system

Abstract

An object of a system according to an embodiment is to realize mutual interference between an AR user and a VR user and enable more natural communication.SOLUTION: A system includes a detection unit, an analysis unit, and a provision unit. The detection unit detects an action of the AR user. The analyzer analyzes the information detected by the detector and provides feedback to the VR user. The provision unit provides visual or audio feedback to the VR user on the basis of the feedback generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to fully realize mutual interaction between AR and VR users, resulting in limitations on communication.

[0005] The system according to the embodiment aims to realize mutual interaction between an AR user and a VR user, enabling more natural communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, an analysis unit, and a provision unit. The detection unit detects an action of an AR user. The analysis unit analyzes the information detected by the detection unit and provides feedback to the VR user. The provision unit provides visual or audio feedback to the VR user based on the feedback generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize mutual interaction between an AR user and a VR user, enabling more natural communication. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system for sharing a VR user's experience with a person not wearing an HMD. This system detects the actions of an AR user, inputs the information into a large-scale language model (LLM) for analysis, and provides appropriate feedback to the VR user. For example, if an AR user waves at a VR user, the LLM analyzes the action and displays a waving animation in the VR user's field of view. In this way, an environment in which the AR user and the VR user can interact with each other is realized. For example, in a scene in which an AR user hands an object to a VR user, the system detects the AR user's hand movement and reproduces that movement in the VR space, allowing the VR user to feel as if they are receiving the object. In addition, in a scene in which a VR user instructs the AR user, the system detects the VR user's instruction using voice recognition technology and conveys the instruction visually or audibly to the AR user. This allows the system to realize an environment in which the AR user and the VR user can interact with each other in real time. For example, the system can detect an AR user waving at a VR user and generate an animation that visually reproduces that action for the VR user. It can also recreate a scene in which an AR user hands over an object to a VR user. Furthermore, it can detect the VR user's instructions using voice recognition technology and convey those instructions to the AR user visually or audibly. This allows the system to create an environment in which AR and VR users can interact with each other in real time.

[0029] The system according to the embodiment includes a detection unit, an analysis unit, and a providing unit. The detection unit detects the actions of the AR user. For example, the detection unit can detect the actions of the AR user using a motion capture sensor or a camera. The detection unit can detect the actions of the AR user in real time and transmit the data to the analysis unit. The analysis unit uses a large-scale language model (LLM) to analyze the information detected by the detection unit and provide appropriate feedback to the VR user. For example, the analysis unit can understand the actions of the AR user using natural language processing technology and generate appropriate feedback. The analysis unit can analyze the actions of the AR user and provide visual or audio feedback to the VR user based on the actions. The providing unit provides visual or audio feedback to the VR user based on the feedback generated by the analysis unit. For example, the providing unit can detect an action of the AR user waving at the VR user and generate an animation that visually reproduces the action for the VR user. The providing unit can also reproduce a scene in which the AR user hands over an object to the VR user. This allows the system according to the embodiment to realize an environment in which AR users and VR users can interact with each other in real time.

[0030] The detection unit can detect the AR user's movements using a motion capture sensor or a camera. Examples of motion capture sensors include optical sensors and inertial sensors. The optical sensor detects the AR user's movements using a camera and transmits the data to the analysis unit. The inertial sensor detects the AR user's movements using an accelerometer or gyroscope and transmits the data to the analysis unit. Examples of cameras include RGB cameras and depth cameras. The RGB camera acquires color images and detects the AR user's movements. The depth camera acquires distance information and detects the AR user's movements. This allows the detection unit to accurately detect the AR user's movements. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data acquired by the motion capture sensor or camera to the generation AI and cause the generation AI to detect the movements.

[0031] The analysis unit can understand the AR user's actions using natural language processing technology and generate appropriate feedback. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides a sentence into words and analyzes the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and clarifies grammatical relationships. Semantic analysis analyzes the meaning of the sentence and generates appropriate feedback based on the context. This allows the analysis unit to accurately understand the AR user's actions and generate appropriate feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using an LLM. For example, the analysis unit can input the AR user's action data into an LLM and cause the LLM to understand the actions and generate feedback.

[0032] The providing unit can detect an action of the AR user waving at the VR user and generate an animation that visually reproduces the action to the VR user. Examples of the animation that is visually reproduced include 3D model animation and 2D animation. The 3D model animation reproduces the AR user's action using a 3D model and visually displays it to the VR user. The 2D animation reproduces the AR user's action using 2D graphics and visually displays it to the VR user. This allows the providing unit to visually reproduce the AR user's action to the VR user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the AR user's action data to a generating AI and cause the generating AI to generate an animation that visually reproduces the action.

[0033] The providing unit can recreate a scene in which an AR user hands over an object to a VR user. The scene in which the object is handed over includes, for example, the type of object and details of the handing action. The type of object includes, for example, a book, a pen, a ball, etc. The details of the handing action include, for example, hand movements and the timing of handing over the object. This allows the providing unit to recreate a scene in which an AR user hands over an object to a VR user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the action data of the AR user into a generating AI and cause the generating AI to recreate a scene in which the object is handed over.

[0034] The providing unit can detect the VR user's instructions using voice recognition technology and convey the instructions to the AR user visually or audibly. Voice recognition technology includes, for example, voice command recognition and natural language understanding. Voice command recognition analyzes the VR user's voice commands and understands their content. Natural language understanding analyzes the VR user's voice and generates appropriate feedback based on the context. This allows the providing unit to accurately convey the VR user's instructions to the AR user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's voice data into a generation AI and have the generation AI perform voice recognition and feedback generation.

