Intuit, Inc.

Shivakumara Narayanaswamy

Byungkyu Kang

Zhewen Fan

Farzaneh Khoshnevisan

Kate Elizabeth Swift-Spong

Janani Kalyanam

Runhua Zhao

Andrew Mattarella-Micke

Wan Yu Zhang

Carlos A Oliveira

A method implements calibrated risk scoring and sampling. Features are extracted from a record. A risk score, associated with the record, is generated from the features using a machine learning model. The record is mapped to a risk bucket using the risk score. The risk bucket may include multiple risk bucket records. The record is selected from the risk bucket records with a sampling threshold corresponding to the risk bucket. A form prepopulated with values from the record is presented to a client device.

Calibrated risk scoring and sampling

Systems and methods dynamically extracting n-grams for automated vocabulary updates. Text is received. An n-gram extracted from the text is matched to a canonical n-gram from a vocabulary to identify a tag for the text. An n-gram weight is computed for the n-gram extracted from the text. The n-gram weight may be computed by adjusting a term frequency of the n-gram. A relevancy score is computed for the tag using the n-gram weight and using an n-gram frequency of the canonical n-gram. The relevancy score is computed by dividing the n-gram weight by a value proportional to the n-gram frequency of the canonical n-gram. The relevancy score of the n-gram is presented.

Dynamically extracting n-grams for automated vocabulary updates

A computer-implemented system and method for predicting and flagging an anomaly entered in a digital form. A server computing device classifies a plurality of data fields of the digital form to identify a set of non-zero value data fields; and obtains an anomaly detection model comprising a statistical tree structure associated with the data field of the digital form. The server computing device receives datasets including a target value of a data field and values of a set of cohorting data features; traverses a statistical tree structure of the anomaly detection model with the target dataset to form a set of target cohorts to determine a target statistic value for the data field; flags the data field value of the target dataset as an anomaly item; and generates one or more confidence scores for a runtime prediction based on one or more variance changes for the data field.

Method and system of automatically predicting anomalies in online forms

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Mountain View, CA, United States of America

Shivakumara Narayanaswamy