Intuit, Inc.

Ronnie Douglas Douthit

Deepankar Mohapatra

Ram Mohan Shamanna

Matthew E Gerde

James A Buffington

Brittany Sumarsono

Andrew Van Cao

Shekinah Cravens

Karpaga Ganesh Patchirajan

Yexin Huang

Amar J Mattey

Chiranjeev Jagannadha Reddy

Mithilesh Kumar Singh

Manish Omprakash Bhatia

Trichur Shivaramakrishnan Subramanian

Jessica Colleen Danby

Chinnadurai Duraisami

Somin Heo

Display device with graphical user interface showing a review plan

A processor may filter data to generate a subset of the data less than an entire set of the data. The subset may include at least one string and at least one numeric value. The processor may match the at least one string and the at least one numeric value to one of a plurality of archetypes by applying a clustering algorithm. Each archetype may include a subset of archetype data less than an entire set of archetype data. The processor may compare the entire set of data to the entire set of archetype data to identify at least one difference between the entire set of data and the entire set of archetype data. The processor may apply at least one optimization to address the at least one difference.

Optimization based on archetypes

Display device with graphical user interface showing a custom strategy

Certain aspects of the present disclosure provide techniques for orchestrating a user experience using natural language input. A user experience is orchestrated within an ecosystem of features for which a plurality of corresponding tokens is defined. Natural language describing a desired user experience result is received by a user experience orchestrator. A sequence of tokens corresponding to operations belonging to an ecosystem of features which produce a correct result for the natural language input can be identified by a trained large language model and executed by the user experience orchestrator using a token operator. The output operations determined by the model to produce or be likely to produce the correct result based on the natural language input can be disambiguated, confirmed, and/or executed.

Automated user experience orchestration using natural language based machine learning techniques

Aspects of the present disclosure provide techniques for application navigation recommendations using machine learning. Embodiments include determining one or more pages accessed by a user within an application. Embodiments include providing one or more inputs to a machine learning model based on the one or more pages accessed by the user. Embodiments include receiving, from the machine learning model based on the one or more inputs, one or more predicted pages. Embodiments include displaying, in a user interface, one or more elements recommending the one or more predicted pages to the user. Embodiments include receiving a selection of a given element of the one or more elements. Embodiments include navigating within the user interface, based on the selection, to a given page of the one or more predicted pages that corresponds to the given element.

Smart navigation

Aspects of the present disclosure provide techniques for document classification through signal processing. Embodiments include receiving a document for classification. Embodiments include generating an image of the document. Embodiments include producing a signal representation of the document based on numbers of non-white pixels in each horizontal scan line or vertical scan line of the image of the document. Embodiments include comparing the signal representation of the document to signal representations of previously-classified documents. Embodiments include determining, based on the comparing, a classification for the document. Embodiments include performing additional processing with respect to the document based on the classification for the document.

Document classification using signal processing

A method including receiving a source file containing a plurality of documents which, to a computer, initially are indistinguishable from each other. A first classification stage is applied to the source file using a convolutional neural network image classification to identify source documents in the multitude of documents and to produce a partially parsed file having a multitude of identified source documents. The partially parsed file includes sub-images corresponding to the plurality of identified source documents. A second classification stage, including a natural language processing artificial intelligence, is applied to sets of text in bounding boxes of the sub-images, to classify each of the multitude of identified source documents as a corresponding sub-type of document. Each of the sets of text corresponding to one of the sub-images. A parsed file having a multitude of identified sub-types of documents is produced. The parsed file is further computer processed.

Automated document extraction and classification

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Frisco, TX, United States of America

Ronnie Douglas Douthit