system

Abstract

The system according to the embodiment aims to replace a real object with a virtual object and display it. [Solution] A system according to an embodiment includes an object recognition unit, a posture estimation unit, a replacement display unit, an interior modification unit, and a scenery modification unit. The object recognition unit recognizes objects. The posture estimation unit estimates posture based on information detected by the object recognition unit. The replacement display unit replaces and displays the object based on information tracked by the posture estimation unit. The interior modification unit modifies the interior based on the information replaced by the replacement display unit. The scenery modification unit modifies the scenery based on the information modified by the interior modification unit.

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 techniques do not adequately replace real objects with virtual objects, and there is room for improvement.

[0005] The system according to the embodiment aims to replace a real object with a virtual object and display it. [Means for solving the problem]

[0006] The system according to the embodiment includes an object recognition unit, a posture estimation unit, a replacement display unit, an interior modification unit, and a scenery modification unit. The object recognition unit recognizes an object. The posture estimation unit estimates a posture based on information detected by the object recognition unit. The replacement display unit replaces and displays the object based on information tracked by the posture estimation unit. The interior modification unit modifies the interior based on the information replaced by the replacement display unit. The scenery modification unit modifies the scenery based on the information modified by the interior modification unit. [Effects of the Invention]

[0007] The system according to the embodiment can replace a real object with a virtual object and display it. [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) An AR system according to an embodiment of the present invention is a system that displays AR images by replacing passersby and vehicles with celebrities or favorite characters. The AR system allows a user to wear AR glasses or AR contact lenses, detect passersby and vehicles in the city using object recognition technology, and track their movements using pose estimation technology. The detected passersby and vehicles are then replaced with data of the celebrity or favorite character designated by the user. Furthermore, the appearance and interior of the furniture in the room can be changed, and the view from the window can be freely specified. For example, in an AR system, a user wears AR glasses or AR contact lenses. The AR system then detects passersby and vehicles in the city using object recognition technology and tracks their movements using pose estimation technology. This allows the position and movement of passersby and vehicles to be accurately determined. The AR system then replaces the detected passersby and vehicles with data of the celebrity or favorite character designated by the user. For example, passersby can be replaced with famous actors or anime characters. Furthermore, the AR system can also change the appearance and interior of the furniture in the room. The appearance of the furniture and interior can be changed based on a design or theme designated by the user. For example, a room can be redesigned to a modern style, or the interior can be restyled to match the worldview of a particular anime. AR systems also allow users to freely specify the view seen from a window. The view seen from a window can be changed using the view data specified by the user. For example, an urban landscape can be changed to a mountain landscape rich in nature. This allows AR systems to augment the real world for users and provide a more enjoyable experience. For example, users can customize the real world to their preferences and find new enjoyment in their daily lives. Furthermore, by changing the interior design of a room or the view from a window, a space can be created that suits the user's mood and environment.

[0029] The AR system according to the embodiment includes an object recognition unit, a posture estimation unit, a replacement display unit, an interior modification unit, and a scenery modification unit. The object recognition unit detects passersby and vehicles. For example, the object recognition unit detects passersby and vehicles using image processing technology. The object recognition unit can also detect passersby and vehicles using a machine learning algorithm. The object recognition unit can also detect passersby and vehicles in real time. The posture estimation unit tracks the movements of passersby and vehicles detected by the object recognition unit. For example, the posture estimation unit tracks the movements of passersby using joint angle calculations. The posture estimation unit can also track the movements of vehicles using motion analysis. The posture estimation unit can also track the movements of passersby and vehicles in real time. The replacement display unit replaces passersby and vehicles tracked by the posture estimation unit with celebrities or characters specified by a user. For example, the replacement display unit replaces passersby with famous actors using image synthesis technology. The replacement display unit can also replace vehicles with anime characters using real-time rendering. The replacement display unit can also replace passersby and vehicles using data specified by the user. The interior modification unit changes the appearance of the furniture and interior of the room based on a design or theme specified by the user. For example, the interior modification unit changes the appearance of the furniture in the room using 3D modeling. The interior modification unit can also change the interior of the room using virtual reality technology. The interior modification unit can also change the interior of the room based on a theme specified by the user. The scenery modification unit changes the view seen through a window using scenery data specified by the user. For example, the scenery modification unit changes the view seen through a window using image processing technology. The scenery modification unit can also change the view seen through a window using real-time rendering. The scenery modification unit can also change the view seen through a window using data specified by the user. This enables the AR system according to the embodiment to perform object recognition, pose estimation, replacement display, interior modification, and scenery modification.

[0030] The object recognition unit can detect passersby or vehicles. The object recognition unit can detect passersby using, for example, image processing technology. For example, the object recognition unit can detect passersby based on pedestrian characteristics. The object recognition unit can also detect vehicles using a machine learning algorithm. For example, the object recognition unit can detect vehicles based on the characteristics of the vehicle model. The object recognition unit can also detect passersby or vehicles in real time. For example, the object recognition unit can detect passersby or vehicles by analyzing movement patterns. This allows for accurate detection of passersby or vehicles.

[0031] The posture estimation unit can track the movement of passersby or vehicles detected by the object recognition unit. The posture estimation unit tracks the movement of passersby, for example, by calculating joint angles. For example, the posture estimation unit calculates the joint angles of passersby and tracks their movement. The posture estimation unit can also track the movement of vehicles by using motion analysis. For example, the posture estimation unit analyzes the movement of a vehicle and tracks its movement. The posture estimation unit can also track the movement of passersby or vehicles in real time. For example, the posture estimation unit analyzes movement patterns to track the movement of passersby or vehicles. This allows the movement of passersby or vehicles to be tracked accurately.

[0032] The replacement display unit can replace passersby or vehicles tracked by the pose estimation unit with celebrities or characters designated by the user. The replacement display unit can, for example, replace passersby with famous actors using image synthesis technology. For example, the replacement display unit can synthesize an image of a passerby with an image of a famous actor. The replacement display unit can also replace vehicles with animated characters using real-time rendering. For example, the replacement display unit can render an image of a vehicle with an image of an animated character in real time. The replacement display unit can also replace passersby or vehicles using data designated by the user. For example, the replacement display unit can replace passersby or vehicles with data of celebrities or characters designated by the user. This allows passersby or vehicles to be replaced with celebrities or characters designated by the user.

[0033] The interior modification unit can change the appearance of the furniture or interior of a room based on a design or theme specified by the user. The interior modification unit can change the appearance of the furniture in the room using, for example, 3D modeling. For example, the interior modification unit can create a 3D model of the furniture and change its appearance. The interior modification unit can also change the interior of the room using virtual reality technology. For example, the interior modification unit can recreate the interior of the room using virtual reality technology and change its appearance. The interior modification unit can also change the interior of the room based on a theme specified by the user. For example, the interior modification unit can change the interior to a modern design specified by the user or to one that matches the worldview of a particular anime. In this way, the appearance of the furniture and interior of the room can be changed based on a design or theme specified by the user.

[0034] The scenery change unit can change the scenery seen through the window using scenery data specified by the user. The scenery change unit changes the scenery seen through the window using, for example, image processing technology. For example, the scenery change unit changes an urban landscape to a mountain landscape rich in nature. The scenery change unit can also change the scenery seen through the window using real-time rendering. For example, the scenery change unit renders the scenery seen through the window in real time and changes it to the scenery specified by the user. The scenery change unit can also change the scenery seen through the window using data specified by the user. For example, the scenery change unit changes the scenery seen through the window using a scenery image or 3D model specified by the user. In this way, the scenery seen through the window can be changed using the scenery data specified by the user.

[0035] When recognizing an object, the object recognition unit can optimize the recognition algorithm by referring to the user's past recognition history. For example, the object recognition unit prioritizes recognizing objects that the user has frequently recognized in the past. For example, the object recognition unit prioritizes recognizing specific objects based on the user's past recognition history. The object recognition unit can also predict objects that will be recognized in a specific time period from the user's past recognition history and optimize the recognition algorithm. For example, the object recognition unit can analyze the user's past recognition history and predict objects that will be recognized in a specific time period. The object recognition unit can also predict objects that will be recognized in a specific location based on the user's past recognition history and optimize the recognition algorithm. For example, the object recognition unit can analyze the user's past recognition history and predict objects that will be recognized in a specific location. In this way, the recognition algorithm can be optimized by referring to the user's past recognition history.

[0036] The object recognition unit can improve the recognition accuracy during object recognition based on the user's current environmental information. For example, the object recognition unit prioritizes recognizing passersby holding umbrellas when it is raining. For example, the object recognition unit prioritizes recognizing passersby holding umbrellas when it is raining based on weather data. The object recognition unit can also prioritize recognizing passersby under street lights at night. For example, the object recognition unit prioritizes recognizing passersby under street lights at night based on time of day information. The object recognition unit can also prioritize recognizing passersby who are close in crowded places. For example, the object recognition unit prioritizes recognizing passersby who are close in crowded places based on environmental information. This makes it possible to improve the recognition accuracy by taking the user's current environmental information into consideration.

[0037] During object recognition, the object recognition unit can prioritize selection of a recognition target using the user's gaze tracking data. The object recognition unit, for example, prioritizes recognition of a passerby that the user is gazing at. For example, the object recognition unit prioritizes recognition of a passerby that the user is gazing at based on the gaze tracking data. The object recognition unit can also prioritize recognition of a vehicle on which the user's gaze remains for a long time. For example, the object recognition unit prioritizes recognition of a vehicle on which the user's gaze remains for a long time based on the gaze tracking data. The object recognition unit can also prioritize recognition of an object at the destination of the gaze when the user frequently moves their gaze. For example, the object recognition unit prioritizes recognition of an object at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to prioritize selection of a recognition target using the user's gaze tracking data.

