System

Abstract

An object of a system according to an embodiment is to visually grasp a reaction of a participant in a remote work era and eliminate a sense of loneliness.SOLUTION: A system according to an embodiment includes an analysis unit, a creation unit, and a visualization unit. The analysis unit analyzes the speaking voice of the participant and the content of the speech. The creator creates a conference space on the Metaverse on the basis of the content analyzed by the analyzer. The visualizer visualizes the reaction of the participant.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] With conventional technology, it is difficult to visually grasp participants' reactions in this era of remote work, which can lead to feelings of isolation.

[0005] The system of the embodiment aims to visually grasp the reactions of participants and eliminate feelings of loneliness in the era of remote work. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a creation unit, and a visualization unit. The analysis unit analyzes the speech and content of the participants. The creation unit creates a conference space on the metaverse based on the content analyzed by the analysis unit. The visualization unit visualizes the participants' reactions. [Effects of the Invention]

[0007] The system according to the embodiment allows participants to visually grasp their reactions and eliminate feelings of loneliness in this era of remote work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The conference system according to an embodiment of the present invention uses a generation AI to analyze the speech and content of participants and create a conference space on the metaverse based on that analysis. This allows the conference system to improve the conference experience in the age of remote work.

[0029] A conference system according to an embodiment includes an analysis unit, a creation unit, and a visualization unit. The analysis unit analyzes the speech of participants and the content of their speech. For example, the analysis unit converts speech into text using speech recognition technology. The analysis unit can also analyze the content of speech using natural language processing technology. The analysis unit can also analyze the tone and pauses of speech to estimate emotions. For example, speech recognition technology analyzes the frequency and volume of speech and converts it into text. Natural language processing technology extracts keywords from the content of speech and understands the context. Analyzing the tone and pauses of speech can estimate the emotional state of participants. The creation unit creates a conference space in the metaverse based on the content analyzed by the analysis unit. For example, the creation unit generates a conference room using 3D modeling technology. The creation unit can also create the conference space using virtual reality technology. The creation unit can also arrange materials and whiteboards according to the theme of the conference. For example, 3D modeling technology designs the layout of the conference room and virtual reality technology creates a realistic conference space. By arranging relevant materials and a whiteboard according to the theme of the meeting, participants can proceed with the meeting efficiently. The visualization unit visualizes the reactions of the participants. For example, the visualization unit generates facial expressions and movements of an avatar. The visualization unit can also generate avatar movements that reflect the emotions of the participants. The visualization unit can also visualize the reactions of the participants in real time. For example, by generating the facial expressions and movements of an avatar, participants' reactions can be visually confirmed. By generating avatar movements that reflect emotions, participants' emotional states can be visually grasped. Visualization in real time facilitates communication between participants. As a result, the conference system according to the embodiment can improve the conference experience in the era of remote work. For example, participants can visually check the reactions of other participants during the meeting, which facilitates communication. Furthermore, the need for makeup and grooming is reduced, thereby reducing the burden on participants.

[0030] The analysis unit can learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, the analysis unit uses a generation AI to learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, for participants who speak quickly, the analysis unit provides a meeting space that makes extensive use of whiteboards to make it easier to organize information. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a relaxed atmosphere for participants who speak slowly. For example, a natural landscape is displayed in the background. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a logical structure for participants who speak logically. For example, a meeting room with sections divided by agenda is provided. This makes it possible to generate a customized meeting space that reflects the characteristics of each individual's speaking style.

[0031] The analysis unit analyzes not only the participants' voices but also their gestures and facial expressions, promoting a more detailed understanding of the meeting content. For example, the generation AI in the analysis unit analyzes the participants' gestures and understands the meeting content from their hand and body movements. For example, if a participant raises their hand, it understands the intention of what is being said. The generation AI in the analysis unit also analyzes the participants' facial expressions and understands their emotional state from their facial expressions. For example, if a participant smiles, it determines that they have a positive reaction. The generation AI in the analysis unit also analyzes the participants' gestures and facial expressions and provides appropriate feedback as the meeting progresses. For example, if a participant looks confused, it displays a message providing additional explanation. In this way, analyzing gestures and facial expressions can promote a more detailed understanding of the meeting content.

