Capital One Services, LLC

Daniele Rosa

Samuel Sharpe

Austin Grant Walters

Jeremy Edward Goodsitt

Galen Rafferty

Brian Chandler Barr

Kenny Bean

Adam Badawy

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a dataset of entity records, identifying, a candidate entity record of the plurality of entity records, utilizing a set of predefined rules to generate a first augmented record, and a second augmented record, utilizing, at least one contrastive loss optimization functions to train parameters of an unsupervised self-contrastive machine learning language model to distinguish between similar entity records representing a same entity and dissimilar entity records.

Computer-based systems configured to pre-train language models for entity resolution and methods of use thereof

The present disclosure provides systems for encoding transforms based on intent to be useable by developers. The system receives input datasets, generates output datasets, and identifies characteristics of the input or output datasets. Using these characteristics, the system can encode transforms associated with the datasets based on intent. The system stores and/or associates the intent-encoded transforms in data profiles, so that the system enables the transforms to be searched, recommended, and/or combined.

Systems for encoding data transforms by intent

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Saugus, MA, United States of America

Daniele Rosa