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. 16, 2024

Filed:

Jan. 25, 2021
Applicant:

Data-core Systems, Inc., Philadelphia, PA (US);

Inventors:

Anshuman Narayan, Bristol, PA (US);

Jishnu Bhattacharyya, Philadelphia, PA (US);

Sin-Min Chang, Fairfield, CT (US);

Rathi Dasgupta, Philadelphia, PA (US);

Pradeep K. Banerjee, Philadelphia, PA (US);

Assignee:

Data-Core Systems, Inc., Philadelphia, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 40/08 (2012.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06N 3/08 (2023.01); G06N 5/045 (2023.01); G06Q 10/10 (2023.01);
U.S. Cl.
CPC ...
G06Q 40/08 (2013.01); G06F 18/2193 (2023.01); G06F 18/285 (2023.01); G06N 3/08 (2013.01); G06N 5/045 (2013.01); G06Q 10/10 (2013.01);
Abstract

Disclosed embodiments include determining whether the insurance claim comprises an anomaly based on whether the claim violates a rule in a Rules Engine or is scored by an Artificial Intelligence/Machine Learning engine below a threshold. When the claim comprises an anomaly, (i) for each feature of the claim, (a) determining a sensitivity value of the feature relative to each other feature of the claim, and (b) determining a saliency value of the feature relative categories relevant to the claim, and (ii) estimating a value of the claim based on a multi-variate linear regression model. And based on the sensitivity values, saliency values, and estimated value, recommending one or more ways to cure the anomaly based on (i) a Local Interpretable Model-Agnostic Explanations based recommendation and/or (ii) a Global Surrogate Model based recommendation.


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