[0038] During object recognition, the object recognition unit can filter recognition targets by taking into account the user's geographical location information. For example, when the user is in a specific area, the object recognition unit preferentially recognizes objects related to the area. For example, when the user is in a specific area, the object recognition unit preferentially recognizes objects related to the area based on the geographical location information. Furthermore, when the user is in a tourist destination, the object recognition unit can preferentially recognize objects related to tourist spots. For example, when the user is in a tourist destination, the object recognition unit preferentially recognizes objects related to tourist spots based on the geographical location information. Furthermore, when the user is in a commercial area, the object recognition unit can preferentially recognize objects related to stores and advertisements. For example, when the user is in a commercial area, the object recognition unit preferentially recognizes objects related to stores and advertisements based on the geographical location information. This makes it possible to filter recognition targets by taking into account the user's geographical location information.

[0039] During object recognition, the object recognition unit can analyze the user's social media activity and prioritize recognition of related objects. For example, the object recognition unit prioritizes recognition of objects related to places where the user has checked in on social media. For example, the object recognition unit prioritizes recognition of objects related to places where the user has checked in based on the social media activity. The object recognition unit can also analyze the content of the user's posts on social media and prioritize recognition of related objects. For example, the object recognition unit prioritizes recognition of objects related to the content of the user's posts based on the social media activity. The object recognition unit can also prioritize recognition of related objects based on the activities of the user's friends on social media. For example, the object recognition unit prioritizes recognition of objects related to the activities of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and related objects can be prioritized.

[0040] The object recognition unit can customize the recognition algorithm by reflecting the user's past feedback when recognizing an object. For example, the object recognition unit preferentially recognizes objects that the user has previously rated highly. For example, the object recognition unit preferentially recognizes objects that the user has previously rated highly based on the user's past feedback. The object recognition unit can also exclude objects that the user has previously rated low from recognition targets. For example, the object recognition unit excludes objects that the user has previously rated low from recognition targets based on the user's past feedback. The object recognition unit can also optimize the recognition algorithm based on the user's past feedback. For example, the object recognition unit analyzes the user's past feedback and optimizes the recognition algorithm. This makes it possible to customize the recognition algorithm by reflecting the user's past feedback.

[0041] The posture estimation unit can optimize the estimation algorithm by referring to historical data of the object's movement when estimating the posture. The posture estimation unit, for example, predicts the object's movement based on historical data of past movement and optimizes the estimation algorithm. For example, the posture estimation unit analyzes historical data of past movement and predicts the object's movement. The posture estimation unit can also analyze the object's movement pattern in a specific time period and optimize the estimation algorithm. For example, the posture estimation unit analyzes the object's movement pattern in a specific time period and optimize the estimation algorithm. The posture estimation unit can also optimize the estimation algorithm based on historical data of the object's movement in a specific location. For example, the posture estimation unit analyzes historical data of the object's movement in a specific location and optimize the estimation algorithm. In this way, the estimation algorithm can be optimized by referring to the historical data of the object's movement.

[0042] The posture estimation unit can apply different estimation methods depending on the type of object when estimating the posture. For example, in the case of a passerby, the posture estimation unit applies an estimation method based on the walking pattern. For example, the posture estimation unit analyzes the walking pattern of the passerby and estimates the movement. In addition, in the case of a vehicle, the posture estimation unit can also apply an estimation method based on the speed and direction. For example, the posture estimation unit analyzes the speed and direction of the vehicle and estimates the movement. In addition, in the case of an animal, the posture estimation unit can also apply an estimation method based on the characteristic movement. For example, the posture estimation unit analyzes the characteristic movement of the animal and estimates the movement. This makes it possible to apply different estimation methods depending on the type of object.

[0043] The posture estimation unit can improve estimation accuracy by using gaze tracking data of the user during posture estimation. The posture estimation unit, for example, prioritizes estimating the movement of an object that the user is gazing at. For example, the posture estimation unit prioritizes estimating the movement of an object that the user is gazing at based on the gaze tracking data. The posture estimation unit can also estimate in detail the movement of an object on which the user's gaze is fixed for a long period of time. For example, the posture estimation unit prioritizes estimating in detail the movement of an object on which the user's gaze is fixed for a long period of time based on the gaze tracking data. The posture estimation unit can also prioritize estimating the movement of an object at the destination of the gaze when the user frequently moves their gaze. For example, the posture estimation unit prioritizes estimating the movement of an object at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to improve estimation accuracy by using the user's gaze tracking data.

[0044] The posture estimation unit can improve the estimation accuracy by taking into account the geographical distribution of the object when estimating the posture. The posture estimation unit, for example, analyzes the movement pattern of the object in a specific area to improve the estimation accuracy. For example, the posture estimation unit analyzes the movement pattern of the object in a specific area using a geographic information system (GIS). The posture estimation unit can also analyze the movement pattern of the object in a tourist destination to improve the estimation accuracy. For example, the posture estimation unit analyzes the movement pattern of the object in a tourist destination to improve the estimation accuracy. The posture estimation unit can also analyze the movement pattern of the object in a commercial area to improve the estimation accuracy. For example, the posture estimation unit analyzes the movement pattern of the object in a commercial area to improve the estimation accuracy. In this way, the estimation accuracy can be improved by taking into account the geographical distribution of the object.

[0045] The posture estimation unit can optimize the estimation algorithm by referring to literature related to the object when estimating the posture. The posture estimation unit, for example, refers to academic papers related to object movement and optimizes the estimation algorithm. For example, the posture estimation unit refers to academic papers related to object movement and optimizes the estimation algorithm based on the knowledge gained therefrom. The posture estimation unit can also optimize the estimation algorithm by referring to patent documents related to object movement. For example, the posture estimation unit refers to patent documents related to object movement and optimizes the estimation algorithm based on the knowledge gained therefrom. The posture estimation unit can also optimize the estimation algorithm by referring to technical literature related to object movement. For example, the posture estimation unit refers to technical literature related to object movement and optimizes the estimation algorithm based on the knowledge gained therefrom. In this way, the estimation algorithm can be optimized by referring to literature related to the object.

[0046] The posture estimation unit can improve estimation accuracy by taking into account the market value of the object when estimating the posture. The posture estimation unit, for example, preferentially estimates the posture of an expensive vehicle. For example, the posture estimation unit preferentially estimates the posture of an expensive vehicle based on market research data. The posture estimation unit can also preferentially estimate the posture of a popular character. For example, the posture estimation unit preferentially estimates the posture of a popular character based on market research data. The posture estimation unit can also preferentially estimate the posture of expensive furniture. For example, the posture estimation unit preferentially estimates the posture of expensive furniture based on market research data. This makes it possible to improve estimation accuracy by taking into account the market value of the object.

[0047] The replacement display unit can optimize the display algorithm by referring to the user's past display history when performing replacement display. The replacement display unit, for example, preferentially displays characters that the user has previously given a high rating. For example, the replacement display unit preferentially displays characters that the user has previously given a high rating based on the user's past display history. The replacement display unit can also exclude characters that the user has previously given a low rating from the display targets. For example, the replacement display unit excludes characters that the user has previously given a low rating from the display targets based on the user's past display history. The replacement display unit can also optimize the display algorithm based on the user's past display history. For example, the replacement display unit analyzes the user's past display history and optimizes the display algorithm. This makes it possible to optimize the display algorithm by referring to the user's past display history.

[0048] The replacement display unit can improve display accuracy by taking into account the user's current environmental information when performing replacement display. For example, the replacement display unit preferentially displays a character holding an umbrella when it is raining. For example, the replacement display unit preferentially displays a character holding an umbrella when it is raining based on weather data. The replacement display unit can also preferentially display a character under a street light at night. For example, the replacement display unit preferentially displays a character under a street light at night based on time of day information. The replacement display unit can also preferentially display a character that is close in a crowded place. For example, the replacement display unit preferentially displays a character that is close in a crowded place based on environmental information. This makes it possible to improve display accuracy by taking into account the user's current environmental information.

[0049] During replacement display, the replacement display unit can preferentially select a display target using the user's gaze tracking data. The replacement display unit, for example, preferentially displays a character that the user is gazing at. For example, the replacement display unit preferentially displays a character that the user is gazing at based on the gaze tracking data. The replacement display unit can also preferentially display a character on which the user's gaze remains for a long time. For example, the replacement display unit preferentially displays a character on which the user's gaze remains for a long time based on the gaze tracking data. The replacement display unit can also preferentially display a character that is located at the destination of the gaze when the user frequently moves their gaze. For example, the replacement display unit preferentially displays a character that is located at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to preferentially select a display target using the user's gaze tracking data.

[0050] The replacement display unit can filter display targets in consideration of the user's geographical location information when performing replacement display. For example, when the user is in a specific area, the replacement display unit preferentially displays characters related to the area. For example, when the user is in a specific area, the replacement display unit preferentially displays characters related to the area based on the geographical location information. Furthermore, when the user is in a tourist spot, the replacement display unit can also preferentially display characters related to tourist spots. For example, when the user is in a tourist spot, the replacement display unit preferentially displays characters related to tourist spots based on the geographical location information. Furthermore, when the user is in a commercial area, the replacement display unit can also preferentially display characters related to stores or advertisements. For example, when the user is in a commercial area, the replacement display unit preferentially displays characters related to stores or advertisements based on the geographical location information. This makes it possible to filter display targets in consideration of the user's geographical location information.

