Digital twinning to validate real-world objects

Abstract

Methods and systems for providing digital twinning access to a real-world object for use in a meta-universe system include receiving a request to validate the real-world object. A real-world object is validated using a plurality of physical attributes of the real-world object. Upon successful verification, the digital twinning is unlocked to allow the digital twinning to be used in the meta-universe system.

Description

Technical Field

[0001] The present disclosure relates to using real-world objects within a virtual world, and more particularly to creating digital twins of real-world objects and using the digital twins in a virtual world. Background Art

[0002] As humans increasingly live their lives online and in virtual worlds (also known as the "metaverse"), interest in creating digital twins of real-world objects has grown. The idea is that if an individual owns an object in the real world, they should also be able to own and "use" that object in the virtual world. For example, if an individual purchases a pair of a particular brand of sneakers in the real world, the individual's virtual world avatar should be able to wear that same pair in the virtual world. In other words, a digital character representing an individual can be displayed as a digital rendering wearing the same brand of sneakers that the individual owns in the real world.

[0003] In order to accomplish this, there needs to be a way to validate an individual's ownership or at least possession of a real-world object. One way to validate ownership or possession is, for example, by connecting a point-of-sale system used to sell real-world objects (such as a particular brand of physical sneakers) to a Metaverse system, such that a physical purchase "unlocks" a digital version of that purchase. This can be accomplished for new items being purchased in a world where such a connected system exists. However, such validation cannot be extended to previously owned items because the connected system is recent and did not exist previously.

[0004] Additionally, certain real-world items may be more valuable, and therefore, ownership of a digital version of such a real-world item may necessarily confer a certain degree of status or prestige due to the value of the real-world item. For example, an individual may own an original famous painting in the real world, which has tremendous value due to its rarity (i.e., a one-of-a-kind piece), is very expensive to purchase, and only a few wealthy people can afford such a hobby. In order to maintain the value of a one-of-a-kind painting and maintain its status or prestige, there needs to be a way to verify that an individual user owns the physical version of the painting. This verification will enhance the value of the digital twin.

[0005] It is in this context that various implementations of the present disclosure are proposed. Summary of the invention

[0006] The implementation of the present disclosure relates to a system and method for associating a digital twin of a real-world object to a user for use in a metaverse system. The system and method first verify that the user possesses the real-world object and is the legal owner. After successful verification, if there is no digital twin for the real-world object owned by the user, a digital twin is created. The digital twin is represented by a virtual object, which includes virtual properties that mimic the physical properties of the real-world object. Alternatively, if a digital twin has been created, a virtual object representing the digital twin is identified. The newly created or previously created virtual object identified for the real-world object is then unlocked and associated with the user who owns the real-world object. The association provides the user with access to a digital twin for use in a metaverse system (including a game system). For example, providing access to a digital twin allows the user to use a virtual object to provide input to a game system. Alternatively, in response to detecting a real-world object on the user, the access allows the virtual object to be presented on a digital character representing the user in the metaverse system or to be controlled by the digital character.

[0007] Any changes in physical properties detected in the real-world object are replicated in the corresponding virtual properties of the virtual object, so that the virtual object represents a faithful replica of the real-world object. In some implementations, before determining the changes in physical properties that need to be updated to the corresponding virtual properties of the virtual object, some of the physical properties of the real-world object are filtered to remove any abnormal data, such as defects caused by wear. In some implementations, one or more virtual properties associated with the virtual object representing the digital twin are normalized by including changes corresponding to the changes detected in the corresponding physical properties of the real-world object. The changes are updated before determining the ownership of the real-world object and the unlocking of the digital twin. Normalization of virtual properties allows virtual objects to become faithful replicas of real-world objects. The creation and / or identification of digital twins of real-world objects allows users to use real-world objects in virtual worlds so as to personalize the user's digital representation using digital twins, thereby greatly improving user engagement in the metaverse.

[0008] Ownership verification of real-world objects is typically performed by connecting a point-of-sale system to a metaverse system so that when a user purchases a real-world object, the purchase “unlocks” a digital version of the real-world object. This connection can be made to verify newly purchased real-world objects. However, for real-world objects that the user already owned before the point-of-sale system was connected, verifying the purchase becomes a challenge. In order to allow a user to create and use a digital version of a real-world object that the user owned / purchased before connecting the point-of-sale system, the user's ownership of the real-world object is first verified. To assist in verification, data related to a specific instance of a real-world object in the possession of the user is captured in real time from multiple sensors, scanners, and devices. The captured data is analyzed to identify the physical properties of the real-world object. Using the captured physical properties and a machine learning object recognition algorithm trained on data from the digital twin, the distinctive characteristics of the real-world object are identified. The distinctive characteristics uniquely identify a specific instance of the real-world object. The real-world object scanned by the various sensors and the user currently in possession of the real-world object are verified, where successful verification establishes that the user is verified as the legitimate owner. After successful identification and ownership verification, the digital twin of the real-world object is unlocked to allow the user to use it in the metaverse.

[0009] In one implementation, a method for providing access to a digital twin of a real-world object in a metaverse system is disclosed. The method includes receiving a request for authenticating a real-world object. The real-world object is identified using a plurality of physical attributes defining a distinct fingerprint. The real-world object is authenticated using the distinct fingerprint, wherein the authentication includes confirming that the user is the legitimate owner of the real-world object. Upon successful authentication of the real-world object in the possession of the user, the digital twin of the real-world object is then unlocked for use by the user in the metaverse system. The digital twin is represented by a virtual object having virtual attributes that replicate a plurality of physical attributes of the real-world object. The unlocking includes associating the virtual object with the user to allow the user to use the virtual object in the metaverse system.

[0010] In another implementation, a method for using a real-world object in a metaverse system is disclosed. The method includes identifying a real-world object in possession of a user. Identifying the real-world object using a plurality of physical attributes defining a distinct fingerprint. Verifying the user's possession of the real-world object. Upon successful verification of possession of the real-world object, creating a digital twin of the real-world object for use in a metaverse system. The digital twin is represented by a virtual object defined using virtual attributes that replicate a plurality of physical attributes of the real-world object. Associating the virtual object with the user to allow use of the virtual object in the metaverse system.

[0011] Other aspects and advantages of the present disclosure will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present disclosure may be best understood by reference to the following description taken in conjunction with the accompanying drawings.

[0013] Figure 1 A simplified block diagram of a system for creating and / or validating a digital twin of a real-world object for use by a user in a metaverse system is provided in accordance with one implementation of the present disclosure.

[0014] Figure 2 Various modules of a digital twin validation engine for creating and / or validating a digital twin of a real-world object are shown in accordance with one implementation of the present disclosure.

[0015] Figure 3 An example of a real-world object worn by a user is shown, the real-world object being replicated on a digital representation of the user in a metaverse system, in accordance with one implementation of the present disclosure.

[0016] Figure 4A An example of a real-world object filtered to remove anomalous data before validation against a digital twin is shown in accordance with one implementation of the present disclosure.

[0017] Figure 4B An example of a virtual object in accordance with one implementation of the present disclosure is shown, wherein one or more virtual attributes are normalized based on corresponding physical attributes of a real-world object.

[0018] Figure 5 A simplified operational flow of a method for validating a digital twin of a real-world object possessed by a user according to one implementation of the present disclosure is shown.

