Intuit, Inc.

Richard J Becker

Daniel Leontin Moise

Pavlo Malynin

Horace Chan

Sheldon Porcina

Pallavika Ramaswamy

Gregory Kenneth Coulombe

Kimia Hassanzadeh

Priya Kothari

Greg Knoblauch

Rakesh Kandpal

Anju Eappen

Greg Coulombe

Carolina Simoes Gomes

Cole MacKenzie

Sanuree Gomes

Corinne Finegan

Goutham Kallepalli

Olabode Idowu

Nigel T Menendez

David A Pisoni

Neha Sikri

Cole Mackenzie

A method, computer program product, and system are provided. A first natural language text is received via a user interface. A generative pretrained transformer machine learning model processes the first natural language text and a context to identify a first intent. The processing is based in part on a syntax determined from a set of natural language completion paradigms. The generative transformer machine learning model maps the first set of parameters to a first query. The mapping is associated with a first confidence. The generative transformer machine learning model processes the first set of parameters and the first query to generate a set of execution steps. The processing is performed when the first confidence satisfies a threshold. The set of execution steps is parsed into a query object that is forwarded to a reporting service.

Natural language query disambiguation

Certain aspects of the present disclosure provide techniques for rendering visual artifacts in virtual worlds using machine learning models. An example method generally includes identifying, based on a machine learning model and a streaming natural language input, an intent associated with the streaming natural language input; generating, based on the identified intent associated with the streaming natural language input, one or more virtual objects for rendering in a virtual environment displayed on one or more displays of an electronic device; and rendering the generated one or more virtual objects in the virtual environment.

Rendering virtual environments based on intents identified in natural language inputs using machine learning models

A method includes receiving a set of text documents. The method also includes generating a summary of the set of text documents by a set of large language machine learning models. The method further includes generating a set of keywords from the summary by the set of large language machine learning models. The method additionally includes generating an image prompt from the set of keywords by the set of large language machine learning models. The method also includes generating a set of images from the image prompt by a text-to-image machine learning model. The method further includes generating a video clip from the set of images. The method additionally includes presenting the video clip.

Context-biased artificial intelligence video generation

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 present disclosure relates to simulating the capture of images. In some embodiments, a document and a camera are simulated using a three-dimensional modeling engine. In certain embodiments, a plurality of images are captured of the simulated document from a perspective of the simulated camera, each of the plurality of images being captured under a different set of simulated circumstances within the three-dimensional modeling engine. In some embodiments, a model is trained based at least on the plurality of images which determines at least a first technique for adjusting a set of parameters in a separate image to prepare the separate image for optical character recognition (OCR).

Simulating image capture

Systems of the present disclosure allow fields and labels to be identified in a digital image of a form without performing OCR. A digital image of a form can be partitioned into image segments using computer-vision image-segmentation techniques. Features for each image segment can be extracted using computer-vision feature-detection methods. The features extracted from an image segment can be included in an input instance for a machine-learning model. The machine-learning model can assign a classification to the input instance. The classification can associate the input instance with a field type or a label type.

Label and field identification without optical character recognition (OCR)

The disclosed embodiments provide a system that describes a semi-structured document for the purpose of acquiring a set of data elements from the semi-structured document. During operation, the system obtains a physics model of a semi-structured document, wherein the physics model includes a set of relationships represented by physical objects that describe relative positions of a set of data elements in the semi-structured document. Next, the system applies the physics model to a representation of the semi-structured document to automatically extract a set of data from the representation. The system then provides the extracted set of data for use with one or more applications without requiring manual input of the data into the one or more applications.

Modeling and extracting elements in semi-structured documents

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

A method for schematizing fields in documents involves segmenting an image of a document into snippets, classifying a first, a second, and a third snippet of the snippets as a first label, a second label, and a data field, respectively. The method further includes generating, for a combination of the first label and the data field, a first image to be classified, and classifying the first image to be classified to obtain a first classification score. The method further includes generating, for a combination of the second label and the data field, a second image to be classified, and classifying the second image to be classified to obtain a second classification score. The method also includes determining that the first classification score is greater than the second classification score and identifying, based on the determining step, that the data field is associated with the first label.

Method and system for schematizing fields in documents

Methods, non-transitory computer-readable storage mediums and systems for classifying features including labels or fields in digital images of forms and identifying any unclassified features utilizing a bitmap approach are provided. Each spatial template includes a set of features and a set of bitmaps, each bitmap indicating a spatial relationship between each feature and respective remaining features in each spatial template. The operations include selecting a spatial template from the spatial templates, classifying each extracted feature as one of features in the spatial template. The operations include generating at least one bitmap representing a spatial relationship for each unclassified feature in relation to classified features, comparing the at least one bitmap with each of a set of bitmaps in the selected spatial template, and identifying the at least one unclassified feature as one of features in the spatial template based on a result of the comparison.

Feature classification with spatial analysis

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.

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Richard J Becker