System

Abstract

An object of the system according to the embodiment is to visualize the content of a discussion during a meeting in real time and to allow all participants to have common recognition.SOLUTION: A system includes a discussion content acquisition part, a chart generation part, and a script generation part. The discussion content acquisition unit acquires statement contents during a conference. The diagram generation unit generates a diagram based on the statement content acquired by the discussion content acquisition unit. The script generation unit generates the chart generated by the chart generation unit as a script.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 had the problem of making it difficult to efficiently visualize the content of discussions during meetings and ensure that all participants have a common understanding.

[0005] The system according to the embodiment aims to visualize the contents of discussions during a meeting in real time, allowing all participants to have a common understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a discussion content acquisition unit, a diagram generation unit, and a script generation unit. The discussion content acquisition unit acquires the content of remarks made during a meeting. The diagram generation unit generates a diagram based on the content of remarks acquired by the discussion content acquisition unit. The script generation unit generates a script from the diagram generated by the diagram generation unit. [Effects of the Invention]

[0007] The system according to the embodiment visualizes the contents of discussions during a meeting in real time, enabling all participants to have a common understanding. [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) The conference support system according to an embodiment of the present invention is a system that graphically represents the content of statements made during a conference in real time and generates the result as a script. This makes it easier to visually grasp the flow of discussion during the conference.

[0029] The conference support system according to the embodiment includes a discussion content acquisition unit, a diagram generation unit, and a script generation unit. The discussion content acquisition unit acquires the content of remarks made during a conference. For example, the discussion content acquisition unit acquires the content of remarks made during a conference as text data using voice recognition technology. The discussion content acquisition unit can also record the remarks made during a conference and convert them into text data later. The discussion content acquisition unit can also convert the remarks made during a conference into text data in real time. For example, the speech recognition technology is used to instantly acquire the content of remarks as text data. The diagram generation unit generates a diagram based on the content of remarks acquired by the discussion content acquisition unit. For example, the diagram generation unit analyzes the content of remarks and generates a flowchart. The diagram generation unit can also generate a mind map based on the content of remarks. The diagram generation unit can also generate graphs and charts based on the content of remarks. For example, the diagram generation unit analyzes the content of remarks and generates a flowchart showing the flow of a discussion. The script generation unit generates a script from the diagram generated by the diagram generation unit. For example, the script generation unit generates the generated flowchart as a script in HTML format. The script generation unit can also generate a presentation-format script from the generated mind map. The script generation unit can also generate a text-format script from the generated graphs and charts. For example, a generated flowchart can be generated as an HTML-format script, and the contents can be modified later or reused in other meetings. This allows the meeting support system according to the embodiment to easily visually grasp the flow of discussion during a meeting. For example, all participants can grasp the overall picture of the discussion using the generated diagrams. The generated script can also be used to modify the contents later or reused in other meetings.

[0030] The diagram generator can track the progress of a discussion in real time and automatically highlight important points and decisions. For example, the generation AI of the diagram generator analyzes the content of comments made during a meeting and automatically highlights important keywords and phrases. For example, it highlights comments containing keywords such as "decision" or "important." The diagram generator also tracks the progress of a discussion in real time and automatically highlights important points. For example, it analyzes the progress of a discussion and displays important points in different colors. The diagram generator also tracks the progress of a discussion in real time and automatically highlights decisions. For example, it highlights decisions and shares them with all participants. This makes it possible to improve the efficiency of discussions by tracking the progress of a discussion in real time and highlighting important points and decisions.

[0031] The discussion content acquisition unit can record the discussion content as audio or video in real time, allowing it to be played back visually later. For example, the discussion content acquisition unit allows the generation AI to record audio during a meeting in real time, allowing it to be played back later. For example, important parts of the discussion can be marked so that they can be easily played back later. The discussion content acquisition unit can also record the discussion content as video in real time, allowing it to be played back visually later. For example, it can record the contents of remarks made during a meeting as video, allowing it to be played back later. The discussion content acquisition unit can also record the discussion content as audio or video in real time, allowing it to be played back visually later. For example, important parts of the discussion can be marked so that they can be easily played back later. In this way, the discussion content can be recorded in real time as audio or video, and then visually played back later, allowing the content of the discussion to be accurately reviewed.

