System

Abstract

An object of a system according to an embodiment is to efficiently visualize progress of a discussion and an idea in a remote conference.SOLUTION: A system according to an embodiment includes an idea visualization unit, a material generation unit, a discussion facilitation unit, and a record generation unit. The idea visualization unit visualizes an idea of a participant. The material generation part generates a material on the basis of the idea visualized by the idea visualization part. The discussion facilitator manages the progress of the discussion based on the material generated by the material generator. The record generating section records the content of the discussion managed by the discussion facilitating 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 has the problem that it is difficult to visualize the progress of discussions and ideas in remote meetings, making it difficult to hold efficient discussions.

[0005] The system according to the embodiment aims to efficiently advance discussions and visualize ideas in remote conferences. [Means for solving the problem]

[0006] The system according to the embodiment comprises an idea visualization unit, a material generation unit, a discussion facilitating unit, and a record generation unit. The idea visualization unit visualizes the ideas of the participants. The material generation unit generates materials based on the ideas visualized by the idea visualization unit. The discussion facilitating unit manages the progress of the discussion based on the materials generated by the material generation unit. The record generation unit records the content of the discussion managed by the discussion facilitating unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently progress discussions and visualize ideas during remote meetings. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A remote meeting support system according to an embodiment of the present invention is a system that supports the efficiency and visualization of discussions in remote meetings. This system utilizes AI technology to visualize discussions, generate materials, facilitate discussions, record meetings, and generate minutes. As a result, the remote meeting support system can improve the efficiency and visualization of remote meetings and reduce the burden on participants.

[0029] A remote conference support system according to an embodiment includes an idea visualization unit, a material generation unit, a discussion facilitator unit, and a record generator unit. The idea visualization unit visualizes participants' ideas. For example, the generation AI analyzes participants' remarks in real time and automatically generates and displays related images and videos. The generation AI can also refer to past conference data to present similar ideas and discussion results. The generation AI also uses an emotion estimation function to analyze participants' emotions and generate visuals that elicit positive emotions. The material generation unit generates materials based on the ideas visualized by the idea visualization unit. For example, the generation AI analyzes participants' remarks and automatically retrieves information from related external databases to reflect the information in the materials. The generation AI can also refer to past conference materials and automatically integrate related data and graphs. The generation AI also uses the emotion estimation function to analyze participants' emotions and generate material designs that elicit positive emotions. The discussion facilitator unit manages the progress of the discussion based on the materials generated by the material generation unit. For example, the generation AI can analyze participants' comments in real time and make suggestions to optimize the discussion. It can also refer to past meeting data and present the results and success stories of similar discussions. It can also use its emotion estimation function to analyze participants' emotions and suggest ways to advance the discussion in a way that elicits positive emotions. The record generation unit records the content of discussions managed by the discussion facilitator unit. For example, when recording the contents of a meeting, the generation AI can automatically tag the content of each speaker's comments and save them in an easily searchable format. When recording the contents of a meeting, the generation AI can also generate minutes in real time and provide a function that participants can review and edit on the spot. The generation AI can also use its emotion estimation function to analyze participants' emotions and generate a minutes format that elicits positive emotions. When recording the contents of a meeting, the generation AI can also simultaneously record audio and video, adding a function to provide minutes in multimedia format.Additionally, when recording meeting content, the generative AI can automatically translate it into different languages, providing a function for obtaining feedback from an international perspective. Furthermore, the generative AI uses emotion estimation to analyze participants' emotions and automatically adjust the layout and design of meeting minutes to elicit positive emotions. This allows the remote meeting support system to improve the efficiency and visualization of remote meetings and reduce the burden on participants. For example, visualizing ideas allows all participants to be on the same page, and generating and providing materials facilitates smooth discussions. Furthermore, facilitating discussions narrows the focus, and recording meetings and generating minutes reduces the burden of post-event processing.

[0030] The idea visualization unit can analyze what is being said in real time and automatically generate and display related images and videos. For example, the idea visualization unit uses a generation AI to analyze what participants are saying in real time and automatically generate and display related images and videos. For example, when a participant proposes an idea for a new product, it instantly generates and displays prototype images of that product and videos of usage scenarios. This helps to concretize discussions by analyzing what participants are saying in real time and automatically generating and displaying related visual content.