[0035] Upon detection, the detection unit can predict an action by referring to the AR user's past action history. The detection unit, for example, predicts the next action based on actions frequently performed by the AR user in the past. For example, the detection unit analyzes the AR user's past action patterns to improve prediction accuracy. The detection unit can also predict actions the AR user will perform during a specific time period to improve detection accuracy. For example, the detection unit predicts the next action based on the AR user's past action history. This allows the detection unit to predict actions based on the AR user's past action history. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the AR user's past action history data into the generation AI and cause the generation AI to predict the action.

[0036] The detection unit can detect motions based on environmental information about the AR user at the time of detection. The detection unit, for example, detects objects around the AR user and predicts motions based on the detected objects. For example, the detection unit analyzes sounds around the AR user to improve motion detection accuracy. The detection unit can also update environmental information about the AR user in real time and reflect the updated information in motion detection. For example, the detection unit detects objects and sounds around the AR user in real time and predicts motions based on the updated information. This allows the detection unit to detect motions based on environmental information about the AR user. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input environmental information data about the AR user to a generation AI and cause the generation AI to detect motions.

[0037] Upon detection, the detection unit can analyze the AR user's movement speed and rhythm and identify the type of movement. The detection unit, for example, analyzes the AR user's movement speed and identifies a specific movement. For example, the detection unit analyzes the AR user's movement rhythm and identifies the type of movement. The detection unit can also combine the AR user's movement speed and rhythm to more accurately identify the movement. For example, the detection unit identifies the type of movement based on the AR user's movement speed and rhythm. This allows the detection unit to identify the type of movement based on the AR user's movement speed and rhythm. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the AR user's movement speed and rhythm data to the generation AI and cause the generation AI to identify the type of movement.

[0038] The detection unit can detect a motion by taking into account the geographical location information of the AR user. For example, when the AR user is in a specific location, the detection unit prioritizes detecting motions related to that location. For example, the detection unit updates the geographical location information of the AR user in real time and reflects it in the motion detection. The detection unit can also predict a motion based on the geographical location information when the AR user is moving. For example, the detection unit detects a motion based on the geographical location information of the AR user. This allows the detection unit to detect a motion based on the geographical location information of the AR user. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the geographical location information data of the AR user to the generation AI and cause the generation AI to detect a motion.

[0039] During detection, the detection unit can analyze the social media activity of the AR user and detect related actions. The detection unit can, for example, analyze the content of posts made by the AR user on social media and detect related actions. For example, the detection unit can predict actions by referring to the activities of the AR user's friends on social media. The detection unit can also detect actions based on the AR user's check-in information on social media. For example, the detection unit can detect actions based on the AR user's social media activity. This allows the detection unit to detect actions based on the AR user's social media activity. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the AR user's social media activity data to the generation AI and cause the generation AI to detect actions.

[0040] The detection unit can customize the detection method by reflecting the AR user's past feedback during detection. The detection unit can adjust the accuracy of motion detection based on, for example, the AR user's past feedback. For example, the detection unit can analyze the AR user's past feedback and optimize the detection method. The detection unit can also improve the motion detection algorithm by referring to the AR user's past feedback. For example, the detection unit customizes the motion detection method based on the AR user's past feedback. This allows the detection unit to customize the detection method based on the AR user's past feedback. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the AR user's past feedback data into the generation AI and cause the generation AI to customize the detection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the feedback based on the importance of the AR user's action. For example, the analysis unit provides detailed feedback for important actions. For example, the analysis unit provides standard feedback for normal actions. The analysis unit can also provide brief feedback for less important actions. For example, the analysis unit adjusts the level of detail of the feedback based on the importance of the AR user's action. This allows the analysis unit to adjust the level of detail of the feedback according to the importance of the AR user's action. Some or all of the above-described processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using the LLM. For example, the analysis unit can input the AR user's action data into the LLM and cause the LLM to adjust the level of detail of the feedback based on the importance of the action.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the AR user's action. For example, the analysis unit applies a specific analysis algorithm to hand movements. For example, the analysis unit applies a different analysis algorithm to voice. Furthermore, when multiple actions are performed simultaneously, the analysis unit can also apply an analysis algorithm depending on each category. For example, the analysis unit applies different analysis algorithms depending on the category of the AR user's action. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the AR user's action. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an LLM (large-scale language model), or may be performed without using an LLM. For example, the analysis unit can input the AR user's action data into an LLM and cause the LLM to apply an analysis algorithm depending on the category of the action.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the AR user's past actions. The analysis unit, for example, optimizes the analysis algorithm based on the AR user's past action data. For example, the analysis unit analyzes the AR user's past action patterns to improve the analysis accuracy. The analysis unit can also improve the analysis accuracy by referring to the AR user's past action history. For example, the analysis unit improves the analysis accuracy based on the AR user's past actions. This allows the analysis unit to improve the analysis accuracy based on the AR user's past actions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using the LLM. For example, the analysis unit can input the AR user's past action data into the LLM and cause the LLM to improve the analysis accuracy.

[0044] During analysis, the analysis unit can determine the priority of feedback based on the time of occurrence of the AR user's action. For example, the analysis unit can provide feedback preferentially for recent actions. For example, the analysis unit can provide standard feedback for past actions. The analysis unit can also adjust the priority and provide feedback for actions performed during a specific time period. For example, the analysis unit can determine the priority of feedback based on the time of occurrence of the AR user's action. This allows the analysis unit to determine the priority of feedback according to the time of occurrence of the AR user's action. Some or all of the above-described processing in the analysis unit can be performed using, for example, a large-scale language model (LLM), or can be performed without using the LLM. For example, the analysis unit can input the AR user's action data into the LLM and cause the LLM to determine the priority of feedback based on the time of occurrence of the action.