[0032] The discussion content acquisition unit can translate discussions in different languages ​​in real time, allowing discussions to proceed smoothly even at international conferences. In the discussion content acquisition unit, for example, the generation AI translates the content of comments made during a meeting in real time and displays it instantly to participants speaking different languages. For example, comments made in English are translated into Japanese and displayed. The discussion content acquisition unit can also translate discussions in different languages ​​in real time, allowing discussions to proceed smoothly even at international conferences. For example, the generation AI translates the content of comments made during a meeting in real time and displays it instantly to participants speaking different languages. The discussion content acquisition unit can also translate discussions in different languages ​​in real time, allowing discussions to proceed smoothly even at international conferences. For example, the generation AI translates the content of comments made during a meeting in real time and displays it instantly to participants speaking different languages. This allows discussions in different languages ​​to be translated in real time, allowing discussions to proceed smoothly even at international conferences.

[0033] The script generation unit can use the generated script to visualize the content of the discussion as a 3D model, allowing for a more intuitive understanding. The script generation unit, for example, uses a script generated by a generation AI to visualize the content of the discussion as a 3D model. For example, the flow of the discussion is displayed as a 3D flowchart. The script generation unit also uses the generated script to visualize the content of the discussion as a 3D model. For example, the flow of the discussion is displayed as a 3D flowchart. The script generation unit also uses the generated script to visualize the content of the discussion as a 3D model. For example, the flow of the discussion is displayed as a 3D flowchart. In this way, by visualizing the content of the discussion as a 3D model using the generated script, participants can intuitively understand the content of the discussion.

[0034] The script generation unit can use the generated script to publish the content of the discussion as an interactive webpage, allowing participants to freely operate it. The script generation unit, for example, uses a script generated by a generation AI to publish the content of the discussion as an interactive webpage. For example, the flow of the discussion is displayed as a flowchart that can be expanded by clicking. The script generation unit also uses the generated script to publish the content of the discussion as an interactive webpage. For example, the flow of the discussion is displayed as a flowchart that can be expanded by clicking. The script generation unit also uses the generated script to publish the content of the discussion as an interactive webpage. For example, the flow of the discussion is displayed as a flowchart that can be expanded by clicking. In this way, by publishing the content of the discussion as an interactive webpage using the generated script, participants can freely operate it and understand the content of the discussion.

[0035] The script generation unit can use the generated script to play back the content of the discussion as an animation, allowing the flow of the discussion to be dynamically understood. The script generation unit, for example, uses a script generated by a generation AI to play back the content of the discussion as an animation. For example, the flow of the discussion can be displayed as an animation, allowing the participants to dynamically understand. The script generation unit also uses the generated script to play back the content of the discussion as an animation. For example, the flow of the discussion can be displayed as an animation, allowing the participants to dynamically understand. The script generation unit also uses the generated script to play back the content of the discussion as an animation. For example, the flow of the discussion can be displayed as an animation, allowing the participants to dynamically understand. In this way, by playing back the content of the discussion as an animation using the generated script, the participants can dynamically understand the flow of the discussion.

[0036] The script generation unit can use the generated script to reproduce the content of the discussion in a VR environment, allowing participants to experience the discussion in a virtual space. The script generation unit, for example, uses a script generated by the generation AI to reproduce the content of the discussion in a VR environment. For example, the flow of the discussion can be displayed in a VR space, allowing participants to experience the discussion in the virtual space. The script generation unit also uses the generated script to reproduce the content of the discussion in a VR environment. For example, the flow of the discussion can be displayed in a VR space, allowing participants to experience the discussion in the virtual space. The script generation unit also uses the generated script to reproduce the content of the discussion in a VR environment. For example, the flow of the discussion can be displayed in a VR space, allowing participants to experience the discussion in the virtual space. In this way, the generated script can be used to reproduce the content of the discussion in a VR environment, allowing participants to experience the discussion in the virtual space.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The diagram generator not only tracks the progress of the discussion in real time, but can also automatically update the agenda as the discussion progresses. For example, if the discussion is focused on a specific topic, that topic is highlighted. Also, if the discussion shifts to a new topic, that topic is automatically added. Furthermore, if the discussion has concluded, the topic is displayed grayed out. In this way, the diagram generator can improve the efficiency of discussions by automatically updating the agenda as the discussion progresses.

