Researchers Submit Patent Application, “Using Machine Learning To Classify Insurance Card Information”, for Approval (USPTO 20190080416)

2019 APR 03 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors Smith, Walter R. (Seattle, WA); Medous, Frederic (San Francisco, CA), filed on September 14, 2018, was made available online on March 14, 2019.

The patent’s assignee is Care Zone Inc. (San Francisco, California, United States).

News editors obtained the following quote from the background information supplied by the inventors: “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.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “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 claims supplied by the inventors are:

“1. A method comprising: receiving, from a client device, at least one image of an insurance card associated with a user operating the client device; extracting features from the at least one image; generating a feature vector associated with the at least one image that includes the features extracted for that image; inputting the feature vector into a machine learning model to determine a classification associated with the at least one image, wherein the machine learning model has been trained using classifications of insurance cards of other users, the classification comprising an association with an insurance plan represented by the insurance card; storing the insurance plan of the classification as a current selection of the user; receiving historical medical information that is specific to the user and indicates a current medical condition of the user; evaluating, based in part on the historical medical information associated with the user, the current selection the user; and providing for display to the user in a user interface a recommendation, based in part on the evaluation, associated with the current selection of the user.

“2. The method of claim 1, wherein the set of features comprises one or more of the following: text information, object information, color information, and shape information extracted from the at least one image.

“3. The method of claim 2, 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, and a customer service number.

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

“5. The method of claim 1, wherein 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.

“6. The method of claim 1, further comprising: selecting the one or more alternative insurance plans for the recommendation based on an analysis of one or more of the following: one or more benefits associated with each insurance plan, a cost associated with each insurance plan, and a savings associated with each of the one or more alternative insurance plans relative to the insurance plan of the user.

“7. The method of claim 1, wherein the user interface displays a comparison between the insurance plan of the user and the one or more alternative insurance plans for the user, the comparison comprising one or more of the following: one or more benefits associated with each insurance plan, a cost associated with each insurance plan, and a savings associated with each of the one or more alternative insurance plans relative to the insurance plan of the user.

“8. The method of claim 1, wherein the 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.

“9. The method of claim 1, further comprising: selecting the one or more alternative pharmacies for the recommendation based on an analysis of one or more of the following: prescription co-payment costs associated with each pharmacy, a savings associated with each of the one or more alternative pharmacies relative to the pharmacy of the user, proximity of each pharmacy to the user, a network associated with each pharmacy, and insurance plan benefits associated with each pharmacy.

“10. The method of claim 1, wherein the user interface 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: prescription co-payment costs associated with each pharmacy, a savings associated with each of the one or more alternative pharmacies relative to the pharmacy of the user, proximity of each pharmacy to the user, a network associated with each pharmacy, and insurance plan benefits associated with each pharmacy.

“11. The method of claim 1, further comprising: analyzing a medical expense history of the user, the medical spending history comprising one or more of a health spending account contributions, health spending account usage, deductible tracking, and out-of-pocket expenses; determining a financial prediction 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.

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

“13. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving, from a client device, at least one image of an insurance card associated with a user operating the client device; extracting features from the at least one image; generating a feature vector associated with the at least one image that includes the features extracted for that image; inputting the feature vector into a machine learning model to determine a classification associated with the at least one image, wherein the machine learning model has been trained using classifications of insurance cards of other users, the classification comprising an association with an insurance plan represented by the insurance card; storing the insurance plan of the classification as a current selection of the user; receiving historical medical information that is specific to the user and indicates a current medical condition of the user; evaluating, based in part on the historical medical information associated with the user, the current selection the user; and providing for display to the user in a user interface a recommendation, based in part on the evaluation, associated with the current selection of the user.

“14. The non-transitory computer-readable storage medium of claim 13, wherein 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.

“15. The non-transitory computer-readable storage medium of claim 13, the operations further comprising: selecting the one or more alternative insurance plans for the recommendation based on an analysis of one or more of the following: one or more benefits associated with each insurance plan, a cost associated with each insurance plan, and a savings associated with each of the one or more alternative insurance plans relative to the insurance plan of the user.