[0032] The analysis unit translates conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, the analysis unit uses a generation AI to translate conversations in different languages ​​in real time, allowing smooth communication even if participants speak different languages. For example, it translates conversations between Japanese and English in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, it translates conversations between French and Chinese in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, allowing participants to understand the content of the conference even if they speak different languages. For example, it translates conversations between Spanish and German in real time. This makes it possible to build a system that can translate conversations in different languages ​​in real time, making it possible to handle international conferences.

[0033] The creation department can generate a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in the creation department, the generation AI generates a conference space specialized for a specific industry according to the theme of the conference. For example, in a medical industry conference, the generation AI generates a conference room where information on medical equipment and pharmaceuticals is displayed. In addition, in the creation department, the generation AI generates a conference room specialized for a specific project according to the theme of the conference. For example, in a conference on a new product development project, the generation AI generates a conference room where product prototypes and blueprints are displayed. In addition, in the creation department, the generation AI generates a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in a conference on a construction project, the generation AI generates a conference room where a 3D model of the construction site is displayed. In this way, it is possible to generate a conference space specialized for a specific industry or project.

[0034] The creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. In the creation department, for example, the generation AI refers to the participants' past meeting history and proposes the optimal layout of the meeting space. For example, it generates a meeting room that is easy for participants to use based on layouts used in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal design of the meeting space. For example, it generates a meeting room that is relaxing for participants based on designs that have been well received in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. For example, it generates an efficient meeting room based on the placement of materials and whiteboards used in past meetings. In this way, the creation department can propose the optimal meeting space by referring to past meeting history.

[0035] The creation department can generate not only meeting spaces, but also spaces for post-meeting reflection and feedback. For example, the creation department's generative AI generates not only meeting spaces, but also spaces for post-meeting reflection. For example, it provides a dedicated space for participants to reflect on the content of the meeting. The creation department's generative AI also generates not only meeting spaces, but also spaces for feedback. For example, it provides a dedicated space for participants to share their evaluations and opinions of the meeting. The creation department's generative AI also generates not only meeting spaces, but also spaces for post-meeting reflection and feedback. For example, it provides a dedicated space for participants to summarize the results of the meeting. This makes it possible to generate a space for post-meeting reflection and feedback.

[0036] The creative department can add interactive elements to meeting spaces to increase participant engagement. For example, the generative AI can add a real-time voting function to meeting spaces to instantly reflect participant opinions. For example, participants can vote for or against an agenda item. The creative department can also add a survey function to meeting spaces to collect participant feedback. For example, it can conduct a survey on the progress of the meeting. The creative department can also add interactive elements to meeting spaces to increase participant engagement. For example, it can provide real-time voting and survey functions. This can add interactive elements to meeting spaces to increase participant engagement.

[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 analysis unit analyzes the speech and content of speech of participants. For example, the analysis unit converts speech into text using speech recognition technology. The analysis unit can also analyze speech content using natural language processing technology. The analysis unit can also infer emotions by analyzing speech tone and pauses. For example, speech recognition technology analyzes the frequency and volume of speech and converts it into text. Natural language processing technology extracts keywords from speech content and understands the context. Analyzing speech tone and pauses can infer participants' emotional states. The creation unit creates a meeting space in the metaverse based on the content analyzed by the analysis unit. For example, the creation unit generates a meeting room using 3D modeling technology. The creation unit can also create a meeting space using virtual reality technology. The creation unit can also arrange materials and whiteboards according to the theme of the meeting. For example, 3D modeling technology designs the layout of the meeting room and creates a realistic meeting space using virtual reality technology. By arranging related materials and whiteboards according to the theme of the meeting, participants can conduct the meeting efficiently. The visualization unit visualizes the reactions of the participants. For example, the visualization unit generates facial expressions and movements of an avatar. The visualization unit can also generate movements of the avatar that reflect the emotions of the participants. The visualization unit can also visualize the reactions of the participants in real time. For example, by generating facial expressions and movements of an avatar, the reactions of the participants can be visually confirmed. By generating movements of the avatar that reflect the emotions, the emotional states of the participants can be visually grasped. Visualization in real time facilitates communication between participants. As a result, the conference system according to the embodiment can improve the conference experience in the era of remote work. For example, participants can visually check the reactions of other participants during the conference, which facilitates communication. Furthermore, the need for makeup and grooming is reduced, thereby reducing the burden on participants.

