System

Abstract

An object of a system according to an embodiment is to efficiently and creatively progress a conference.SOLUTION: In one embodiment, a system includes a display technology, a generation AI, and a progress plan generator. The display technology enables a plurality of people to simultaneously enjoy an image. The production AI acts as the facilitator of the meeting. The progress plan generation unit analyzes an instruction input by the user and generates an appropriate progress plan.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 made it difficult to conduct meetings efficiently and creatively.

[0005] The system according to the embodiment aims to conduct a conference efficiently and creatively. [Means for solving the problem]

[0006] The system according to the embodiment includes a display technology, a generation AI, and a progress plan generation unit. The display technology allows multiple people to enjoy the video simultaneously. The generation AI acts as the facilitator of the meeting. The progress plan generation unit analyzes instructions input by the user and generates an appropriate progress plan. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and creatively conduct a conference. [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 next-generation conferencing solution according to an embodiment of the present invention is a system that uses display technology that allows multiple people to enjoy video simultaneously without requiring special equipment, and generative AI to act as a moderator for the conference. This enables the next-generation conferencing solution to make meetings more free and creative.

[0029] A next-generation conference solution according to an embodiment includes display technology, a generation AI, and a progress plan generation unit. The display technology allows multiple people to view video simultaneously. For example, by using display technology installed in a conference room, all participants can share the same video while advancing a discussion. Furthermore, the display technology uses a display with a wide viewing angle and an innovative audio distribution method, allowing multiple people to view the video simultaneously. The generation AI acts as the conference moderator. For example, the generation AI can manage the order of speeches, present agenda items, and manage time. Furthermore, the generation AI analyzes instructions entered by a user and generates an appropriate progress plan. For example, the generation AI can analyze user instructions using natural language processing technology and generate a progress plan according to the type of instruction. This enables the next-generation conference solution to make meetings more flexible and creative.

[0030] The display technology can display 3D holograms in real time, providing three-dimensional visual information. For example, by utilizing the display technology of Looking Glass Go, a system can be built that displays 3D holograms in real time. For example, a 3D model of a product can be displayed during a meeting, allowing participants to share the three-dimensional visual information. This improves the efficiency of meetings by providing three-dimensional visual information.

[0031] Display technology can add a haptic feedback function, allowing users to interact with the image by touching it. For example, adding a haptic feedback function to the Looking Glass Go display allows users to interact with the image by touching it. For example, it would be possible to rotate or zoom in and out on a 3D model during a meeting. This would allow users to interact with the image by touching it.

[0032] Display technology is being developed as a portable device, making it easy to set up and use anywhere. For example, the Looking Glass Go is being developed as a portable device, making it easy to set up and use anywhere. For example, it is designed to be portable so that it can be used in conference rooms or outside the office. This increases the flexibility of meetings by making it easy to set up and use anywhere.

[0033] Display technology can be applied to educational and medical settings, and can be used as a support tool for distance learning and telemedicine. For example, the display technology of Looking Glass Go can be applied to educational settings and used as a support tool for distance learning. For example, a teacher can use a 3D model in a lesson and students can share the video. This can be used in educational and medical settings to support distance learning and telemedicine.

[0034] Generative AI can summarize the key points of a discussion in real time and provide them to participants. Generative AI can, for example, build a system that not only manages the progress of a meeting, but also summarizes the key points of a discussion in real time and provides them to participants. For example, it can automatically extract the main points of a discussion and display a summary. This improves the efficiency of meetings by summarizing the key points of a discussion in real time and providing them to participants.

[0035] Generative AI can analyze participants' comments and automatically search for and present relevant materials and information. For example, generative AI can analyze participants' comments in real time and build a system that automatically searches for and presents relevant materials and information. For example, it can instantly display literature and data related to the content of comments. This improves the quality of meetings by automatically providing relevant materials and information based on participants' comments.

[0036] Generative AI can automatically generate and distribute minutes. For example, generative AI can build a system that not only manages the progress of a meeting, but also automatically generates and distributes minutes. For example, it can automatically record statements made during a meeting and distribute them as minutes. This automatically generates and distributes minutes, improving the efficiency of meetings.