[0019] Figure 6 Components of an example computing device that may be used to perform aspects of various implementations of the present disclosure are shown. DETAILED DESCRIPTION

[0020] In the following description, a number of specific details are set forth to provide a thorough understanding of the present disclosure. However, it will be appreciated by those skilled in the art that the present disclosure may be practiced without some or all of these specific details. In other cases, well-known process steps are not described in detail in order to avoid making the present disclosure unclear.

[0021] As humans increasingly live their lives online and in virtual worlds, there is a growing interest in creating digital twins of real-world objects owned by users and allowing users to use these digital twins in virtual worlds. Digital twins are created as digital replicas of real-world objects by defining virtual properties that mimic the physical properties of real-world objects. The physical properties of real-world objects are identified using data captured by multiple data capture devices such as sensors, devices, scanners, etc. For example, physical properties can be defined based on visual data captured using an image capture device, audio data captured using an audio device (e.g., microphone, microphone array, etc.), physical data captured using standard or specialized devices / equipment (including weight, volume, sonar data, x-ray data, magnetic resonance imaging (MRI) data, infrared data, ultrasonic data, etc.). Physical properties are used together with machine learning object recognition algorithms to identify the distinct characteristics of instances of real-world objects to define unique fingerprints. If the corresponding digital twin of the real-world object has not yet been created, the digital twin is created using a unique fingerprint. Digital twins are represented as virtual objects with virtual properties that match the physical properties of real-world objects. After the digital twin is created, the digital twin is associated with the user to provide the user with access to the digital twin for use in the metaverse system. For example, the digital twin can be used to decorate or render on the user's digital representation (e.g., avatar) in a video game or social media application (e.g., chat application) to mimic the user's real-world appearance in the virtual world. In another example, the digital twin can be used to provide input in a video game, or as a collectible asset, or to improve the value of the user's digital assets, etc. Various uses provide users with the flexibility to customize their virtual worlds with digital replicas of real-world objects they own in the real world, thereby allowing users to have a more personalized online presence in the virtual world. After confirming that the user is the actual owner of the real-world object, the user is provided with access to the digital twin of the real-world object for use in the virtual world (i.e., the metaverse system).

[0022] Alternatively, a digital twin may have been created for the real-world object, and the digital twin may already exist in the virtual world. In such cases, ownership verification is performed using the unique fingerprint of the real-world object, and upon successful verification, the digital twin is unlocked for the user. Unlocking the digital twin provides the user with access to the virtual object for use in the virtual world.

[0023] In some implementations, a machine learning algorithm is used to perform identification and / or verification of a real-world object in the possession of a user. The machine learning algorithm is trained using an object recognition algorithm to use the physical properties of the real-world object to match the corresponding virtual properties of a specific virtual object in the virtual object. The virtual object is a digital twin of the real-world object and may be shown as being in a pristine state, while the real-world object may exhibit certain flaws or marks attributable to wear or manufacturing defects or user customization. In such cases, the physical properties of the real-world object may not completely match the corresponding virtual properties of the virtual object representing the digital twin. In such cases, before comparing the corresponding properties to identify the virtual object of the real-world object, either the virtual properties of the digital twin (i.e., the virtual object) are updated to include similar defects, or the physical properties of the real-world object are filtered to remove such defects. Once a match is established, the virtual object related to the real-world object is unlocked to allow the user to use the virtual object in the metaverse system.

[0024] With the present disclosure generally understood, specific implementations will be described with reference to the various figures.

[0025] Figure 1 An embodiment of a system 10 for interacting with an interactive application (such as a video game) via a user computing device 102 is shown. In some implementations, the user computing device 102 may be a desktop computer or a mobile computing device, such as a portable game console, a smart phone, a laptop computer, a notebook computer, a tablet computer, a book reader, a PDA, a mobile phone, etc. Figure 1 , a user 100 is shown as physically located in a real world space 108 playing a video game using a computing device 102. The interactive application with which the user 100 is interacting is not limited to a video game, but may include any other type of interactive application, including a chat application, a social media application, etc. In one implementation, the cloud computing and gaming system 106 maintains and executes the video game played by the user 100. A first portion (e.g., a device portion) of the interactive application is executed on the user computing device 102, while a second portion (e.g., a server portion) of the interactive application is executed on a server (not shown) that is part of the cloud computing and gaming system 106. Input provided by the user via the computing device 102 or a controller (not shown) is transmitted to the cloud computing and gaming system 106 via the network 104.

[0026] In the illustrated implementation, the computing device 102 is shown as being wirelessly connected to the cloud computing and gaming system 106 via a network 104. In other implementations, the computing device 102 may communicate with the cloud computing and gaming system 106 wirelessly or via a wired connection via an alternative mechanism or channel such as a cellular network (not shown). In some implementations, the cloud computing and gaming system 106 is configured to process input 109 transmitted by the computing device 102 or a controller that affects the state of an executing interactive application (such as a video game). Content output (such as video data, audio data) from the executing video game is compressed and transmitted to the computing device 102 for decoding and rendering. In alternative implementations, the interactive application may be a social media application or a chat application or any other application other than a video game, and the content output may include content of a target category, including social media streams, chat streams, interactive narratives, virtual reality experiences, augmented reality experiences, interactive live streams, other multimedia content, and the like.

[0027] The user 100 may be associated with a set of real-world objects 112. For example, the user may be associated with a user-customized backpack 112a, a soccer ball 112b, and a polo shirt 112c with a customized design. The user 100 may acquire these real-world objects by purchasing, gifting, trading, transferring, etc. In response to detecting the presence of the user 100 in the real-world space 108, data defining the physical attributes 107 of the real-world objects 112 is captured using sensors, scanners, devices including image capture devices, and transmitted to the cloud computing and gaming system 106 for processing. The cloud computing and gaming system 106 processes the physical attributes 107 of the real-world objects 112 with the help of a machine learning algorithm with an embedded object recognition algorithm for generating and training an artificial intelligence (AI) model 120. The AI ​​model 120 is trained using the captured data of the real-world objects to define outputs. The captured data of the real-world objects extracts relevant features, identifies appropriate classifiers (not shown) for the relevant features and provides the appropriate classifiers as inputs to the AI ​​model 120, and generates various outputs. In addition to data related to the real-world object, user data for the user currently in possession of the real-world object and the user known to own the real-world object is extracted from the user profile data, and an appropriate user data classifier is identified and provided as input to the AI ​​model. The user in possession of the real-world object may be the same user known to own the real-world object. The output from the AI ​​model 120 is used to identify the real-world object 112 and confirm ownership of the real-world object 112 to the user currently in possession of the real-world object. In addition to identifying the real-world object and confirming ownership, the output from the AI ​​model 120 is also used to identify virtual objects having virtual attributes that match corresponding physical attributes of each real-world object associated with the user 100.

[0028] After confirming ownership of each real-world object 112 and identifying the corresponding virtual object, the cloud computing and gaming system 106 is configured to unlock the virtual object so that the virtual object can be used in the metaverse system. The unlocking provides the user 100 with access to the virtual object for use in a virtual world (such as a video game). For example, the real-world object may be a polo shirt 112c owned by the user 100, and the unlocking of the virtual representation of the real-world object 112c (i.e., the digital twin of the polo shirt 112c) allows the user or system to use the virtual representation of the polo shirt 112c with a unique design in the video game. One such use may be to dress up the avatar (i.e., digital representation) 100A of the user 100 in the video game using the virtual representation of the polo shirt 112c. The cloud computing and gaming system 106 may be configured to automatically update the avatar 100A of the user 100 in the video game after detecting that the user is wearing the polo shirt 112c while interacting with the video game. In an alternative example, while the user is interacting with the video game, the avatar 100A of the user 100 may be updated in response to input provided by the user 100. In yet another example, instead of dressing up the user, a virtual representation of a polo shirt 112c may be available for trading, selling, purchasing, or collecting as a valuable asset in a virtual marketplace available within the video game.

