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US 2026/0037871 A1Published application
Data aggregation and model training based on sparse datasets
OwnerGontran Jerome de Quillacqinventor
Life of this patent
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Abstract
A system may access a set of training data and determine a timeframe associated with a positively labeled data item of the training data. A system may generate at least two new positively labeled data items based on the positively labeled data item to generate augmented training data. A system may train a machine learning model by applying the augmented training data as input to a machine learning model, and modifying a weight of the machine learning model.
Claims20
A method of training machine learning models comprising:
- accessing a set of training data comprising a subset of positively labeled data items and a subset of negatively labeled data items;
- augmenting the set of training data, wherein augmenting the set of training data comprises:
- identifying a positively labeled data item of the subset of positively labeled data items;
- generating, based on the positively labeled data item, at least two new positively labeled data items, wherein each of the at least two new positively labeled data items is distinct from the subset of positively labeled data items;
- and
- combining the at least two positively labeled data items with the set of training data to generate augmented training data;
- applying the augmented training data as input to a machine learning model to cause the machine learning model to generate a training response;
- analyzing the training response to generate an analysis result;
- modifying a value of the machine learning model based on the analysis result to generate a trained machine learning model;
- and
- storing the trained machine learning model.
8 more elements
A non-transitory, computer-readable medium encoded with computer-executable instructions executable by a processor of a computing device, wherein the computer-executable instructions, when executed by the processor, cause the computing device to:
- access a set of training data comprising a subset of positively labeled data items and a subset of negatively labeled data items;
- augment the set of training data, wherein augmenting the set of training data comprises:
- identify a positively labeled data item of the subset of positively labeled data items;
- generate, based on the positively labeled data item, at least two new positively labeled data items, wherein each of the at least two new positively labeled data items is distinct from the subset of positively labeled data items;
- and
- combine at least one of the at least two positively labeled data items with the set of training data to generate augmented training data;
- apply the augmented training data as input to a machine learning model to cause the machine learning model to generate a training response;
- analyzing the training response to generate an analysis result;
- modify a value of the machine learning model based on the analysis result to generate a trained machine learning model;
- and
- store the trained machine learning model.
8 more elements
A system comprising:
- a non-transitory computer-readable memory storing computer-executable instructions;
- and
- one or more processors in communication with the memory, wherein the computer-executable instructions, when executed by the one or more processors, causes the one or more processors to at least:
- access a set of training data comprising a subset of positively labeled data items and a subset of negatively labeled data items;
- augment the set of training data, wherein augmenting the set of training data comprises:
- identify a positively labeled data item of the subset of positively labeled data items;
- generate, based on the positively labeled data item, at least two new augmented data items, wherein each of the at least two new augmented data items is distinct from the subset of positively labeled data items;
- and
- combine the at least two augmented data items with the set of training data to generate augmented training data;
- apply the augmented training data as input to a machine learning model to cause the machine learning model to generate a training response;
- analyzing the training response to generate an analysis result;
- modify a value associated with the machine learning model based on the analysis result to generate a trained machine learning model;
- and
- store the trained machine learning model.
11 more elements
Dependencies are inferred from the claim text.