[0031] The idea visualization unit can refer to past meeting data and present similar ideas and discussion results. For example, the idea visualization unit uses a generation AI to refer to past meeting data and present similar ideas and discussion results. For example, it compares ideas that came up in previous meetings with current discussions and displays progress and areas for improvement. This supports the progress of discussions by referring to past meeting data and presenting similar ideas and discussion results.

[0032] The material generation unit can analyze the content of comments and automatically retrieve information from related external databases to reflect it in the materials. For example, the generation AI in the material generation unit analyzes the content of participants' comments and automatically retrieves information from related external databases to reflect it in the materials. For example, it obtains the latest market data and technical information in real time and adds it to the materials. In this way, the quality of discussions is improved by analyzing the content of participants' comments and automatically retrieving information from related external databases to reflect it in the materials.

[0033] The material generation unit can refer to past meeting materials and automatically integrate related data and graphs. For example, the generation AI in the material generation unit refers to past meeting materials and automatically integrates related data and graphs. For example, the latest materials are generated based on data used in the previous meeting. This improves the quality of materials by referencing past meeting materials and automatically integrating related data and graphs.

[0034] The discussion facilitator can analyze the content of comments in real time and make suggestions to optimize the progress of the discussion. For example, the generative AI can analyze the content of participants' comments in real time and make suggestions to optimize the progress of the discussion. For example, if the discussion is about to go off track, it can encourage participants to return to the original topic. In this way, the efficiency of the discussion is improved by analyzing the content of participants' comments in real time and making suggestions to optimize the progress of the discussion.

[0035] The discussion facilitator can refer to past meeting data and present the results and success stories of similar discussions. For example, the generative AI in the discussion facilitator can refer to past meeting data and present the results and success stories of similar discussions. For example, it can provide information useful for the current discussion based on success stories from previous meetings. This improves the quality of discussions by referring to past meeting data and presenting the results and success stories of similar discussions.

[0036] When recording the contents of a meeting, the record generation unit can automatically tag the content of what was said by each speaker and save it in an easily searchable format. For example, when the generation AI records the contents of a meeting, the record generation unit can automatically tag the content of what was said by each speaker and save it in an easily searchable format. For example, it can tag the speaker's name and the topic of what was said. This allows the contents of a meeting to be managed efficiently by automatically tagging the content of what was said by each speaker and saving it in an easily searchable format when recording the contents of a meeting.

[0037] The record generation unit can generate minutes in real time when recording the contents of a meeting, and provide a function that allows participants to check and modify them on the spot. For example, when the generation AI records the contents of a meeting, the record generation unit can generate minutes in real time, and provide a function that allows participants to check and modify them on the spot. For example, the contents of the minutes can be shared online and participants can add comments. This improves the accuracy of the minutes by generating minutes in real time when recording the contents of a meeting, and providing a function that allows participants to check and modify them on the spot.

[0038] The record generation unit can add a function to simultaneously record audio and video when recording the contents of a meeting and provide minutes in multimedia format. For example, the record generation unit can add a function to simultaneously record audio and video when the generation AI records the contents of a meeting and provide minutes in multimedia format. For example, the audio and video of the meeting can be linked to the minutes. This improves the quality of the minutes by simultaneously recording audio and video when recording the contents of the meeting and providing minutes in multimedia format.

[0039] The record generation unit can provide a function for automatically translating the contents of a meeting into different languages ​​and obtaining feedback from an international perspective when recording the contents of the meeting. For example, the record generation unit provides a function for automatically translating the contents of a meeting into different languages ​​and obtaining feedback from an international perspective when the generation AI records the contents of the meeting. For example, the minutes of a meeting can be automatically translated into English, French, Chinese, etc., and feedback from an international team can be collected. This improves the quality of discussions by automatically translating the contents of a meeting into different languages ​​and obtaining feedback from an international perspective when recording the contents of the meeting.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The remote meeting support system can further include an audio recording unit that records what participants say. For example, the audio recording unit uses a generation AI to record what participants say in real time and save it as an audio file. This allows the contents of the meeting to be recorded in audio format and played back for later review. The audio recording unit can also refer to past meeting data and present audio recordings of similar discussions. This allows current discussions to proceed while referring to past audio recordings. Furthermore, the audio recording unit can record what participants say directly without using an emotion estimation function. This makes it possible to provide objective records that are not influenced by emotions.