[0037] Generative AI can support meetings in different languages ​​and can also accommodate international conferences. Generative AI can build a system that supports meetings in different languages ​​and can also accommodate international conferences. For example, it can translate what is being said in real time and provide it to participants. This can support meetings in different languages ​​and can also accommodate international conferences.

[0038] Generative AI can analyze participants' comments in real time and dynamically change the priority of agenda items. For example, generative AI can build a system that analyzes participants' comments in real time while a meeting is in progress and dynamically changes the priority of agenda items. For example, if an important comment is made, that topic will be prioritized. This improves the efficiency of meetings by dynamically changing the priority of agenda items based on participants' comments.

[0039] Generative AI can aggregate the opinions of participants and instantly conduct voting and surveys. For example, generative AI can build a system that aggregates the opinions of participants while a meeting is in progress and instantly conducts voting and surveys. For example, it can tally the pros and cons of agenda items in real time. This allows participants' opinions to be aggregated and voting and surveys to be conducted instantly, improving the efficiency of meetings.

[0040] Generative AI can automatically invite external experts while a meeting is in progress and provide the necessary knowledge. Generative AI builds a system that automatically invites external experts while a meeting is in progress and provides the necessary knowledge. For example, it invites experts on a specific topic in real time. This improves the quality of meetings by automatically inviting external experts and providing the necessary knowledge.

[0041] Generative AI can automatically set up brainstorming sessions while a meeting is in progress to draw out creative ideas. Generative AI can, for example, build a system that automatically sets up brainstorming sessions while a meeting is in progress to draw out creative ideas. For example, brainstorming time can be set aside for a specific agenda item, allowing participants to freely share ideas. This makes it possible to draw out creative ideas by automatically setting up brainstorming sessions.

[0042] Generative AI can analyze what participants say as a meeting progresses and automatically generate relevant creative proposals. For example, generative AI can build a system that analyzes what participants say as a meeting progresses and automatically generates relevant creative proposals. For example, it can generate new ideas and proposals based on what is said. This improves the quality of meetings by automatically generating creative proposals based on what participants say.

[0043] Generative AI can introduce examples from different industries as a meeting progresses, fusing knowledge from different fields. For example, generative AI can build a system that introduces examples from different industries as a meeting progresses, fusing knowledge from different fields. For example, in a technical meeting, a case study from the design field can be introduced to provide a new perspective. This allows for the introduction of examples from different industries and the fusing of knowledge from different fields to elicit creative ideas.

[0044] Generative AI can visualize participants' ideas in real time as a meeting progresses, promoting visual discussions. For example, generative AI can build a system that visualizes participants' ideas in real time as a meeting progresses, promoting visual discussions. For example, it can display ideas as diagrams and graphs to visually support discussions. This makes it possible to promote visual discussions by visualizing participants' ideas in real time.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] Next-generation conference solutions can also be equipped with a translation unit that translates what participants say in real time, facilitating communication between participants who speak different languages. For example, something said in English can be instantly translated into Japanese and displayed on the screen. The translation unit can also take into account the nuances and emotions of the comments. This will enable smooth communication even in international conferences, without any language barriers.

[0047] Next-generation conferencing solutions can also be equipped with an information provider that analyzes participants' comments and automatically searches for and presents relevant materials and information. For example, it can instantly display literature and data related to the content of a comment. The information provider can also understand the context of the comment and prioritize presenting the most relevant information. This allows for quick provision of necessary information during a meeting, improving the quality of discussions.

[0048] Next-generation conferencing solutions can also be equipped with a summary section that summarizes participants' comments in real time and displays the key points. For example, the summary section can automatically extract the main points of a discussion and display them on the screen. The summary section can also evaluate the importance of each comment and highlight the most important points. This makes it easier for all participants to grasp the main points of the discussion, improving meeting efficiency.

[0049] Next-generation meeting solutions can also be equipped with a voting section that aggregates participants' opinions and conducts instant voting and surveys. For example, it can tally up votes for and against a topic in real time and display the results on a display. The voting section can also aggregate participants' opinions anonymously to provide fair voting results. This enables quick and fair decision-making and improves meeting efficiency.