[0039] The analysis unit can learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, the analysis unit uses a generation AI to learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, for participants who speak quickly, the analysis unit provides a meeting space that makes extensive use of whiteboards to make it easier to organize information. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a relaxed atmosphere for participants who speak slowly. For example, a natural landscape is displayed in the background. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a logical structure for participants who speak logically. For example, a meeting room with sections divided by agenda is provided. This makes it possible to generate a customized meeting space that reflects the characteristics of each individual's speaking style.

[0040] The analysis unit analyzes not only the participants' voices but also their gestures and facial expressions, promoting a more detailed understanding of the meeting content. For example, the generation AI in the analysis unit analyzes the participants' gestures and understands the meeting content from their hand and body movements. For example, if a participant raises their hand, it understands the intention of what is being said. The generation AI in the analysis unit also analyzes the participants' facial expressions and understands their emotional state from their facial expressions. For example, if a participant smiles, it determines that they have a positive reaction. The generation AI in the analysis unit also analyzes the participants' gestures and facial expressions and provides appropriate feedback as the meeting progresses. For example, if a participant looks confused, it displays a message providing additional explanation. In this way, analyzing gestures and facial expressions can promote a more detailed understanding of the meeting content.

[0041] The analysis unit translates conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, the analysis unit uses a generation AI to translate conversations in different languages ​​in real time, allowing smooth communication even if participants speak different languages. For example, it translates conversations between Japanese and English in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, it translates conversations between French and Chinese in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, allowing participants to understand the content of the conference even if they speak different languages. For example, it translates conversations between Spanish and German in real time. This makes it possible to build a system that can translate conversations in different languages ​​in real time, making it possible to handle international conferences.

[0042] The creation department can generate a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in the creation department, the generation AI generates a conference space specialized for a specific industry according to the theme of the conference. For example, in a medical industry conference, the generation AI generates a conference room where information on medical equipment and pharmaceuticals is displayed. In addition, in the creation department, the generation AI generates a conference room specialized for a specific project according to the theme of the conference. For example, in a conference on a new product development project, the generation AI generates a conference room where product prototypes and blueprints are displayed. In addition, in the creation department, the generation AI generates a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in a conference on a construction project, the generation AI generates a conference room where a 3D model of the construction site is displayed. In this way, it is possible to generate a conference space specialized for a specific industry or project.

[0043] The creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. In the creation department, for example, the generation AI refers to the participants' past meeting history and proposes the optimal layout of the meeting space. For example, it generates a meeting room that is easy for participants to use based on layouts used in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal design of the meeting space. For example, it generates a meeting room that is relaxing for participants based on designs that have been well received in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. For example, it generates an efficient meeting room based on the placement of materials and whiteboards used in past meetings. In this way, the creation department can propose the optimal meeting space by referring to past meeting history.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The analysis unit analyzes the speech and content of speech of participants. For example, the analysis unit converts speech into text using speech recognition technology. The analysis unit can also analyze speech content using natural language processing technology. The analysis unit can also analyze speech tone and pauses to infer emotions. Speech recognition technology analyzes the frequency and volume of speech and converts it into text. Natural language processing technology extracts keywords from speech content and understands the context. By analyzing speech tone and pauses, the emotional state of participants can be inferred. Step 2: The creation department creates a meeting space in the metaverse based on the content analyzed by the analysis department. For example, the creation department generates a meeting room using 3D modeling technology. The creation department can also create the meeting space using virtual reality technology. Furthermore, the creation department can arrange materials and whiteboards according to the theme of the meeting. 3D modeling technology designs the layout of the meeting room, and virtual reality technology is used to generate a realistic meeting space. By arranging related materials and whiteboards according to the theme of the meeting, participants can conduct the meeting efficiently. Step 3: The visualization unit visualizes the participants' reactions. For example, the visualization unit generates facial expressions and movements of an avatar. The visualization unit can also generate avatar movements that reflect the participants' emotions. Furthermore, the visualization unit can visualize the participants' reactions in real time. By generating avatar facial expressions and movements, the participants' reactions can be visually confirmed. By generating avatar movements that reflect the participants' emotions, the emotional state of the participants can be visually grasped. Visualization in real time facilitates smooth communication between participants.