[0045] During analysis, the analysis unit can adjust the order of feedback based on the relevance of the AR user's actions. For example, the analysis unit provides preferential feedback for highly relevant actions. For example, the analysis unit provides standard feedback for less relevant actions. Furthermore, when multiple actions are related, the analysis unit can provide consistent feedback for those actions. For example, the analysis unit adjusts the order of feedback based on the relevance of the AR user's actions. This allows the analysis unit to adjust the order of feedback according to the relevance of the AR user's actions. Some or all of the above-described processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using the LLM. For example, the analysis unit can input the AR user's action data into the LLM and cause the LLM to adjust the order of feedback based on the relevance of the actions.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the feedback according to the expertise level of the AR user. For example, the analysis unit provides feedback that uses a lot of technical terms to a user with high expertise. For example, the analysis unit provides feedback in simple language to a user with low expertise. The analysis unit can also analyze the user's expertise level and provide feedback accordingly. For example, the analysis unit adjusts the use of technical terms in the feedback according to the expertise level of the AR user. This allows the analysis unit to adjust the use of technical terms in the feedback according to the expertise level of the AR user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using an LLM. For example, the analysis unit can input the expertise level data of the AR user into an LLM and cause the LLM to adjust the use of technical terms in the feedback.

[0047] The providing unit can select the display method by referring to the VR user's past feedback history when providing the data. The providing unit, for example, selects the optimal display method based on the VR user's past feedback history. For example, the providing unit analyzes the VR user's past feedback history and optimizes the display method. The providing unit can also customize the display method by referring to the VR user's past feedback history. For example, the providing unit selects the display method based on the VR user's past feedback history. This allows the providing unit to select the optimal display method based on the VR user's past feedback history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's past feedback history data to the generating AI and cause the generating AI to select the display method.

[0048] The providing unit can customize the display content according to the VR user's current task when providing the display content. For example, when the VR user is performing a specific task, the providing unit provides display content related to the task. For example, the providing unit analyzes the VR user's current task and provides optimal display content. The providing unit can also update the display content in real time according to the progress of the VR user's task. For example, the providing unit customizes the display content according to the VR user's current task. This allows the providing unit to customize the display content according to the VR user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's task data to a generating AI and cause the generating AI to customize the display content.

[0049] The providing unit can improve the display method by reflecting the VR user's feedback at the time of providing. The providing unit improves the display method, for example, based on the VR user's feedback. For example, the providing unit analyzes the VR user's feedback and optimizes the display method. The providing unit can also customize the display method by referring to the VR user's feedback. For example, the providing unit improves the display method based on the VR user's feedback. This allows the providing unit to improve the display method based on the VR user's feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the VR user's feedback data into the generating AI and cause the generating AI to improve the display method.

[0050] The providing unit can select the optimal feedback method by taking into account the geographical location information of the VR user when providing the feedback. For example, when the VR user is in a specific location, the providing unit provides feedback related to that location. For example, the providing unit updates the geographical location information of the VR user in real time and optimizes the feedback method. Furthermore, when the VR user is moving, the providing unit can also adjust the feedback method based on the geographical location information. For example, the providing unit selects the feedback method based on the geographical location information of the VR user. This allows the providing unit to select the optimal feedback method based on the geographical location information of the VR user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the VR user to the generating AI and cause the generating AI to select the feedback method.

[0051] At the time of providing, the providing unit can analyze the VR user's social media activity and suggest a means of feedback. The providing unit, for example, analyzes the content posted by the VR user on social media and suggests a relevant means of feedback. For example, the providing unit suggests a means of feedback by referring to the activities of the VR user's friends on social media. The providing unit can also suggest a means of feedback based on the VR user's check-in information on social media. For example, the providing unit suggests a means of feedback based on the VR user's social media activity. This allows the providing unit to suggest a means of feedback based on the VR user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's social media activity data into the generating AI and cause the generating AI to suggest a means of feedback.

[0052] The providing unit can customize the feedback method by reflecting the VR user's past feedback when providing the feedback. The providing unit customizes the feedback method based on, for example, the VR user's past feedback. For example, the providing unit analyzes the VR user's past feedback and optimizes the feedback method. The providing unit can also improve the feedback method by referring to the VR user's past feedback. For example, the providing unit customizes the feedback method based on the VR user's past feedback. This allows the providing unit to customize the feedback method based on the VR user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's past feedback data into the generating AI and cause the generating AI to customize the feedback method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can take into account background information of the action when analyzing the AR user's action. For example, the analysis unit can take into account the location and time of day when the AR user performed the action, thereby more accurately understanding the intention of the action. In addition, if the AR user's action is related to a specific event or situation, the analysis unit can generate feedback based on that related information. Furthermore, the analysis unit can analyze how the AR user's action is related to past actions and identify patterns of action. This allows the analysis unit to provide more appropriate feedback by taking into account background information of the AR user's action.

[0055] When receiving feedback from a VR user, the providing unit can automatically classify the content of the feedback and suggest appropriate actions. For example, if the VR user's feedback is positive, the providing unit can suggest actions to strengthen the feedback. In addition, if the providing unit receives negative feedback, the providing unit can identify the cause and suggest improvements. Furthermore, the providing unit can guide the VR user on the next step based on the content of the feedback. This allows the providing unit to effectively utilize the VR user's feedback and improve the user experience.