[0050] Next-generation conferencing solutions can also be equipped with a suggestion unit that analyzes participants' comments and automatically generates relevant creative suggestions. For example, new ideas and suggestions can be generated based on the content of the comments and displayed on a screen. The suggestion unit can also understand the context of the comments and prioritize the most relevant suggestions. This allows for the automatic generation of creative suggestions based on participants' comments, improving the quality of meetings.

[0051] Next-generation conferencing solutions can also be equipped with an expert invitation module that automatically invites external experts to provide necessary knowledge while a meeting is in progress. For example, experts on a specific topic can be invited in real time and their knowledge can be provided to participants via a display. The expert invitation module can also select and invite the most appropriate expert depending on the content of the agenda. This automatically inviting external experts and providing necessary knowledge improves the quality of meetings.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: Display technology allows multiple people to enjoy video at the same time. For example, by using display technology installed in a conference room, all participants can share the same video and advance discussions. In addition, by using a display with a wide viewing angle and devising a method for distributing audio, multiple people can enjoy video at the same time. Step 2: The generation AI acts as the moderator of the meeting. For example, the generation AI can manage the order of speaking, present the agenda, and manage the time. Step 3: The progress plan generation unit analyzes the instructions entered by the user and generates an appropriate progress plan. For example, the generation AI uses natural language processing technology to analyze the user's instructions and generate a progress plan according to the type of instruction.

[0054] (Example 2) The next-generation conferencing solution according to an embodiment of the present invention is a system that uses display technology that allows multiple people to enjoy video simultaneously without requiring special equipment, and generative AI to act as a moderator for the conference. This enables the next-generation conferencing solution to make meetings more free and creative.

[0055] A next-generation conference solution according to an embodiment includes display technology, a generation AI, and a progress plan generation unit. The display technology allows multiple people to view video simultaneously. For example, by using display technology installed in a conference room, all participants can share the same video while advancing a discussion. Furthermore, the display technology uses a display with a wide viewing angle and an innovative audio distribution method, allowing multiple people to view the video simultaneously. The generation AI acts as the conference moderator. For example, the generation AI can manage the order of speeches, present agenda items, and manage time. Furthermore, the generation AI analyzes instructions entered by a user and generates an appropriate progress plan. For example, the generation AI can analyze user instructions using natural language processing technology and generate a progress plan according to the type of instruction. This enables the next-generation conference solution to make meetings more flexible and creative.

[0056] The display technology can display 3D holograms in real time, providing three-dimensional visual information. For example, by utilizing the display technology of Looking Glass Go, a system can be built that displays 3D holograms in real time. For example, a 3D model of a product can be displayed during a meeting, allowing participants to share the three-dimensional visual information. This improves the efficiency of meetings by providing three-dimensional visual information.

[0057] Display technology can add a haptic feedback function, allowing users to interact with the image by touching it. For example, adding a haptic feedback function to the Looking Glass Go display allows users to interact with the image by touching it. For example, it would be possible to rotate or zoom in and out on a 3D model during a meeting. This would allow users to interact with the image by touching it.

[0058] Display technology adds an emotion estimation function, making it possible to adjust video effects in real time according to the emotions of participants. For example, display technology uses the emotion estimation function to build a system that adjusts video effects in real time according to the emotions of participants. For example, if a participant is excited, the color tone of the video can be brightened. This makes it possible to optimize the atmosphere of a meeting by adjusting video effects in real time according to the emotions of participants.

[0059] Display technology is being developed as a portable device, making it easy to set up and use anywhere. For example, the Looking Glass Go is being developed as a portable device, making it easy to set up and use anywhere. For example, it is designed to be portable so that it can be used in conference rooms or outside the office. This increases the flexibility of meetings by making it easy to set up and use anywhere.