“16. The non-transitory computer-readable storage medium of claim 13, wherein the user interface displays a comparison between the insurance plan of the user and the one or more alternative insurance plans for the user, the comparison comprising one or more of the following: one or more benefits associated with each insurance plan, a cost associated with each insurance plan, and a savings associated with each of the one or more alternative insurance plans relative to the insurance plan of the user.

“17. The non-transitory computer-readable storage medium of claim 13, wherein the 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.

“18. The non-transitory computer-readable storage medium of claim 13, the operations further comprising: selecting the one or more alternative pharmacies for the recommendation based on an analysis of one or more of the following: prescription co-payment costs associated with each pharmacy, a savings associated with each of the one or more alternative pharmacies relative to the pharmacy of the user, proximity of each pharmacy to the user, a network associated with each pharmacy, and insurance plan benefits associated with each pharmacy.

“19. The non-transitory computer-readable storage medium of claim 13, wherein the user interface 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: prescription co-payment costs associated with each pharmacy, a savings associated with each of the one or more alternative pharmacies relative to the pharmacy of the user, proximity of each pharmacy to the user, a network associated with each pharmacy, and insurance plan benefits associated with each pharmacy.

“20. The non-transitory computer-readable storage medium of claim 13, the operations further comprising: analyzing a medical expense history of the user, the medical spending history comprising one or more of a health spending account contributions, health spending account usage, deductible tracking, and out-of-pocket expenses; determining a financial prediction 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.

“21. The non-transitory computer-readable storage medium of claim 13, wherein the machine learning model is configured to: determine, based on the set of features, a template of an insurance card that is similar to the insurance card in the at least one image, wherein the template is a geometrical representation of the insurance card known to the insurance management system and comprises one or more slots in various locations on the template where each slot is associated with a type of information.

“22. A method for training comprising: receiving, by an insurance management system, a set of features extracted from at least one image of an insurance card associated with a user; generating a feature vector associated with the at least one image that includes the features extracted for that image; training, by the insurance management system, a machine learning algorithm using the feature vector to determine a classification of the insurance card, wherein the classification comprises an association with an insurance plan; receiving historical medical information that is specific to the user and indicates a current medical condition of the user; and providing a recommendation to the user based on the classification of the insurance card and historical medical information of the user.

“23. The method of claim 22, further comprising: determining, based on the set of features, a template of an insurance card that is similar to the insurance card in the at least one image, wherein the template is a geometrical representation of the insurance card known to the insurance management system.

“24. The method of claim 23, wherein the classification further comprises a confidence level that the insurance card in the at least one image matches the template.

“25. The method of claim 22, wherein training the machine learning algorithm comprises: calculating a difference between the determined classification and a received classification of the insurance card, wherein the received classification comprises an identification of the insurance plan associated with the insurance card; and adjusting, based on the difference, one or more coefficients of the machine learning algorithm to reduce the difference.

“26. The method of claim 22, further comprising: forming a positive training set of images of other insurance cards that are classified as associated with a specific insurance plan or a specific insurance company; and providing the positive training set to the machine learning algorithm for training.

“27. The method of claim 22, further comprising: forming a negative training set of images of other insurance cards that are classified as not associated with a specific insurance plan or a specific insurance company; and providing the negative training set to the machine learning algorithm for training.

“28. The method of claim 22, further comprising: forming a validation training set of images of other insurance cards that are classified as associated or not associated with a specific insurance plan or a specific insurance company; and providing the validation training set to the machine learning algorithm for training.

“29. The method of claim 22, wherein 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.

“30. The method of claim 22, wherein the 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.”

For additional information on this patent application, see: Smith, Walter R.; Medous, Frederic. Using Machine Learning To Classify Insurance Card Information. Filed September 14, 2018 and posted March 14, 2019. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220190080416%22.PGNR.&OS=DN/20190080416&RS=DN/20190080416

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