Intuit, Inc.

Mithun Ghosh

Vignesh Thirukazhukundram Subrahmaniam

Hrishikesh Vidyadhar Ganu

Ankit Agarwal

Shashank Mendiratta

David Matz

Karen Wai

Azal Fatima

Tiffany Dolan

Systems and methods for automatically populating documents about special entities are disclosed herein. An example method is performed by one or more processors of a computing system. The example method may include receiving user data, extracting a list of entities associated with the user and a list of events that occurred between the user and the entities, transforming metadata for the events associated with entities of interest into vectorized embeddings, selectively classifying, using a binary classifier model, ones of the entities as special entities and ones of the events as special events for a set of documents, assigning, using a multi-class classifier model, one of a plurality of categories to each special event associated with each special entity, each of the categories mapping to a corresponding section within the set of documents, and populating, for each special entity, the corresponding sections within the set of documents based on the categories.

Automatically populating documents about special entities

Aspects of the present disclosure provide techniques for automated data classification error correction through machine learning. Embodiments include receiving a set of predicted labels corresponding to a set of consecutive text strings that appear in a particular order in a document, including: a first text string corresponding to a first predicted label; a second text string that follows the first text string in the particular order and corresponds to a second predicted label; and a third text string that follows the second text string in the particular order and corresponds to a third predicted label. Embodiments include providing inputs to a machine learning model based on: the third text string; the second text string; the second predicted label; and the first predicted label. Embodiments include determining a corrected third label for the third text string based on an output provided by the machine learning model in response to the inputs.

Automated data classification error correction through spatial analysis using machine learning

A rule having text is pre-processed by replacing terms with dummy tokens. A first machine learning model (MLM) uses the dummy tokens to generate a dependency graph with nodes related by edges tagged with dependency tags. A second MLM uses the dependency graph to generate a canonical version with node labels. The node labels are sorted into a lexicographic order to form a document. A third MLM uses the document to generate a machine readable vector (MRV) that embeds the document as a sequence of numbers representative of a structure of the rule. The MRV is compared to additional MRVs corresponding to additional rules for which computer useable program code blocks have been generated. A set of MRVs is identified that match the MRV within a range. The set of MRVs correspond to a set of rules from the additional rules. The set of rules is displayed to a user.

Representation learning for tax rule bootstrapping

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Bengaluru, India

Mithun Ghosh