[0029] Figure 2 An example digital item validation engine 122 is shown executed at a cloud computing and gaming system 106 in some implementations, which is configured to perform various operations to identify and / or verify a real-world object in the possession of a user. The digital item validation engine (referred to herein as the "validation engine") 122 first identifies a real-world object in the possession of a user (operation 124). Data related to the real-world object is captured using a plurality of sensors, scanners, image capture devices, audio devices, etc. available within or coupled to the computing device 102 and / or disposed in a real-world space (i.e., the user's physical location) 108 in which the user 100 is interacting with an interactive application (such as a video game). The various sensors, devices, scanners capture data in real time after the user is detected to have the real-world object while the user is interacting with the video game. In some implementations, the visual data of the real-world object is identified using image data captured by one or more image capture devices by utilizing techniques such as photogrammetry. Images of the real-world object are captured from various angles so that a virtual three-dimensional replica of the real-world object capturing all visual attributes can be constructed.

[0030] In addition to the visual data captured in the image, other descriptive attributes may be identified from data captured by a plurality of sensors, audio devices, x-ray machines, sonar, magnetic resonance imaging (MRI) scanners, infrared scanners, ultrasound scanners, and the like. The validation engine 122 uses the data captured by the various sensors, devices, and scanners to identify physical and other attributes (e.g., dynamic, chemical, etc.) of instances of real-world objects. For example, the physical and other attributes may be used to define a unique fingerprint that identifies various characteristics of a real-world object. For example, physical attributes may be used to define size, shape, color, texture, brightness, unique identifying marks, and the like. Dynamic attributes are defined as volume, weight, kinetic energy, and the like of a real-world object.

[0031] Analyze the physical and other properties of the real-world object to verify the specific instance of the real-world object in the possession of the user (operation 126). In some implementations, the verification of the real-world object can be divided into two parts—(a) identifying the specific instance of the real-world object; and (b) determining the ownership of the specific instance of the real-world object. In some implementations, the specific instance of the real-world object is identified using a machine learning algorithm 140 in which an object recognition algorithm is embedded. The machine learning algorithm 140 creates and updates an artificial intelligence (AI) model 120, which is trained using data captured for various virtual objects. The data is used to identify the unique characteristics of each real-world object. For example, if the real-world object is a red ball, the unique characteristics may include visible characteristics of the red ball, such as size (e.g., diameter, surface area, etc.), color (e.g., hue, saturation, brightness, etc.), texture, etc. The visible characteristics of the specific instance of the red ball may also include user customization, distinctive markings, visible defects, etc. that can further distinguish the specific instance of the real-world object. In addition to visible characteristics, other attributes may also be used to identify other inherent characteristics (e.g., weight, volume, etc.) that define, for example, physical properties, dynamic properties, chemical properties. These characteristics can be used in the metaverse to apply to a digital character or perform a task or generate a desired result. The object recognition algorithm is configured to identify specific instances of real-world objects using unique characteristics identified by the machine learning algorithm 140 from analyzing the physical properties of the real-world objects captured by multiple sensors / devices.

[0032] In addition to identifying real-world objects, in some implementations, user data of users obtained from a user profile data repository (not shown) is used to determine ownership of real-world objects. In some other implementations, a unique fingerprint of a specific instance of a real-world object is used to generate a non-fungible token (NFT) within a blockchain (operation 132), which acts as a digital ledger for keeping track of the use of a specific instance of a real-world object. The blockchain can be a proprietary blockchain or a general-purpose blockchain. In some implementations, real-world objects and virtual objects are tracked using NFTs maintained in separate blockchains. In other implementations, real-world objects and virtual objects are tracked via NFTs maintained in a single blockchain. In yet other implementations, a blockchain can be maintained separately for each interactive application, where different NFTs are used to maintain and track real-world objects and virtual objects used in interactive applications. In alternative implementations, a blockchain can be maintained separately for each type of interactive application. For example, a first blockchain can be used to generate an NFT for tracking real-world objects and virtual objects used in a video game, a second blockchain can be used to generate an NFT for tracking real-world objects and / or virtual objects used in a social media application, and the like.

[0033] The blockchain works by registering each transaction representing the use of a real-world or virtual object as a separate entry within the blockchain. The data included in the NFT within the blockchain provides sufficient details that can be used to confirm the authenticity of the real-world object, past use history, past and current ownership, characteristics of the real-world object or virtual object, physical attributes of the real-world object, virtual attributes of the virtual object, etc. The blockchain that maintains the use history of the real-world object and the virtual object is stored in multiple servers and is used to further authenticate the real-world object or virtual object by ensuring that the data for the real-world object or virtual object contained in the blockchain on one server is the same as the blockchain data contained for the real-world object or virtual object on most servers. When the real-world object or virtual object is gifted or transferred to another user or acquired by another user, the ownership of the real-world object or virtual object is automatically updated to the corresponding NFT in the blockchain using a new block to reflect the current owner. The real-world object identified in operation 124 can be further verified (operation 126) using the details included in the corresponding NFT within the blockchain, wherein the verification includes verifying the identity of the real-world object and verifying the ownership of the real-world object.

[0034] Once the real-world object and the associated owner are successfully authenticated, the virtual object associated with the real-world object that has been created is identified (operation 128). In such implementations, the physical properties of the real-world object are used to query the virtual object database 129 to identify virtual objects whose virtual properties match the physical properties of the real-world object. The identified virtual object is a digital twin of the real-world object. After identifying the virtual object of the real-world object, the virtual object is unlocked (operation 136) to allow the user to use the virtual object in the metaverse. As described, the metaverse can be a virtual game world in which the user's digital representation can use virtual objects. The user's digital representation can be an avatar or a digital icon or a game character. The unlocked virtual object can be clothing, accessories (including fashion accessories), wearable devices, etc. that can be used to dress up the user's digital representation, or can be a game item or accessory that can be used to provide input to the game. The unlocking of the virtual object provides access to the virtual object for use in the virtual world (e.g., a video game). In some implementations, the unlocked virtual object can be a virtual representation of a shirt or shoe worn by the user or a specific instance of a wearable clothing / accessory item. In these implementations, the validation engine 122 can detect that a user is wearing a specific instance of a shirt, a shoe, or a wearable clothing / accessory item while the user is interacting with a video game, and in response, can automatically dress the user's avatar in the video game using the corresponding unlocked virtual object so that the avatar mimics the user's appearance. Thus, the validation engine 122 allows the user to be personalized in the virtual world using digital representations associated with real-world objects (i.e., virtual objects). In alternative implementations, a digital twin can be used to dress the user's avatar or game character based on input provided by the user.