[0042] The remote meeting support system can further include a text converter that converts participants' comments into text. For example, the text converter uses a generation AI to transcribe participants' comments in real time and save them as a text file. This allows the contents of the meeting to be recorded in text format, making it easy to search and edit later. The text converter can also refer to past meeting data and present text records of similar discussions. This allows participants to advance the current discussion while referring to past text records. Furthermore, the text converter can convert participants' comments directly into text without using an emotion estimation function. This makes it possible to provide an objective record that is not influenced by emotions.

[0043] The remote meeting support system can further include a classification unit that classifies the content of participants' comments. For example, the classification unit uses a generation AI to analyze the content of participants' comments in real time and classify them based on the theme or category of the comments. This makes it easier to organize the content of the meeting and makes it convenient for later reference. The classification unit can also refer to past meeting data and present classification results for similar discussions. This allows current discussions to proceed while referring to past classification results. Furthermore, the classification unit can classify the content of participants' comments directly without using an emotion estimation function. This makes it possible to provide objective classifications that are not influenced by emotions.

[0044] The remote meeting support system can further include a statistical analysis unit that performs statistical analysis of participants' comments. For example, the statistical analysis unit uses a generative AI to analyze participants' comments in real time and statistically analyze the frequency and patterns of comments. This makes it easier to grasp the trends and important points of discussions. The statistical analysis unit can also refer to past meeting data and present the results of statistical analysis of similar discussions. This allows current discussions to proceed while referring to past statistical analysis results. Furthermore, the statistical analysis unit can perform statistical analysis of participants' comments directly without using an emotion estimation function. This makes it possible to provide objective analysis that is not influenced by emotions.

[0045] The remote meeting support system can further include a feedback unit that provides feedback on participants' comments. For example, the feedback unit uses a generation AI to analyze participants' comments in real time and provide feedback on the comments. This makes it easier for participants to understand the impact of their comments. The feedback unit can also refer to past meeting data and present feedback results from similar discussions. This allows participants to advance the current discussion while referring to past feedback results. Furthermore, the feedback unit can provide feedback on participants' comments as they are, without using an emotion estimation function. This makes it possible to provide objective feedback that is not influenced by emotions.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The idea visualization unit visualizes participants' ideas. For example, the generation AI analyzes participants' comments in real time and automatically generates and displays related images and videos. The generation AI can also refer to past meeting data to present similar ideas and discussion results. Furthermore, the generation AI uses an emotion estimation function to analyze participants' emotions and generate visuals that elicit positive emotions. Step 2: The material generation unit generates materials based on the ideas visualized by the idea visualization unit. For example, the generation AI analyzes participants' comments and automatically retrieves information from relevant external databases to reflect in the materials. The generation AI can also refer to past meeting materials and automatically integrate related data and graphs. Furthermore, the generation AI uses an emotion estimation function to analyze participants' emotions and generate a material design that elicits positive emotions. Step 3: The discussion facilitator manages the progress of the discussion based on the materials generated by the material generator. For example, the generation AI analyzes participants' comments in real time and makes suggestions to optimize the progress of the discussion. The generation AI can also refer to past meeting data to present the results of similar discussions and success stories. Furthermore, the generation AI uses an emotion estimation function to analyze participants' emotions and suggests ways to progress the discussion in a way that elicits positive emotions. Step 4: The record generation unit records the content of the discussion managed by the discussion facilitator unit. For example, when recording the contents of a meeting, the generation AI automatically tags the content of each speaker's remarks and saves them in an easily searchable format. The generation AI can also generate minutes in real time when recording the contents of a meeting, providing a function that allows participants to check and edit them on the spot. Furthermore, the generation AI can use an emotion estimation function to analyze participants' emotions and generate a minutes format that elicits positive emotions. The generation AI can also add a function to simultaneously record audio and video when recording the contents of a meeting, providing minutes in multimedia format. Furthermore, when recording the contents of a meeting, the generation AI can automatically translate the contents into different languages, providing a function to obtain feedback from an international perspective.

[0048] (Example 2) A remote meeting support system according to an embodiment of the present invention is a system that supports the efficiency and visualization of discussions in remote meetings. This system utilizes AI technology to visualize discussions, generate materials, facilitate discussions, record meetings, and generate minutes. As a result, the remote meeting support system can improve the efficiency and visualization of remote meetings and reduce the burden on participants.