[0046] (Example 2) The conference system according to an embodiment of the present invention uses a generation AI to analyze the speech and content of participants and create a conference space on the metaverse based on that analysis. This allows the conference system to improve the conference experience in the age of remote work.

[0047] A conference system according to an embodiment includes an analysis unit, a creation unit, and a visualization unit. The analysis unit analyzes the speech of participants and the content of their speech. For example, the analysis unit converts speech into text using speech recognition technology. The analysis unit can also analyze the content of speech using natural language processing technology. The analysis unit can also analyze the tone and pauses of speech to estimate emotions. For example, speech recognition technology analyzes the frequency and volume of speech and converts it into text. Natural language processing technology extracts keywords from the content of speech and understands the context. Analyzing the tone and pauses of speech can estimate the emotional state of participants. The creation unit creates a conference space in the metaverse based on the content analyzed by the analysis unit. For example, the creation unit generates a conference room using 3D modeling technology. The creation unit can also create the conference space using virtual reality technology. The creation unit can also arrange materials and whiteboards according to the theme of the conference. For example, 3D modeling technology designs the layout of the conference room and virtual reality technology creates a realistic conference space. By arranging relevant materials and a whiteboard according to the theme of the meeting, participants can proceed with the meeting efficiently. The visualization unit visualizes the reactions of the participants. For example, the visualization unit generates facial expressions and movements of an avatar. The visualization unit can also generate avatar movements that reflect the emotions of the participants. The visualization unit can also visualize the reactions of the participants in real time. For example, by generating the facial expressions and movements of an avatar, participants' reactions can be visually confirmed. By generating avatar movements that reflect emotions, participants' emotional states can be visually grasped. Visualization in real time facilitates communication between participants. As a result, the conference system according to the embodiment can improve the conference experience in the era of remote work. For example, participants can visually check the reactions of other participants during the meeting, which facilitates communication. Furthermore, the need for makeup and grooming is reduced, thereby reducing the burden on participants.

[0048] The analysis unit analyzes the emotional tone of the participants' speech and can provide appropriate feedback in real time as the meeting progresses. For example, the generation AI analyzes the emotional tone of the speech and, if a participant appears tense, provides feedback to help them relax. For example, if a participant's voice tone becomes higher, the generation AI displays a message such as "Please speak more relaxedly." The analysis unit also analyzes the emotional tone of the speech and provides appropriate feedback as the meeting progresses. For example, if a participant appears excited, the generation AI displays a message such as "Please calm down a bit while you speak." The analysis unit also analyzes the emotional tone of the speech and, if a participant appears tired, provides feedback such as "Let's take a short break." For example, if the voice tone becomes lower, the generation AI suggests taking a break. This makes it possible to provide appropriate feedback as the meeting progresses.

[0049] The analysis unit can learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, the analysis unit uses a generation AI to learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, for participants who speak quickly, the analysis unit provides a meeting space that makes extensive use of whiteboards to make it easier to organize information. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a relaxed atmosphere for participants who speak slowly. For example, a natural landscape is displayed in the background. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a logical structure for participants who speak logically. For example, a meeting room with sections divided by agenda is provided. This makes it possible to generate a customized meeting space that reflects the characteristics of each individual's speaking style.