[0035] Figure 3One such implementation is shown in which a digital representation of a user in a virtual world is personalized to mimic the user's appearance in the real world. As shown, a user 100 is shown playing a video game executed on a cloud gaming system 106 via a network 104 and providing input using a controller 111. The video game includes a game character or avatar 100A representing the user 100. The user is shown wearing a specific instance of a pair of shoes 150 that the user has customized. The confirmation engine 122 detects that the user is wearing the customized pair of shoes 150, identifies a specific instance of a virtual pair of shoes 150' from a virtual object database 129, and unlocks the specific instance of the virtual pair of shoes 150'. In some implementations, the confirmation engine 122 is configured to use the unlocked specific instance of the virtual pair of shoes 150' to dress up the feet of the game character / avatar 100A to mimic the appearance of the user's feet. In an alternative implementation, the validation engine 122 is configured to send a signal to the game logic of the video game to adjust the appearance of the avatar 100A rendered on the display screen 110, and the game logic is configured to adjust the appearance of the avatar to mimic the appearance of the user's feet. In other words, the adjustment can be made automatically by the validation engine 122 or in response to input from the user 100.

[0036] Return to reference Figure 2 In some implementations, the physical properties of the real-world object may not perfectly match the virtual properties of the virtual object. This may be due to the fact that the virtual object may be a native representation of the real-world object, while the real-world object may exhibit some flaws or defects. In some implementations, depending on the type of the real-world object, the flaws or defects may be due to wear and tear or due to manufacturing defects or due to user customization. For example, the flaws may include fading, stretching, tearing, soiling or staining, scratches, customization marks, etc., and such flaws or defects are captured in real time by one or more sensors and used to define the physical properties. Therefore, during the comparison of the physical properties of the real-world object with the virtual properties of the virtual object stored in the virtual object database 129, a perfect match is not found.

[0037] In order to correctly identify the digital twin of the real-world object, the machine learning algorithm may allow a certain error margin for each physical attribute of the real-world object when comparing the corresponding attributes (operation 130). In some implementations, the allowable error margin may be based on the properties of the real-world object, including the object category (e.g., clothing may be considered to have a larger allowable error margin than furniture), the general material used (e.g., fabric may be considered to have a larger allowable error margin than steel or metal), the specific material used (e.g., silk may be considered to have a larger allowable error margin than cotton), fragility (e.g., glass may be considered to have a larger allowable error margin than wood), monetary value (e.g., low-value items such as T-shirts may be considered to have a larger allowable error margin than high-value items such as paintings by Picasso), fungibility (e.g., a regular coffee cup may be considered to have a larger allowable error margin than a designer coffee cup, or a sculpture designed by a lesser-known artist may be considered to have a larger allowable error margin than a one-of-a-kind sculpture by a well-known artist such as Michelangelo), etc. In some implementations, a real-world object is considered a match for a virtual object stored in the virtual object database 129 as long as each virtual attribute of the virtual object falls within an allowable error margin defined for the corresponding physical attribute of the real-world object. For example, the real-world object may be a red ball of a certain hue, size, volume, etc. The color of the red ball may be slightly faded, deflated, or the color and circumference (i.e., physical attributes) may not exactly match the corresponding virtual attributes of the virtual red ball in the virtual object database 129. In this example, the virtual red ball is defined as a match for the physical red ball as long as the difference between the physical red ball and the virtual red ball in color, volume, circumference, etc. is within the allowable error margin defined for each physical attribute (e.g., within 10%, 12%, etc.).

[0038] For example, the difference between the physical attributes defining the distinctive characteristics of the real-world object and the virtual attributes of the virtual object is used to calculate a match score (operation 130a). When the calculated match score is defined to be within a predefined threshold (e.g., 10 or 12%), the virtual object is declared to be a match. The match score is calculated, for example, by identifying a real score for each of the physical attributes identified for the real-world object possessed by the user. The real scores of all physical attributes identified for the real-world object are integrated to define a physical total score. Similarly, a virtual score is identified for each virtual attribute identified for each virtual object within the virtual object database 129. The virtual scores of all virtual attributes of each virtual object are integrated to define a virtual total score. The physical total score is compared to the virtual total score of each virtual object to define a difference (i.e., a delta). When the difference is within a predefined threshold limit, the virtual object having the total virtual score corresponding to the difference is declared to be a match for the real-world object.

[0039] In some implementations, the calculation of the matching score takes into account the error margin defined for each physical attribute identified for the real-world object, wherein the error margin is affected based on the different characteristics of the real-world object. In some implementations, the calculation of the matching score and the error margin are performed using a classifier. The machine learning algorithm is trained to identify the properties of the real-world object and the error margin evaluated for each physical attribute, use these details to calculate the matching score between the real-world object and each virtual object in the virtual object database 129, and identify the virtual object with a virtual attribute whose matching score falls within a predefined threshold (i.e., the matching score is within the allowable error margin). When identifying the virtual object, it is also confirmed that the ownership of the real-world object belongs to the user who possesses the real-world object. Therefore, the digital twin does not have to be the same as the real-world object, because some of the virtual attributes of the digital twin may be different from the physical attributes of the real-world object. In some implementations, there may be an overlap of some attributes in the attributes, while the remaining attributes may be different. In these implementations, as long as the matching score falls within the predefined threshold, the virtual object can still be regarded as a digital twin of the real-world object.

[0040] In some implementations, the difference between the physical and virtual properties and the error margin defined for each physical property can be used to alter the digital twin to more accurately resemble the real-world object. For example, if the red ball in the user's possession is faded and its color saturation deviates by approximately 10%, the color saturation of the virtual object representing the digital twin is also altered by approximately 10% so that the virtual object more accurately resembles the real-world object.

[0041] In some implementations, after detecting abnormal data on the real-world object, the physical attributes of the real-world object are normalized before determining the match score (operation 130b). In the normalization step, the abnormal data is first cleared from the data of the real-world object captured by multiple sensors / devices before being compared with the virtual attributes of the virtual object to identify the digital twin. For example, a machine learning algorithm can be trained to recognize wear and tear on the real-world object and exclude such data when comparing the physical attributes with the virtual attributes to identify the digital twin. For example, a machine learning algorithm can be trained to recognize stains, recognize tears, cracks, holes, etc., and exclude (i.e., filter out) such abnormal data when considering the physical attributes for comparison with the virtual attributes. Virtual data is generally shown as being in its original state (i.e., never defective) unless certain defects or markings are intentionally included. Therefore, excluding defects from the physical object can reduce the error margin when calculating the match score with the remaining physical attributes.

[0042] Figure 4A One such example of filtering out anomalous data associated with a real-world object in some implementations is shown. The real-world object is shown to include some damage in one area, while the digital twin is shown to be in a pristine condition. Therefore, to ensure that the correct digital twin is identified, the damage in the area is excluded when calculating the matching score. The exclusion of the damage will result in the area being undamaged. The exclusion includes adjusting certain of the physical properties to filter out anomalous data related to the damage, so that the adjusted physical properties are related to the undamaged real-world object. The resulting real-world object is almost pristine. The altered physical properties of the real-world object are then used to identify a virtual object, which is in a pristine condition.

[0043] Return to reference Figure 2 Instead of removing anomalous data associated with the detected defects from the real-world object in order to reduce the margin of error, the virtual object representing the digital twin may be altered to incorporate any defects or special markings detected in the real-world object (operation 130c). For example, if the real-world object has special markings, such as paint streaks or paint spots, the special markings are also applied to the virtual object so that the resulting virtual object closely resembles the real-world object. To incorporate the defects or special markings, the validation engine 122 analyzes the special markings or defects to identify attributes (e.g., location, size, orientation, shape, depth, etc.), and uses the identified attributes to update the corresponding virtual attributes of the virtual object identified as matching the real-world object.