[0039] The discussion content acquisition unit not only records the content of the discussion as audio and video in real time, but also automatically summarizes the important parts of the discussion, making them easy to refer to later. For example, if an important keyword appears in the discussion, that part is automatically summarized and saved. Also, if a decision is made in the discussion, that part is automatically summarized and saved. Furthermore, if a problem is pointed out in the discussion, that part is automatically summarized and saved. In this way, the discussion content acquisition unit automatically summarizes the important parts of the discussion, making them easy to refer to later, allowing the content of the discussion to be accurately reviewed.

[0040] The discussion content acquisition unit not only translates discussions in different languages ​​in real time, but also plays the translated content aloud, allowing participants to understand the discussion in a natural way. For example, it can translate comments in English into Japanese and play the translated content aloud. It can also translate comments in Japanese into English and play the translated content aloud. It can also translate comments in multiple languages ​​simultaneously and play the audio in each language. In this way, the discussion content acquisition unit can play the translated content aloud, allowing participants to understand the discussion in a natural way.

[0041] The script generator uses the generated script to not only visualize the content of the discussion as a 3D model, but also allows participants to freely explore the flow of the discussion by manipulating the 3D model. For example, participants can click on each node in the 3D flowchart to display detailed information. They can also rotate the 3D model to check the flow of the discussion from different perspectives. They can also zoom in on parts of the 3D model to check the details of the discussion. In this way, the script generator allows participants to freely explore the flow of the discussion by manipulating the 3D model, thereby enabling a deeper understanding of the content of the discussion.

[0042] The script generation unit not only publishes the content of the discussion as an interactive web page using the generated script, but also enables participants to add comments and feedback in real time. For example, participants can add comments to each point in the discussion. Also, participants can add feedback on the flow of the discussion. Furthermore, participants can reply to comments made by other participants. In this way, the script generation unit can enrich the content of the discussion by enabling participants to add comments and feedback in real time.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The discussion content acquisition unit acquires the content of what is said during the meeting. For example, speech recognition technology can be used to acquire what is said during the meeting as text data. It is also possible to record what is said during the meeting and convert it into text data later. It is also possible to convert what is said during the meeting into text data in real time. Step 2: The diagram generation unit generates diagrams based on the utterances acquired by the discussion content acquisition unit. For example, the utterances can be analyzed to generate flowcharts, mind maps, graphs, and charts. Step 3: The script generator generates the diagrams generated by the diagram generator as scripts. For example, the generated flowchart can be generated as an HTML script, and the generated mind map can be generated as a presentation script. Furthermore, the generated graphs and charts can be generated as text scripts.

[0045] (Example 2) The conference support system according to an embodiment of the present invention is a system that graphically represents the content of statements made during a conference in real time and generates the result as a script. This makes it easier to visually grasp the flow of discussion during the conference.

[0046] The conference support system according to the embodiment includes a discussion content acquisition unit, a diagram generation unit, and a script generation unit. The discussion content acquisition unit acquires the content of remarks made during a conference. For example, the discussion content acquisition unit acquires the content of remarks made during a conference as text data using voice recognition technology. The discussion content acquisition unit can also record the remarks made during a conference and convert them into text data later. The discussion content acquisition unit can also convert the remarks made during a conference into text data in real time. For example, the speech recognition technology is used to instantly acquire the content of remarks as text data. The diagram generation unit generates a diagram based on the content of remarks acquired by the discussion content acquisition unit. For example, the diagram generation unit analyzes the content of remarks and generates a flowchart. The diagram generation unit can also generate a mind map based on the content of remarks. The diagram generation unit can also generate graphs and charts based on the content of remarks. For example, the diagram generation unit analyzes the content of remarks and generates a flowchart showing the flow of a discussion. The script generation unit generates a script from the diagram generated by the diagram generation unit. For example, the script generation unit generates the generated flowchart as a script in HTML format. The script generation unit can also generate a presentation-format script from the generated mind map. The script generation unit can also generate a text-format script from the generated graphs and charts. For example, a generated flowchart can be generated as an HTML-format script, and the contents can be modified later or reused in other meetings. This allows the meeting support system according to the embodiment to easily visually grasp the flow of discussion during a meeting. For example, all participants can grasp the overall picture of the discussion using the generated diagrams. The generated script can also be used to modify the contents later or reused in other meetings.