[0050] The analysis unit can use the emotion estimation function to analyze the emotional state of participants and automatically select relaxing music and backgrounds as the meeting progresses. For example, the analysis unit uses the emotion estimation function to automatically select relaxing music if a participant is tense. For example, the generation AI analyzes the tone of the participant's voice and plays music that has a relaxing effect. The analysis unit also uses the emotion estimation function to automatically select a refreshing background if a participant is tired. For example, the generation AI analyzes the tone of the participant's voice and displays a natural landscape as the background. The analysis unit also uses the emotion estimation function to automatically select calming music if a participant is excited. For example, the generation AI analyzes the tone of the participant's voice and plays classical music. This makes it possible to automatically select music and backgrounds according to the participants' emotional state.

[0051] The analysis unit analyzes not only the participants' voices but also their gestures and facial expressions, promoting a more detailed understanding of the meeting content. For example, the generation AI in the analysis unit analyzes the participants' gestures and understands the meeting content from their hand and body movements. For example, if a participant raises their hand, it understands the intention of what is being said. The generation AI in the analysis unit also analyzes the participants' facial expressions and understands their emotional state from their facial expressions. For example, if a participant smiles, it determines that they have a positive reaction. The generation AI in the analysis unit also analyzes the participants' gestures and facial expressions and provides appropriate feedback as the meeting progresses. For example, if a participant looks confused, it displays a message providing additional explanation. In this way, analyzing gestures and facial expressions can promote a more detailed understanding of the meeting content.

[0052] The analysis unit translates conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, the analysis unit uses a generation AI to translate conversations in different languages ​​in real time, allowing smooth communication even if participants speak different languages. For example, it translates conversations between Japanese and English in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, it translates conversations between French and Chinese in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, allowing participants to understand the content of the conference even if they speak different languages. For example, it translates conversations between Spanish and German in real time. This makes it possible to build a system that can translate conversations in different languages ​​in real time, making it possible to handle international conferences.

[0053] The analysis unit can use the emotion estimation function to automatically suggest icebreakers and refreshment times based on the emotions of the participants. For example, the analysis unit uses the emotion estimation function to automatically suggest icebreakers to relax participants when they are nervous. For example, it can suggest simple games or quizzes. The analysis unit also uses the emotion estimation function to automatically suggest refreshment times when participants are tired. For example, it can suggest short breaks or stretching. The analysis unit also uses the emotion estimation function to automatically suggest icebreakers to calm participants when they are excited. For example, it can play music that has a relaxing effect. In this way, it is possible to automatically suggest icebreakers and refreshment times based on the emotions of the participants.

[0054] The creation department can generate a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in the creation department, the generation AI generates a conference space specialized for a specific industry according to the theme of the conference. For example, in a medical industry conference, the generation AI generates a conference room where information on medical equipment and pharmaceuticals is displayed. In addition, in the creation department, the generation AI generates a conference room specialized for a specific project according to the theme of the conference. For example, in a conference on a new product development project, the generation AI generates a conference room where product prototypes and blueprints are displayed. In addition, in the creation department, the generation AI generates a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in a conference on a construction project, the generation AI generates a conference room where a 3D model of the construction site is displayed. In this way, it is possible to generate a conference space specialized for a specific industry or project.

[0055] The creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. In the creation department, for example, the generation AI refers to the participants' past meeting history and proposes the optimal layout of the meeting space. For example, it generates a meeting room that is easy for participants to use based on layouts used in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal design of the meeting space. For example, it generates a meeting room that is relaxing for participants based on designs that have been well received in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. For example, it generates an efficient meeting room based on the placement of materials and whiteboards used in past meetings. In this way, the creation department can propose the optimal meeting space by referring to past meeting history.

[0056] The creation unit can use the emotion estimation function to adjust colors and lighting according to the emotional state of the participants, thereby optimizing the atmosphere of the meeting. For example, the creation unit uses the emotion estimation function to adjust colors and lighting to relax participants when they are tense. For example, warm lighting or soft colors are used. The creation unit also uses the emotion estimation function to adjust colors and lighting to refresh participants when they are tired. For example, bright lighting or refreshing colors are used. The creation unit also uses the emotion estimation function to adjust colors and lighting to calm participants when they are excited. For example, cool colors or gentle lighting are used. In this way, the colors and lighting can be adjusted according to the emotional state of the participants, thereby optimizing the atmosphere of the meeting.