[0044] Figure 4BOne such implementation is shown in which a virtual object is shown in a pristine condition and a real-world object is shown with visual damage. To make the virtual object a digital twin of the real-world object, the virtual object is adjusted to include the visual damage identified in the real-world object (operation 130c). The adjustment of the virtual object includes updating or adjusting virtual attributes of the virtual object to include attributes of the defect detected in the real-world object, so that the virtual object, when rendered in a metaverse (e.g., a virtual world of a video game) according to the adjusted virtual attributes, will be a digital twin and show damage similar to the visual damage of the real-world object.

[0045] Return to reference Figure 2 , each real-world object will have slight imperfections due to stains, marks, scratches, etc., due to wear or manufacturing variability, or customization provided by the user. Machine learning algorithms are used to identify imperfections to determine the margin of error, perform data normalization and / or filtering, and identify the unique characteristics of each instance of a real-world object. For example, a first user and a second user both own a pair of shoes of the same brand, same color, same style, and same size. From a distance, the two pairs of shoes look the same. However, upon closer inspection, the first pair of shoes owned by the first user may include a small black tar mark left by the first user scraping against the asphalt on the street at the right toe of the right shoe. Alternatively, the second pair of shoes owned by the second user may include paint spots from an art project on both the right shoe and the left shoe. These small deviations from the ideal form define the unique characteristics of each pair of shoes that can be used to distinguish which pair belongs to which user. Once ownership of each pair of shoes (i.e., a specific instance of the pair of shoes) is established, the validation engine 122 can provide the digital representation of the corresponding owner (e.g., a digital character or avatar or a game character of a video game) with access to the digital twin of each pair of shoes to allow the digital character of the corresponding owner to use the pair of shoes in the virtual world (operation 136a). In one implementation, the digital character is shown wearing the specific instance of the pair of shoes on its feet.

[0046] It is extremely unlikely that any two real-world objects have the same flaws or markings. Machine learning algorithms can be used to calculate such possibilities and declare with high confidence that two real-world objects do represent two different instances. In addition to identifying two different instances differently, different characteristics including any flaws can also be used to prevent any fraud. For example, if a specific instance of a real-world object is scanned more than once, data from the two scans are used to determine whether the real-world object was accidentally scanned twice, or whether the second user to whom the real-world object was transferred performed the scan, or whether any fraud is being carried out. If the two different scans are performed by the same user, it can be established that the second scan is accidental. In this case, the confirmation engine 122 will recognize that the digital twin of the real-world object has been unlocked for use by the user and prevent another instance of the digital twin from being unlocked. In some implementations, the confirmation engine 122 generates a notification about the second scan and sends the notification to the user.

[0047] On the other hand, if the two different scans are performed by two different users, in one implementation, the confirmation engine 122 determines whether the ownership of the real-world object has been transferred from the first user to the second user (operation 136b). In one implementation, the transfer option can be provided on the user's computing device 102 next to the content of the interactive application, and the first user may have initiated the transfer option to transfer the ownership of the real-world object to the second user. In one implementation, the confirmation engine 122 checks whether the first user has initiated the transfer option for the real-world object and obtains the details of the transfer, such as the identity of the second user, the real-world object identifier, the transfer time, etc. In an alternative implementation, the confirmation engine 122 sends a notification to the first user (i.e., the original owner) who unlocked the digital twin to request them to confirm that the ownership of the real-world object has been transferred to the second user. When the first user confirms that the real-world object has indeed been transferred to the second user, this means that the ownership of the digital twin has also been transferred to the second user along with the transfer of the real-world object to allow the second user to use the digital twin in a virtual world (e.g., a video game). In some implementations, the confirmation engine 122 can use the NFT of the real-world object to query the blockchain to obtain the details of the transfer. Because the blockchain uses NFTs to keep track of the use of real-world objects, querying the blockchain provides the necessary details of past owners, current owners, transfer times, current locations of current owners, etc. On the other hand, if the original owner responds that the real-world object was not transferred but was actually stolen, the confirmation engine 122 initiates an alert notification (operation 134). In some implementations, the alert notification is sent to the original owner, who can then call the police to the relevant authorities. In an alternative implementation, the alert notification is sent to both the original owner and the relevant authorities, so that the second user who stole the real-world object can be arrested. Because the second user who scans the real-world object for the second time must have possession of the real-world object, the data related to the location of the second user can be used to capture the second user and request the return of the stolen real-world object. In some implementations, when the real-world object is marked as stolen, the confirmation engine 122 preemptively marks the digital twin as stolen, thereby preventing any user (including the second user) from accessing and using the digital twin in the virtual world (operation 136c). Thus, when the second user scans the real-world object, an alert notification is automatically sent to the authorities by the validation engine 122, and such notification may be initiated without confirmation from the original owner (ie, the first user).

[0048] In addition to keeping track of the use of real-world objects, NFTs are also used to maintain the value of real-world objects. The verification of real-world objects and ownership of real-world objects by the confirmation engine 122 enhances the value of the corresponding digital twin and also gives the digital twin the same status or reputation as the corresponding real-world object. When each instance of a real-world object is identified and verified, the value associated with the real-world object is assigned to the corresponding digital twin. Various implementations described herein use digital twins of real-world objects (including mimicking the way users use real-world objects in the physical world to the corresponding use of virtual objects in the virtual world) to provide users with the flexibility to personalize their virtual worlds, thereby providing an attractive digital experience.

[0049] Figure 5 A flow operation of a method for providing access to a digital twin of a real-world object in some implementations is shown. The method begins at operation 510, where a request for verifying a real-world object is received at a verification engine 122. The request may be initiated from a client device by a user having possession of the real-world object. In response to receiving the request, a plurality of sensors and image capture devices disposed in the client device and in the user's location are activated. The activated sensors, scanners, devices scan the real-world object and capture data including images from different angles. The data captured by the image capture devices and sensors are analyzed to identify physical properties of the real-world object. Distinct characteristics of the real-world object are defined using the physical properties of the real-world object.

[0050] The real-world object is verified using various physical properties defined based on data captured by multiple sensors, scanners, and devices, as shown in operation 520. The verification includes verifying the real-world object and verifying the ownership of the real-world object. In some implementations, a machine learning algorithm is used to verify the real-world object. The machine learning algorithm uses an object recognition algorithm to identify the real-world object from the physical properties defined based on the data captured by the sensors, scanners, and devices. In one implementation, the ownership of the real-world object is verified using the details included in the NFT of the real-world object maintained in the blockchain. After successful confirmation, if a digital twin exists for the real-world object, the digital twin of the real-world object is unlocked for use in the metaverse system, as shown in operation 530. If there is no digital twin, a digital twin is created for the real-world object and the digital twin is associated with the user. The unlocking provides the user with access to a digital twin for use by or on a digital character representing the user in the virtual world of the metaverse system. Users use the digital twin of the real-world object to personalize their digital characters to mimic their real-world appearance in the virtual world. Other advantages of the present disclosure can be envisioned after reading the various details provided with reference to the accompanying drawings.