[0060] Display technology can be applied to educational and medical settings, and can be used as a support tool for distance learning and telemedicine. For example, the display technology of Looking Glass Go can be applied to educational settings and used as a support tool for distance learning. For example, a teacher can use a 3D model in a lesson and students can share the video. This can be used in educational and medical settings to support distance learning and telemedicine.

[0061] The display technology adds an emotion estimation function and can provide customized video content based on the emotions of participants. For example, the display technology uses the emotion estimation function to build a system that provides customized video content based on the emotions of participants. For example, if a participant is excited, an energetic video is displayed. This improves the quality of the meeting by providing customized video content based on the emotions of participants.

[0062] Generative AI can summarize the key points of a discussion in real time and provide them to participants. Generative AI can, for example, build a system that not only manages the progress of a meeting, but also summarizes the key points of a discussion in real time and provides them to participants. For example, it can automatically extract the main points of a discussion and display a summary. This improves the efficiency of meetings by summarizing the key points of a discussion in real time and providing them to participants.

[0063] Generative AI can analyze participants' comments and automatically search for and present relevant materials and information. For example, generative AI can analyze participants' comments in real time and build a system that automatically searches for and presents relevant materials and information. For example, it can instantly display literature and data related to the content of comments. This improves the quality of meetings by automatically providing relevant materials and information based on participants' comments.

[0064] The generative AI can add an emotion estimation function to generate a progress plan based on the emotions of the participants and adjust the atmosphere of the meeting. For example, the generative AI can use the emotion estimation function to create a system that generates a progress plan based on the emotions of the participants and adjusts the atmosphere of the meeting. For example, if the participants are relaxed, the progress can be slowed down. This allows the generation of a progress plan based on the emotions of the participants and optimizes the atmosphere of the meeting.

[0065] Generative AI can automatically generate and distribute minutes. For example, generative AI can build a system that not only manages the progress of a meeting, but also automatically generates and distributes minutes. For example, it can automatically record statements made during a meeting and distribute them as minutes. This automatically generates and distributes minutes, improving the efficiency of meetings.

[0066] Generative AI can support meetings in different languages ​​and can also accommodate international conferences. Generative AI can build a system that supports meetings in different languages ​​and can also accommodate international conferences. For example, it can translate what is being said in real time and provide it to participants. This can support meetings in different languages ​​and can also accommodate international conferences.

[0067] Generative AI can improve the quality of meetings by adding an emotion estimation function and providing feedback based on participants' emotions in real time. For example, generative AI uses the emotion estimation function to build a system that provides feedback based on participants' emotions in real time to improve the quality of meetings. For example, if a participant is relaxed, it provides positive feedback. This improves the quality of meetings by providing feedback based on participants' emotions in real time.

[0068] Generative AI can analyze participants' comments in real time and dynamically change the priority of agenda items. For example, generative AI can build a system that analyzes participants' comments in real time while a meeting is in progress and dynamically changes the priority of agenda items. For example, if an important comment is made, that topic will be prioritized. This improves the efficiency of meetings by dynamically changing the priority of agenda items based on participants' comments.

[0069] Generative AI can aggregate the opinions of participants and instantly conduct voting and surveys. For example, generative AI can build a system that aggregates the opinions of participants while a meeting is in progress and instantly conducts voting and surveys. For example, it can tally the pros and cons of agenda items in real time. This allows participants' opinions to be aggregated and voting and surveys to be conducted instantly, improving the efficiency of meetings.

[0070] Generative AI can add an emotion estimation function and adjust the progress plan in real time according to the emotions of the participants, optimizing the flow of the meeting. For example, generative AI can use the emotion estimation function to build a system that adjusts the progress plan in real time according to the emotions of the participants, optimizing the flow of the meeting. For example, if the participants are relaxed, the progress can be slowed down. This makes it possible to optimize the flow of the meeting by adjusting the progress plan in real time according to the emotions of the participants.

[0071] Generative AI can automatically invite external experts while a meeting is in progress and provide the necessary knowledge. Generative AI builds a system that automatically invites external experts while a meeting is in progress and provides the necessary knowledge. For example, it invites experts on a specific topic in real time. This improves the quality of meetings by automatically inviting external experts and providing the necessary knowledge.