[0056] When detecting the AR user's movements, the detection unit can analyze the speed and rhythm of the movements to identify the type of movement. For example, the detection unit can determine that if the AR user's movements are fast, the movement is in a hurry. Also, if the rhythm of the movements is constant, the detection unit can determine that the movement is habitual. Furthermore, the detection unit can detect changes in the speed and rhythm of the movements and analyze what the changes mean. This allows the detection unit to more accurately identify the type of movement based on the speed and rhythm of the AR user's movements.

[0057] The analysis unit can take into account the context of the action when analyzing the AR user's action. For example, the analysis unit can analyze the circumstances before and after the AR user performs the action to understand the context in which the action was performed. The analysis unit can also evaluate how the AR user's action is related to the actions of other users and generate feedback based on that relationship. Furthermore, the analysis unit can analyze how the AR user's action contributes to a specific task or goal and understand the intention of the action based on that information. This allows the analysis unit to provide more appropriate feedback by taking into account the context of the AR user's action.

[0058] The detection unit can combine multiple sensors to improve the accuracy of detecting the AR user's movements. For example, the detection unit can combine a motion capture sensor and a camera to more accurately detect the AR user's movements. The detection unit can also combine an accelerometer and a gyroscope to analyze the direction and speed of the movements in detail. Furthermore, the detection unit can combine an audio sensor and a biometric sensor to collect background information about the movements and improve the accuracy of the movements based on that information. As a result, the detection unit can combine multiple sensors to more accurately detect the AR user's movements.

[0059] When analyzing the actions of an AR user, the analysis unit can learn patterns of actions and make predictions based on those patterns. For example, the analysis unit can predict the next action that the AR user is likely to perform based on data on actions that the AR user has performed in the past. The analysis unit can also analyze the patterns of the AR user's actions and generate feedback based on those patterns. Furthermore, if the pattern of the AR user's actions changes, the analysis unit can detect that change and perform analysis based on the new pattern. This allows the analysis unit to learn the patterns of the AR user's actions, make predictions based on those patterns, and provide more appropriate feedback.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The detection unit detects the actions of the AR user. For example, the AR user's movements can be detected using a motion capture sensor or a camera. The detection unit also detects the AR user's movements in real time and sends the data to the analysis unit. Step 2: The analysis unit uses a large-scale language model (LLM) to analyze the information detected by the detection unit and provide appropriate feedback to the VR user. For example, it uses natural language processing technology to understand the AR user's actions and generate appropriate feedback. The analysis unit also analyzes the AR user's actions and provides visual or audio feedback to the VR user based on those actions. Step 3: The providing unit provides visual or audio feedback to the VR user based on the feedback generated by the analyzing unit. For example, the providing unit detects the AR user waving their hand to the VR user and generates an animation that visually reproduces that action for the VR user. The providing unit also reproduces a scene in which the AR user hands over an object to the VR user.

[0062] (Example 2) A system according to an embodiment of the present invention is a system for sharing a VR user's experience with a person not wearing an HMD. This system detects the actions of an AR user, inputs the information into a large-scale language model (LLM) for analysis, and provides appropriate feedback to the VR user. For example, if an AR user waves at a VR user, the LLM analyzes the action and displays a waving animation in the VR user's field of view. In this way, an environment in which the AR user and the VR user can interact with each other is realized. For example, in a scene in which an AR user hands an object to a VR user, the system detects the AR user's hand movement and reproduces that movement in the VR space, allowing the VR user to feel as if they are receiving the object. In addition, in a scene in which a VR user instructs the AR user, the system detects the VR user's instruction using voice recognition technology and conveys the instruction visually or audibly to the AR user. This allows the system to realize an environment in which the AR user and the VR user can interact with each other in real time. For example, the system can detect an AR user waving at a VR user and generate an animation that visually reproduces that action for the VR user. It can also recreate a scene in which an AR user hands over an object to a VR user. Furthermore, it can detect the VR user's instructions using voice recognition technology and convey those instructions to the AR user visually or audibly. This allows the system to create an environment in which AR and VR users can interact with each other in real time.

[0063] The system according to the embodiment includes a detection unit, an analysis unit, and a providing unit. The detection unit detects the actions of the AR user. For example, the detection unit can detect the actions of the AR user using a motion capture sensor or a camera. The detection unit can detect the actions of the AR user in real time and transmit the data to the analysis unit. The analysis unit uses a large-scale language model (LLM) to analyze the information detected by the detection unit and provide appropriate feedback to the VR user. For example, the analysis unit can understand the actions of the AR user using natural language processing technology and generate appropriate feedback. The analysis unit can analyze the actions of the AR user and provide visual or audio feedback to the VR user based on the actions. The providing unit provides visual or audio feedback to the VR user based on the feedback generated by the analysis unit. For example, the providing unit can detect an action of the AR user waving at the VR user and generate an animation that visually reproduces the action for the VR user. The providing unit can also reproduce a scene in which the AR user hands over an object to the VR user. This allows the system according to the embodiment to realize an environment in which AR users and VR users can interact with each other in real time.

[0064] The detection unit can detect the AR user's movements using a motion capture sensor or a camera. Examples of motion capture sensors include optical sensors and inertial sensors. The optical sensor detects the AR user's movements using a camera and transmits the data to the analysis unit. The inertial sensor detects the AR user's movements using an accelerometer or gyroscope and transmits the data to the analysis unit. Examples of cameras include RGB cameras and depth cameras. The RGB camera acquires color images and detects the AR user's movements. The depth camera acquires distance information and detects the AR user's movements. This allows the detection unit to accurately detect the AR user's movements. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data acquired by the motion capture sensor or camera to the generation AI and cause the generation AI to detect the movements.