[0051] Figure 6 Components of an example device 600 that can be used to perform various aspects of various embodiments of the present disclosure are shown. The block diagram shows a device 600 that can be incorporated into or can be a personal computer, a video game console, a personal digital assistant, a head-mounted display (HMD), a wearable computing device, a laptop or desktop computing device, a server, or any other digital device suitable for practicing the embodiments of the present disclosure. For example, in various implementations discussed herein, the device 600 represents a first device and a second device. The device 600 includes a central processing unit (CPU) 602 for running software applications and optionally an operating system. Further, the CPU 602 may include a machine learning algorithm 140 embedded with an object recognition algorithm (not shown) to use the physical attributes captured for the real-world object to identify the real-world object. The CPU 602 may be composed of one or more homogeneous or heterogeneous processing cores. For example, the CPU 602 is one or more general-purpose microprocessors with one or more processing cores. Other embodiments may be implemented using one or more CPUs with a microprocessor architecture that is particularly well suited for highly parallel and computationally intensive applications, such as processing operations to interpret queries, identify context-relevant resources, and immediately implement and render context-relevant resources in a video game. Device 600 may be local to the player playing the game segment (e.g., a game console), or remote from the player (e.g., a backend server processor), or one of many servers in a gaming cloud system that uses virtualization to remotely stream game play to client devices.

[0052] Memory 604 stores applications and data for use by CPU 602. Storage 606 provides non-volatile storage and other computer-readable media for applications and data, and may include fixed disk drives, removable disk drives, flash memory devices, and CD-ROM, DVD-ROM, Blu-ray, HD-DVD or other optical storage devices, as well as signal transmission and storage media. User input device 608 transmits user input from one or more users to device 600, examples of which may include keyboards, mice, joysticks, touch pads, touch screens, static or video recorders / cameras, tracking devices for identifying gestures, and / or microphones. Network interface 614 allows device 600 to communicate with other computer systems via an electronic communication network, and may include wired or wireless communications through local area networks and wide area networks (such as the Internet). Audio processor 612 is suitable for generating analog or digital audio output from instructions and / or data provided by CPU 602, memory 604 and / or storage 606. The components of device 600 , including CPU 602 , memory 604 , data storage 606 , user input device 608 , network interface 614 , and audio processor 612 , are connected via one or more data buses 622 .

[0053] The graphics subsystem 620 is also connected to the data bus 622 and the components of the device 600. The graphics subsystem 620 includes a graphics processing unit (GPU) 616 and a graphics memory 618. The graphics memory 618 includes a display memory (e.g., a frame buffer) for storing pixel data for each pixel of an output image. The graphics memory 618 may be integrated into the same device as the GPU 616, connected to the GPU 616 as a separate device, and / or implemented in the memory 604. The pixel data may be provided directly from the CPU 602 to the graphics memory 618. Alternatively, the CPU 602 provides the GPU 616 with data and / or instructions defining the desired output image, and the GPU 616 generates pixel data for one or more output images based on the data and / or instructions. The data and / or instructions defining the desired output image may be stored in the memory 604 and / or the graphics memory 618. In one embodiment, GPU 616 includes 3D rendering capabilities that generate pixel data for output images from instructions and data defining the geometry, lighting, shading, textures, motion, and / or camera parameters of a scene. GPU 616 may further include one or more programmable execution units capable of executing shader programs.

[0054] Graphics subsystem 620 periodically outputs pixel data of an image from graphics memory 618 for display on display device 610. Display device 610 may be any device capable of displaying visual information in response to a signal from device 600, including CRT, LCD, plasma, and OLED displays. In addition to display device 610, the pixel data may also be projected onto a projection surface. For example, device 600 may provide an analog or digital signal to display device 610.

[0055] It should be noted that access services delivered over a wide geographic area (such as providing access to games of the current embodiment) often use cloud computing. Cloud computing is a computing method in which dynamically scalable and usually virtualized resources are provided as services over the Internet. Users do not need to be experts in the technical infrastructure in the "cloud" that supports them. Cloud computing can be divided into different services, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Cloud computing services typically provide common applications (such as video games) accessed from a web browser online, while the software and data are stored on servers in the cloud. Based on the way the Internet is depicted in computer network diagrams, the term cloud is used as a metaphor for the Internet and is an abstraction of the complex infrastructure that it hides.

[0056] In some embodiments, a game server can be used to perform the operation of a duration information platform for video game players. Most video games played over the Internet are operated by connection to a game server. Typically, games use a dedicated server application that collects data from players and distributes it to other players. In other embodiments, video games can be performed by a distributed game engine. In these embodiments, a distributed game engine can be performed on multiple processing entities (PEs) so that each PE performs a functional fragment of a given game engine on which a video game runs. The game engine simply regards each processing entity as a computing node. The game engine typically performs a series of operations with diverse functions to perform video game applications and additional services for user experience. For example, a game engine implements game logic, performs game calculations, physical effects, geometric transformations, rendering, lighting, shading, audio, and additional in-game or game-related services. Additional services may include, for example, messaging, social utilities, audio communications, game play / playback functions, help functions, etc. Although a game engine can sometimes be performed on an operating system virtualized by a hypervisor of a specific server, in other embodiments, the game engine itself is distributed between multiple processing entities, each of which can reside on different server units in a data center.

[0057] According to this embodiment, the corresponding processing entity for performing the operation can be a server unit, a virtual machine, or a container, depending on the needs of each game engine fragment. For example, if a game engine fragment is responsible for camera transformations, a virtual machine associated with a graphics processing unit (GPU) can be provided to this particular game engine fragment, because it will perform a large number of relatively simple mathematical operations (e.g., matrix transformations). Other game engine fragments that require fewer but more complex operations can be provided with processing entities associated with one or more higher-powered central processing units (CPUs).

[0058] By distributing the game engine, the game engine is provided with elastic computing properties that are not constrained by the capabilities of the physical server units. Instead, more or fewer computing nodes are provided to the game engine as needed to meet the needs of the video game. From the perspective of the video game and the video game player, a game engine distributed across multiple computing nodes is no different than a non-distributed game engine executed on a single processing entity, because the game engine manager or supervisor distributes the workload and seamlessly integrates the results to provide the video game output components to the end user.

[0059] The user accesses the remote service using a client device, which includes at least a CPU, a display, and an I / O. The client device can be a PC, a mobile phone, a notebook computer, a PDA, etc. In one embodiment, the network executed on the game server recognizes the type of device used by the client and adjusts the communication method used. In other cases, the client device uses a standard communication method such as HTML to access the application on the game server through the Internet.

[0060] It should be understood that a given video game or gaming application may be developed for a specific platform and a specific associated controller device. However, when such a game is made available via a gaming cloud system as presented herein, a user may access the video game with different controller devices. For example, a game may have been developed for a gaming console and its associated controller, while a user may access a cloud-based version of the game from a personal computer using a keyboard and mouse. In such scenarios, an input parameter configuration may define a mapping of inputs generated from a user's available controller devices (i.e., a keyboard and mouse in this case) to acceptable inputs for execution of the video game.

[0061] In another example, a user may access the cloud gaming system via a tablet computing device, a touch screen smart phone, or other touch screen driven device. In this case, the client device and the controller device are integrally formed in the same device, wherein input is provided by means of a detected touch screen input / gesture. For such devices, the input parameter configuration may define a specific touch screen input corresponding to the game input of the video game. For example, during the running of a video game, a button, a directional pad, or other type of input element may be displayed or overlaid to indicate a position on the touch screen where the user can touch to generate a game input. Gestures (such as swipes or specific touch movements in a particular direction) may also be detected as game inputs. In one embodiment, guidance may be provided to the user, indicating, for example, how to provide input for gameplay via a touch screen before starting to play a video game, so as to adapt the user to the operation of the controls on the touch screen.

