Patent Issued for Using machine learning to classify insurance card information (USPTO 11074656)

2021 AUG 17 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Walmart Apollo LLC (Bentonville, Arkansas, United States) has been issued patent number 11074656, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Medous, Frederic (San Francisco, CA, US), Smith, Walter R. (Seattle, WA, US).

This patent was filed on September 14, 2018 and was published online on July 27, 2021.

From the background information supplied by the inventors, news correspondents obtained the following quote: “This disclosure relates generally to medical insurance and more specifically to machine learning algorithms applied to insurance cards for identifying and recommending insurance plans.

“In today’s complex medical insurance market, there are a myriad of choices for medical insurance plans for consumers. The various types of medical insurance plans range from health maintenance organizations (HMOs), preferred provider organizations (PPOs), exclusive provider organizations (EPOs), point-of-service (POS) plans, to high-deductible health plans (HDHPs). Evaluating medical insurance plans as a consumer can be overwhelming when trying to balance the benefits of each plan against the costs of each plan. While insurance policies define intricate cost calculations for healthcare providers and healthcare consumers, they are often written with complicated language and terminology, making the policies challenging to decipher and making it more difficult for consumers to comparison shop. As a result, many consumers enroll in insurance plans that are not the most appropriate for their medical needs and financial budget.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “The present invention involves a method for applying machine-learning algorithms to identify an insurance plan associated with an insurance card of a user and providing a recommendation to the user based on the user’s insurance plan. An insurance management system receives, from a client device, at least one image of an insurance card associated with the user operating the client device. The at least one image may include, e.g., a front side of the insurance card and a back side of the insurance card. The insurance management system extracts features from the at least one image, such as text information, object information, color information, and shape information. The insurance management system generates a feature vector associated with the at least one image and inputs the feature vector into a machine learning model that has been trained using classifications of insurance cards of other users. The machine learning model outputs a classification, which comprises an association with an insurance plan. The insurance management system stores the insurance plan of the classification as a current selection of an insurance plan of the user. In one embodiment, the insurance management system receives historical medical information that is specific to the user and indicates a current medical condition of the user. The historical medical information comprises one or more of a prescription history, a medical expense history, a medical claims history, a medical procedure history, and a co-payment history of the user. Based in part on the historical medical information, the insurance management system evaluates the current selection of the insurance plan of the user. Based on the evaluation, the insurance management system provides for display to the user in a user interface a recommendation associated with the current selection of the insurance plan of the user. In one embodiment, the recommendation comprises one or more alternative insurance plans for the user for the user to consider as alternatives to the insurance plan of the user associated with the insurance card. In one embodiment, the historical medical information indicates a pharmacy of the user, and the recommendation comprises one or more alternative pharmacies for the user for the user to consider as alternatives to the pharmacy of the user.

“The figures depict various embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.”

The claims supplied by the inventors are:

“1. A method comprising: training, by a processor, a set of images of insurance cards for a machine learning model, wherein inputs to the machine learning model comprise historical data for one or more classifications associated with one or more types of insurance cards during a historical time period, wherein outputs of the machine learning model comprise at least one insurance plan matching at least one classification of the one or more classifications associated with the one or more types of insurance cards, wherein each classification of the one or more classifications is associated with a respective insurance plan; receiving, by the processor, at least one image of an insurance card of a user scanned from a mobile device of the user; extracting, by the processor, one or more feature values from the at least one image of the insurance card of the user, the one or more feature values, as extracted, comprising of text information, object information, color information, and shape information; generating, by the processor, a feature vector associated with the at least one image of the insurance card of the user comprising an ordered list of the one or more feature values, as extracted, for the at least one image of the insurance card of the user; reducing an amount of data in the feature vector of the at least one image of the insurance card of the user to a set of data using dimensionality reduction; determining, by the machine learning model, as trained, a first insurance plan of the user based on input data comprising the set of data of the feature vector, as reduced, of the at least one image of the insurance card of the user; storing, by the processor, the first insurance plan, as determined; receiving, by the processor, historical medical information of the user comprising medical conditions of the user; identifying, by the processor, at least one alternative insurance plan to be displayed to the user, comprising: evaluating, based in part on the historical medical information associated with the user, the first insurance plan, as determined, of the user; generating a comparison of a plurality of feature values of the first insurance plan of the user to other feature values of one or more alternative insurance plans, wherein the other feature values comprise one or more of the following: (i) one or more respective benefits associated with the first insurance plan of the user and each alternative insurance plan of the one or more alternative insurance plans; (ii) a respective cost associated with the first insurance plan of the user and the each alternative insurance plan; and (iii) a respective savings associated with the each alternative insurance plan relative to the first insurance plan of the user; and selecting the at least one alternative insurance plan of the one or more alternative insurance plans based on the comparison; and sending, by the processor, instructions to display to the user on a user interface of the mobile device of the user a recommendation comprising the at least one alternative insurance plan, as selected.