[0065] The analysis unit can understand the AR user's actions using natural language processing technology and generate appropriate feedback. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides a sentence into words and analyzes the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and clarifies grammatical relationships. Semantic analysis analyzes the meaning of the sentence and generates appropriate feedback based on the context. This allows the analysis unit to accurately understand the AR user's actions and generate appropriate feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using an LLM. For example, the analysis unit can input the AR user's action data into an LLM and cause the LLM to understand the actions and generate feedback.

[0066] The providing unit can detect an action of the AR user waving at the VR user and generate an animation that visually reproduces the action to the VR user. Examples of the animation that is visually reproduced include 3D model animation and 2D animation. The 3D model animation reproduces the AR user's action using a 3D model and visually displays it to the VR user. The 2D animation reproduces the AR user's action using 2D graphics and visually displays it to the VR user. This allows the providing unit to visually reproduce the AR user's action to the VR user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the AR user's action data to a generating AI and cause the generating AI to generate an animation that visually reproduces the action.

[0067] The providing unit can recreate a scene in which an AR user hands over an object to a VR user. The scene in which the object is handed over includes, for example, the type of object and details of the handing action. The type of object includes, for example, a book, a pen, a ball, etc. The details of the handing action include, for example, hand movements and the timing of handing over the object. This allows the providing unit to recreate a scene in which an AR user hands over an object to a VR user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the action data of the AR user into a generating AI and cause the generating AI to recreate a scene in which the object is handed over.

[0068] The providing unit can detect the VR user's instructions using voice recognition technology and convey the instructions to the AR user visually or audibly. Voice recognition technology includes, for example, voice command recognition and natural language understanding. Voice command recognition analyzes the VR user's voice commands and understands their content. Natural language understanding analyzes the VR user's voice and generates appropriate feedback based on the context. This allows the providing unit to accurately convey the VR user's instructions to the AR user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's voice data into a generation AI and have the generation AI perform voice recognition and feedback generation.

[0069] The detection unit can estimate the emotion of the AR user and adjust the accuracy of motion detection based on the estimated emotion. For example, the detection unit captures the AR user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression. The detection unit can also record the AR user's voice and estimate the emotion using voice analysis technology. For example, the detection unit analyzes the tone and speed of the voice and calculates an emotion score. The detection unit can also collect the AR user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on heart rate fluctuations. This allows the detection unit to adjust the accuracy of motion detection according to the AR user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data of the AR user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0070] Upon detection, the detection unit can predict an action by referring to the AR user's past action history. The detection unit, for example, predicts the next action based on actions frequently performed by the AR user in the past. For example, the detection unit analyzes the AR user's past action patterns to improve prediction accuracy. The detection unit can also predict actions the AR user will perform during a specific time period to improve detection accuracy. For example, the detection unit predicts the next action based on the AR user's past action history. This allows the detection unit to predict actions based on the AR user's past action history. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the AR user's past action history data into the generation AI and cause the generation AI to predict the action.

[0071] The detection unit can detect motions based on environmental information about the AR user at the time of detection. The detection unit, for example, detects objects around the AR user and predicts motions based on the detected objects. For example, the detection unit analyzes sounds around the AR user to improve motion detection accuracy. The detection unit can also update environmental information about the AR user in real time and reflect the updated information in motion detection. For example, the detection unit detects objects and sounds around the AR user in real time and predicts motions based on the updated information. This allows the detection unit to detect motions based on environmental information about the AR user. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input environmental information data about the AR user to a generation AI and cause the generation AI to detect motions.

[0072] Upon detection, the detection unit can analyze the AR user's movement speed and rhythm and identify the type of movement. The detection unit, for example, analyzes the AR user's movement speed and identifies a specific movement. For example, the detection unit analyzes the AR user's movement rhythm and identifies the type of movement. The detection unit can also combine the AR user's movement speed and rhythm to more accurately identify the movement. For example, the detection unit identifies the type of movement based on the AR user's movement speed and rhythm. This allows the detection unit to identify the type of movement based on the AR user's movement speed and rhythm. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the AR user's movement speed and rhythm data to the generation AI and cause the generation AI to identify the type of movement.

[0073] The detection unit can estimate the emotion of the AR user and determine the priority of actions based on the estimated emotion. For example, the detection unit captures the AR user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression. The detection unit can also record the AR user's voice and estimate the emotion using voice analysis technology. For example, the detection unit analyzes the tone and speed of the voice and calculates the emotion score. The detection unit can also collect the AR user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates the emotion score based on heart rate fluctuations. This allows the detection unit to determine the priority of actions based on the AR user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data of the AR user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0074] The detection unit can detect a motion by taking into account the geographical location information of the AR user. For example, when the AR user is in a specific location, the detection unit prioritizes detecting motions related to that location. For example, the detection unit updates the geographical location information of the AR user in real time and reflects it in the motion detection. The detection unit can also predict a motion based on the geographical location information when the AR user is moving. For example, the detection unit detects a motion based on the geographical location information of the AR user. This allows the detection unit to detect a motion based on the geographical location information of the AR user. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the geographical location information data of the AR user to the generation AI and cause the generation AI to detect a motion.

[0075] During detection, the detection unit can analyze the social media activity of the AR user and detect related actions. The detection unit can, for example, analyze the content of posts made by the AR user on social media and detect related actions. For example, the detection unit can predict actions by referring to the activities of the AR user's friends on social media. The detection unit can also detect actions based on the AR user's check-in information on social media. For example, the detection unit can detect actions based on the AR user's social media activity. This allows the detection unit to detect actions based on the AR user's social media activity. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the AR user's social media activity data to the generation AI and cause the generation AI to detect actions.