[0049] A remote conference support system according to an embodiment includes an idea visualization unit, a material generation unit, a discussion facilitator unit, and a record generator unit. The idea visualization unit visualizes participants' ideas. For example, the generation AI analyzes participants' remarks in real time and automatically generates and displays related images and videos. The generation AI can also refer to past conference data to present similar ideas and discussion results. The generation AI also uses an emotion estimation function to analyze participants' emotions and generate visuals that elicit positive emotions. The material generation unit generates materials based on the ideas visualized by the idea visualization unit. For example, the generation AI analyzes participants' remarks and automatically retrieves information from related external databases to reflect the information in the materials. The generation AI can also refer to past conference materials and automatically integrate related data and graphs. The generation AI also uses the emotion estimation function to analyze participants' emotions and generate material designs that elicit positive emotions. The discussion facilitator unit manages the progress of the discussion based on the materials generated by the material generation unit. For example, the generation AI can analyze participants' comments in real time and make suggestions to optimize the discussion. It can also refer to past meeting data and present the results and success stories of similar discussions. It can also use its emotion estimation function to analyze participants' emotions and suggest ways to advance the discussion in a way that elicits positive emotions. The record generation unit records the content of discussions managed by the discussion facilitator unit. For example, when recording the contents of a meeting, the generation AI can automatically tag the content of each speaker's comments and save them in an easily searchable format. When recording the contents of a meeting, the generation AI can also generate minutes in real time and provide a function that participants can review and edit on the spot. The generation AI can also use its emotion estimation function to analyze participants' emotions and generate a minutes format that elicits positive emotions. When recording the contents of a meeting, the generation AI can also simultaneously record audio and video, adding a function to provide minutes in multimedia format.Additionally, when recording meeting content, the generative AI can automatically translate it into different languages, providing a function for obtaining feedback from an international perspective. Furthermore, the generative AI uses emotion estimation to analyze participants' emotions and automatically adjust the layout and design of meeting minutes to elicit positive emotions. This allows the remote meeting support system to improve the efficiency and visualization of remote meetings and reduce the burden on participants. For example, visualizing ideas allows all participants to be on the same page, and generating and providing materials facilitates smooth discussions. Furthermore, facilitating discussions narrows the focus, and recording meetings and generating minutes reduces the burden of post-event processing.

[0050] The idea visualization unit can analyze what is being said in real time and automatically generate and display related images and videos. For example, the idea visualization unit uses a generation AI to analyze what participants are saying in real time and automatically generate and display related images and videos. For example, when a participant proposes an idea for a new product, it instantly generates and displays prototype images of that product and videos of usage scenarios. This helps to concretize discussions by analyzing what participants are saying in real time and automatically generating and displaying related visual content.

[0051] The idea visualization unit can refer to past meeting data and present similar ideas and discussion results. For example, the idea visualization unit uses a generation AI to refer to past meeting data and present similar ideas and discussion results. For example, it compares ideas that came up in previous meetings with current discussions and displays progress and areas for improvement. This supports the progress of discussions by referring to past meeting data and presenting similar ideas and discussion results.

[0052] The idea visualization unit can use the emotion estimation function to analyze the emotions of participants and generate visuals that elicit positive emotions. The idea visualization unit, for example, uses the emotion estimation function to analyze the emotions of participants in real time and generate visuals that elicit positive emotions. For example, if a participant is excited, it displays bright colors and fun images. In this way, the emotion estimation function is used to analyze the emotions of participants and generate visuals that elicit positive emotions, improving the atmosphere of the discussion.

[0053] The material generation unit can analyze the content of comments and automatically retrieve information from related external databases to reflect it in the materials. For example, the generation AI in the material generation unit analyzes the content of participants' comments and automatically retrieves information from related external databases to reflect it in the materials. For example, it obtains the latest market data and technical information in real time and adds it to the materials. In this way, the quality of discussions is improved by analyzing the content of participants' comments and automatically retrieving information from related external databases to reflect it in the materials.

[0054] The material generation unit can refer to past meeting materials and automatically integrate related data and graphs. For example, the generation AI in the material generation unit refers to past meeting materials and automatically integrates related data and graphs. For example, the latest materials are generated based on data used in the previous meeting. This improves the quality of materials by referencing past meeting materials and automatically integrating related data and graphs.