[0047] The diagram generation unit can analyze the emotional tone of a discussion and automatically generate color coding and icons to emphasize positive arguments. For example, the diagram generation unit uses a generation AI to analyze the content of comments made during a meeting in real time and identify the emotional tone. For example, it automatically applies a green background to positive comments and a red background to negative comments. The diagram generation unit also analyzes the emotional tone and automatically generates icons to emphasize positive arguments. For example, it applies a smiling icon to positive comments and a sad icon to negative comments. The diagram generation unit also analyzes the emotional tone and uses color coding to facilitate the progress of the discussion. For example, it applies a green background to positive comments and a red background to negative comments. This makes it possible to analyze the emotional tone of a discussion and emphasize positive arguments, thereby smoothing the progress of the discussion.

[0048] The diagram generator can track the progress of a discussion in real time and automatically highlight important points and decisions. For example, the generation AI of the diagram generator analyzes the content of comments made during a meeting and automatically highlights important keywords and phrases. For example, it highlights comments containing keywords such as "decision" or "important." The diagram generator also tracks the progress of a discussion in real time and automatically highlights important points. For example, it analyzes the progress of a discussion and displays important points in different colors. The diagram generator also tracks the progress of a discussion in real time and automatically highlights decisions. For example, it highlights decisions and shares them with all participants. This makes it possible to improve the efficiency of discussions by tracking the progress of a discussion in real time and highlighting important points and decisions.

[0049] The diagram generation unit uses the emotion estimation function to analyze the emotional state of participants in real time and suggest appropriate intervention if the discussion is stalling. For example, the generation AI of the diagram generation unit analyzes the facial expressions and tone of voice of participants to estimate their emotional state in real time. For example, if a participant is tired, it suggests a break. The diagram generation unit also uses the emotion estimation function to analyze the emotional state of participants in real time and suggest appropriate intervention if the discussion is stalling. For example, if the discussion is stalling, it suggests the intervention of a facilitator. The diagram generation unit also uses the emotion estimation function to analyze the emotional state of participants in real time and suggest a change in the agenda. For example, if a participant is tired, it suggests a change in the agenda. In this way, the emotion estimation function can be used to suggest appropriate intervention if the discussion is stalling, supporting the smooth progress of the discussion.

[0050] The discussion content acquisition unit can record the discussion content as audio or video in real time, allowing it to be played back visually later. For example, the discussion content acquisition unit allows the generation AI to record audio during a meeting in real time, allowing it to be played back later. For example, important parts of the discussion can be marked so that they can be easily played back later. The discussion content acquisition unit can also record the discussion content as video in real time, allowing it to be played back visually later. For example, it can record the contents of remarks made during a meeting as video, allowing it to be played back later. The discussion content acquisition unit can also record the discussion content as audio or video in real time, allowing it to be played back visually later. For example, important parts of the discussion can be marked so that they can be easily played back later. In this way, the discussion content can be recorded in real time as audio or video, and then visually played back later, allowing the content of the discussion to be accurately reviewed.

[0051] The discussion content acquisition unit can translate discussions in different languages ​​in real time, allowing discussions to proceed smoothly even at international conferences. In the discussion content acquisition unit, for example, the generation AI translates the content of comments made during a meeting in real time and displays it instantly to participants speaking different languages. For example, comments made in English are translated into Japanese and displayed. The discussion content acquisition unit can also translate discussions in different languages ​​in real time, allowing discussions to proceed smoothly even at international conferences. For example, the generation AI translates the content of comments made during a meeting in real time and displays it instantly to participants speaking different languages. The discussion content acquisition unit can also translate discussions in different languages ​​in real time, allowing discussions to proceed smoothly even at international conferences. For example, the generation AI translates the content of comments made during a meeting in real time and displays it instantly to participants speaking different languages. This allows discussions in different languages ​​to be translated in real time, allowing discussions to proceed smoothly even at international conferences.

