US 2026/0093748 A1 · SYSTEMS AND METHODS FOR AN AI FRAMEWORK FOR CLASSIFYING LABELLED DATA

Abstract

Disclosed are systems and methods that provide a computerized framework for classifying data at scale, particularly when dealing with complex datasets that include ambiguous, incomplete and/or non-standard data. The disclosed framework provides a computerized solution that is scalable, accurate and capable of handling the complexities of the dataset while ensuring data treatment was done in line with legal and security requirements. The framework can operate to classify text-based data even when the data itself is incomplete, ambiguous, overlapping, and/or inconsistent, which can be particularly important for a wireless provider, where data is often scattered across different systems, and the information available for classification might be fragmented or contradictory. The framework provides functionality for classifying data automatically and efficiently, even as the volume of data grows exponentially.

Claims23

Dependent claims

A method comprising:

identifying a dataset;
analyzing the dataset, and determining, based on the analysis, a data profile and metadata;
executing a first computer model based on the data profile and metadata, and determining a classification of information within the dataset, the first computer model being trained to classify information based on statistical patterns and learned features;
executing a second computer model based on the data profile and metadata, and determining embeddings for the dataset, the embeddings capturing semantic relationships;
determining, based on the execution of the first computer model and the second computer model, a data classification, the data classification being based on the classification of the information within the dataset and the embeddings;
treating, based on the data classification, the dataset to generate a modified version of the dataset, the treatment comprising at least one of anonymizing or encrypting the dataset to enable sharing of the dataset across different environments in accordance with a policy;
and
outputting, over a network, a device-consumable version of the modified dataset to a receiving entity, the outputting enabling usage of the modified dataset by a device of the receiving entity.

5 more elements

A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor, perform a method comprising:

identifying a dataset;
analyzing the dataset, and determining, based on the analysis, a data profile and metadata;
executing a first computer model based on the data profile and metadata, and determining a classification of information within the dataset, the first computer model being trained to classify information based on statistical patterns and learned features;
executing a second computer model based on the data profile and metadata, and determining embeddings for the dataset, the embeddings capturing semantic relationships;
determining, based on the execution of the first computer model and the second computer model, a data classification, the data classification being based on the classification of the information within the dataset and the embeddings;
treating, based on the data classification, the dataset to generate a modified version of the dataset, the treatment comprising at least one of anonymizing or encrypting the dataset to enable sharing of the dataset across different environments in accordance with a policy;
and
outputting, over a network, a device-consumable version of the modified dataset to a receiving entity, the outputting enabling usage of the modified dataset by a device of the receiving entity.

5 more elements

Dependencies are inferred from the claim text.

Systems and methods for an ai framework for classifying labelled data

Life of this patent

Systems and methods for an ai framework for classifying labelled data

Life of this patent

Abstract

Claims23