[0055] The material generation unit can use the emotion estimation function to analyze the emotions of the participants and generate a material design that elicits positive emotions. The material generation unit, for example, uses the emotion estimation function to analyze the emotions of the participants and generate a material design that elicits positive emotions. For example, the material generation unit uses a design that includes bright colors and positive messages. This improves the atmosphere of the discussion by analyzing the emotions of the participants and generating a material design that elicits positive emotions using the emotion estimation function.

[0056] The discussion facilitator can analyze the content of comments in real time and make suggestions to optimize the progress of the discussion. For example, the generative AI can analyze the content of participants' comments in real time and make suggestions to optimize the progress of the discussion. For example, if the discussion is about to go off track, it can encourage participants to return to the original topic. In this way, the efficiency of the discussion is improved by analyzing the content of participants' comments in real time and making suggestions to optimize the progress of the discussion.

[0057] The discussion facilitator can refer to past meeting data and present the results and success stories of similar discussions. For example, the generative AI in the discussion facilitator can refer to past meeting data and present the results and success stories of similar discussions. For example, it can provide information useful for the current discussion based on success stories from previous meetings. This improves the quality of discussions by referring to past meeting data and presenting the results and success stories of similar discussions.

[0058] The discussion facilitator unit can use the emotion estimation function to analyze the emotions of participants and propose ways to progress the discussion that will elicit positive emotions. For example, the discussion facilitator unit can use the emotion estimation function to analyze the emotions of participants in real time and propose ways to progress the discussion that will elicit positive emotions. For example, if a participant is feeling down, the unit can provide words of encouragement and a positive perspective. In this way, the atmosphere of the discussion can be improved by using the emotion estimation function to analyze the emotions of participants and propose ways to progress the discussion that will elicit positive emotions.

[0059] When recording the contents of a meeting, the record generation unit can automatically tag the content of what was said by each speaker and save it in an easily searchable format. For example, when the generation AI records the contents of a meeting, the record generation unit can automatically tag the content of what was said by each speaker and save it in an easily searchable format. For example, it can tag the speaker's name and the topic of what was said. This allows the contents of a meeting to be managed efficiently by automatically tagging the content of what was said by each speaker and saving it in an easily searchable format when recording the contents of a meeting.

[0060] The record generation unit can generate minutes in real time when recording the contents of a meeting, and provide a function that allows participants to check and modify them on the spot. For example, when the generation AI records the contents of a meeting, the record generation unit can generate minutes in real time, and provide a function that allows participants to check and modify them on the spot. For example, the contents of the minutes can be shared online and participants can add comments. This improves the accuracy of the minutes by generating minutes in real time when recording the contents of a meeting, and providing a function that allows participants to check and modify them on the spot.

[0061] The record generation unit can use the emotion estimation function to analyze the emotions of the participants and generate a minutes format that elicits positive emotions. The record generation unit, for example, uses the emotion estimation function to analyze the emotions of the participants and generate a minutes format that elicits positive emotions. For example, the record generation unit uses a format that includes bright colors and positive messages. This improves the atmosphere of the minutes by using the emotion estimation function to analyze the emotions of the participants and generate a minutes format that elicits positive emotions.

[0062] The record generation unit can add a function to simultaneously record audio and video when recording the contents of a meeting and provide minutes in multimedia format. For example, the record generation unit can add a function to simultaneously record audio and video when the generation AI records the contents of a meeting and provide minutes in multimedia format. For example, the audio and video of the meeting can be linked to the minutes. This improves the quality of the minutes by simultaneously recording audio and video when recording the contents of the meeting and providing minutes in multimedia format.

[0063] The record generation unit can provide a function for automatically translating the contents of a meeting into different languages ​​and obtaining feedback from an international perspective when recording the contents of the meeting. For example, the record generation unit provides a function for automatically translating the contents of a meeting into different languages ​​and obtaining feedback from an international perspective when the generation AI records the contents of the meeting. For example, the minutes of a meeting can be automatically translated into English, French, Chinese, etc., and feedback from an international team can be collected. This improves the quality of discussions by automatically translating the contents of a meeting into different languages ​​and obtaining feedback from an international perspective when recording the contents of the meeting.

