Patent ID: 11893632
Assignee: CAPITAL ONE SERVICES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. A system for dynamic detection of security features based on self-supervised natural language extraction from unstructured data sets, comprising:
one or more processors;
a memory in communication with the one or more processors and storing instructions that, when executed by the processor, are configured to cause the system to:
retrieve a first unstructured data array comprising a full-text corpus indicative of one or more financial assets narratives and one or more financial liabilities narratives associated with a user, wherein retrieving the first unstructured data array comprises retrieving the first unstructured data array from a database;
serialize the first unstructured data array based on a pickling algorithm to form one or more first data arrays each indicative of a portion of the full-text corpus, wherein each portion is indicative of a financial assets or a financial liability narrative and includes an associated timestamp, wherein the portions of the narrative are classified as financial assets or financial liabilities at least in part by accessing a user information database which includes semantic data indicative of whether a financial event in the full-text corpus is a desired positive event or a hazardous negative event;
apply a self-tokenization aspect of a natural language processing algorithm to determine key words and nearby word correlations for each word contained in the full-text corpus using a neural network implemented by the one or more processors, wherein determining key words and nearby word correlations includes determining which words of the full-text corpus convey an outcome of the one or more financial assets narratives or one or more financial liabilities narratives, wherein the one or more financial assets narratives and the one or more financial liabilities narratives are stored in the database;
determine, using a self-supervised machine learning aspect of the natural language processing algorithm, a condensed summary of each portion of the full-text corpus corresponding to a subset of the one or more first data arrays;
determine a relevancy score for each condensed summary based at least in part on the associated timestamp;
determine, using a sentiment analysis aspect of the natural language processing algorithm, a sentiment score for each condensed summary;
determine an overall risk score for each condensed summary based on a weighted average of the relevancy score and the sentiment score;
when at least one overall risk score exceeds a predetermined threshold, execute one or more security actions, wherein the one or more security actions comprise at least flagging a user account for external review;
generate a reconstructed full-text corpus by providing the condensed summaries of each portion of the full-text corpus as input to the neural network;
iteratively calculate an error measurement between the reconstructed full-text corpus and the full-text corpus; and
iteratively modify one or more weights of one or more layers of the neural network based on the calculated error measurement.