[0051] The replacement display unit can analyze the user's social media activity and prioritize displaying related characters when performing replacement display. The replacement display unit, for example, prioritizes displaying characters related to places where the user has checked in on social media. For example, the replacement display unit prioritizes displaying characters related to places where the user has checked in based on the social media activity. The replacement display unit can also analyze the content of the user's posts on social media and prioritize displaying related characters. For example, the replacement display unit prioritizes displaying characters related to the content of the user's posts based on the social media activity. The replacement display unit can also prioritize displaying related characters based on the activities of the user's friends on social media. For example, the replacement display unit prioritizes displaying characters related to the activities of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and related characters can be prioritized.

[0052] The replacement display unit can customize the display algorithm by reflecting the user's past feedback when performing replacement display. The replacement display unit, for example, preferentially displays characters that the user has previously given a high rating. For example, the replacement display unit preferentially displays characters that the user has previously given a high rating based on the user's past feedback. The replacement display unit can also exclude characters that the user has previously given a low rating from the display targets. For example, the replacement display unit excludes characters that the user has previously given a low rating from the display targets based on the user's past feedback. The replacement display unit can also optimize the display algorithm based on the user's past feedback. For example, the replacement display unit analyzes the user's past feedback and optimizes the display algorithm. This makes it possible to customize the display algorithm by reflecting the user's past feedback.

[0053] When changing the interior, the interior modification unit can optimize the design algorithm by referring to the user's past interior modification history. The interior modification unit, for example, preferentially applies designs that the user has previously given a high rating. For example, the interior modification unit preferentially applies designs that the user has previously given a high rating based on the user's past interior modification history. The interior modification unit can also exclude designs that the user has previously given a low rating from the targets for application. For example, the interior modification unit excludes designs that the user has previously given a low rating from the targets for application based on the user's past interior modification history. The interior modification unit can also optimize the design algorithm based on the user's past interior modification history. For example, the interior modification unit analyzes the user's past interior modification history and optimizes the design algorithm. In this way, the design algorithm can be optimized by referring to the user's past interior modification history.

[0054] The interior modification unit can improve design accuracy by taking into account the user's current environmental information when modifying the interior. The interior modification unit, for example, provides a warm design when it is raining. For example, the interior modification unit provides a warm design when it is raining based on weather data. The interior modification unit can also provide a design with muted colors at night. For example, the interior modification unit provides a design with muted colors at night based on time of day information. The interior modification unit can also provide a design with bright colors when it is sunny. For example, the interior modification unit provides a design with bright colors when it is sunny based on weather data. This makes it possible to improve design accuracy by taking into account the user's current environmental information.

[0055] When changing the interior, the interior modification unit can prioritize selecting a design target using the user's gaze tracking data. The interior modification unit, for example, prioritizes changing the design of furniture that the user is gazing at. For example, the interior modification unit prioritizes changing the design of furniture that the user is gazing at based on the gaze tracking data. The interior modification unit can also prioritize changing an interior design on which the user's gaze remains for a long time. For example, the interior modification unit prioritizes changing an interior design on which the user's gaze remains for a long time based on the gaze tracking data. The interior modification unit can also prioritize changing an interior design at a destination of the gaze if the user frequently moves their gaze. For example, the interior modification unit prioritizes changing an interior design at a destination of the gaze if the user frequently moves their gaze based on the gaze tracking data. In this way, it is possible to prioritize selecting a design target using the user's gaze tracking data.

[0056] When modifying the interior, the interior modification unit can filter design targets by taking into account the user's geographical location information. For example, when the user is in a specific area, the interior modification unit preferentially applies a design related to that area. For example, when the user is in a specific area, the interior modification unit preferentially applies a design related to that area based on the geographical location information. Furthermore, when the user is in a tourist destination, the interior modification unit can preferentially apply a design related to a tourist spot. For example, when the user is in a tourist destination, the interior modification unit preferentially applies a design related to a tourist spot based on the geographical location information. Furthermore, when the user is in a commercial area, the interior modification unit can preferentially apply a design related to a store or advertisement. For example, when the user is in a commercial area, the interior modification unit preferentially applies a design related to a store or advertisement based on the geographical location information. In this way, it is possible to filter design targets by taking into account the user's geographical location information.

[0057] When changing the interior, the interior modification unit can analyze the user's social media activity and preferentially apply a related design. For example, the interior modification unit preferentially applies a design related to a place where the user has checked in on social media. For example, the interior modification unit preferentially applies a design related to a place where the user has checked in based on the social media activity. The interior modification unit can also analyze the content posted by the user on social media and preferentially apply a related design. For example, the interior modification unit preferentially applies a design related to the content posted by the user based on the social media activity. The interior modification unit can also preferentially apply a related design based on the activity of the user's friends on social media. For example, the interior modification unit preferentially applies a design related to the activity of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and a related design can be preferentially applied.

[0058] When changing the interior, the interior modification unit can customize the design algorithm by reflecting the user's past feedback. The interior modification unit, for example, preferentially applies designs that the user has previously given a high rating. For example, the interior modification unit preferentially applies designs that the user has previously given a high rating based on the user's past feedback. The interior modification unit can also exclude designs that the user has previously given a low rating from the targets for application. For example, the interior modification unit excludes designs that the user has previously given a low rating from the targets for application based on the user's past feedback. The interior modification unit can also optimize the design algorithm based on the user's past feedback. For example, the interior modification unit analyzes the user's past feedback and optimizes the design algorithm. In this way, the design algorithm can be customized by reflecting the user's past feedback.

[0059] When changing the scenery, the scenery change unit can optimize the display algorithm by referring to the user's past scenery change history. The scenery change unit, for example, preferentially displays scenery that the user has previously given a high rating. For example, the scenery change unit preferentially displays scenery that the user has given a high rating based on the user's past scenery change history. The scenery change unit can also exclude scenery that the user has previously given a low rating from the display targets. For example, the scenery change unit excludes scenery that the user has given a low rating from the display targets based on the user's past scenery change history. The scenery change unit can also optimize the display algorithm based on the user's past scenery change history. For example, the scenery change unit analyzes the user's past scenery change history and optimizes the display algorithm. In this way, the display algorithm can be optimized by referring to the user's past scenery change history.

[0060] The scenery change unit can improve display accuracy by taking into account the user's current environmental information when changing the scenery. The scenery change unit, for example, displays a warm scenery when it is raining. For example, the scenery change unit displays a warm scenery when it is raining based on weather data. The scenery change unit can also display a scenery with subdued colors at night. For example, the scenery change unit displays a scenery with subdued colors at night based on time of day information. The scenery change unit can also display a scenery with bright colors when it is sunny. For example, the scenery change unit displays a scenery with bright colors when it is sunny based on weather data. This makes it possible to improve display accuracy by taking into account the user's current environmental information.

[0061] When changing the scenery, the scenery change unit can preferentially select a display target using the user's gaze tracking data. The scenery change unit, for example, preferentially displays a scenery that the user is gazing at. For example, the scenery change unit preferentially displays a scenery that the user is gazing at based on the gaze tracking data. The scenery change unit can also preferentially display a scenery on which the user's gaze remains for a long time. For example, the scenery change unit preferentially displays a scenery on which the user's gaze remains for a long time based on the gaze tracking data. The scenery change unit can also preferentially display a scenery at the destination of the gaze when the user frequently moves their gaze. For example, the scenery change unit preferentially displays a scenery at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to preferentially select a display target using the user's gaze tracking data.

[0062] When changing the scenery, the scenery change unit can filter the display objects taking into account the user's geographical location information. For example, when the user is in a specific area, the scenery change unit prioritizes displaying scenery related to the area. For example, when the user is in a specific area, the scenery change unit prioritizes displaying scenery related to the area based on the geographical location information. Furthermore, when the user is in a tourist destination, the scenery change unit can also prioritize displaying scenery related to tourist spots. For example, when the user is in a tourist destination, the scenery change unit prioritizes displaying scenery related to tourist spots based on the geographical location information. Furthermore, when the user is in a commercial area, the scenery change unit can also prioritize displaying scenery related to stores and advertisements. For example, when the user is in a commercial area, the scenery change unit prioritizes displaying scenery related to stores and advertisements based on the geographical location information. In this way, the display objects can be filtered taking into account the user's geographical location information.

[0063] When changing the scenery, the scenery changing unit can analyze the user's social media activity and prioritize displaying related scenery. For example, the scenery changing unit prioritizes displaying scenery related to places where the user has checked in on social media. For example, the scenery changing unit prioritizes displaying scenery related to places where the user has checked in based on the social media activity. The scenery changing unit can also analyze the content of the user's posts on social media and prioritize displaying related scenery. For example, the scenery changing unit prioritizes displaying scenery related to the content of the user's posts based on the social media activity. The scenery changing unit can also prioritize displaying related scenery based on the activity of the user's friends on social media. For example, the scenery changing unit prioritizes displaying scenery related to the activities of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and related scenery can be prioritized.

[0064] When changing the scenery, the scenery change unit can customize the display algorithm by reflecting the user's past feedback. The scenery change unit, for example, preferentially displays scenery that the user has previously rated highly. For example, the scenery change unit preferentially displays scenery that the user has previously rated highly based on the user's past feedback. The scenery change unit can also exclude scenery that the user has previously rated poorly from the display targets. For example, the scenery change unit excludes scenery that the user has previously rated poorly from the display targets based on the user's past feedback. The scenery change unit can also optimize the display algorithm based on the user's past feedback. For example, the scenery change unit analyzes the user's past feedback and optimizes the display algorithm. In this way, the display algorithm can be customized by reflecting the user's past feedback.

