Validating a real-world object's digital twin

A method, comprising:

• receiving, from a device of a user, a request to validate a real-world object that has a plurality of physical attributes defining a distinct fingerprint;
• capturing, using a plurality of sensors including at least one image capturing device, data representing the plurality of physical attributes of the real-world object;
• validating the real-world object using the distinct fingerprint, wherein validating comprises verifying ownership of the real-world object by at least:
• analyzing, using the plurality of sensors collecting data associated with the real-world object, the plurality of physical attributes based on the collected data to determine the distinct fingerprint;
• analyzing the data captured by the sensors in a multi-step process, wherein the analyzing comprises:
• identifying, by a machine learning algorithm, one or more visible imperfections, markings, wear-and-tear, or user customizations present in the physical attributes of the real-world object captured by the sensors;
• determining, by the machine learning algorithm, a margin of error for validating the real-world object, wherein the margin of error is influenced by one or more distinct characteristics of the real-world object selected from the group consisting of type, class, material, fragility, monetary value, or fungibility;
• filtering out, by the machine learning algorithm, anomalous data in the captured data corresponding to stains, tears, cracks, holes, or other imperfections;
• normalizing, by the machine learning algorithm, the physical attributes of the real-world object prior to comparison;
• and
• identifying, by the machine learning algorithm, unique characteristics of each instance of the real-world object;
• in response to analyzing the data, comparing the physical attributes with virtual attributes of a virtual object representing a digital twin to determine a match score, wherein the match score is computed to account for a margin of error defined for each physical attribute of one or more of the plurality of physical attributes of the real-world object and the virtual object representing the digital twin,
• identifying, by the machine learning algorithm, one or more imperfections for determining the margin of error;
• performing, by the machine learning algorithm, data normalization and/or filtering;
• identifying, by the machine learning algorithm, unique characteristics of each instance of the real-world object
• declaring, upon the match score being within a pre-defined threshold, the virtual object to be a match of the real-world object in possession of the user;
• and
• unlocking the digital twin of the real-world object for use by the user in a metaverse system, upon successfully validating the real-world object, wherein the digital twin is represented by the virtual object with virtual attributes replicating the plurality of physical attributes of the real-world object, wherein the unlocking comprises associating the virtual object with the user for use in the metaverse system;
• and
• controlling, based on the unlocking, a presentation of the virtual object on a display, wherein operations of the method are performed by a processor of a computing system, wherein analyzing the data and validation are performed by a dedicated object recognition module integrated with a secure processor, and validation results are stored in a protected memory segment to prevent unauthorized access or tampering.