[0057] The creation department can generate not only meeting spaces, but also spaces for post-meeting reflection and feedback. For example, the creation department's generative AI generates not only meeting spaces, but also spaces for post-meeting reflection. For example, it provides a dedicated space for participants to reflect on the content of the meeting. The creation department's generative AI also generates not only meeting spaces, but also spaces for feedback. For example, it provides a dedicated space for participants to share their evaluations and opinions of the meeting. The creation department's generative AI also generates not only meeting spaces, but also spaces for post-meeting reflection and feedback. For example, it provides a dedicated space for participants to summarize the results of the meeting. This makes it possible to generate a space for post-meeting reflection and feedback.

[0058] The creative department can add interactive elements to meeting spaces to increase participant engagement. For example, the generative AI can add a real-time voting function to meeting spaces to instantly reflect participant opinions. For example, participants can vote for or against an agenda item. The creative department can also add a survey function to meeting spaces to collect participant feedback. For example, it can conduct a survey on the progress of the meeting. The creative department can also add interactive elements to meeting spaces to increase participant engagement. For example, it can provide real-time voting and survey functions. This can add interactive elements to meeting spaces to increase participant engagement.

[0059] The creation department can use the emotion estimation function to suggest interactive exercises and breakout sessions based on the emotions of participants during a meeting. For example, the creation department can use the emotion estimation function to suggest interactive exercises to relax participants when they are tense. For example, it can suggest simple stretching or relaxation exercises. The creation department can also use the emotion estimation function to suggest breakout sessions to refresh participants when they are tired. For example, it can suggest short breaks or refreshment time. The creation department can also use the emotion estimation function to suggest interactive exercises to calm participants when they are excited. For example, it can suggest breathing exercises or meditation. This makes it possible to suggest interactive exercises and breakout sessions based on the emotions of participants during a meeting.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The analysis unit analyzes the speech and content of speech of participants. For example, the analysis unit converts speech into text using speech recognition technology. The analysis unit can also analyze speech content using natural language processing technology. The analysis unit can also infer emotions by analyzing speech tone and pauses. For example, speech recognition technology analyzes the frequency and volume of speech and converts it into text. Natural language processing technology extracts keywords from speech content and understands the context. Analyzing speech tone and pauses can infer participants' emotional states. The creation unit creates a meeting space in the metaverse based on the content analyzed by the analysis unit. For example, the creation unit generates a meeting room using 3D modeling technology. The creation unit can also create a meeting space using virtual reality technology. The creation unit can also arrange materials and whiteboards according to the theme of the meeting. For example, 3D modeling technology designs the layout of the meeting room and creates a realistic meeting space using virtual reality technology. By arranging related materials and whiteboards according to the theme of the meeting, participants can conduct the meeting efficiently. The visualization unit visualizes the reactions of the participants. For example, the visualization unit generates facial expressions and movements of an avatar. The visualization unit can also generate movements of the avatar that reflect the emotions of the participants. The visualization unit can also visualize the reactions of the participants in real time. For example, by generating facial expressions and movements of an avatar, the reactions of the participants can be visually confirmed. By generating movements of the avatar that reflect the emotions, the emotional states of the participants can be visually grasped. Visualization in real time facilitates communication between participants. As a result, the conference system according to the embodiment can improve the conference experience in the era of remote work. For example, participants can visually check the reactions of other participants during the conference, which facilitates communication. Furthermore, the need for makeup and grooming is reduced, thereby reducing the burden on participants.

[0062] The analysis unit analyzes the emotional tone of the participants' speech and can provide appropriate feedback in real time as the meeting progresses. For example, the generation AI analyzes the emotional tone of the speech and, if a participant appears tense, provides feedback to help them relax. For example, if a participant's voice tone becomes higher, the generation AI displays a message such as "Please speak more relaxedly." The analysis unit also analyzes the emotional tone of the speech and provides appropriate feedback as the meeting progresses. For example, if a participant appears excited, the generation AI displays a message such as "Please calm down a bit while you speak." The analysis unit also analyzes the emotional tone of the speech and, if a participant appears tired, provides feedback such as "Let's take a short break." For example, if the voice tone becomes lower, the generation AI suggests taking a break. This makes it possible to provide appropriate feedback as the meeting progresses.