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

[0066] The object recognition unit can learn and predict a user's past behavioral patterns. For example, if a user tends to visit a specific location at a specific time of day, the object recognition unit can improve the accuracy of object recognition at that location. Also, if a user tends to perform a specific activity on a specific day of the week, the object recognition unit can preferentially recognize objects related to that activity. Furthermore, the object recognition unit can predict future behavior based on the user's past behavioral patterns and optimize the object recognition algorithm based on the predicted behavior. In this way, the object recognition unit can achieve more accurate object recognition by learning and predicting the user's behavioral patterns.

[0067] The posture estimation unit can collect the user's exercise data and provide fitness advice. For example, the posture estimation unit can analyze the user's walking pattern and provide advice on correct walking posture. The posture estimation unit can also analyze the user's running form and suggest an efficient running form. Furthermore, the posture estimation unit can analyze the user's stretching movements and provide advice on effective stretching methods. In this way, the posture estimation unit can provide fitness advice based on the user's exercise data.

[0068] The interior modification unit can customize the interior based on the user's hobbies and interests. For example, if the user likes music, the interior modification unit can provide a music studio-style interior. If the user likes reading, the interior modification unit can provide a library-style interior. Furthermore, if the user likes cooking, the interior modification unit can provide a kitchen-style interior. In this way, the interior modification unit can customize the interior based on the user's hobbies and interests.

[0069] The object recognition unit can improve the accuracy of object recognition by utilizing sensory information other than the user's vision. For example, the object recognition unit can use audio data to identify the source of a sound and recognize that object. The object recognition unit can also use tactile data to recognize the shape and texture of a touched object. Furthermore, the object recognition unit can also use olfactory data to recognize objects that emit a specific odor. This allows the object recognition unit to improve the accuracy of object recognition by utilizing sensory information other than vision.

[0070] The replacement display unit can customize the movements of the characters to be displayed based on the user's past display history. For example, it can reproduce the movements of characters that the user liked to see in the past. It can also prevent the display of movements of characters that the user avoided in the past. Furthermore, it can also suggest new character movements based on the user's past display history. This allows the replacement display unit to customize the movements of the characters to be displayed based on the user's past display history.

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

[0072] Step 1: The object recognition unit detects passersby and vehicles. For example, the object recognition unit uses image processing technology and machine learning algorithms to detect passersby and vehicles in real time. Step 2: The posture estimation unit tracks the movements of pedestrians and vehicles detected by the object recognition unit, for example, by calculating joint angles and analyzing motion to track the movements of pedestrians and vehicles in real time. Step 3: The replacement display unit replaces the passersby and vehicles tracked by the posture estimation unit with celebrities or characters specified by the user. For example, by using image synthesis technology or real-time rendering, passersby can be replaced with famous actors or anime characters. Step 4: The interior modification unit modifies the appearance of the room's furniture and interior based on the user-specified design or theme, for example, by using 3D modeling or virtual reality technology. Step 5: The scenery change unit changes the scenery seen through the window using the scenery data specified by the user, for example, by using image processing technology or real-time rendering.

[0073] (Example 2) An AR system according to an embodiment of the present invention is a system that displays AR images by replacing passersby and vehicles with celebrities or favorite characters. The AR system allows a user to wear AR glasses or AR contact lenses, detect passersby and vehicles in the city using object recognition technology, and track their movements using pose estimation technology. The detected passersby and vehicles are then replaced with data of the celebrity or favorite character designated by the user. Furthermore, the appearance and interior of the furniture in the room can be changed, and the view from the window can be freely specified. For example, in an AR system, a user wears AR glasses or AR contact lenses. The AR system then detects passersby and vehicles in the city using object recognition technology and tracks their movements using pose estimation technology. This allows the position and movement of passersby and vehicles to be accurately determined. The AR system then replaces the detected passersby and vehicles with data of the celebrity or favorite character designated by the user. For example, passersby can be replaced with famous actors or anime characters. Furthermore, the AR system can also change the appearance and interior of the furniture in the room. The appearance of the furniture and interior can be changed based on a design or theme designated by the user. For example, a room can be redesigned to a modern style, or the interior can be restyled to match the worldview of a particular anime. AR systems also allow users to freely specify the view seen from a window. The view seen from a window can be changed using the view data specified by the user. For example, an urban landscape can be changed to a mountain landscape rich in nature. This allows AR systems to augment the real world for users and provide a more enjoyable experience. For example, users can customize the real world to their preferences and find new enjoyment in their daily lives. Furthermore, by changing the interior design of a room or the view from a window, a space can be created that suits the user's mood and environment.

[0074] The AR system according to the embodiment includes an object recognition unit, a posture estimation unit, a replacement display unit, an interior modification unit, and a scenery modification unit. The object recognition unit detects passersby and vehicles. For example, the object recognition unit detects passersby and vehicles using image processing technology. The object recognition unit can also detect passersby and vehicles using a machine learning algorithm. The object recognition unit can also detect passersby and vehicles in real time. The posture estimation unit tracks the movements of passersby and vehicles detected by the object recognition unit. For example, the posture estimation unit tracks the movements of passersby using joint angle calculations. The posture estimation unit can also track the movements of vehicles using motion analysis. The posture estimation unit can also track the movements of passersby and vehicles in real time. The replacement display unit replaces passersby and vehicles tracked by the posture estimation unit with celebrities or characters specified by a user. For example, the replacement display unit replaces passersby with famous actors using image synthesis technology. The replacement display unit can also replace vehicles with anime characters using real-time rendering. The replacement display unit can also replace passersby and vehicles using data specified by the user. The interior modification unit changes the appearance of the furniture and interior of the room based on a design or theme specified by the user. For example, the interior modification unit changes the appearance of the furniture in the room using 3D modeling. The interior modification unit can also change the interior of the room using virtual reality technology. The interior modification unit can also change the interior of the room based on a theme specified by the user. The scenery modification unit changes the view seen through a window using scenery data specified by the user. For example, the scenery modification unit changes the view seen through a window using image processing technology. The scenery modification unit can also change the view seen through a window using real-time rendering. The scenery modification unit can also change the view seen through a window using data specified by the user. This enables the AR system according to the embodiment to perform object recognition, pose estimation, replacement display, interior modification, and scenery modification.

[0075] The object recognition unit can detect passersby or vehicles. The object recognition unit can detect passersby using, for example, image processing technology. For example, the object recognition unit can detect passersby based on pedestrian characteristics. The object recognition unit can also detect vehicles using a machine learning algorithm. For example, the object recognition unit can detect vehicles based on the characteristics of the vehicle model. The object recognition unit can also detect passersby or vehicles in real time. For example, the object recognition unit can detect passersby or vehicles by analyzing movement patterns. This allows for accurate detection of passersby or vehicles.

[0076] The posture estimation unit can track the movement of passersby or vehicles detected by the object recognition unit. The posture estimation unit tracks the movement of passersby, for example, by calculating joint angles. For example, the posture estimation unit calculates the joint angles of passersby and tracks their movement. The posture estimation unit can also track the movement of vehicles by using motion analysis. For example, the posture estimation unit analyzes the movement of a vehicle and tracks its movement. The posture estimation unit can also track the movement of passersby or vehicles in real time. For example, the posture estimation unit analyzes movement patterns to track the movement of passersby or vehicles. This allows the movement of passersby or vehicles to be tracked accurately.

[0077] The replacement display unit can replace passersby or vehicles tracked by the pose estimation unit with celebrities or characters designated by the user. The replacement display unit can, for example, replace passersby with famous actors using image synthesis technology. For example, the replacement display unit can synthesize an image of a passerby with an image of a famous actor. The replacement display unit can also replace vehicles with animated characters using real-time rendering. For example, the replacement display unit can render an image of a vehicle with an image of an animated character in real time. The replacement display unit can also replace passersby or vehicles using data designated by the user. For example, the replacement display unit can replace passersby or vehicles with data of celebrities or characters designated by the user. This allows passersby or vehicles to be replaced with celebrities or characters designated by the user.

[0078] The interior modification unit can change the appearance of the furniture or interior of a room based on a design or theme specified by the user. The interior modification unit can change the appearance of the furniture in the room using, for example, 3D modeling. For example, the interior modification unit can create a 3D model of the furniture and change its appearance. The interior modification unit can also change the interior of the room using virtual reality technology. For example, the interior modification unit can recreate the interior of the room using virtual reality technology and change its appearance. The interior modification unit can also change the interior of the room based on a theme specified by the user. For example, the interior modification unit can change the interior to a modern design specified by the user or to one that matches the worldview of a particular anime. In this way, the appearance of the furniture and interior of the room can be changed based on a design or theme specified by the user.

[0079] The scenery change unit can change the scenery seen through the window using scenery data specified by the user. The scenery change unit changes the scenery seen through the window using, for example, image processing technology. For example, the scenery change unit changes an urban landscape to a mountain landscape rich in nature. The scenery change unit can also change the scenery seen through the window using real-time rendering. For example, the scenery change unit renders the scenery seen through the window in real time and changes it to the scenery specified by the user. The scenery change unit can also change the scenery seen through the window using data specified by the user. For example, the scenery change unit changes the scenery seen through the window using a scenery image or 3D model specified by the user. In this way, the scenery seen through the window can be changed using the scenery data specified by the user.