[0076] The detection unit can customize the detection method by reflecting the AR user's past feedback during detection. The detection unit can adjust the accuracy of motion detection based on, for example, the AR user's past feedback. For example, the detection unit can analyze the AR user's past feedback and optimize the detection method. The detection unit can also improve the motion detection algorithm by referring to the AR user's past feedback. For example, the detection unit customizes the motion detection method based on the AR user's past feedback. This allows the detection unit to customize the detection method based on the AR user's past feedback. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the AR user's past feedback data into the generation AI and cause the generation AI to customize the detection method.

[0077] The analysis unit can estimate the emotion of the AR user and adjust the content of the feedback based on the estimated emotion. For example, the analysis unit captures the AR user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the AR user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the AR user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to adjust the content of the feedback based on the AR user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the AR user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0078] During analysis, the analysis unit can adjust the level of detail of the feedback based on the importance of the AR user's action. For example, the analysis unit provides detailed feedback for important actions. For example, the analysis unit provides standard feedback for normal actions. The analysis unit can also provide brief feedback for less important actions. For example, the analysis unit adjusts the level of detail of the feedback based on the importance of the AR user's action. This allows the analysis unit to adjust the level of detail of the feedback according to the importance of the AR user's action. Some or all of the above-described processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using the LLM. For example, the analysis unit can input the AR user's action data into the LLM and cause the LLM to adjust the level of detail of the feedback based on the importance of the action.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the AR user's action. For example, the analysis unit applies a specific analysis algorithm to hand movements. For example, the analysis unit applies a different analysis algorithm to voice. Furthermore, when multiple actions are performed simultaneously, the analysis unit can also apply an analysis algorithm depending on each category. For example, the analysis unit applies different analysis algorithms depending on the category of the AR user's action. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the AR user's action. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an LLM (large-scale language model), or may be performed without using an LLM. For example, the analysis unit can input the AR user's action data into an LLM and cause the LLM to apply an analysis algorithm depending on the category of the action.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the AR user's past actions. The analysis unit, for example, optimizes the analysis algorithm based on the AR user's past action data. For example, the analysis unit analyzes the AR user's past action patterns to improve the analysis accuracy. The analysis unit can also improve the analysis accuracy by referring to the AR user's past action history. For example, the analysis unit improves the analysis accuracy based on the AR user's past actions. This allows the analysis unit to improve the analysis accuracy based on the AR user's past actions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using the LLM. For example, the analysis unit can input the AR user's past action data into the LLM and cause the LLM to improve the analysis accuracy.

[0081] The analysis unit can estimate the emotion of the AR user and adjust the length of the feedback based on the estimated emotion. For example, the analysis unit captures the AR user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the AR user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the AR user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to adjust the length of the feedback based on the AR user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the AR user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0082] During analysis, the analysis unit can determine the priority of feedback based on the time of occurrence of the AR user's action. For example, the analysis unit can provide feedback preferentially for recent actions. For example, the analysis unit can provide standard feedback for past actions. The analysis unit can also adjust the priority and provide feedback for actions performed during a specific time period. For example, the analysis unit can determine the priority of feedback based on the time of occurrence of the AR user's action. This allows the analysis unit to determine the priority of feedback according to the time of occurrence of the AR user's action. Some or all of the above-described processing in the analysis unit can be performed using, for example, a large-scale language model (LLM), or can be performed without using the LLM. For example, the analysis unit can input the AR user's action data into the LLM and cause the LLM to determine the priority of feedback based on the time of occurrence of the action.

[0083] During analysis, the analysis unit can adjust the order of feedback based on the relevance of the AR user's actions. For example, the analysis unit provides preferential feedback for highly relevant actions. For example, the analysis unit provides standard feedback for less relevant actions. Furthermore, when multiple actions are related, the analysis unit can provide consistent feedback for those actions. For example, the analysis unit adjusts the order of feedback based on the relevance of the AR user's actions. This allows the analysis unit to adjust the order of feedback according to the relevance of the AR user's actions. Some or all of the above-described processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using the LLM. For example, the analysis unit can input the AR user's action data into the LLM and cause the LLM to adjust the order of feedback based on the relevance of the actions.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the feedback according to the expertise level of the AR user. For example, the analysis unit provides feedback that uses a lot of technical terms to a user with high expertise. For example, the analysis unit provides feedback in simple language to a user with low expertise. The analysis unit can also analyze the user's expertise level and provide feedback accordingly. For example, the analysis unit adjusts the use of technical terms in the feedback according to the expertise level of the AR user. This allows the analysis unit to adjust the use of technical terms in the feedback according to the expertise level of the AR user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a large-scale language model (LLM), or may be performed without using an LLM. For example, the analysis unit can input the expertise level data of the AR user into an LLM and cause the LLM to adjust the use of technical terms in the feedback.

[0085] The providing unit can estimate the VR user's emotions and adjust the feedback display method based on the estimated emotions. For example, the providing unit captures the VR user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions. The providing unit can also record the VR user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the VR user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows the providing unit to adjust the feedback display method according to the VR user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input image data of a VR user taken with a camera into the generating AI and have the generating AI estimate emotions.

