The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 30, 2024

Filed:

Mar. 06, 2023
Applicant:

Intuit Inc., Mountain View, CA (US);

Inventors:

Janani Kalyanam, San Diego, CA (US);

Zhewen Fan, San Diego, CA (US);

Byungkyu Kang, San Diego, CA (US);

Kate Elizabeth Swift-Spong, San Diego, CA (US);

Shivakumara Narayanaswamy, Mountain View, CA (US);

Farzaneh Khoshnevisan, Raleigh, NC (US);

Assignee:

INTUIT INC., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/23 (2019.01); G06N 20/00 (2019.01); G06F 40/279 (2020.01); G06F 40/174 (2020.01); G06F 17/18 (2006.01);
U.S. Cl.
CPC ...
G06F 16/2365 (2019.01); G06F 17/18 (2013.01); G06F 40/174 (2020.01); G06F 40/279 (2020.01); G06N 20/00 (2019.01);
Abstract

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.


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