[0080] The object recognition unit can estimate the user's emotion and adjust the accuracy of object recognition based on the estimated user emotion. For example, when the user is excited, the object recognition unit increases the accuracy of object recognition to obtain more detailed information. For example, the object recognition unit analyzes the user's facial expression and increases the accuracy of object recognition when it determines that the user is excited. Furthermore, when the user is relaxed, the object recognition unit can maintain the accuracy of object recognition at a normal level to reduce the processing load. For example, the object recognition unit analyzes the user's voice and maintains the accuracy of object recognition at a normal level when it determines that the user is relaxed. Furthermore, when the user is stressed, the object recognition unit can adjust the accuracy of object recognition to improve the recognition speed. For example, the object recognition unit analyzes the user's biometric data and adjusts the accuracy of object recognition when it determines that the user is stressed. This allows the accuracy of object recognition to be adjusted based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When recognizing an object, the object recognition unit can optimize the recognition algorithm by referring to the user's past recognition history. For example, the object recognition unit prioritizes recognizing objects that the user has frequently recognized in the past. For example, the object recognition unit prioritizes recognizing specific objects based on the user's past recognition history. The object recognition unit can also predict objects that will be recognized in a specific time period from the user's past recognition history and optimize the recognition algorithm. For example, the object recognition unit can analyze the user's past recognition history and predict objects that will be recognized in a specific time period. The object recognition unit can also predict objects that will be recognized in a specific location based on the user's past recognition history and optimize the recognition algorithm. For example, the object recognition unit can analyze the user's past recognition history and predict objects that will be recognized in a specific location. In this way, the recognition algorithm can be optimized by referring to the user's past recognition history.

[0082] The object recognition unit can improve the recognition accuracy during object recognition based on the user's current environmental information. For example, the object recognition unit prioritizes recognizing passersby holding umbrellas when it is raining. For example, the object recognition unit prioritizes recognizing passersby holding umbrellas when it is raining based on weather data. The object recognition unit can also prioritize recognizing passersby under street lights at night. For example, the object recognition unit prioritizes recognizing passersby under street lights at night based on time of day information. The object recognition unit can also prioritize recognizing passersby who are close in crowded places. For example, the object recognition unit prioritizes recognizing passersby who are close in crowded places based on environmental information. This makes it possible to improve the recognition accuracy by taking the user's current environmental information into consideration.

[0083] During object recognition, the object recognition unit can prioritize selection of a recognition target using the user's gaze tracking data. The object recognition unit, for example, prioritizes recognition of a passerby that the user is gazing at. For example, the object recognition unit prioritizes recognition of a passerby that the user is gazing at based on the gaze tracking data. The object recognition unit can also prioritize recognition of a vehicle on which the user's gaze remains for a long time. For example, the object recognition unit prioritizes recognition of a vehicle on which the user's gaze remains for a long time based on the gaze tracking data. The object recognition unit can also prioritize recognition of an object at the destination of the gaze when the user frequently moves their gaze. For example, the object recognition unit prioritizes recognition of an object at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to prioritize selection of a recognition target using the user's gaze tracking data.

[0084] The object recognition unit can estimate the user's emotion and determine the priority of recognition targets based on the estimated user emotion. For example, if the user is excited, the object recognition unit prioritizes recognizing fast-moving objects. For example, if the object recognition unit analyzes the user's facial expression and determines that the user is excited, it prioritizes recognizing fast-moving objects. The object recognition unit can also prioritize recognizing stationary objects if the user is relaxed. For example, if the object recognition unit analyzes the user's voice and determines that the user is relaxed, it prioritizes recognizing stationary objects. The object recognition unit can also prioritize recognizing close-range objects if the user is stressed. For example, if the object recognition unit analyzes the user's biometric data and determines that the user is stressed, it prioritizes recognizing close-range objects. This makes it possible to determine the priority of recognition targets based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During object recognition, the object recognition unit can filter recognition targets by taking into account the user's geographical location information. For example, when the user is in a specific area, the object recognition unit preferentially recognizes objects related to the area. For example, when the user is in a specific area, the object recognition unit preferentially recognizes objects related to the area based on the geographical location information. Furthermore, when the user is in a tourist destination, the object recognition unit can preferentially recognize objects related to tourist spots. For example, when the user is in a tourist destination, the object recognition unit preferentially recognizes objects related to tourist spots based on the geographical location information. Furthermore, when the user is in a commercial area, the object recognition unit can preferentially recognize objects related to stores and advertisements. For example, when the user is in a commercial area, the object recognition unit preferentially recognizes objects related to stores and advertisements based on the geographical location information. This makes it possible to filter recognition targets by taking into account the user's geographical location information.

[0086] During object recognition, the object recognition unit can analyze the user's social media activity and prioritize recognition of related objects. For example, the object recognition unit prioritizes recognition of objects related to places where the user has checked in on social media. For example, the object recognition unit prioritizes recognition of objects related to places where the user has checked in based on the social media activity. The object recognition unit can also analyze the content of the user's posts on social media and prioritize recognition of related objects. For example, the object recognition unit prioritizes recognition of objects related to the content of the user's posts based on the social media activity. The object recognition unit can also prioritize recognition of related objects based on the activities of the user's friends on social media. For example, the object recognition unit prioritizes recognition of objects related to the activities of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and related objects can be prioritized.

[0087] The object recognition unit can customize the recognition algorithm by reflecting the user's past feedback when recognizing an object. For example, the object recognition unit preferentially recognizes objects that the user has previously rated highly. For example, the object recognition unit preferentially recognizes objects that the user has previously rated highly based on the user's past feedback. The object recognition unit can also exclude objects that the user has previously rated low from recognition targets. For example, the object recognition unit excludes objects that the user has previously rated low from recognition targets based on the user's past feedback. The object recognition unit can also optimize the recognition algorithm based on the user's past feedback. For example, the object recognition unit analyzes the user's past feedback and optimizes the recognition algorithm. This makes it possible to customize the recognition algorithm by reflecting the user's past feedback.

[0088] The posture estimation unit can estimate the user's emotion and adjust the accuracy of the posture estimation based on the estimated emotion. For example, when the user is excited, the posture estimation unit increases the accuracy of the posture estimation to track more detailed movements. For example, the posture estimation unit analyzes the user's facial expression and increases the accuracy of the posture estimation if it determines that the user is excited. The posture estimation unit can also maintain the accuracy of the posture estimation at a normal level to reduce the processing load if the user is relaxed. For example, the posture estimation unit analyzes the user's voice and maintains the accuracy of the posture estimation at a normal level if it determines that the user is relaxed. The posture estimation unit can also adjust the accuracy of the posture estimation to improve the tracking speed if the user is stressed. For example, the posture estimation unit analyzes the user's biometric data and adjusts the accuracy of the posture estimation if it determines that the user is stressed. This allows the accuracy of the posture estimation to be adjusted based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The posture estimation unit can optimize the estimation algorithm by referring to historical data of the object's movement when estimating the posture. The posture estimation unit, for example, predicts the object's movement based on historical data of past movement and optimizes the estimation algorithm. For example, the posture estimation unit analyzes historical data of past movement and predicts the object's movement. The posture estimation unit can also analyze the object's movement pattern in a specific time period and optimize the estimation algorithm. For example, the posture estimation unit analyzes the object's movement pattern in a specific time period and optimize the estimation algorithm. The posture estimation unit can also optimize the estimation algorithm based on historical data of the object's movement in a specific location. For example, the posture estimation unit analyzes historical data of the object's movement in a specific location and optimize the estimation algorithm. In this way, the estimation algorithm can be optimized by referring to the historical data of the object's movement.

[0090] The posture estimation unit can apply different estimation methods depending on the type of object when estimating the posture. For example, in the case of a passerby, the posture estimation unit applies an estimation method based on the walking pattern. For example, the posture estimation unit analyzes the walking pattern of the passerby and estimates the movement. In addition, in the case of a vehicle, the posture estimation unit can also apply an estimation method based on the speed and direction. For example, the posture estimation unit analyzes the speed and direction of the vehicle and estimates the movement. In addition, in the case of an animal, the posture estimation unit can also apply an estimation method based on the characteristic movement. For example, the posture estimation unit analyzes the characteristic movement of the animal and estimates the movement. This makes it possible to apply different estimation methods depending on the type of object.

[0091] The posture estimation unit can improve estimation accuracy by using gaze tracking data of the user during posture estimation. The posture estimation unit, for example, prioritizes estimating the movement of an object that the user is gazing at. For example, the posture estimation unit prioritizes estimating the movement of an object that the user is gazing at based on the gaze tracking data. The posture estimation unit can also estimate in detail the movement of an object on which the user's gaze is fixed for a long period of time. For example, the posture estimation unit prioritizes estimating in detail the movement of an object on which the user's gaze is fixed for a long period of time based on the gaze tracking data. The posture estimation unit can also prioritize estimating the movement of an object at the destination of the gaze when the user frequently moves their gaze. For example, the posture estimation unit prioritizes estimating the movement of an object at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to improve estimation accuracy by using the user's gaze tracking data.

[0092] The posture estimation unit can estimate the user's emotions and determine the priority of posture estimation based on the estimated user emotions. For example, if the user is excited, the posture estimation unit prioritizes estimating the posture of a fast-moving object. For example, if the posture estimation unit analyzes the user's facial expression and determines that the user is excited, it prioritizes estimating the posture of a fast-moving object. The posture estimation unit can also prioritize estimating the posture of a stationary object if the user is relaxed. For example, if the posture estimation unit analyzes the user's voice and determines that the user is relaxed, it prioritizes estimating the posture of a stationary object. The posture estimation unit can also prioritize estimating the posture of a nearby object if the user is stressed. For example, if the posture estimation unit analyzes the user's biometric data and determines that the user is stressed, it prioritizes estimating the posture of a nearby object. This makes it possible to determine the priority of posture estimation based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The posture estimation unit can improve the estimation accuracy by taking into account the geographical distribution of the object when estimating the posture. The posture estimation unit, for example, analyzes the movement pattern of the object in a specific area to improve the estimation accuracy. For example, the posture estimation unit analyzes the movement pattern of the object in a specific area using a geographic information system (GIS). The posture estimation unit can also analyze the movement pattern of the object in a tourist destination to improve the estimation accuracy. For example, the posture estimation unit analyzes the movement pattern of the object in a tourist destination to improve the estimation accuracy. The posture estimation unit can also analyze the movement pattern of the object in a commercial area to improve the estimation accuracy. For example, the posture estimation unit analyzes the movement pattern of the object in a commercial area to improve the estimation accuracy. In this way, the estimation accuracy can be improved by taking into account the geographical distribution of the object.

