Patent ID: 11893641
Assignee: AGBLOX, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 22:
23. A method, comprising:
classifying input data comprised of structured data sources and unstructured data sources to develop a data set for a multi-layer machine learning-based model configured to analyze a state of a selected equity within a specified future time, by
identifying one or more sentiment predictors for the selected equity from the unstructured data sources to develop a taxonomy comprising equity-specific keywords and keyword pairings for a selected equity, and modifying the modifying the taxonomy with temporal parameters relative to the selected equity for the specific future time representing discrete-time data points constructed from the structured data sources, to create a set of classified content representing a temporally-relative sentiment for the selected equity, and
deriving knowledge-based rules representing specific knowledge relative to the selected equity from economic and equity-specific indicators in the input data;

modeling the set of classified content and the knowledge-based rules within a neural network modeling layer comprised of one or more neural networks and at least one deep learning meta network, the neural network modeling layer configured to
represent the set of classified content and the specific knowledge as threshold activation functions for initiating a plurality of nodes and connections comprising a topology of the one or more neural networks to map the taxonomy and the knowledge-based rules into the one or more neural networks, and
tune the one or more neural networks in the at least one deep learning meta network, by identifying additional, temporally-dynamic predictors representing patterns in the taxonomy and the knowledge-based rules that quantify relationships between data points within the set of classified content and the specific knowledge, to modify the threshold activation functions for the plurality of nodes and connections of the one or more neural networks, and identify and apply adjusted topologies for the one or more neural networks based on the additional, temporally-dynamic predictors; and

performing the multi-layer machine learning-based model to forecast the state of the selected equity at the specified future time.