Techniques to embed a data object into a multidimensional frame
AssigneeCapital One Services, LLC
InventorsAustin Grant WALTERS, Jeremy Edward GOODSITT, Mark Louis WATSON, Anh TRUONG
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Abstract
Various embodiments are generally directed to techniques for embedding a data object into a multidimensional frame, such as for training an autoencoder to generate latent space representations of the data object based on the multidimensional frame, for instance. Additionally, in one or more embodiments latent space representations of data objects may be classified, such as with a machine learning algorithm. Some embodiments are particularly directed to embedding a data object comprising a plurality of object entries into a three-dimensional (3D) frame.
Claims20
At least one non-transitory computer-readable medium comprising a set of instructions that, in response to being executed via a processing unit of a computing device, cause the computing device to:
- access a data object comprising a set of object entries;
- generate a multidimensional frame based on the data object;
- and
- utilize a trained encoder to embed the multidimensional frame into a latent space representation of the multidimensional frame, wherein the trained encoder is trained with a machine learning model to generate latent space representations of multidimensional frames, and a respective latent space representation generated with the trained encoder based on a respective multidimensional frame enables creation of an approximation of the respective multidimensional frame with a decoder.
A computing device comprising:
- a processing circuit;
- and
- a memory having executable instructions stored thereon, which when executed by the processing circuit, causes the processing circuit to:
- access a data object comprising a set of object entries;
- generate a multidimensional frame based on the data object;
- and
- utilize a trained encoder to embed the multidimensional frame into a latent space representation of the multidimensional frame, wherein the trained encoder is trained with a machine learning model to generate latent space representations of multidimensional frames, and a respective latent space representation generated with the trained encoder based on a respective multidimensional frame enables creation of an approximation of the respective multidimensional frame with a decoder.
A computer-implemented method of monitoring at least one system component, the method comprising, via at least one processor of a computing device:
- accessing a JSON object for the at least one system component indicating a status of the at least one system component;
- generating a multidimensional frame based on the JSON object;
- embedding the multidimensional frame into a latent space representation of the multidimensional frame;
- generating a set of correlation scores based on the latent space representation, wherein each correlation score in the set of correlation scores corresponds to a different latent space representation of a different multidimensional frame generated based on a different JSON object;
- comparing the set of correlation scores to determine a classification of the latent space representation;
- and
- monitoring component operation of the at least one system component based on the classification.
Dependencies are inferred from the claim text.