the meeting support system to create minutes taking into account the geographical location information of participants and provide more appropriate minutes.

[0115] The meeting support system can further include an emotion-prioritized minutes-taking unit that estimates the emotions of participants and determines the priority of minutes based on the estimated emotions. For example, if a participant is feeling stressed, the emotion-prioritized minutes-taking unit allows the AI ​​to prioritize recording important remarks in the minutes. In addition, if a participant is relaxed, the emotion-prioritized minutes-taking unit can also prioritize recording detailed remarks in the minutes. Furthermore, if a participant is in a hurry, the emotion-prioritized minutes-taking unit can also prioritize recording remarks that quickly get to the point in the minutes. This allows the meeting support system to prioritize minutes according to the emotions of participants and efficiently record important remarks.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection department collects and organizes meeting materials. For example, minutes of past meetings and related documents can be retrieved from a database and automatically collected and organized using AI. Step 2: The progress management department manages the progress of the meeting based on the materials collected by the collection department. For example, it monitors the progress of the meeting in real time and adjusts the agenda so that it proceeds within the scheduled time. Step 3: The Interpretation Department interprets the contents of the conference managed by the Conference Management Department in real time, for example, to enable smooth communication between participants who speak different languages. Step 4: The minutes-taking department creates minutes based on the content interpreted by the interpretation department. For example, it records what is said during the meeting in real time and automatically generates minutes after the meeting ends.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects and organizes meeting materials; a progress management unit that manages the progress of the meeting based on the materials collected by the collection unit; an interpretation unit that interprets in real time the content of the meeting whose progress is managed by the progress management unit; and a minutes creation unit that creates minutes based on the content interpreted by the interpretation unit.

2. The system according to claim 1 , wherein the collection unit collects and organizes minutes of past meetings and related documents.

3. 2. The system according to claim 1, wherein the progress management unit monitors the progress of the meeting in real time and adjusts the agenda so that the agenda is progressed within the time limit.

4. 2. The system of claim 1, wherein the interpretation unit provides real-time interpretation to enable participants who speak different languages ​​to communicate with each other.

5. 2. The system according to claim 1, wherein the minutes-taking section records the contents of statements made during a meeting in real time and generates minutes after the meeting ends.

6. The collecting unit Estimate user emotions and adjust the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze past meeting minutes and related documents and select the best collection method 2. The system of claim 1.

8. The collecting unit When collecting materials, filter them based on the theme and purpose of the meeting.

2. The system of claim 1.

Images