[0086] The providing unit can select the display method by referring to the VR user's past feedback history when providing the data. The providing unit, for example, selects the optimal display method based on the VR user's past feedback history. For example, the providing unit analyzes the VR user's past feedback history and optimizes the display method. The providing unit can also customize the display method by referring to the VR user's past feedback history. For example, the providing unit selects the display method based on the VR user's past feedback history. This allows the providing unit to select the optimal display method based on the VR user's past feedback history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's past feedback history data to the generating AI and cause the generating AI to select the display method.

[0087] The providing unit can customize the display content according to the VR user's current task when providing the display content. For example, when the VR user is performing a specific task, the providing unit provides display content related to the task. For example, the providing unit analyzes the VR user's current task and provides optimal display content. The providing unit can also update the display content in real time according to the progress of the VR user's task. For example, the providing unit customizes the display content according to the VR user's current task. This allows the providing unit to customize the display content according to the VR user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's task data to a generating AI and cause the generating AI to customize the display content.

[0088] The providing unit can improve the display method by reflecting the VR user's feedback at the time of providing. The providing unit improves the display method, for example, based on the VR user's feedback. For example, the providing unit analyzes the VR user's feedback and optimizes the display method. The providing unit can also customize the display method by referring to the VR user's feedback. For example, the providing unit improves the display method based on the VR user's feedback. This allows the providing unit to improve the display method based on the VR user's feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the VR user's feedback data into the generating AI and cause the generating AI to improve the display method.

[0089] The providing unit can estimate the VR user's emotions and determine the priority of feedback based on the estimated emotions. For example, the providing unit captures the VR user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions. The providing unit can also record the VR user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the VR user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows the providing unit to determine the priority of feedback according to the VR user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of a VR user taken with a camera into the generating AI and have the generating AI estimate emotions.

[0090] The providing unit can select the optimal feedback method by taking into account the geographical location information of the VR user when providing the feedback. For example, when the VR user is in a specific location, the providing unit provides feedback related to that location. For example, the providing unit updates the geographical location information of the VR user in real time and optimizes the feedback method. Furthermore, when the VR user is moving, the providing unit can also adjust the feedback method based on the geographical location information. For example, the providing unit selects the feedback method based on the geographical location information of the VR user. This allows the providing unit to select the optimal feedback method based on the geographical location information of the VR user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the VR user to the generating AI and cause the generating AI to select the feedback method.

[0091] At the time of providing, the providing unit can analyze the VR user's social media activity and suggest a means of feedback. The providing unit, for example, analyzes the content posted by the VR user on social media and suggests a relevant means of feedback. For example, the providing unit suggests a means of feedback by referring to the activities of the VR user's friends on social media. The providing unit can also suggest a means of feedback based on the VR user's check-in information on social media. For example, the providing unit suggests a means of feedback based on the VR user's social media activity. This allows the providing unit to suggest a means of feedback based on the VR user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's social media activity data into the generating AI and cause the generating AI to suggest a means of feedback.

[0092] The providing unit can customize the feedback method by reflecting the VR user's past feedback when providing the feedback. The providing unit customizes the feedback method based on, for example, the VR user's past feedback. For example, the providing unit analyzes the VR user's past feedback and optimizes the feedback method. The providing unit can also improve the feedback method by referring to the VR user's past feedback. For example, the providing unit customizes the feedback method based on the VR user's past feedback. This allows the providing unit to customize the feedback method based on the VR user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the VR user's past feedback data into the generating AI and cause the generating AI to customize the feedback method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned detection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit can detect the movement of the AR user using the camera 42 or a motion capture sensor of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the detected information using the LLM, and provides appropriate feedback to the VR user. The provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides the feedback generated by the analysis unit to the VR user visually or audibly. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned detection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit can detect the movement of the AR user using the camera 42 or a motion capture sensor of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the detected information using the LLM, and provides appropriate feedback to the VR user. The provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides the feedback generated by the analysis unit to the VR user visually or audibly. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit can detect the movement of the AR user using the camera 42 or a motion capture sensor of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the detected information using the LLM, and provides appropriate feedback to the VR user. The provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the feedback generated by the analysis unit to the VR user visually or audibly. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned detection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit can detect the movement of the AR user using the camera 42 or a motion capture sensor of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the detected information using the LLM, and provides appropriate feedback to the VR user. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides the feedback generated by the analysis unit to the VR user visually or audibly.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can take into account background information of the action when analyzing the AR user's action. For example, the analysis unit can take into account the location and time of day when the AR user performed the action, thereby more accurately understanding the intention of the action. In addition, if the AR user's action is related to a specific event or situation, the analysis unit can generate feedback based on that related information. Furthermore, the analysis unit can analyze how the AR user's action is related to past actions and identify patterns of action. This allows the analysis unit to provide more appropriate feedback by taking into account background information of the AR user's action.

[0095] When receiving feedback from a VR user, the providing unit can automatically classify the content of the feedback and suggest appropriate actions. For example, if the VR user's feedback is positive, the providing unit can suggest actions to strengthen the feedback. In addition, if the providing unit receives negative feedback, the providing unit can identify the cause and suggest improvements. Furthermore, the providing unit can guide the VR user on the next step based on the content of the feedback. This allows the providing unit to effectively utilize the VR user's feedback and improve the user experience.

[0096] When detecting the AR user's movements, the detection unit can analyze the speed and rhythm of the movements to identify the type of movement. For example, the detection unit can determine that if the AR user's movements are fast, the movement is in a hurry. Also, if the rhythm of the movements is constant, the detection unit can determine that the movement is habitual. Furthermore, the detection unit can detect changes in the speed and rhythm of the movements and analyze what the changes mean. This allows the detection unit to more accurately identify the type of movement based on the speed and rhythm of the AR user's movements.