[0052] The script generation unit can use the generated script to visualize the content of the discussion as a 3D model, allowing for a more intuitive understanding. The script generation unit, for example, uses a script generated by a generation AI to visualize the content of the discussion as a 3D model. For example, the flow of the discussion is displayed as a 3D flowchart. The script generation unit also uses the generated script to visualize the content of the discussion as a 3D model. For example, the flow of the discussion is displayed as a 3D flowchart. The script generation unit also uses the generated script to visualize the content of the discussion as a 3D model. For example, the flow of the discussion is displayed as a 3D flowchart. In this way, by visualizing the content of the discussion as a 3D model using the generated script, participants can intuitively understand the content of the discussion.

[0053] The script generation unit can use the generated script to publish the content of the discussion as an interactive webpage, allowing participants to freely operate it. The script generation unit, for example, uses a script generated by a generation AI to publish the content of the discussion as an interactive webpage. For example, the flow of the discussion is displayed as a flowchart that can be expanded by clicking. The script generation unit also uses the generated script to publish the content of the discussion as an interactive webpage. For example, the flow of the discussion is displayed as a flowchart that can be expanded by clicking. The script generation unit also uses the generated script to publish the content of the discussion as an interactive webpage. For example, the flow of the discussion is displayed as a flowchart that can be expanded by clicking. In this way, by publishing the content of the discussion as an interactive webpage using the generated script, participants can freely operate it and understand the content of the discussion.

[0054] The script generation unit uses the emotion estimation function to add emotional elements to diagrams based on the generated script, thereby visually expressing changes in emotions. For example, the script generation unit adds emotional elements to a script generated by the generation AI based on emotion estimation data. For example, a green background is applied to discussion sections with positive emotions, and a red background is applied to discussion sections with negative emotions. The script generation unit also uses the emotion estimation function to add emotional elements to diagrams based on the generated script. For example, a green background is applied to discussion sections with positive emotions, and a red background is applied to discussion sections with negative emotions. The script generation unit also uses the emotion estimation function to add emotional elements to diagrams based on the generated script. For example, a green background is applied to discussion sections with positive emotions, and a red background is applied to discussion sections with negative emotions. In this way, by adding emotional elements to a script generated using the emotion estimation function, changes in emotions can be visually expressed.

[0055] The script generation unit can use the generated script to play back the content of the discussion as an animation, allowing the flow of the discussion to be dynamically understood. The script generation unit, for example, uses a script generated by a generation AI to play back the content of the discussion as an animation. For example, the flow of the discussion can be displayed as an animation, allowing the participants to dynamically understand. The script generation unit also uses the generated script to play back the content of the discussion as an animation. For example, the flow of the discussion can be displayed as an animation, allowing the participants to dynamically understand. The script generation unit also uses the generated script to play back the content of the discussion as an animation. For example, the flow of the discussion can be displayed as an animation, allowing the participants to dynamically understand. In this way, by playing back the content of the discussion as an animation using the generated script, the participants can dynamically understand the flow of the discussion.

[0056] The script generation unit can use the generated script to reproduce the content of the discussion in a VR environment, allowing participants to experience the discussion in a virtual space. The script generation unit, for example, uses a script generated by the generation AI to reproduce the content of the discussion in a VR environment. For example, the flow of the discussion can be displayed in a VR space, allowing participants to experience the discussion in the virtual space. The script generation unit also uses the generated script to reproduce the content of the discussion in a VR environment. For example, the flow of the discussion can be displayed in a VR space, allowing participants to experience the discussion in the virtual space. The script generation unit also uses the generated script to reproduce the content of the discussion in a VR environment. For example, the flow of the discussion can be displayed in a VR space, allowing participants to experience the discussion in the virtual space. In this way, the generated script can be used to reproduce the content of the discussion in a VR environment, allowing participants to experience the discussion in the virtual space.