“2. The method of claim 1, wherein the text information includes one or more of the following: a patient name, an insurance carrier name, an insurance plan name, a patient identification number, an issuer identification number, a group number, a coverage effective date, an employer name, a bank identification number (BIN), co-payment information, or a customer service number.

“3. The method of claim 1, wherein the historical medical information comprises one or more of the following: a prescription history, a medical expense history, a medical claims history, a medical procedure history, or a co-payment history of the user.

“4. The method of claim 1, further comprising: forming a positive training set of images of other insurance cards that are classified as associated with the respective insurance plan or a respective insurance company; providing the positive training set to the machine learning model for training of the machine learning model; forming a negative training set of images of the other insurance cards that are classified as not associated with the respective insurance plan or the respective insurance company; and providing the negative training set to the machine learning model for training of the machine learning model.

“5. The method of claim 1, wherein the user interface of the mobile device of the user displays the comparison between the first insurance plan of the user and the one or more alternative insurance plans for the user.

“6. The method of claim 1, wherein: the historical medical information of the user further comprises: a pharmacy of the user; and the recommendation further comprises: one or more alternative pharmacies for the user to consider as alternatives to the pharmacy of the user.

“7. The method of claim 6, further comprising: selecting the one or more alternative pharmacies for the recommendation based on an analysis of one or more of the following: (i) respective prescription co-payment costs associated with each pharmacy, (ii) a respective savings associated with each of the one or more alternative pharmacies relative to the pharmacy of the user, (iii) a respective proximity of the each pharmacy to the user, (iv) a respective network associated with the each pharmacy, or (v) respective insurance plan benefits associated with the each pharmacy.

“8. The method of claim 6, wherein the user interface of the mobile device of the user displays a comparison between the pharmacy of the user and the one or more alternative pharmacies for the user, the comparison comprising one or more of the following: (i) respective prescription co-payment costs associated with each pharmacy, (ii) a respective savings associated with each of the one or more alternative pharmacies relative to the pharmacy of the user, (iii) a respective proximity of the each pharmacy to the user, (iv) a respective network associated with the each pharmacy, or (v) respective insurance plan benefits associated with the each pharmacy.

“9. The method of claim 1, further comprising: analyzing a medical expense history of the user, the medical spending history comprising one or more of health spending account contributions, health spending account usage, deductible tracking, and out-of-pocket expenses of the user; determining a financial prediction for the user based on the medical expense history; and providing the recommendation based on the financial prediction, wherein the recommendation comprises a recommended timeframe for medical visits or medical procedures for the user.

“10. The method of claim 1, wherein the machine learning model is configured to: determine, based on a set of feature values, a template of a first insurance card that is similar to the insurance card of the user in the at least one image, wherein the template is a geometrical representation of the first insurance card known to an insurance management system and comprises one or more slots in various locations on the template where each slot is associated with a respective type of information.”

There are additional claims. Please visit full patent to read further.

For the URL and additional information on this patent, see: Medous, Frederic. Using machine learning to classify insurance card information. U.S. Patent Number 11074656, filed September 14, 2018, and published online on July 27, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11074656.PN.&OS=PN/11074656RS=PN/11074656

(Our reports deliver fact-based news of research and discoveries from around the world.)