[0097] The analysis unit can take into account the emotional aspects of the actions when analyzing the AR user's actions. For example, the analysis unit can analyze the AR user's facial expressions and tone of voice when performing the action and infer the emotion behind the action. The analysis unit can also evaluate the emotional impact of the AR user's actions on other users and generate feedback taking that impact into account. Furthermore, the analysis unit can analyze how the AR user's emotional state affects the action and understand the intention of the action based on that information. This allows the analysis unit to provide more appropriate feedback by taking into account the emotional aspects of the AR user's actions.

[0098] The providing unit can estimate the emotion of the VR user and adjust the way in which the feedback is displayed based on the estimated emotion. For example, if the VR user is excited, the providing unit can visually emphasize the feedback to attract the user's attention. In addition, if the VR user is relaxed, the providing unit can display the feedback in a calm tone. Furthermore, if the VR user is feeling stressed, the providing unit can simplify the feedback to reduce the burden on the user. In this way, the providing unit can adjust the way in which the feedback is displayed according to the emotion of the VR user and improve the user experience.

[0099] The analysis unit can take into account the context of the action when analyzing the AR user's action. For example, the analysis unit can analyze the circumstances before and after the AR user performs the action to understand the context in which the action was performed. The analysis unit can also evaluate how the AR user's action is related to the actions of other users and generate feedback based on that relationship. Furthermore, the analysis unit can analyze how the AR user's action contributes to a specific task or goal and understand the intention of the action based on that information. This allows the analysis unit to provide more appropriate feedback by taking into account the context of the AR user's action.

[0100] The providing unit can estimate the emotion of the VR user and adjust the content of the feedback based on the estimated emotion. For example, if the VR user is happy, the providing unit can provide positive feedback that further enhances the emotion. Also, if the VR user is sad, the providing unit can provide comforting feedback that alleviates the emotion. Furthermore, if the VR user is angry, the providing unit can provide calming feedback that calms the emotion. In this way, the providing unit can adjust the content of the feedback according to the emotion of the VR user and improve the user experience.

[0101] The detection unit can combine multiple sensors to improve the accuracy of detecting the AR user's movements. For example, the detection unit can combine a motion capture sensor and a camera to more accurately detect the AR user's movements. The detection unit can also combine an accelerometer and a gyroscope to analyze the direction and speed of the movements in detail. Furthermore, the detection unit can combine an audio sensor and a biometric sensor to collect background information about the movements and improve the accuracy of the movements based on that information. As a result, the detection unit can combine multiple sensors to more accurately detect the AR user's movements.

[0102] When analyzing the actions of an AR user, the analysis unit can learn patterns of actions and make predictions based on those patterns. For example, the analysis unit can predict the next action that the AR user is likely to perform based on data on actions that the AR user has performed in the past. The analysis unit can also analyze the patterns of the AR user's actions and generate feedback based on those patterns. Furthermore, if the pattern of the AR user's actions changes, the analysis unit can detect that change and perform analysis based on the new pattern. This allows the analysis unit to learn the patterns of the AR user's actions, make predictions based on those patterns, and provide more appropriate feedback.

[0103] The providing unit can estimate the emotion of the VR user and adjust the timing of the feedback based on the estimated emotion. For example, if the VR user is concentrating, the providing unit can delay the timing of the feedback so as not to disturb the concentration. Also, if the VR user is relaxed, the providing unit can adjust the timing of the feedback to maintain the relaxation. Furthermore, if the VR user is feeling stressed, the providing unit can optimize the timing of the feedback to reduce the stress. In this way, the providing unit can adjust the timing of the feedback according to the emotion of the VR user and improve the user experience.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The detection unit detects the actions of the AR user. For example, the AR user's movements can be detected using a motion capture sensor or a camera. The detection unit also detects the AR user's movements in real time and sends the data to the analysis unit. Step 2: The analysis unit uses a large-scale language model (LLM) to analyze the information detected by the detection unit and provide appropriate feedback to the VR user. For example, it uses natural language processing technology to understand the AR user's actions and generate appropriate feedback. The analysis unit also analyzes the AR user's actions and provides visual or audio feedback to the VR user based on those actions. Step 3: The providing unit provides visual or audio feedback to the VR user based on the feedback generated by the analyzing unit. For example, the providing unit detects the AR user waving their hand to the VR user and generates an animation that visually reproduces that action for the VR user. The providing unit also reproduces a scene in which the AR user hands over an object to the VR user.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 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.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0120] 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.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a detection unit that detects the actions of an AR user; an analysis unit that analyzes the information detected by the detection unit and provides feedback to a VR user; a providing unit that provides visual or audio feedback to the VR user based on the feedback generated by the analyzing unit. A system characterized by:

2. The detection unit Detecting AR user movements using motion capture sensors or cameras 2. The system of claim 1.

3. The analysis unit Using natural language processing technology to understand AR user actions and generate appropriate feedback 2. The system of claim 1.

4. The providing unit Detects the AR user's hand waving action towards the VR user and generates an animation that visually reproduces that action for the VR user.

2. The system of claim 1.

5. The providing unit Recreate a scene where an AR user hands something over to a VR user 2. The system of claim 1.

6. The providing unit Detects the VR user's instructions using voice recognition technology and conveys those instructions to the AR user visually or audibly.

2. The system of claim 1.

7. The detection unit Estimate the AR user's emotions and adjust the accuracy of motion detection based on the estimated emotions.

2. The system of claim 1.

8. The detection unit Upon detection, the AR user's past behavior history is referenced to predict their behavior.

2. The system of claim 1.

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