[0064] The record generation unit can use the emotion estimation function to analyze the emotions of the participants and automatically adjust the layout and design of the minutes to elicit positive emotions. The record generation unit, for example, uses the emotion estimation function to automatically adjust the layout and design of the minutes according to the emotions of the participants. For example, if the participants are relaxed, calm colors and a simple layout are used. In this way, the emotion estimation function is used to analyze the emotions of the participants and automatically adjust the layout and design of the minutes to elicit positive emotions, thereby improving the atmosphere of the minutes.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The remote meeting support system can further include a summary generation unit that summarizes what participants say. For example, the summary generation unit uses a generation AI to analyze what participants say in real time, extract important points, and generate a summary. This allows for a concise understanding of the meeting content and improves the efficiency of discussions. The summary generation unit can also refer to past meeting data and present summaries of similar discussions. This allows current discussions to proceed while referring to the flow of past discussions. Furthermore, the summary generation unit can use an emotion estimation function to analyze participants' emotions and generate summaries that elicit positive emotions. For example, the atmosphere of a meeting can be improved by using words and expressions that evoke positive emotions in participants.

[0067] The remote meeting support system can also be equipped with a translation unit that translates what participants say. For example, the translation unit uses a generation AI to analyze what participants say in real time and automatically translate it into different languages. This allows participants who speak different languages ​​to communicate smoothly. The translation unit can also refer to past meeting data and present translation results for similar discussions. This allows participants to advance the current discussion while referring to past translation results. Furthermore, the translation unit can use an emotion estimation function to analyze the emotions of participants and generate translations that elicit positive emotions. For example, the atmosphere of a meeting can be improved by using words and expressions that evoke positive emotions in participants.

[0068] The remote meeting support system can further include an evaluation unit that evaluates the content of participants' comments. For example, the evaluation unit uses a generation AI to analyze the content of participants' comments in real time and evaluate the quality and importance of the comments. This allows for objective evaluation of participants' comments and optimizes the progress of discussions. The evaluation unit can also refer to past meeting data and present evaluation results of similar discussions. This allows current discussions to proceed while referring to past evaluation results. Furthermore, the evaluation unit can use an emotion estimation function to analyze participants' emotions and make evaluations that elicit positive emotions. For example, providing feedback that encourages participants to feel positive can improve the atmosphere of a meeting.

[0069] The remote meeting support system can further include an analysis unit that analyzes participants' comments. For example, the analysis unit uses a generative AI to analyze participants' comments in real time and analyze trends and patterns in the discussion. This optimizes the progress of the discussion and supports efficient discussions. The analysis unit can also refer to past meeting data and present analysis results of similar discussions. This allows participants to advance the current discussion while referring to past analysis results. Furthermore, the analysis unit can use an emotion estimation function to analyze participants' emotions and provide analysis results that elicit positive emotions. For example, presenting analysis results that encourage participants to have positive emotions can improve the atmosphere of the meeting.

[0070] The remote meeting support system can further include a visualization unit that visualizes participants' comments. For example, the visualization unit uses a generation AI to analyze participants' comments in real time and automatically generate and display related graphs and charts. This makes it easier to visually grasp what participants are saying and helps to flesh out discussions. The visualization unit can also refer to past meeting data and present visualization results of similar discussions. This allows participants to advance current discussions while referring to past visualization results. Furthermore, the visualization unit can use an emotion estimation function to analyze participants' emotions and create visualizations that elicit positive emotions. For example, the atmosphere of a meeting can be improved by using colors and designs that evoke positive emotions in participants.

[0071] The remote meeting support system can further include an audio recording unit that records what participants say. For example, the audio recording unit uses a generation AI to record what participants say in real time and save it as an audio file. This allows the contents of the meeting to be recorded in audio format and played back for later review. The audio recording unit can also refer to past meeting data and present audio recordings of similar discussions. This allows current discussions to proceed while referring to past audio recordings. Furthermore, the audio recording unit can record what participants say directly without using an emotion estimation function. This makes it possible to provide objective records that are not influenced by emotions.

[0072] The remote meeting support system can further include a text converter that converts participants' comments into text. For example, the text converter uses a generation AI to transcribe participants' comments in real time and save them as a text file. This allows the contents of the meeting to be recorded in text format, making it easy to search and edit later. The text converter can also refer to past meeting data and present text records of similar discussions. This allows participants to advance the current discussion while referring to past text records. Furthermore, the text converter can convert participants' comments directly into text without using an emotion estimation function. This makes it possible to provide an objective record that is not influenced by emotions.