[0063] The analysis unit can learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, the analysis unit uses a generation AI to learn the speech patterns of participants and generate a customized meeting space that reflects the characteristics of each individual's speaking style. For example, for participants who speak quickly, the analysis unit provides a meeting space that makes extensive use of whiteboards to make it easier to organize information. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a relaxed atmosphere for participants who speak slowly. For example, a natural landscape is displayed in the background. The analysis unit also uses a generation AI to learn the speech patterns of participants and generate a meeting space with a logical structure for participants who speak logically. For example, a meeting room with sections divided by agenda is provided. This makes it possible to generate a customized meeting space that reflects the characteristics of each individual's speaking style.

[0064] The analysis unit can use the emotion estimation function to analyze the emotional state of participants and automatically select relaxing music and backgrounds as the meeting progresses. For example, the analysis unit uses the emotion estimation function to automatically select relaxing music if a participant is tense. For example, the generation AI analyzes the tone of the participant's voice and plays music that has a relaxing effect. The analysis unit also uses the emotion estimation function to automatically select a refreshing background if a participant is tired. For example, the generation AI analyzes the tone of the participant's voice and displays a natural landscape as the background. The analysis unit also uses the emotion estimation function to automatically select calming music if a participant is excited. For example, the generation AI analyzes the tone of the participant's voice and plays classical music. This makes it possible to automatically select music and backgrounds according to the participants' emotional state.

[0065] The analysis unit analyzes not only the participants' voices but also their gestures and facial expressions, promoting a more detailed understanding of the meeting content. For example, the generation AI in the analysis unit analyzes the participants' gestures and understands the meeting content from their hand and body movements. For example, if a participant raises their hand, it understands the intention of what is being said. The generation AI in the analysis unit also analyzes the participants' facial expressions and understands their emotional state from their facial expressions. For example, if a participant smiles, it determines that they have a positive reaction. The generation AI in the analysis unit also analyzes the participants' gestures and facial expressions and provides appropriate feedback as the meeting progresses. For example, if a participant looks confused, it displays a message providing additional explanation. In this way, analyzing gestures and facial expressions can promote a more detailed understanding of the meeting content.

[0066] The analysis unit translates conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, the analysis unit uses a generation AI to translate conversations in different languages ​​in real time, allowing smooth communication even if participants speak different languages. For example, it translates conversations between Japanese and English in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, making it possible to build a system that can handle international conferences. For example, it translates conversations between French and Chinese in real time. The analysis unit also uses a generation AI to translate conversations in different languages ​​in real time, allowing participants to understand the content of the conference even if they speak different languages. For example, it translates conversations between Spanish and German in real time. This makes it possible to build a system that can translate conversations in different languages ​​in real time, making it possible to handle international conferences.

[0067] The analysis unit can use the emotion estimation function to automatically suggest icebreakers and refreshment times based on the emotions of the participants. For example, the analysis unit uses the emotion estimation function to automatically suggest icebreakers to relax participants when they are nervous. For example, it can suggest simple games or quizzes. The analysis unit also uses the emotion estimation function to automatically suggest refreshment times when participants are tired. For example, it can suggest short breaks or stretching. The analysis unit also uses the emotion estimation function to automatically suggest icebreakers to calm participants when they are excited. For example, it can play music that has a relaxing effect. In this way, it is possible to automatically suggest icebreakers and refreshment times based on the emotions of the participants.

