US 12,762,406 B1Granted

Generating predictive data segments using machine learning models and natural language processing techniques

AssigneeChalice Custom Algorithms, Inc.

InventorsRobert Mooney, Tylynn Pettrey, Adam Heimlich, Ronald Pacheco

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Abstract

Systems, methods, and devices generate predictive data segments using machine learning and natural language processing. The method receives training data with digital identifiers and binary classification labels indicating target outcomes. It accesses external data sources containing textual attribute descriptions, which are preprocessed by concatenating multiple descriptions per identifier for large language model input. The model outputs high-dimensional vector embeddings encoding semantic relationships. A neural network trains using these embeddings and classification labels to predict probability scores. For new data, the method preprocesses textual descriptions, generates embeddings via the language model, and applies the neural network to produce probability scores representing conversion propensity values. It selects digital identifiers with scores exceeding a configurable threshold and transmits them to a digital platform for prioritized resource allocation.

Claims20

Dependent claims

A computer-implemented method for generating predictive data segments using machine learning models and natural language processing techniques, the computer-implemented method comprising:

receiving training data comprising a plurality of training digital identifiers and corresponding binary classification labels, wherein each binary classification label indicates whether a corresponding training digital identifier is associated with a target outcome;
accessing one or more external data sources comprising structured and unstructured data, wherein the structured and unstructured data comprises training textual attribute descriptions associated with the plurality of training digital identifiers;
preprocessing the training textual attribute descriptions by (i) concatenating multiple training textual attribute descriptions per training digital identifier and (ii) formatting the concatenated training textual attribute descriptions into a unified text string for input to a large language model;
inputting the unified text string into the large language model to cause the large language model to output a plurality of training high-dimensional vector embeddings encoding semantic relationships and contextual information of the training textual attribute descriptions in a continuous vector space;
training a neural network using the plurality of training high-dimensional vector embeddings and the corresponding binary classification labels, wherein the neural network learns to predict probability scores for high-dimensional vector embeddings;
receiving new data comprising a plurality of new digital identifiers;
preprocessing new textual attribute descriptions associated with the plurality of new digital identifiers derived from the one or more external data sources by concatenating multiple new textual attribute descriptions per new digital identifier and formatting the concatenated new textual attribute descriptions into a new unified text string for input to the large language model;
inputting the new unified text string into the large language model to cause the large language model to output a plurality of new high-dimensional vector embeddings;
generating a plurality of probability scores for the plurality of new digital identifiers by applying the neural network to the plurality of new high-dimensional vector embeddings, wherein each probability score represents a conversion propensity value for a corresponding new digital identifier;
selecting a subset of the plurality of new digital identifiers having probability scores exceeding a configurable threshold;
and
transmitting the subset of the plurality of new digital identifiers to a digital platform for prioritized resource allocation.

9 more elements

A computer-implemented method comprising:

receiving training data comprising a plurality of training digital identifiers and corresponding binary classification labels, wherein each binary classification label indicates whether a corresponding training digital identifier is associated with a target outcome;
accessing one or more external data sources comprising training textual attribute descriptions associated with the plurality of training digital identifiers;
preprocessing the training textual attribute descriptions by concatenating multiple training textual attribute descriptions per training digital identifier and formatting the concatenated training textual attribute descriptions into a unified text string for input to a large language model;
inputting the unified text string into the large language model to cause the large language model to output a plurality of training high-dimensional vector embeddings encoding semantic relationships and contextual information of the training textual attribute descriptions;
and
training a neural network using the plurality of training high-dimensional vector embeddings and the corresponding binary classification labels, wherein the neural network learns to predict probability scores for high-dimensional vector embeddings.

3 more elements

A system comprising:

at least one hardware processor;
and
at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
receive data comprising a plurality of digital identifiers;
preprocess textual attribute descriptions associated with the plurality of digital identifiers derived from one or more external data sources by concatenating multiple textual attribute descriptions per digital identifier and formatting the concatenated textual attribute descriptions into a unified alphanumeric set for input to an artificial intelligence (AI) model;
input the unified alphanumeric set into the AI model to cause the AI model to output a plurality of high-dimensional vector embeddings encoding semantic relationships and new contextual information of the textual attribute descriptions;
generate a plurality of probability scores for the plurality of digital identifiers by applying a trained neural network to the plurality of high-dimensional vector embeddings, wherein each probability score represents a conversion propensity value for a corresponding digital identifier;
and
create a plurality of data partitions by selecting a subset of the plurality of digital identifiers having probability scores exceeding a threshold.

6 more elements

Dependencies are inferred from the claim text.