[0094] The posture estimation unit can optimize the estimation algorithm by referring to literature related to the object when estimating the posture. The posture estimation unit, for example, refers to academic papers related to object movement and optimizes the estimation algorithm. For example, the posture estimation unit refers to academic papers related to object movement and optimizes the estimation algorithm based on the knowledge gained therefrom. The posture estimation unit can also optimize the estimation algorithm by referring to patent documents related to object movement. For example, the posture estimation unit refers to patent documents related to object movement and optimizes the estimation algorithm based on the knowledge gained therefrom. The posture estimation unit can also optimize the estimation algorithm by referring to technical literature related to object movement. For example, the posture estimation unit refers to technical literature related to object movement and optimizes the estimation algorithm based on the knowledge gained therefrom. In this way, the estimation algorithm can be optimized by referring to literature related to the object.

[0095] The posture estimation unit can improve estimation accuracy by taking into account the market value of the object when estimating the posture. The posture estimation unit, for example, preferentially estimates the posture of an expensive vehicle. For example, the posture estimation unit preferentially estimates the posture of an expensive vehicle based on market research data. The posture estimation unit can also preferentially estimate the posture of a popular character. For example, the posture estimation unit preferentially estimates the posture of a popular character based on market research data. The posture estimation unit can also preferentially estimate the posture of expensive furniture. For example, the posture estimation unit preferentially estimates the posture of expensive furniture based on market research data. This makes it possible to improve estimation accuracy by taking into account the market value of the object.

[0096] The replacement display unit can estimate the user's emotion and adjust the representation of the replacement display based on the estimated user's emotion. For example, if the user is excited, the replacement display unit adds a visually stimulating effect to the replacement display. For example, if the replacement display unit analyzes the user's facial expression and determines that the user is excited, the replacement display unit adds a visually stimulating effect to the replacement display. Furthermore, if the user is relaxed, the replacement display unit can also display a replacement display in a calming color. For example, if the replacement display unit analyzes the user's voice and determines that the user is relaxed, the replacement display unit can display a replacement display in a calming color. Furthermore, if the user is stressed, the replacement display unit can also display a simple, highly visible replacement display. For example, if the replacement display unit analyzes the user's biometric data and determines that the user is stressed, the replacement display unit displays a simple, highly visible replacement display. This makes it possible to adjust the representation of the replacement display based on the 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The replacement display unit can optimize the display algorithm by referring to the user's past display history when performing replacement display. The replacement display unit, for example, preferentially displays characters that the user has previously given a high rating. For example, the replacement display unit preferentially displays characters that the user has previously given a high rating based on the user's past display history. The replacement display unit can also exclude characters that the user has previously given a low rating from the display targets. For example, the replacement display unit excludes characters that the user has previously given a low rating from the display targets based on the user's past display history. The replacement display unit can also optimize the display algorithm based on the user's past display history. For example, the replacement display unit analyzes the user's past display history and optimizes the display algorithm. This makes it possible to optimize the display algorithm by referring to the user's past display history.

[0098] The replacement display unit can improve display accuracy by taking into account the user's current environmental information when performing replacement display. For example, the replacement display unit preferentially displays a character holding an umbrella when it is raining. For example, the replacement display unit preferentially displays a character holding an umbrella when it is raining based on weather data. The replacement display unit can also preferentially display a character under a street light at night. For example, the replacement display unit preferentially displays a character under a street light at night based on time of day information. The replacement display unit can also preferentially display a character that is close in a crowded place. For example, the replacement display unit preferentially displays a character that is close in a crowded place based on environmental information. This makes it possible to improve display accuracy by taking into account the user's current environmental information.

[0099] During replacement display, the replacement display unit can preferentially select a display target using the user's gaze tracking data. The replacement display unit, for example, preferentially displays a character that the user is gazing at. For example, the replacement display unit preferentially displays a character that the user is gazing at based on the gaze tracking data. The replacement display unit can also preferentially display a character on which the user's gaze remains for a long time. For example, the replacement display unit preferentially displays a character on which the user's gaze remains for a long time based on the gaze tracking data. The replacement display unit can also preferentially display a character that is located at the destination of the gaze when the user frequently moves their gaze. For example, the replacement display unit preferentially displays a character that is located at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to preferentially select a display target using the user's gaze tracking data.

[0100] The replacement display unit can estimate the user's emotions and determine the priority of characters to be displayed based on the estimated user emotions. For example, if the user is excited, the replacement display unit prioritizes displaying characters that move quickly. For example, if the replacement display unit analyzes the user's facial expression and determines that the user is excited, it prioritizes displaying characters that move quickly. The replacement display unit can also prioritize displaying still characters if the user is relaxed. For example, if the replacement display unit analyzes the user's voice and determines that the user is relaxed, it prioritizes displaying still characters. The replacement display unit can also prioritize displaying characters that are close to the user if the user is stressed. For example, if the replacement display unit analyzes the user's biometric data and determines that the user is stressed, it prioritizes displaying characters that are close to the user. This makes it possible to determine the priority of characters to be displayed based on the 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The replacement display unit can filter display targets in consideration of the user's geographical location information when performing replacement display. For example, when the user is in a specific area, the replacement display unit preferentially displays characters related to the area. For example, when the user is in a specific area, the replacement display unit preferentially displays characters related to the area based on the geographical location information. Furthermore, when the user is in a tourist spot, the replacement display unit can also preferentially display characters related to tourist spots. For example, when the user is in a tourist spot, the replacement display unit preferentially displays characters related to tourist spots based on the geographical location information. Furthermore, when the user is in a commercial area, the replacement display unit can also preferentially display characters related to stores or advertisements. For example, when the user is in a commercial area, the replacement display unit preferentially displays characters related to stores or advertisements based on the geographical location information. This makes it possible to filter display targets in consideration of the user's geographical location information.

[0102] The replacement display unit can analyze the user's social media activity and prioritize displaying related characters when performing replacement display. The replacement display unit, for example, prioritizes displaying characters related to places where the user has checked in on social media. For example, the replacement display unit prioritizes displaying characters related to places where the user has checked in based on the social media activity. The replacement display unit can also analyze the content of the user's posts on social media and prioritize displaying related characters. For example, the replacement display unit prioritizes displaying characters related to the content of the user's posts based on the social media activity. The replacement display unit can also prioritize displaying related characters based on the activities of the user's friends on social media. For example, the replacement display unit prioritizes displaying characters related to the activities of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and related characters can be prioritized.

[0103] The replacement display unit can customize the display algorithm by reflecting the user's past feedback when performing replacement display. The replacement display unit, for example, preferentially displays characters that the user has previously given a high rating. For example, the replacement display unit preferentially displays characters that the user has previously given a high rating based on the user's past feedback. The replacement display unit can also exclude characters that the user has previously given a low rating from the display targets. For example, the replacement display unit excludes characters that the user has previously given a low rating from the display targets based on the user's past feedback. The replacement display unit can also optimize the display algorithm based on the user's past feedback. For example, the replacement display unit analyzes the user's past feedback and optimizes the display algorithm. This makes it possible to customize the display algorithm by reflecting the user's past feedback.

[0104] The interior modification unit can estimate the user's emotions and adjust the interior design based on the estimated user's emotions. For example, if the user is relaxed, the interior modification unit provides a design with calm colors. For example, if the interior modification unit analyzes the user's facial expression and determines that the user is relaxed, it provides a design with calm colors. The interior modification unit can also provide a visually stimulating design if the user is excited. For example, if the interior modification unit analyzes the user's voice and determines that the user is excited, it provides a visually stimulating design. The interior modification unit can also provide a simple, highly visible design if the user is stressed. For example, if the interior modification unit analyzes the user's biometric data and determines that the user is stressed, it provides a simple, highly visible design. This makes it possible to adjust the interior design based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] When changing the interior, the interior modification unit can optimize the design algorithm by referring to the user's past interior modification history. The interior modification unit, for example, preferentially applies designs that the user has previously given a high rating. For example, the interior modification unit preferentially applies designs that the user has previously given a high rating based on the user's past interior modification history. The interior modification unit can also exclude designs that the user has previously given a low rating from the targets for application. For example, the interior modification unit excludes designs that the user has previously given a low rating from the targets for application based on the user's past interior modification history. The interior modification unit can also optimize the design algorithm based on the user's past interior modification history. For example, the interior modification unit analyzes the user's past interior modification history and optimizes the design algorithm. In this way, the design algorithm can be optimized by referring to the user's past interior modification history.

[0106] The interior modification unit can improve design accuracy by taking into account the user's current environmental information when modifying the interior. The interior modification unit, for example, provides a warm design when it is raining. For example, the interior modification unit provides a warm design when it is raining based on weather data. The interior modification unit can also provide a design with muted colors at night. For example, the interior modification unit provides a design with muted colors at night based on time of day information. The interior modification unit can also provide a design with bright colors when it is sunny. For example, the interior modification unit provides a design with bright colors when it is sunny based on weather data. This makes it possible to improve design accuracy by taking into account the user's current environmental information.