[0057] The script generation unit uses the emotion estimation function to add emotional feedback to diagrams based on the generated script, making it possible to present the content of the discussion in a way that participants can easily empathize with emotionally. For example, the script generation unit adds emotional feedback to a script generated by the generation AI based on emotion estimation data. For example, a green background is applied to sections of discussion that have positive emotions, and a red background is applied to sections of discussion that have negative emotions. The script generation unit also uses the emotion estimation function to add emotional feedback to diagrams based on the generated script. For example, a green background is applied to sections of discussion that have positive emotions, and a red background is applied to sections of discussion that have negative emotions. The script generation unit also uses the emotion estimation function to add emotional feedback to diagrams based on the generated script. For example, a green background is applied to sections of discussion that have positive emotions, and a red background is applied to sections of discussion that have negative emotions. In this way, by adding emotional feedback to a script generated using the emotion estimation function, it is possible to present the content of the discussion in a way that participants can easily empathize with emotionally.

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

[0059] The discussion content acquisition unit not only acquires the content of what is said during a meeting, but also analyzes the gestures and facial expressions of the speakers in real time, allowing for a more accurate understanding of the nuances of the discussion. For example, if a speaker raises their hand, it indicates that the statement is important. Also, if the speaker is smiling while speaking, it indicates that the statement is positive. Furthermore, if the speaker is frowning, it indicates that the statement is negative. In this way, the discussion content acquisition unit can more accurately grasp the nuances of the discussion by analyzing not only the content of what is said, but also the gestures and facial expressions of the speakers.

[0060] The diagram generator not only analyzes the emotional tone of the discussion, but also the tempo and rhythm of the discussion, and is able to visually represent the flow of the discussion. For example, if the discussion is progressing rapidly, an arrow is added to the diagram to indicate the speed. If the discussion is stagnating, a clock icon is added to the diagram to indicate the delay. Furthermore, if the discussion is repeating, a loop icon is added to the diagram to indicate the repetition. In this way, the diagram generator can visually represent the flow of the discussion by analyzing the tempo and rhythm of the discussion.

[0061] The diagram generator not only tracks the progress of the discussion in real time, but can also automatically update the agenda as the discussion progresses. For example, if the discussion is focused on a specific topic, that topic is highlighted. Also, if the discussion shifts to a new topic, that topic is automatically added. Furthermore, if the discussion has concluded, the topic is displayed grayed out. In this way, the diagram generator can improve the efficiency of discussions by automatically updating the agenda as the discussion progresses.

[0062] The diagram generation unit uses the emotion estimation function to not only analyze the emotional state of participants in real time, but also predict changes in emotions and suggest breaks at appropriate times. For example, if participants' emotions are gradually becoming negative, it will suggest a break. Also, if participants' emotions are changing rapidly, it will suggest adjusting the pace of the discussion. Furthermore, if participants' emotions are stable, it will suggest continuing the discussion. In this way, by using the emotion estimation function to predict changes in emotions and suggest breaks at appropriate times, it is possible to support the smooth progress of discussions.

[0063] The discussion content acquisition unit not only records the content of the discussion as audio and video in real time, but also automatically summarizes the important parts of the discussion, making them easy to refer to later. For example, if an important keyword appears in the discussion, that part is automatically summarized and saved. Also, if a decision is made in the discussion, that part is automatically summarized and saved. Furthermore, if a problem is pointed out in the discussion, that part is automatically summarized and saved. In this way, the discussion content acquisition unit automatically summarizes the important parts of the discussion, making them easy to refer to later, allowing the content of the discussion to be accurately reviewed.

[0064] The discussion content acquisition unit not only translates discussions in different languages ​​in real time, but also plays the translated content aloud, allowing participants to understand the discussion in a natural way. For example, it can translate comments in English into Japanese and play the translated content aloud. It can also translate comments in Japanese into English and play the translated content aloud. It can also translate comments in multiple languages ​​simultaneously and play the audio in each language. In this way, the discussion content acquisition unit can play the translated content aloud, allowing participants to understand the discussion in a natural way.

