LEVERAGING SPATIAL COMPUTING LiDAR FEEDBACK TO DYNAMICALLY GENERATE IDENTITY PROFILES

A method, comprising:

• receiving, by a computing platform, the computing platform having at least one processor and memory, and via light detection and ranging (LiDAR) functionality of a spatial computing device, first image data of a first user environment at a first time;
• extracting, by the at least one processor and from the first image data, metadata;
• generating, by the at least one processor and based on the extracted metadata, a first spatial computing map of the first user environment at the first time;
• scoring, by the at least one processor, the first spatial computing map of the first user environment at the first time, wherein the score of the first spatial computing map indicates a likelihood that a user of the spatial computing device is a valid user;
• storing the first spatial computing map and score;
• receiving, by the at least one processor and via the spatial computing device, a request to process a transaction;
• responsive to receiving the request to process the transaction, transmitting, by the at least one processor and to the spatial computing device, an instruction causing the spatial computing device to capture, via the LiDAR functionality, second image data of a current environment of the user of the spatial computing device at a current time;
• extracting, by the at least one processor and from the second image data, metadata;
• generating, by the at least one processor and based on the extracted metadata from the second image data, a second spatial computing map of the current environment of the user at the current time;
• analyzing, by the at least one processor and by executing a machine learning model, the second spatial computing map to detect any anomalies between the second image data and the first image data;
• scoring, by the at least one processor and based on the analysis, the second spatial computing map, wherein the score of the second spatial computing map indicates a likelihood that the user of the spatial computing device is the valid user;
• comparing, by the at least one processor, the score of the second spatial computing map to a threshold;
• based on determining, based on the comparing, that the score of the second spatial computing map meets or exceeds a threshold, validating, by the at least one processor, the user of the spatial computing device and authorize processing of requested transaction;
• and
• based on determining, based on the comparing that the score of the second spatial computing map is below the threshold, initiating, by the at least one processor, one or more validation failure actions.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to:

• receive, via light detection and ranging (LiDAR) functionality of a spatial computing device, first image data of a first user environment at a first time;
• extract, from the first image data, metadata;
• generate, based on the extracted metadata, a first spatial computing map of the first user environment at the first time;
• score the first spatial computing map of the first user environment at the first time, wherein the score of the first spatial computing map indicates a likelihood that a user of the spatial computing device is a valid user;
• store the first spatial computing map and score;
• receive, via the spatial computing device, a request to process a transaction;
• responsive to receiving the request to process the transaction, transmit, to the spatial computing device, an instruction causing the spatial computing device to capture, via the LiDAR functionality, second image data of a current environment of the user of the spatial computing device at a current time;
• extract, from the second image data, metadata;
• generate, based on the extracted metadata from the second image data, a second spatial computing map of the current environment of the user at the current time;
• analyze, by executing a machine learning model, the second spatial computing map to detect any anomalies between the second image data and the first image data;
• score, based on the analysis, the second spatial computing map, wherein the score of the second spatial computing map indicates a likelihood that the user of the spatial computing device is the valid user;
• compare the score of the second spatial computing map to a threshold;
• responsive to determining, based on the comparing, that the score of the second spatial computing map meets or exceeds a threshold, validate the user of the spatial computing device and authorize processing of requested transaction;
• and
• responsive to determining, based on the comparing that the score of the second spatial computing map is below the threshold, initiate one or more validation failure actions.