[0072] The generative AI can add an emotion estimation function and automatically set refresh times based on participants' emotions, improving meeting efficiency. For example, the generative AI uses the emotion estimation function to build a system that automatically sets refresh times based on participants' emotions and improves meeting efficiency. For example, if a participant is tired, a refresh time is set. This improves meeting efficiency by automatically setting refresh times based on participants' emotions.

[0073] Generative AI can automatically set up brainstorming sessions while a meeting is in progress to draw out creative ideas. Generative AI can, for example, build a system that automatically sets up brainstorming sessions while a meeting is in progress to draw out creative ideas. For example, brainstorming time can be set aside for a specific agenda item, allowing participants to freely share ideas. This makes it possible to draw out creative ideas by automatically setting up brainstorming sessions.

[0074] Generative AI can analyze what participants say as a meeting progresses and automatically generate relevant creative proposals. For example, generative AI can build a system that analyzes what participants say as a meeting progresses and automatically generates relevant creative proposals. For example, it can generate new ideas and proposals based on what is said. This improves the quality of meetings by automatically generating creative proposals based on what participants say.

[0075] Generative AI can add an emotion estimation function and suggest creative activities according to the emotions of participants, thereby invigorating meetings. For example, generative AI can use the emotion estimation function to build a system that suggests creative activities according to the emotions of participants, thereby invigorating meetings. For example, if participants are relaxed, it can suggest an idea-generating activity. This makes it possible to invigorate meetings by suggesting creative activities according to the emotions of participants.

[0076] Generative AI can introduce examples from different industries as a meeting progresses, fusing knowledge from different fields. For example, generative AI can build a system that introduces examples from different industries as a meeting progresses, fusing knowledge from different fields. For example, in a technical meeting, a case study from the design field can be introduced to provide a new perspective. This allows for the introduction of examples from different industries and the fusing of knowledge from different fields to elicit creative ideas.

[0077] Generative AI can visualize participants' ideas in real time as a meeting progresses, promoting visual discussions. For example, generative AI can build a system that visualizes participants' ideas in real time as a meeting progresses, promoting visual discussions. For example, it can display ideas as diagrams and graphs to visually support discussions. This makes it possible to promote visual discussions by visualizing participants' ideas in real time.

[0078] Generative AI can improve the quality of meetings by adding an emotion estimation function and automatically setting up creative workshops based on participants' emotions. For example, generative AI can use the emotion estimation function to build a system that automatically sets up creative workshops based on participants' emotions and improves the quality of meetings. For example, if participants are relaxed, it sets up an idea generation workshop. This improves the quality of meetings by automatically setting up creative workshops based on participants' emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] Next-generation conference solutions can also be equipped with a translation unit that translates what participants say in real time, facilitating communication between participants who speak different languages. For example, something said in English can be instantly translated into Japanese and displayed on the screen. The translation unit can also take into account the nuances and emotions of the comments. This will enable smooth communication even in international conferences, without any language barriers.

[0081] Next-generation conferencing solutions can also be equipped with an information provider that analyzes participants' comments and automatically searches for and presents relevant materials and information. For example, it can instantly display literature and data related to the content of a comment. The information provider can also understand the context of the comment and prioritize presenting the most relevant information. This allows for quick provision of necessary information during a meeting, improving the quality of discussions.

[0082] Next-generation conferencing solutions can also be equipped with an emotion adjustment unit that estimates participants' emotions and adjusts the progress of the meeting based on the estimated emotions. For example, if participants are tired, it can suggest a break and set a time to refresh. Also, if participants are excited, it can suggest activities to stimulate discussion. This allows the progress to be adjusted according to the participants' emotions, improving the efficiency and quality of the meeting.

[0083] Next-generation conferencing solutions can also be equipped with a summary section that summarizes participants' comments in real time and displays the key points. For example, the summary section can automatically extract the main points of a discussion and display them on the screen. The summary section can also evaluate the importance of each comment and highlight the most important points. This makes it easier for all participants to grasp the main points of the discussion, improving meeting efficiency.

