Intuit, Inc.

Pavlo Malynin

Richard J Becker

Roger Charles Meike

Sricharan Kallur Palli Kumar

Cynthia Joann Osmon

Gregory Kenneth Coulombe

Sheldon Porcina

Rakesh Kandpal

Priya Kothari

Greg Knoblauch

Anju Eappen

Igor A Podgorny

Matthew Cannon

Faraz Sharafi

Greg Coulombe

Yason Khaburzaniya

Jeff W Geisler

Aspects of the present disclosure provide techniques for intent matching. Embodiments include receiving input of text by a user via a user interface. Embodiments include determining weights for portions of the text based on a plurality of keywords. Embodiment include generating an embedding of the text. Embodiments include determining an intent of the text by weighting, based on the weights, word mover's distances from the embedding of the text to a known embedding of known text associated with the intent in order to determine a similarity measure between the text and the known text. Embodiments include providing content to the user via the user interface based on the intent.

Enhanced intent matching using keyword-based word mover's distance

Certain aspects of the present disclosure provide techniques for node matching with accuracy by combining statistical methods with a knowledge graph to assist in responding (e.g., providing content) to a user query in a user support system. In order to provide content, a keyword matching algorithm, statistical method (e.g., a trained BERT model), and data retrieval are each implemented to identify node(s) in a knowledge graph with encoded content relevant to the user's query. The implementation of the keyword matching algorithm, statistical method, and data retrieval results in a matching metric score, semantic score, and graph metric data, respectively. Each score associated with a node is combined to generate an overall score that can be used to rank nodes. Once the nodes are ranked, the top ranking nodes are displayed to the user for selection. Based on the selection, content encoded in the node is displayed to the user.

Combining statistical methods with a knowledge graph

Certain aspects of the present disclosure provide techniques for mapping natural language to stored information. The method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application associated with a set of topics and providing the natural language utterance to a natural language model configured to identify nodes of a knowledge graph. The method further includes, based on output of the natural language model, identifying a node of a knowledge graph associated with the natural language utterance, wherein the output of the natural language model includes a node identifier for the node of the knowledge graph and providing the node identifier to the knowledge engine. The method further includes receiving a response associated with the node of the knowledge graph from the knowledge engine and transmitting the response to the user in response to the long-tail query.

Mapping natural language utterances to nodes in a knowledge graph

Techniques are disclosed for performing optical character recognition (OCR) by assessing and improving quality of electronic documents to perform the OCR. For example a method for identifying information in an electronic document includes obtaining a reference image of the electronic document, distorting the reference image by adjusting different sets of one or more parameters associated with a quality of the reference image to generate a plurality of distorted images, analyzing each distorted image to detect the adjusted set of parameters and corresponding adjusted values, determining an accuracy of detection of the set of parameters and the adjusted values, and training a model based at least on the plurality of distorted images and the accuracy of the detection, wherein the trained model determines at least a first technique for adjusting a set of parameters in a second image to prepare the second image for optical character recognition.

Image quality assessment and improvement for performing optical character recognition

The invention relates to a method. The method includes receiving a flawed input comprising a domain specific misspelling. The method further includes encoding, by an encoder machine learning model executing on a computer processor, the flawed input on a per character basis to create a context vector. The method further includes decoding, by a decoder machine learning model executing on the computer processor, the context vector on the per character basis to create a rephrased input lacking the domain specific misspelling. The method further includes presenting the rephrased input.

Misspelling correction based on deep learning architecture

Techniques are disclosed to identify a form document in an image using a digital fingerprint of the form document. To do so, the image is evaluated to detect features of the image. For each feature, a pixel is plotted in a second image. The second image is the digital fingerprint of the form. To identify the form corresponding to the digital fingerprint, the digital fingerprint may be compared to digital fingerprints of known forms.

Identifying document forms using digital fingerprints

Techniques are disclosed to identify a form document in an image using a digital fingerprint of the form document. To do so, the image is evaluated to detect features of the image and determine a polygon bounding each feature. For each polygon, pixels are plotted in a second image based on coordinates of a center of the polygon. The second image is the digital fingerprint of the form. To identify the form corresponding to the digital fingerprint, the digital fingerprint may be compared to digital fingerprints of known forms.

Techniques are disclosed to identify a form document in an image using a digital fingerprint of the form document. To do so, the image is evaluated to detect features of the image and generate a boundary around each feature. For each boundary, dimensions of the boundary may be stored in a color channel of a pixel in a second image. Thus, the color of the pixel represents the size of the boundary. The second image is the digital fingerprint of the form. To identify the form corresponding to the digital fingerprint, the digital fingerprint may be compared to digital fingerprints of known forms.

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Edmonton, Canada

Pavlo Malynin