Dell Products Inc.

Dell & Products L.p.

Saurabh Jha

Atul Kumar

Guhesh Swaminathan

Michael David Shepherd

Sailendu Kumar Patra

Kailash Talreja

Kuruba Ajay Kumar

Methods, apparatus, and processor-readable storage media for character-based representation learning for information extraction using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying, from unstructured documents, words and corresponding document position information using artificial intelligence-based text extraction techniques; generating an intermediate output by implementing at least one character embedding with respect to the unstructured documents using at least one artificial intelligence-based encoder; determining structure-related information for at least a portion of the unstructured documents using one or more artificial intelligence-based graph-related techniques; generating a character-based representation of at least a portion of the unstructured documents using at least one artificial intelligence-based decoder; classifying one or more portions of the character-based representation using one or more artificial intelligence-based statistical modeling techniques; and performing one or more automated actions based on the classifying.

Character-based representation learning for information extraction using artificial intelligence techniques

Methods, apparatus, and processor-readable storage media for multi-level time series forecasting using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining entity-related features and temporal features from at least a portion of one or more sets of time series data pertaining to at least one entity; creating multiple embeddings by encoding at least a portion of the entity-related features and at least a portion of the temporal features using at least one artificial intelligence-based embedding technique; processing the multiple embeddings using at least one neural network-based attention technique; generating one or more data forecasts across one or more temporal granularity levels by processing at least a portion of results from the processing of the multiple embeddings using at least one artificial intelligence-based categorization technique; and performing one or more automated actions based at least in part on the one or more data forecasts.

Multi-level time series forecasting using artificial intelligence techniques

Methods, apparatus, and processor-readable storage media for automated address data determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes extracting address information from one or more documents using at least one artificial intelligence-based address extraction model; parsing, into one or more address components, at least a portion of the extracted address information using at least one artificial intelligence-based parsing model; comparing at least a portion of the one or more parsed address components to stored address information using at least one artificial intelligence-based scoring model; and performing one or more automated actions based at least in part on results from the comparing of at least a portion of the one or more parsed address components to stored address information.

Automated address data determinations using artificial intelligence techniques

Methods, apparatus, and processor-readable storage media for character-based representation learning for table data extraction using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying, from unstructured documents comprising tabular data, items of text and corresponding document position information using artificial intelligence-based text extraction techniques; generating an intermediate output by implementing character embedding with respect to the unstructured documents using an artificial intelligence-based encoder; determining structure-related information for the unstructured documents using one or more artificial intelligence-based graph-related techniques by inferring columns from the tabular data; generating a character-based representation of the unstructured documents using an artificial intelligence-based decoder by converting the inferred columns into one or more line items; classifying portions of the character-based representation using artificial intelligence-based statistical modeling techniques; and performing one or more automated actions based on the classifying.

Character-based representation learning for table data extraction using artificial intelligence techniques

An information handling system is provided for extracting data associated with a data field of a database from a randomly formatted source document. A processor of the information handling system receives the document, embeds a text-based representation of text of the document into vector data associated with the text, and encodes the vector data through a first neural network into reconstructed text activations. The processor further concatenates the vector data with an image-based representation of the document to provide first concatenated data, and encodes the first concatenated data through a second neural network into extracted visual feature activations. The processor further concatenates the reconstructed text activations with the extracted visual feature activations to provide second concatenated data, decodes the second concatenated data to identify particular data associated with the data field, and updates an entry of the database with the particular data in the data field.

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Bengaluru, India

Saurabh Jha