Intuit, Inc.

Conrad De Peuter

Sricharan Kallur Palli Kumar

Deepankar Mohapatra

Saikat Mukherjee

Karpaga Ganesh Patchirajan

Peter Caton Anthony

Preeti Duraipandian

Roger Charles Meike

Tharathorn Joy Rimchala

Cynthia Joann Osmon

Gregory Kenneth Coulombe

Efraim David Feinstein

Pavlo Malynin

Yi Xu Ng

Apoorva Banubakode

Mohsen Samadani

Na Xu

Yi Wei

Ian Andrew Sebanja

Seth Harris Rubin

Nagaraj Janardhana

Jadiel De Armas

Harsha Ilapakurty

Surendra Maharjan

Subathra Sundararajan

Sricharan Kallur Kumar

Image pages are generated from a document. Assembling the image pages generates a collage. Two-dimensional text and bounding boxes are extracted from the image pages. A structure verbalizer spatially formats the two-dimensional text in one-dimension with spatial information to generate spatial-formatted text. The spatial-formatted text is concatenated to generate a text extraction. A multimodal embedding model is applied to the collage and the text extraction to generate a target artifact vector. The target artifact vector is compared against a set of preexisting artifact vectors to identify a corresponding artifact vector associated with a corresponding document having a corresponding metadata tag. A distance is determined between the corresponding artifact vector and the target artifact vector. Responsive to the distance being within a threshold distance additional steps are performed, including performing both applying the corresponding metadata tag to the document to generate a modified document and outputting the modified document.

Computationally efficient artifact tagging for document management

A sequence of data entry screens are configured to collect the data from a user. The method and system receive data entered by a user into a data entry screen. The method and system then determine metrics of the collected data, and ranks the collected data and the data entry screens based on the determined metrics. The ranking is then used to display the next best screen in the sequence for collecting data.

Quality, availability and AI model predictions

Certain aspects of the present disclosure provide techniques for processing natural language utterances in a knowledge graph. An example method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application. Operands and operators are extracted from the natural language utterance using a natural language model. Operands may be mapped to nodes in a knowledge graph, the nodes representing values calculated from data input into the application, and operators may be mapped to operations to be performed on data extracted from the knowledge graph. The functions associated with the operators are executed using data extracted from the nodes in the knowledge graph associated with the operands to generate a query result. The query result is returned as a response to the received long-tail query.

Mapping natural language utterances to operations over a knowledge graph

A method of blank detection involves receiving a document from a user, where the document includes derived text; applying a trained blank detection model to the document to make a first prediction, where the first prediction indicates whether at least one field in the document is blank; comparing the first prediction with a second prediction, where the second prediction is made by an extraction model; and extracting the at least one field using the extraction model.

Detection of blanks in documents

Aspects of the present disclosure provide techniques for machine learning and rules integration. Embodiments include receiving input values corresponding to a subset of a set of input variables associated with an automated determination. Embodiments include generating a directed acyclic graph (DAG) representing a set of constraints corresponding to the set of input variables. The set of constraints relate to one or more machine learning models and one or more rules. Embodiments include receiving one or more outputs from the one or more machine learning models based on one or more of the input values. Embodiments include determining outcomes for the one or more rules based on at least one of the input values. Embodiments include populating the DAG based on the input values, the one or more outputs, and the outcomes. Embodiments include making the automated determination based on logic represented by the DAG.

Integrated machine learning and rules platform for improved accuracy and root cause analysis

Systems and methods for recognizing domain specific named entities are disclosed. An example method may be performed by one or more processors of a text incorporation system and include extracting a number of terms from a text under consideration, identifying, among the number of terms, a set of unmatched terms that do not match any of a plurality of known terms, passing each respective unmatched term to a vectorization module, embedding a vectorized version of each respective unmatched term in a vector space, comparing each vectorized version to known term vectors, passing, to a machine learning model, candidate terms corresponding to known term vectors closest to the vectorized versions, identifying, using the machine learning model, a best candidate term for each respective unmatched term, mapping the best candidate terms to unmatched terms in the text under consideration, and incorporating the text under consideration into the system based on the mappings.

System and method for recognizing domain specific named entities using domain specific word embeddings

Certain aspects of the present disclosure provide techniques for generating a compliance graph based on a compliance rule to implement in a software program product for determining user compliance. To generate a compliance graph, an encoder receives a compliance rule in a source language and generates a set of corresponding vectors. The decoder, which has been trained using verified training pairs and synthetic data, generates a sequence of operations based on the vectors from the encoder. The sequence of operations is the used to build a graph in which each operation is a node in the graph and each node is connected to at least one other node in the same graph or a separate graph.

Compliance graph generation

Systems and methods that may be used to determine that input form field data is accurate or not, and associate a level of confidence with that determination. The systems and methods may use a multi part confidence model that uses inter-field correlation to tie the correctness of a particular field to the pattern of values seen in other fields of the document the field data is input from.

Systems and methods utilizing distribution trees for confidence modeling and use of form field data

A natural language processing method and system utilizes a combination of rules-based processes, vector-based processes, and machine learning-based processes to identify the meaning of terms extracted from data management system related text. Once the meaning of the terms has been identified, the method and system can automatically incorporate new forms and text into a data management system.

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Mountain View, CA, United States of America

Conrad De Peuter