[0107] When changing the interior, the interior modification unit can prioritize selecting a design target using the user's gaze tracking data. The interior modification unit, for example, prioritizes changing the design of furniture that the user is gazing at. For example, the interior modification unit prioritizes changing the design of furniture that the user is gazing at based on the gaze tracking data. The interior modification unit can also prioritize changing an interior design on which the user's gaze remains for a long time. For example, the interior modification unit prioritizes changing an interior design on which the user's gaze remains for a long time based on the gaze tracking data. The interior modification unit can also prioritize changing an interior design at a destination of the gaze if the user frequently moves their gaze. For example, the interior modification unit prioritizes changing an interior design at a destination of the gaze if the user frequently moves their gaze based on the gaze tracking data. In this way, it is possible to prioritize selecting a design target using the user's gaze tracking data.

[0108] The interior modification unit can estimate the user's emotions and determine the priority of interior modifications based on the estimated user emotions. For example, if the user is relaxed, the interior modification unit prioritizes changing the interior to a more subdued color scheme. For example, if the interior modification unit analyzes the user's facial expression and determines that the user is relaxed, it prioritizes changing the interior to a more subdued color scheme. Furthermore, if the user is excited, the interior modification unit can prioritize changing the interior to a more visually stimulating color scheme. For example, if the interior modification unit analyzes the user's voice and determines that the user is excited, it prioritizes changing the interior to a more visually stimulating color scheme. Furthermore, if the user is stressed, the interior modification unit can prioritize changing the interior to a simple, highly visible color scheme. For example, if the interior modification unit analyzes the user's biometric data and determines that the user is stressed, it prioritizes changing the interior to a simple, highly visible color scheme. This allows the priority of interior modifications to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] When modifying the interior, the interior modification unit can filter design targets by taking into account the user's geographical location information. For example, when the user is in a specific area, the interior modification unit preferentially applies a design related to that area. For example, when the user is in a specific area, the interior modification unit preferentially applies a design related to that area based on the geographical location information. Furthermore, when the user is in a tourist destination, the interior modification unit can preferentially apply a design related to a tourist spot. For example, when the user is in a tourist destination, the interior modification unit preferentially applies a design related to a tourist spot based on the geographical location information. Furthermore, when the user is in a commercial area, the interior modification unit can preferentially apply a design related to a store or advertisement. For example, when the user is in a commercial area, the interior modification unit preferentially applies a design related to a store or advertisement based on the geographical location information. In this way, it is possible to filter design targets by taking into account the user's geographical location information.

[0110] When changing the interior, the interior modification unit can analyze the user's social media activity and preferentially apply a related design. For example, the interior modification unit preferentially applies a design related to a place where the user has checked in on social media. For example, the interior modification unit preferentially applies a design related to a place where the user has checked in based on the social media activity. The interior modification unit can also analyze the content posted by the user on social media and preferentially apply a related design. For example, the interior modification unit preferentially applies a design related to the content posted by the user based on the social media activity. The interior modification unit can also preferentially apply a related design based on the activity of the user's friends on social media. For example, the interior modification unit preferentially applies a design related to the activity of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and a related design can be preferentially applied.

[0111] When changing the interior, the interior modification unit can customize the design algorithm by reflecting the user's past feedback. The interior modification unit, for example, preferentially applies designs that the user has previously given a high rating. For example, the interior modification unit preferentially applies designs that the user has previously given a high rating based on the user's past feedback. The interior modification unit can also exclude designs that the user has previously given a low rating from the targets for application. For example, the interior modification unit excludes designs that the user has previously given a low rating from the targets for application based on the user's past feedback. The interior modification unit can also optimize the design algorithm based on the user's past feedback. For example, the interior modification unit analyzes the user's past feedback and optimizes the design algorithm. In this way, the design algorithm can be customized by reflecting the user's past feedback.

[0112] When changing the scenery, the scenery change unit can optimize the display algorithm by referring to the user's past scenery change history. The scenery change unit, for example, preferentially displays scenery that the user has previously given a high rating. For example, the scenery change unit preferentially displays scenery that the user has given a high rating based on the user's past scenery change history. The scenery change unit can also exclude scenery that the user has previously given a low rating from the display targets. For example, the scenery change unit excludes scenery that the user has given a low rating from the display targets based on the user's past scenery change history. The scenery change unit can also optimize the display algorithm based on the user's past scenery change history. For example, the scenery change unit analyzes the user's past scenery change history and optimizes the display algorithm. In this way, the display algorithm can be optimized by referring to the user's past scenery change history.

[0113] The scenery change unit can improve display accuracy by taking into account the user's current environmental information when changing the scenery. The scenery change unit, for example, displays a warm scenery when it is raining. For example, the scenery change unit displays a warm scenery when it is raining based on weather data. The scenery change unit can also display a scenery with subdued colors at night. For example, the scenery change unit displays a scenery with subdued colors at night based on time of day information. The scenery change unit can also display a scenery with bright colors when it is sunny. For example, the scenery change unit displays a scenery with bright colors when it is sunny based on weather data. This makes it possible to improve display accuracy by taking into account the user's current environmental information.

[0114] When changing the scenery, the scenery change unit can preferentially select a display target using the user's gaze tracking data. The scenery change unit, for example, preferentially displays a scenery that the user is gazing at. For example, the scenery change unit preferentially displays a scenery that the user is gazing at based on the gaze tracking data. The scenery change unit can also preferentially display a scenery on which the user's gaze remains for a long time. For example, the scenery change unit preferentially displays a scenery on which the user's gaze remains for a long time based on the gaze tracking data. The scenery change unit can also preferentially display a scenery at the destination of the gaze when the user frequently moves their gaze. For example, the scenery change unit preferentially displays a scenery at the destination of the gaze when the user frequently moves their gaze based on the gaze tracking data. This makes it possible to preferentially select a display target using the user's gaze tracking data.

[0115] The scenery change unit can estimate the user's emotions and determine the priority of the scenery to be displayed based on the estimated user's emotions. For example, if the user is relaxed, the scenery change unit prioritizes displaying scenery with calm colors. For example, if the scenery change unit analyzes the user's facial expression and determines that the user is relaxed, it prioritizes displaying scenery with calm colors. Furthermore, if the user is excited, the scenery change unit can prioritize displaying visually stimulating scenery. For example, if the scenery change unit analyzes the user's voice and determines that the user is excited, it prioritizes displaying visually stimulating scenery. Furthermore, if the user is stressed, the scenery change unit can prioritize displaying simple, highly visible scenery. For example, if the scenery change unit analyzes the user's biometric data and determines that the user is stressed, it prioritizes displaying simple, highly visible scenery. This makes it possible to determine the priority of the scenery to be displayed based on the 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] When changing the scenery, the scenery change unit can filter the display objects taking into account the user's geographical location information. For example, when the user is in a specific area, the scenery change unit prioritizes displaying scenery related to the area. For example, when the user is in a specific area, the scenery change unit prioritizes displaying scenery related to the area based on the geographical location information. Furthermore, when the user is in a tourist destination, the scenery change unit can also prioritize displaying scenery related to tourist spots. For example, when the user is in a tourist destination, the scenery change unit prioritizes displaying scenery related to tourist spots based on the geographical location information. Furthermore, when the user is in a commercial area, the scenery change unit can also prioritize displaying scenery related to stores and advertisements. For example, when the user is in a commercial area, the scenery change unit prioritizes displaying scenery related to stores and advertisements based on the geographical location information. In this way, the display objects can be filtered taking into account the user's geographical location information.

[0117] When changing the scenery, the scenery changing unit can analyze the user's social media activity and prioritize displaying related scenery. For example, the scenery changing unit prioritizes displaying scenery related to places where the user has checked in on social media. For example, the scenery changing unit prioritizes displaying scenery related to places where the user has checked in based on the social media activity. The scenery changing unit can also analyze the content of the user's posts on social media and prioritize displaying related scenery. For example, the scenery changing unit prioritizes displaying scenery related to the content of the user's posts based on the social media activity. The scenery changing unit can also prioritize displaying related scenery based on the activity of the user's friends on social media. For example, the scenery changing unit prioritizes displaying scenery related to the activities of the user's friends based on the social media activity. In this way, the user's social media activity can be analyzed and related scenery can be prioritized.