[0073] The remote meeting support system can further include a classification unit that classifies the content of participants' comments. For example, the classification unit uses a generation AI to analyze the content of participants' comments in real time and classify them based on the theme or category of the comments. This makes it easier to organize the content of the meeting and makes it convenient for later reference. The classification unit can also refer to past meeting data and present classification results for similar discussions. This allows current discussions to proceed while referring to past classification results. Furthermore, the classification unit can classify the content of participants' comments directly without using an emotion estimation function. This makes it possible to provide objective classifications that are not influenced by emotions.

[0074] The remote meeting support system can further include a statistical analysis unit that performs statistical analysis of participants' comments. For example, the statistical analysis unit uses a generative AI to analyze participants' comments in real time and statistically analyze the frequency and patterns of comments. This makes it easier to grasp the trends and important points of discussions. The statistical analysis unit can also refer to past meeting data and present the results of statistical analysis of similar discussions. This allows current discussions to proceed while referring to past statistical analysis results. Furthermore, the statistical analysis unit can perform statistical analysis of participants' comments directly without using an emotion estimation function. This makes it possible to provide objective analysis that is not influenced by emotions.

[0075] The remote meeting support system can further include a feedback unit that provides feedback on participants' comments. For example, the feedback unit uses a generation AI to analyze participants' comments in real time and provide feedback on the comments. This makes it easier for participants to understand the impact of their comments. The feedback unit can also refer to past meeting data and present feedback results from similar discussions. This allows participants to advance the current discussion while referring to past feedback results. Furthermore, the feedback unit can provide feedback on participants' comments as they are, without using an emotion estimation function. This makes it possible to provide objective feedback that is not influenced by emotions.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The idea visualization unit visualizes participants' ideas. For example, the generation AI analyzes participants' comments in real time and automatically generates and displays related images and videos. The generation AI can also refer to past meeting data to present similar ideas and discussion results. Furthermore, the generation AI uses an emotion estimation function to analyze participants' emotions and generate visuals that elicit positive emotions. Step 2: The material generation unit generates materials based on the ideas visualized by the idea visualization unit. For example, the generation AI analyzes participants' comments and automatically retrieves information from relevant external databases to reflect in the materials. The generation AI can also refer to past meeting materials and automatically integrate related data and graphs. Furthermore, the generation AI uses an emotion estimation function to analyze participants' emotions and generate a material design that elicits positive emotions. Step 3: The discussion facilitator manages the progress of the discussion based on the materials generated by the material generator. For example, the generation AI analyzes participants' comments in real time and makes suggestions to optimize the progress of the discussion. The generation AI can also refer to past meeting data to present the results of similar discussions and success stories. Furthermore, the generation AI uses an emotion estimation function to analyze participants' emotions and suggests ways to progress the discussion in a way that elicits positive emotions. Step 4: The record generation unit records the content of the discussion managed by the discussion facilitator unit. For example, when recording the contents of a meeting, the generation AI automatically tags the content of each speaker's remarks and saves them in an easily searchable format. The generation AI can also generate minutes in real time when recording the contents of a meeting, providing a function that allows participants to check and edit them on the spot. Furthermore, the generation AI can use an emotion estimation function to analyze participants' emotions and generate a minutes format that elicits positive emotions. The generation AI can also add a function to simultaneously record audio and video when recording the contents of a meeting, providing minutes in multimedia format. Furthermore, when recording the contents of a meeting, the generation AI can automatically translate the contents into different languages, providing a function to obtain feedback from an international perspective.

[0078] 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.

[0079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] 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.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0145] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an idea visualization section that visualizes participants' ideas; a material generation unit that generates materials based on the ideas visualized by the idea visualization unit; a discussion facilitator unit that manages the progress of the discussion based on the materials generated by the material generation unit; a record generating unit that records the content of the discussion managed by the discussion facilitating unit. A system characterized by:

2. The idea visualization unit Analyzes speech content in real time and automatically generates and displays related images and videos 2. The system of claim 1.

3. The material generation unit Analyze the content of the statement, automatically retrieve information from related external databases, and reflect it in the above materials.

2. The system of claim 1.

4. The discussion facilitator unit Analyzes comments in real time and makes suggestions to optimize the progress of the discussion 2. The system of claim 1.

5. The record generation unit When recording meeting content, the content of each speaker is automatically tagged and saved in an easy-to-search format.

2. The system of claim 1.

6. The idea visualization unit Analyzing the emotions of the participants and generating visuals that elicit positive emotions 2. The system of claim 1.

Images