[0065] The discussion content acquisition unit not only acquires the content of the discussion, but also monitors the progress of the discussion in real time and can make suggestions to balance the discussion if it becomes unbalanced. For example, if a particular participant is monopolizing the discussion, it can suggest that other participants be given an opportunity to speak. Also, if the discussion is biased towards a particular topic, it can suggest that participants turn their attention to other topics. Furthermore, if the discussion becomes emotionally biased, it can make suggestions to encourage a calm discussion. In this way, the discussion content acquisition unit can maintain fairness in the discussion by monitoring the progress of the discussion in real time and making suggestions to balance the discussion.

[0066] The script generator uses the generated script to not only visualize the content of the discussion as a 3D model, but also allows participants to freely explore the flow of the discussion by manipulating the 3D model. For example, participants can click on each node in the 3D flowchart to display detailed information. They can also rotate the 3D model to check the flow of the discussion from different perspectives. They can also zoom in on parts of the 3D model to check the details of the discussion. In this way, the script generator allows participants to freely explore the flow of the discussion by manipulating the 3D model, thereby enabling a deeper understanding of the content of the discussion.

[0067] The script generation unit not only publishes the content of the discussion as an interactive web page using the generated script, but also enables participants to add comments and feedback in real time. For example, participants can add comments to each point in the discussion. Also, participants can add feedback on the flow of the discussion. Furthermore, participants can reply to comments made by other participants. In this way, the script generation unit can enrich the content of the discussion by enabling participants to add comments and feedback in real time.

[0068] The script generation unit uses the emotion estimation function to not only add emotional elements to a chart based on the generated script, but also to express changes in emotion with animation, making it easier to visually understand the flow of emotions. For example, in areas where positive emotions are increasing, an animation is applied in which the green background gradually becomes brighter. Also, in areas where negative emotions are increasing, an animation is applied in which the red background gradually becomes darker. Furthermore, in areas where emotions change drastically, an animation in which the background color changes rapidly can be applied. In this way, by using the emotion estimation function to express changes in emotion with animation, it is possible to visually make it easier to understand the flow of emotions.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The discussion content acquisition unit acquires the content of what is said during the meeting. For example, speech recognition technology can be used to acquire what is said during the meeting as text data. It is also possible to record what is said during the meeting and convert it into text data later. It is also possible to convert what is said during the meeting into text data in real time. Step 2: The diagram generation unit generates diagrams based on the utterances acquired by the discussion content acquisition unit. For example, the utterances can be analyzed to generate flowcharts, mind maps, graphs, and charts. Step 3: The script generator generates the diagrams generated by the diagram generator as scripts. For example, the generated flowchart can be generated as an HTML script, and the generated mind map can be generated as a presentation script. Furthermore, the generated graphs and charts can be generated as text scripts.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The 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.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 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.

[0097] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the 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.

[0100] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] The data processing system 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a discussion content acquisition unit that acquires the content of comments made during a meeting; a diagram generation unit that generates a diagram based on the utterance content acquired by the discussion content acquisition unit; a script generation unit that generates a script from the diagram generated by the diagram generation unit; A system characterized by:

2. The diagram generation unit Analyze the emotional tone of a discussion and automatically generate colors and icons to highlight positive arguments 2. The system of claim 1.

3. The discussion content acquisition unit Record the contents of the discussion as audio and video in real time and replay it visually later 2. The system of claim 1.

4. The script generation unit Using the generated script, the discussion content can be visualized as a 3D model for more intuitive understanding.

2. The system of claim 1.

5. The diagram generation unit Analyze participants' emotional states in real time and suggest appropriate interventions if the discussion stalls 2. The system of claim 1.

6. The discussion content acquisition unit Translating discussions in different languages ​​in real time to ensure smooth discussions even at international conferences 2. The system of claim 1.

7. The script generation unit Using the generated script, the discussion content is published as an interactive web page, allowing participants to freely manipulate it.

2. The system of claim 1.

8. The script generation unit Add emotional elements to generated script-based diagrams to visually express emotional changes 2. The system of claim 1.

Images