[0062] In some embodiments, the client device is used as a connection point for the controller device. That is, the controller device communicates with the client device via a wireless or wired connection to transmit input from the controller device to the client device. The client device can process these inputs in turn, and then transmit the input data to the cloud game server via a network (e.g., accessed via a local networking device such as a router). However, in other embodiments, the controller itself can be a networked device with the ability to directly transmit input to the cloud game server via a network without first transmitting such input through a client device. For example, the controller can be connected to a local networking device (such as the above-mentioned router) to send data to and receive data from the cloud game server. Therefore, although the client device may still be required to receive video output from a cloud-based video game and render it on a local display, input latency can be reduced by allowing the controller to send input directly to the game cloud server over the network to bypass the client device.

[0063] In one embodiment, the networked controller and client device may be configured to send certain types of input directly from the controller to the cloud gaming server, and to send other types of input via the client device. For example, inputs whose detection does not rely on any additional hardware or processing other than the controller itself can be sent directly from the controller to the cloud gaming server via the network, thereby bypassing the client device. Such inputs may include button inputs, joystick inputs, embedded motion detection inputs (e.g., accelerometers, magnetometers, gyroscopes), etc. However, inputs that utilize additional hardware or require processing by the client device can be sent by the client device to the cloud gaming server. These may include video or audio captured from the gaming environment, which may be processed by the client device before being sent to the cloud gaming server. In addition, inputs from the motion detection hardware of the controller may be processed by the client device in conjunction with the captured video to detect the position and motion of the controller, which is then transmitted to the cloud gaming server by the client device. It should be understood that the controller device according to various embodiments may also receive data (e.g., feedback data) from the client device or directly from the cloud gaming server.

[0064] In one embodiment, various technical examples can be implemented using a virtual environment via a head-mounted display (HMD). The HMD may also be referred to as a virtual reality (VR) head-mounted device. As used herein, the term "virtual reality" (VR) generally refers to the interaction of a user with a virtual space / environment, which involves viewing the virtual space in a real-time response to the movement of the HMD (controlled by the user) through the HMD (or VR head-mounted device) to provide the user with a feeling of being in a virtual space or metaverse. For example, a user can see a three-dimensional (3D) view of the virtual space when facing a given direction, and when the user turns to one side and thus turns the HMD in the same manner, the view of the side in the virtual space is rendered on the HMD. The HMD can be worn in a manner similar to glasses, goggles, or a helmet, and is configured to display video games or other metaverse content to the user. The HMD can provide a very immersive experience to the user by means of its display mechanism being close to the user's eyes. Therefore, the HMD can provide a display area that occupies most of the user's or even the entire field of view to each eye of the user, and can also provide viewing with three-dimensional depth and perspective.

[0065] In one embodiment, the HMD may include a gaze tracking camera configured to capture images of the user's eyes as the user interacts with the VR scene. The gaze information captured by the gaze tracking camera may include information related to the user's gaze direction and specific virtual objects and content items in the VR scene that the user is paying attention to or interested in interacting with. Thus, based on the user's gaze direction, the system can detect specific virtual objects and content items that may be of potential interest to the user (where the user is interested in interacting with and engaging with them), such as game characters, game objects, game props, etc.

[0066] In some embodiments, the HMD may include an outward-facing camera configured to capture images of the user's real-world space, such as the user's body movements and any real-world objects that may be located in the real-world space. In some embodiments, the images captured by the outward-facing camera may be analyzed to determine the position / orientation of the real-world object relative to the HMD. Using the HMD, the known position / orientation of the real-world object, and the inertial sensor data from the inertial motion unit (IMU) sensor, the user's posture and movement may be continuously monitored and tracked during the user's interaction with the VR scene. For example, when interacting with a scene in a game, a user may make various gestures, such as pointing and walking toward a specific content item in the scene. In one embodiment, the system may track and process gestures to generate predictions of interactions with specific content items in the game scene. In some embodiments, machine learning may be used to facilitate or assist the predictions.

[0067] During the use of the HMD, various single-hand and two-hand controllers can be used. In some implementations, the controller itself can be tracked by tracking the lights included in the controller or tracking the shapes, sensors and inertial data associated with the controller. Using these different types of controllers or even just using gestures made and captured by one or more cameras, the virtual reality environment or metaverse rendered on the HMD can be docked, controlled, manipulated, interacted with and participated in the virtual reality environment or metaverse. In some cases, the HMD can be wirelessly connected to the cloud computing and game system through a network. In one embodiment, the cloud computing and game system maintain and execute the video game played by the user. In some embodiments, the cloud computing and game system are configured to receive input from the HMD and interface objects through the network. The cloud computing and game system are configured to process input to affect the game state of the video game being executed. Output (such as video data, audio data and tactile feedback data) from the video game being executed is transmitted to the HMD and the interface object. In other implementations, the HMD can communicate wirelessly with the cloud computing and game system through alternative mechanisms or channels such as cellular networks.

[0068] In addition, although the implementations in the present disclosure may be described with reference to a head-mounted display, it should be understood that in other implementations, a non-head-mounted display may be substituted, including but not limited to a portable device screen (e.g., a tablet, a smart phone, a laptop computer, etc.) or any other type of display that can be configured to render video and / or provide a display of an interactive scene or virtual environment according to the present implementation. It should be understood that the various embodiments defined herein may be combined or assembled into a specific implementation using the various features disclosed herein. Therefore, the examples provided are only some possible examples and are not limited to various implementations that can define more implementations by combining various elements. In some examples, some implementations may include fewer elements without departing from the spirit of the disclosed or equivalent implementations.

[0069] As noted, embodiments of the present disclosure for communicating between computing devices may be practiced using a variety of computer device configurations, including handheld devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, head-mounted displays, wearable computing devices, etc. Embodiments of the present disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked over wired or wireless networks.

[0070] In some embodiments, wireless technology can be used to facilitate communication. Such technology can include, for example, 5G wireless communication technology. 5G is the fifth generation of cellular network technology. 5G network is a digital cellular network in which the service area covered by the provider is divided into small geographical areas called cells. Analog signals representing sound and images are digitized in the phone, converted by analog-to-digital converters and transmitted as bit streams. Through the channels allocated by the transceiver from the frequency pool reused in other cells, all 5G wireless devices in the cell communicate with the local antenna array and low-power automatic transceiver (transmitter and receiver) in the cell through radio waves. The local antenna is connected to the telephone network and the Internet through high-bandwidth optical fiber or wireless backhaul connection. As in other cell networks, mobile devices that cross from one cell to another automatically go to the new cell. It should be understood that the 5G network is only an exemplary type of communication network, and the embodiments of the present disclosure can utilize earlier generations of wireless or wired communications, as well as newer generations of wired or wireless technologies after 5G.

[0071] In view of the above embodiments, it should be understood that the present disclosure may employ various computer-implemented operations involving data stored in a computer system. These operations are those operations that require physical manipulation of physical quantities. Any operation described herein that forms a part of the present disclosure is a useful machine operation. The present disclosure also relates to a device or apparatus for performing these operations. The apparatus may be specially constructed for the desired purpose, or the apparatus may be a general-purpose computer selectively activated or configured by a computer program stored in a computer. Specifically, various general-purpose machines may be used together with a computer program written according to the teachings of this article, or it may be more convenient to construct a more specialized apparatus to perform the desired operation.

