Intuit, Inc.

Sricharan Kallur Palli Kumar

Roger Charles Meike

Cynthia Joann Osmon

Gregory Kenneth Coulombe

Pavlo Malynin

Conrad De Peuter

Preeti Duraipandian

Ashok N Srivastava

Kamalika Das

Jiaxin Zhang

Peter Caton Anthony

Sambarta Dasgupta

Tharathorn Joy Rimchala

Philipp Hertel

Xiao Xiao

Deepankar Mohapatra

Homa Foroughi

Efraim David Feinstein

Tristan Cooper Baker

Sameeksha Khillan

Sumanth Venkatasubbaiah

Dominic Miguel Rossi

Alon Amit

Caio Vinicius Soares

Joanne Locascio

Yi Xu Ng

Allegra Latimer

Thrathorn Rimchala

Hui Chen

Ian Andrew Sebanja

Nagaraj Janardhana

Malhar Siddhesh Jere

Surendra Maharjan

Nilanjan Ray

Alexander Hertel

Ayantha Randika Ponnamperuma Arachchige

A method may include extracting, from a document, a first key-value pair including a key and a first value and corresponding to a first confidence score, extracting a second key-value pair including the key and a second value corresponding to a second confidence score, classifying a first match probability for the first key-value pair and a second match probability for the second key-value pair, generating a first calibrated confidence score for the first confidence score and a second calibrated confidence score for the second confidence score by transforming, using precision lookup tables constructed from training records, the first match probability to the first calibrated confidence score and the second match probability to second calibrated confidence score, selecting, using the first and second calibrated confidence scores, one of the first key-value pair and the second key-value pair, and presenting, in a graphical user interface (GUI), the selected key-value pair.

Machine learning model-agnostic confidence calibration system and method

Augmented Denoising Diffusion Implicit Models ('DDIMs') using a latent trajectory optimization process can be used for image generation and manipulation using text input and one or more source images to create an output image. Noise bias and textual bias inherent in the model representing the image and text input is corrected by correcting trajectories previously determined by the model at each step of a diffusion inversion process by iterating multiple starts the trajectories to find determine augmented trajectories that minimizes loss at each step. The trajectories can be used to determine an augmented noise vector, enabling use of an augmented DDIM and resulting in more accurate, stable, and responsive text-based image manipulation.

Augmented diffusion inversion using latent trajectory optimization

A method implements a dataset rank metric for measuring dataset relevance. Metadata is identified for a plurality of datasets. A graph structure is generated in storage. The graph structure includes a multitude of nodes connected by a multitude of edges. Each node of the multitude of nodes representing a respective dataset of a multitude of datasets, the multitude of edges connecting the multitude of nodes according to a data lineage determined from metadata of the multitude of datasets. A composite score is generated for each node of the graph. The computer processor iteratively processes the composite scores for the multitude of nodes of the graph to generate a dataset rank for each dataset. The multitude of datasets is presented in an interface, sorted according to the respective dataset rank of each dataset.

Dataset ranking based on composite score

Certain aspects of the present disclosure provide techniques for executing a function in a software application through a conversational user interface based on a knowledge graph associated with the function. An example method generally includes receiving a request to execute a function in a software application through a conversational user interface. A graph definition of the function is retrieved from a knowledge engine. Input is iteratively requested through the conversational user interface for each parameter of the parameters identified in the graph definition of the function based on a traversal of the graph definition of the function. Based on a completeness graph associated with the function, it is determined that the requested inputs corresponding to the parameters identified in the graph definition of the function have been provided through the conversational user interface. The function is executed using the requested inputs as parameters for executing the function.

Conversational user interfaces based on knowledge graphs

Certain aspects of the present disclosure provide techniques for optimizing results generated by functions executed using a rule-based knowledge graph. The method generally includes generating a neural network based on a knowledge graph and inputs for performing a function using the knowledge graph. Inputs for the function are received and used to generate a result of the function. A request to optimize the generated result of the function is received. A loss function is generated for the neural network. Generally, the loss function identifies a desired optimization for the function. Values of parameters in the neural network are adjusted to optimize the generated result based on the generated loss function, and the adjusted values of the parameters in the neural network are output in response to the request to optimize the generated result of the function.

Method and system for optimizing results of a function in a knowledge graph using neural networks

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

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 includes executing a Optical Character Recognition (OCR) preprocessor on training images to obtain OCR preprocessor output, executing an OCR engine on the OCR preprocessor output to obtain OCR engine output, and executing an approximator on the OCR preprocessor output to obtain approximator output. The method further includes iteratively adjusting the approximator to simulate the OCR engine using the OCR engine output and the approximator output, and generating OCR preprocessor losses using the approximator output and target labels. The method further includes iteratively adjusting the OCR preprocessor using the OCR preprocessor losses to obtain a customized OCR preprocessor.

Preprocessor training for optical character recognition

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

A method for generating a synthetic dataset involves generating discretized synthetic data based on driving a model of a cumulative distribution function (CDF) with random numbers. The CDF is based on a source dataset. The method further includes generating the synthetic dataset from the discretized synthetic data by selecting, for inclusion into the synthetic dataset, values from a multitude of entries of the source dataset, based on the discretized synthetic data, and providing the synthetic dataset to a downstream application that is configured to operate on the source dataset.

Method and system for generating synthetic data using a regression model while preserving statistical properties of underlying data

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

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

In one embodiment a finite rank deep kernel learning method includes: receiving a training dataset; forming a plurality of training data subsets from the training dataset; for each respective training data subset of the plurality of training data subsets: calculating a subset-specific loss based on a loss function and the respective training data subset; and optimizing a model based on the subset-specific loss; determining a set of embeddings based on the optimized model; determining, based on the set of embeddings, a plurality of dot kernels; combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.

Extending finite rank deep kernel learning to forecasting over long time horizons

A method including retrieving first data from first and second databases. The databases include different, incompatible formats and described different aspect of subjects. The data in the respective databases is referenceable using a common key type. The method also includes converting, into combined data, the first data and the second data into a canonical form configured for use as input to a machine learning model. The method also includes generating, using the common key type, pre-processed data by correlating, for ones of the subjects in the combined data, the first aspect of the subjects to the second aspect of the subjects. The machine learning model takes, as an input, the first aspect and the second aspect for each of the subjects in the pre-processed data, and generates, as an output, a prediction for a selected subject in the subjects. The method also includes presenting the output.

Growing community of inventors

Mountain View, CA, United States of America

Sricharan Kallur Palli Kumar