[0118] When changing the scenery, the scenery change unit can customize the display algorithm by reflecting the user's past feedback. The scenery change unit, for example, preferentially displays scenery that the user has previously rated highly. For example, the scenery change unit preferentially displays scenery that the user has previously rated highly based on the user's past feedback. The scenery change unit can also exclude scenery that the user has previously rated poorly from the display targets. For example, the scenery change unit excludes scenery that the user has previously rated poorly from the display targets based on the user's past feedback. The scenery change unit can also optimize the display algorithm based on the user's past feedback. For example, the scenery change unit analyzes the user's past feedback and optimizes the display algorithm. In this way, the display algorithm can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the object recognition unit, posture estimation unit, replacement display unit, interior change unit, and scenery change unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the object recognition unit detects passersby and vehicles using the camera 42 of the smart device 14, and is realized by the specific processing unit 290 of the data processing device 12 using image processing technology and machine learning algorithms. The posture estimation unit tracks the movements of passersby and vehicles using the control unit 46A of the smart device 14, and calculates joint angles and performs motion analysis using the specific processing unit 290 of the data processing device 12. The replacement display unit replaces the display 40A of the smart device 14 with a celebrity or character specified by the user, and is realized by the specific processing unit 290 of the data processing device 12 using image synthesis technology and real-time rendering. The interior change unit changes the appearance of the furniture and interior of a room using the control unit 46A of the smart device 14, and is realized by the specific processing unit 290 of the data processing device 12 using 3D modeling and virtual reality technology. The scenery change unit changes the scenery seen through the window using the display 40A of the smart device 14, and is realized by the specific processing unit 290 of the data processing device 12 using image processing technology and real-time rendering. === Hard Collateral 1-2 === Each of the multiple elements, including the object recognition unit, posture estimation unit, replacement display unit, interior change unit, and scenery change unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the object recognition unit detects passersby and vehicles using the camera 42 of the smart glasses 214, and is realized by the specific processing unit 290 of the data processing device 12 using image processing technology and machine learning algorithms. The posture estimation unit tracks the movements of passersby and vehicles using the control unit 46A of the smart glasses 214, and calculates joint angles and performs motion analysis using the specific processing unit 290 of the data processing device 12. The replacement display unit replaces the display of the smart glasses 214 with a celebrity or character designated by the user, and is realized by the specific processing unit 290 of the data processing device 12 using image synthesis technology and real-time rendering. The interior change unit changes the appearance of the furniture and interior of the room using the control unit 46A of the smart glasses 214, and is realized by the specific processing unit 290 of the data processing device 12 using 3D modeling and virtual reality technology. The scenery change unit changes the scenery seen through the window using the display of the smart glasses 214, and is realized by the specific processing unit 290 of the data processing device 12 using image processing techniques and real-time rendering. === Hard Collateral 1-3 === Each of the multiple elements, including the object recognition unit, posture estimation unit, replacement display unit, interior change unit, and scenery change unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the object recognition unit detects passersby and vehicles using the camera 42 of the headset-type terminal 314, and is realized by the specific processing unit 290 of the data processing device 12 using image processing technology and machine learning algorithms. The posture estimation unit tracks the movements of passersby and vehicles using the control unit 46A of the headset-type terminal 314, and calculates joint angles and analyzes movements using the specific processing unit 290 of the data processing device 12. The replacement display unit replaces the display 343 of the headset-type terminal 314 with a celebrity or character designated by the user, and is realized by the specific processing unit 290 of the data processing device 12 using image synthesis technology and real-time rendering. The interior change unit changes the appearance of the furniture and interior of the room using the control unit 46A of the headset-type terminal 314, and is realized by the specific processing unit 290 of the data processing device 12 using 3D modeling and virtual reality technology. The scenery change unit changes the scenery seen from the window using the display 343 of the headset terminal 314, and is realized by the specific processing unit 290 of the data processing device 12 using image processing technology and real-time rendering. === Hard Collateral 1-4 === Each of the multiple elements, including the object recognition unit, posture estimation unit, replacement display unit, interior change unit, and scenery change unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the object recognition unit detects passersby and vehicles using the camera 42 of the robot 414, and is realized by the specific processing unit 290 of the data processing device 12 using image processing technology and machine learning algorithms. The posture estimation unit tracks the movements of passersby and vehicles using the control unit 46A of the robot 414, and calculates joint angles and analyzes movements using the specific processing unit 290 of the data processing device 12. The replacement display unit replaces the display of the robot 414 with a celebrity or character designated by the user, and is realized by the specific processing unit 290 of the data processing device 12 using image synthesis technology and real-time rendering. The interior change unit changes the appearance of the furniture and interior of the room using the control unit 46A of the robot 414, and is realized by the specific processing unit 290 of the data processing device 12 using 3D modeling and virtual reality technology. The scenery change unit changes the scenery seen from the window using the display of the robot 414, and is realized by the specific processing unit 290 of the data processing device 12 using image processing techniques and real-time rendering.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The AR system may further include a health management unit that monitors the user's health condition. The health management unit acquires biometric data, such as the user's heart rate and blood pressure, in real time to monitor the user's health condition. For example, if the user's heart rate is abnormally high, the health management unit can automatically display relaxing scenery or interior decor. Alternatively, if the user's blood pressure is high, the health management unit can provide interior decor with calming colors. Furthermore, the health management unit can provide appropriate exercise and rest advice according to the user's health condition. This allows the AR system to provide a customized experience that takes the user's health condition into consideration.

[0121] The object recognition unit can learn and predict a user's past behavioral patterns. For example, if a user tends to visit a specific location at a specific time of day, the object recognition unit can improve the accuracy of object recognition at that location. Also, if a user tends to perform a specific activity on a specific day of the week, the object recognition unit can preferentially recognize objects related to that activity. Furthermore, the object recognition unit can predict future behavior based on the user's past behavioral patterns and optimize the object recognition algorithm based on the predicted behavior. In this way, the object recognition unit can achieve more accurate object recognition by learning and predicting the user's behavioral patterns.

[0122] The posture estimation unit can collect the user's exercise data and provide fitness advice. For example, the posture estimation unit can analyze the user's walking pattern and provide advice on correct walking posture. The posture estimation unit can also analyze the user's running form and suggest an efficient running form. Furthermore, the posture estimation unit can analyze the user's stretching movements and provide advice on effective stretching methods. In this way, the posture estimation unit can provide fitness advice based on the user's exercise data.

[0123] The replacement display unit can estimate the user's emotions and adjust the facial expression of the character to be displayed based on the estimated emotions. For example, if the user is sad, the character can display an encouraging facial expression. If the user is happy, the character can display an empathetic facial expression. Furthermore, if the user is angry, the character can display a calming facial expression. This allows the replacement display unit to adjust the facial expression of the character based on the user's emotions.

[0124] The interior modification unit can customize the interior based on the user's hobbies and interests. For example, if the user likes music, the interior modification unit can provide a music studio-style interior. If the user likes reading, the interior modification unit can provide a library-style interior. Furthermore, if the user likes cooking, the interior modification unit can provide a kitchen-style interior. In this way, the interior modification unit can customize the interior based on the user's hobbies and interests.

[0125] The scenery change unit can estimate the user's emotions and adjust the movement of the scenery to be displayed based on the estimated emotions. For example, if the user is relaxed, it can display scenery with slow movement. If the user is excited, it can display scenery with fast movement. Furthermore, if the user is stressed, it can display a still scenery. In this way, the scenery change unit can adjust the movement of the scenery based on the user's emotions.

[0126] The object recognition unit can improve the accuracy of object recognition by utilizing sensory information other than the user's vision. For example, the object recognition unit can use audio data to identify the source of a sound and recognize that object. The object recognition unit can also use tactile data to recognize the shape and texture of a touched object. Furthermore, the object recognition unit can also use olfactory data to recognize objects that emit a specific odor. This allows the object recognition unit to improve the accuracy of object recognition by utilizing sensory information other than vision.

[0127] The posture estimation unit can estimate the user's emotion and adjust the posture estimation algorithm based on the estimated emotion. For example, if the user is relaxed, the posture estimation algorithm can be made lenient. If the user is excited, the posture estimation algorithm can be made strict. Furthermore, if the user is feeling stressed, the posture estimation algorithm can be made flexible. This allows the posture estimation unit to adjust the posture estimation algorithm based on the user's emotion.

[0128] The replacement display unit can customize the movements of the characters to be displayed based on the user's past display history. For example, it can reproduce the movements of characters that the user liked to see in the past. It can also prevent the display of movements of characters that the user avoided in the past. Furthermore, it can also suggest new character movements based on the user's past display history. This allows the replacement display unit to customize the movements of the characters to be displayed based on the user's past display history.

[0129] The interior modification unit can estimate the user's emotions and adjust the acoustic environment of the interior based on the estimated emotions. For example, if the user is relaxed, calm music can be played. If the user is excited, lively music can be played. Furthermore, if the user is stressed, natural sounds can be played. In this way, the interior modification unit can adjust the acoustic environment of the interior based on the user's emotions.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The object recognition unit detects passersby and vehicles. For example, the object recognition unit uses image processing technology and machine learning algorithms to detect passersby and vehicles in real time. Step 2: The posture estimation unit tracks the movements of pedestrians and vehicles detected by the object recognition unit, for example, by calculating joint angles and analyzing motion to track the movements of pedestrians and vehicles in real time. Step 3: The replacement display unit replaces the passersby and vehicles tracked by the posture estimation unit with celebrities or characters specified by the user. For example, by using image synthesis technology or real-time rendering, passersby can be replaced with famous actors or anime characters. Step 4: The interior modification unit modifies the appearance of the room's furniture and interior based on the user-specified design or theme, for example, by using 3D modeling or virtual reality technology. Step 5: The scenery change unit changes the scenery seen through the window using the scenery data specified by the user, for example, by using image processing technology or real-time rendering.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an object recognition unit that detects pedestrians and vehicles; a posture estimation unit based on information detected by the object recognition unit; a replacement display unit based on information tracked by the posture estimation unit; an interior modification unit based on the information replaced by the replacement display unit; a scenery change unit based on the information changed by the interior change unit. A system characterized by:

2. The object recognition unit Detecting pedestrians or vehicles 2. The system of claim 1.

3. The posture estimation unit Tracking the movement of passersby or vehicles detected by the object recognition unit 2. The system of claim 1.

4. The replacement display unit The passerby or vehicle tracked by the posture estimation unit is replaced with a celebrity or character designated by the user.

2. The system of claim 1.

5. The interior modification section includes: Change the appearance of a room's furniture or decor based on a user-specified design or theme 2. The system of claim 1.

6. The scenery change unit Change the view from the window using user-specified view data 2. The system of claim 1.

7. The object recognition unit Estimate the user's emotions and adjust the accuracy of object recognition based on the estimated user emotions.

2. The system of claim 1.

8. The object recognition unit When recognizing objects, the recognition algorithm is optimized by referring to the user's past recognition history.

2. The system of claim 1.

Images