[0072] Although the method operations are described in a particular order, it should be understood that other housekeeping operations may be performed between the operations, or the operations may be adjusted so that they occur at slightly different times, or the operations may be distributed in a system that allows the processing operations to occur at various time intervals related to the processing, so long as the processing of telemetry and game state data used to generate the modified game state is performed in the desired manner.

[0073] One or more embodiments may also be manufactured as computer readable code on a computer readable medium. A computer readable medium is any data storage device that can store data that can then be read by a computer system. Examples of computer readable media include hard drives, network attached storage (NAS), read-only memory, random access memory, CD-ROM, CD-R, CD-RW, magnetic tape, and other optical and non-optical data storage devices. The computer readable medium may include a computer readable tangible medium distributed on a network coupled computer system so that the computer readable code is stored and executed in a distributed manner.

[0074] Although the foregoing embodiments have been described in some detail for the purpose of clarity of understanding, it is apparent that certain changes and modifications may be practiced within the scope of the appended claims. Therefore, the present embodiments should be considered as illustrative rather than restrictive, and the embodiments are not limited to the details given herein, but may be modified within the scope and equivalent range of the appended claims.

[0075] It should be understood that the various embodiments defined herein may be combined or assembled into specific implementations using the various features disclosed herein. Therefore, the examples provided are only some possible examples and are not limited to various implementations that may define more implementations by combining various elements. In some examples, some implementations may include fewer elements without departing from the spirit of the disclosed or equivalent implementations.

Claims

1. A method for providing access to a digital twin of a real-world object in a metaverse system, comprising: receiving a request from a user to authenticate the real-world object, the real-world object having a plurality of physical attributes defining a distinct fingerprint; authenticating the real-world object using the distinct fingerprint, the authentication comprising confirming ownership of the real-world object; as well as upon successful authentication of the real-world object owned by the user, unlocking a digital twin of the real-world object for use by the user in the metaverse system, the digital twin being represented by a virtual object having virtual properties that replicate the plurality of physical properties of the real-world object, the unlocking comprising associating the virtual object with the user for use in the metaverse system, The operations of the method are performed by a processor of a computing system.

2. The method of claim 1 , wherein the virtual properties of the virtual object are automatically updated to replicate changes detected in one or more physical properties of the real-world object, and Wherein the virtual attributes of the virtual object are used to construct a three-dimensional replica of a physical version of the real-world object. 3 . The method of claim 2 , wherein the automatic updating is performed after detecting the change to the one or more physical properties of the real-world object.

4. The method of claim 1 , wherein the plurality of physical attributes correspond to visual data that distinctively identifies the real-world object, and wherein a three-dimensional replica of a physical version of the real-world object is constructed using the virtual attributes of the virtual object that replicate the plurality of physical attributes of the real-world object.

5. The method of claim 1, wherein the multiple physical properties of the real-world object are identified using any one or a combination of the following: visual data captured in an image of the real-world object using one or more image capture devices, or audio data captured using an audio capture device, or physical data identified using one or more sensors.

6. The method of claim 1 , wherein verifying possession of the real-world object comprises: analyzing the plurality of physical properties to determine distinct characteristics that uniquely identify the real-world object; comparing the plurality of physical properties of the real-world object with corresponding virtual properties of the virtual object representing the digital twin to determine a match score; as well as When the match score is within a predefined threshold, declaring the virtual object a match for the real-world object possessed by the user and unlocking the digital twin.

7. The method of claim 6, wherein determining the matching score comprises: calculating a total physical score for the real-world object by integrating the real scores defined for each of the plurality of physical properties of the real-world object; calculating a virtual total score of the virtual object by integrating the virtual scores defined for each virtual attribute of the virtual object; as well as The match score is calculated as the difference between the physical total score and the virtual total score.

8. The method of claim 6, wherein the matching score is calculated to account for an error margin defined for each of the one or more of the plurality of physical properties of the real-world object and the virtual object representing the digital twin; and Wherein the error margin is affected by the one or more of the distinct characteristics of the real-world object and is identified using a machine learning algorithm.

9. A method as claimed in claim 8, wherein the corresponding virtual attributes of the virtual object representing the digital twin are normalized using physical attributes among the multiple physical attributes that affect the error margin so that the digital twin accurately resembles the real-world object, and the normalization of the corresponding virtual attributes of the virtual object is performed using the machine learning algorithm before comparing the multiple physical properties of the real-world object with the virtual attributes of the virtual object.

10. The method of claim 6, wherein analyzing the plurality of physical properties further comprises filtering out abnormal data included in one or more of the plurality of physical properties of the real-world object before comparing the plurality of physical properties of the real-world object with the virtual property of the virtual object to determine the match score, and Wherein the anomalous data is identified using a machine learning algorithm as corresponding to visible imperfections observed in the one or more of the plurality of physical properties of the real-world object.

11. The method of claim 6, wherein analyzing the plurality of physical properties further comprises modifying the digital twin by updating one or more of the virtual properties of the virtual object using changes detected in corresponding one or more of the plurality of physical properties of the real-world object before comparing the plurality of physical properties of the real-world object with the virtual properties of the virtual object to determine the match score, the modification being performed to make the digital twin similar to the real-world object.

12. The method of claim 6, wherein the distinctive characteristics include any one or combination of the following: type, category, material used, fragility, monetary value, or fungibility of the real-world object.

13. A method as claimed in claim 6, wherein the distinctive characteristics of the real-world object provide a unique fingerprint of the real-world object, the unique fingerprint is used to generate a non-fungible token (NFT) of the real-world object and store the NFT in a blockchain, the NFT is used to maintain the value of the real-world object and track the ownership and use of the real-world object, and the NFT in the blockchain is updated to reflect any changes in the ownership or use of the real-world object.

14. A method as claimed in claim 13, wherein when the real-world object is detected as stolen, the NFT is updated and a flag is generated to indicate that the virtual object representing the real-world object is stolen, the flag is used to alert the system to prevent the use of the virtual object in the metaverse system, and the update will be to lock the NFT to prevent the use of the real-world object.

15. The method of claim 1, wherein unlocking the digital twin comprises: providing the user with an option to transfer ownership of the real-world object associated with the digital twin to a second user, the transfer of ownership providing the second user with access to the virtual object for use in the metaverse system, and Notification is provided to the user and the second user that ownership of the real-world object is transferred to the second user.

16. The method of claim 1, wherein unlocking the digital twin comprises providing access to the virtual object for use by a digital character representing the user in the metaverse system.

17. A method for using a real-world object in a metaverse system, comprising: identifying the real-world object in possession of a user, the real-world object being identified using a plurality of physical attributes defining a distinct fingerprint; verifying said possession of said real-world object by said user; upon successfully verifying the possession of the real-world object by the user, creating a digital twin of the real-world object for use in the metaverse system, the digital twin represented by a virtual object having virtual properties that replicate the plurality of physical properties of the real-world object; as well as associating the virtual object with the user to enable use of the virtual object in the metaverse system, The operations of the method are performed by a processor of a computing system.

18. The method of claim 17, wherein the use of the virtual object in the metaverse system comprises providing access to the virtual object to a digital character representing the user in the metaverse system.

19. A method as claimed in claim 17, wherein the multiple physical properties define a unique fingerprint of the real-world object, the unique fingerprint is used to generate a non-fungible token (NFT) of the real-world object and store the NFT in a blockchain, the NFT maintains the value of the real-world object and is used to track the ownership of the real-world object and the use of the real-world object within the metaverse system.

Images