[0068] The creation department can generate a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in the creation department, the generation AI generates a conference space specialized for a specific industry according to the theme of the conference. For example, in a medical industry conference, the generation AI generates a conference room where information on medical equipment and pharmaceuticals is displayed. In addition, in the creation department, the generation AI generates a conference room specialized for a specific project according to the theme of the conference. For example, in a conference on a new product development project, the generation AI generates a conference room where product prototypes and blueprints are displayed. In addition, in the creation department, the generation AI generates a customized conference space specialized for a specific industry or project according to the theme of the conference. For example, in a conference on a construction project, the generation AI generates a conference room where a 3D model of the construction site is displayed. In this way, it is possible to generate a conference space specialized for a specific industry or project.

[0069] The creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. In the creation department, for example, the generation AI refers to the participants' past meeting history and proposes the optimal layout of the meeting space. For example, it generates a meeting room that is easy for participants to use based on layouts used in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal design of the meeting space. For example, it generates a meeting room that is relaxing for participants based on designs that have been well received in the past. In addition, the creation department can refer to the participants' past meeting history and propose the optimal layout and design of the meeting space. For example, it generates an efficient meeting room based on the placement of materials and whiteboards used in past meetings. In this way, the creation department can propose the optimal meeting space by referring to past meeting history.

[0070] The creation unit can use the emotion estimation function to adjust colors and lighting according to the emotional state of the participants, thereby optimizing the atmosphere of the meeting. For example, the creation unit uses the emotion estimation function to adjust colors and lighting to relax participants when they are tense. For example, warm lighting or soft colors are used. The creation unit also uses the emotion estimation function to adjust colors and lighting to refresh participants when they are tired. For example, bright lighting or refreshing colors are used. The creation unit also uses the emotion estimation function to adjust colors and lighting to calm participants when they are excited. For example, cool colors or gentle lighting are used. In this way, the colors and lighting can be adjusted according to the emotional state of the participants, thereby optimizing the atmosphere of the meeting.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The analysis unit analyzes the speech and content of speech of participants. For example, the analysis unit converts speech into text using speech recognition technology. The analysis unit can also analyze speech content using natural language processing technology. The analysis unit can also analyze speech tone and pauses to infer emotions. Speech recognition technology analyzes the frequency and volume of speech and converts it into text. Natural language processing technology extracts keywords from speech content and understands the context. By analyzing speech tone and pauses, the emotional state of participants can be inferred. Step 2: The creation department creates a meeting space in the metaverse based on the content analyzed by the analysis department. For example, the creation department generates a meeting room using 3D modeling technology. The creation department can also create the meeting space using virtual reality technology. Furthermore, the creation department can arrange materials and whiteboards according to the theme of the meeting. 3D modeling technology designs the layout of the meeting room, and virtual reality technology is used to generate a realistic meeting space. By arranging related materials and whiteboards according to the theme of the meeting, participants can conduct the meeting efficiently. Step 3: The visualization unit visualizes the participants' reactions. For example, the visualization unit generates facial expressions and movements of an avatar. The visualization unit can also generate avatar movements that reflect the participants' emotions. Furthermore, the visualization unit can visualize the participants' reactions in real time. By generating avatar facial expressions and movements, the participants' reactions can be visually confirmed. By generating avatar movements that reflect the participants' emotions, the emotional state of the participants can be visually grasped. Visualization in real time facilitates smooth communication between participants.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] 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.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes the participants' speech and what they are saying; a creation unit that creates a conference space on the metaverse based on the content analyzed by the analysis unit; a visualization unit that visualizes the reactions of the participants. A system characterized by:

2. The analysis unit Analyze the emotional tone of the participants' speech and provide appropriate feedback in real time as the meeting progresses 2. The system of claim 1.

3. The analysis unit Learn the speech patterns of the participants and create a customized meeting space that reflects the characteristics of each individual's speech.

2. The system of claim 1.

4. The analysis unit Analyze the emotional state of the participants and automatically select relaxing music and backgrounds according to the progress of the meeting.

2. The system of claim 1.

5. The analysis unit Analyze not only the participants' voices but also their gestures and facial expressions to facilitate a more detailed understanding of the meeting content.

2. The system of claim 1.

6. The analysis unit Build a system that translates conversations in different languages ​​in real time and can be used for international conferences 2. The system of claim 1.

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