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

Claim 3:
4. A method for dynamic detection of security features based on self-supervised natural language extraction from unstructured data sets, comprising:
retrieving a first unstructured data array indicative of a plurality of first discrete user events, wherein retrieving the first unstructured data array comprises retrieving the first unstructured data array from a database;
converting the unstructured data array by serializing the unstructured data array based on at least one indicator to form one or more first data arrays, the one or more first data arrays each indicative of a discrete user event;
for each data entry of the one or more first data arrays, determining a tokenization library using a neural network implemented by one or more processors;
generating a value for each data entry of the one or more first data arrays based on the determined tokenization library, wherein the value for each data entry is also determined at least in part by accessing the database to find the respective first discrete user event for each data entry to determine whether the value represents a desired event with a positive value or a hazardous event with a negative value;
determining one or more second data arrays corresponding to a subset of the one or more first data arrays based on selecting a plurality of highest value data entries from the one or more first data arrays using the neural network, wherein selecting the plurality of highest value data entries includes dynamically generating narratives associated with each respective first discrete user event of the one or more first data arrays and storing the narratives as the one or more second data arrays in the database;
determining a security weight for each discrete user event based on the one or more second data arrays and the at least one indicator associated with a respective discrete user event, the at least one indicator comprising a timestamp;
determining an associated sentiment score for each security weight;
determining a security score based on a weighted average of the sentiment score and the security weight;
when the security score of any discrete user event exceeds a predetermined threshold, executing one or more security actions, wherein at least one of the one or more security actions comprises generating an indication that the security score of a user account associated with the discrete user events exceeds the predetermined threshold;
computing one or more third data arrays from the one or more second data arrays using the neural network by providing the one or more second data arrays as input to the neural network;
iteratively calculating an error measurement between the one or more third data arrays and the one or more first data arrays; and
iteratively modifying one or more weights of one or more layers of the neural network based on the calculated error measurement.