[0084] The next-generation conferencing solution can further include a feedback unit that estimates participants' emotions and provides customized feedback based on the estimated emotions. For example, if a participant is relaxed, positive feedback is provided, and if a participant is nervous, advice on how to relax is provided. The feedback unit can also adjust the feedback content in real time according to changes in emotions. This allows for providing appropriate feedback according to participants' emotions and improving the quality of the conference.

[0085] Next-generation meeting solutions can also be equipped with a voting section that aggregates participants' opinions and conducts instant voting and surveys. For example, it can tally up votes for and against a topic in real time and display the results on a display. The voting section can also aggregate participants' opinions anonymously to provide fair voting results. This enables quick and fair decision-making and improves meeting efficiency.

[0086] Next-generation conferencing solutions can also be equipped with an atmosphere adjustment unit that estimates participants' emotions and adjusts the atmosphere of the meeting based on the estimated emotions. For example, if participants are relaxed, the pace may be slowed down, and if participants are excited, activities may be suggested to stimulate discussion. The atmosphere adjustment unit can also make adjustments in real time according to changes in emotions. This makes it possible to provide an optimal meeting atmosphere according to participants' emotions and improve the quality of the meeting.

[0087] Next-generation conferencing solutions can also be equipped with a suggestion unit that analyzes participants' comments and automatically generates relevant creative suggestions. For example, new ideas and suggestions can be generated based on the content of the comments and displayed on a screen. The suggestion unit can also understand the context of the comments and prioritize the most relevant suggestions. This allows for the automatic generation of creative suggestions based on participants' comments, improving the quality of meetings.

[0088] The next-generation meeting solution can also include an activity suggestion unit that estimates participants' emotions and suggests creative activities based on the estimated emotions. For example, if a participant is relaxed, it suggests an idea-generating activity, and if a participant is tired, it suggests a refreshing activity. The activity suggestion unit can also adjust the suggested content in real time according to changes in emotions. This makes it possible to suggest creative activities that match the participants' emotions and to liven up meetings.

[0089] Next-generation conferencing solutions can also be equipped with an expert invitation module that automatically invites external experts to provide necessary knowledge while a meeting is in progress. For example, experts on a specific topic can be invited in real time and their knowledge can be provided to participants via a display. The expert invitation module can also select and invite the most appropriate expert depending on the content of the agenda. This automatically inviting external experts and providing necessary knowledge improves the quality of meetings.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: Display technology allows multiple people to enjoy video at the same time. For example, by using display technology installed in a conference room, all participants can share the same video and advance discussions. In addition, by using a display with a wide viewing angle and devising a method for distributing audio, multiple people can enjoy video at the same time. Step 2: The generation AI acts as the moderator of the meeting. For example, the generation AI can manage the order of speaking, present the agenda, and manage the time. Step 3: The progress plan generation unit analyzes the instructions entered by the user and generates an appropriate progress plan. For example, the generation AI uses natural language processing technology to analyze the user's instructions and generate a progress plan according to the type of instruction.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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. Display technology that allows multiple people to enjoy video simultaneously, A generative AI that acts as a moderator for meetings using generative AI, a progress plan generation unit that analyzes instructions input by a user and generates an appropriate progress plan. A system characterized by:

2. The display technology includes: Displaying 3D holograms in real time to provide three-dimensional visual information 2. The system of claim 1.

3. The display technology includes: Developed as a portable device, it can be easily installed and used anywhere 2. The system of claim 1.

4. The generated AI is Provide real-time summaries of key points of discussion to participants 2. The system of claim 1.

5. The generated AI is Analyze participants' comments in real time and dynamically change the priorities of the agenda 2. The system of claim 1.

6. The generated AI is Automatically set up brainstorming sessions during the meeting to spark creative ideas 2. The system of claim 1.

7. The display technology includes: Add emotion estimation function to adjust video effects in real time according to participants' emotions 2. The system of claim 1.

8. The generated AI is Adding an emotion estimation function, generating the progress plan according to the emotions of the participants, and adjusting the atmosphere of the meeting 2. The system of claim 1.

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