Walmart Apollo, LLC

Frédéric Médous

Walter R Smith

Jonathan I Schwartz

Christian Hagel-Sorensen

Chi-Kai Chien

Paul Teixeira

Peyman Oreizy

Brittany Staten

A system comprising one or more processors, and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: displaying, one or more instructions on a user interface of a mobile device to instruct a user to capture each respective image of a prescription label; determining, using contrast, a location of each respective image in the set of images that is associated with the respective different portions of the prescription label on the medication package; reconstructing each of the respective different portions of the prescription label on the medication package as a flattened reconstruction of the respective different portions of the prescription label; and providing for display on the user interface a reconstructed prescription label. Other embodiments are disclosed herein.

Importing structured prescription records from a prescription label on a medication package

A system including one or more processor and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: extracting one or more feature values from at least one image of an insurance card of a user; generating a feature vector associated with the at least one image of the insurance card of the user; reducing, using dimensionality reduction, an amount of data in the feature vector to a reduced set of data; determining, by a machine learning model, a first insurance plan of the user based on machine learning model input data comprising the reduced set of data; identifying at least one alternative insurance plan for the user; and sending instructions to display a recommendation of the at least one alternative insurance plan on a user interface. Other embodiments are disclosed.

Using machine learning to classify insurance card information

The system captures portions of a label on a package in a set of images, reconstructs the label based on the set of images, identifies text in the label, determines associations of identified text and types of information, and stores the set of images, the reconstructed label, the identified text in the label, and the determined associations as, for example, a batch in a review queue. During a review process, the batch is reviewed and a structured prescription record is determined for the batch which is further used by the system and user of the system associated with the batch to provide various features to the user.

Generating prescription records from a prescription label on a medication package

A system can include one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors to perform: receiving at least one image of an insurance card of a user; extracting one or more feature values from the at least one image of the insurance card of the user; reducing an amount of data in a 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 a machine learning model, as trained, a first insurance plan of the user based on machine learning model input data; identifying at least one alternative insurance plan to be displayed to the user based at least in part on historical medical information associated with the user; and a recommendation comprises the at least one alternative insurance plan. Other embodiments are disclosed.

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 insurance management system extracts features from the at least one image and generates a feature vector associated with the at least one image. A machine learning model receives the feature vector as input and outputs a classification comprising an association with an insurance plan. The insurance management system receives historical medical information that is specific to the user and indicates a current medical condition of the user and evaluates the insurance plan of the user in view of their historical medical information. Based on the evaluation, the insurance management system provides a recommendation that comprises one or more alternative insurance plans or one or more alternative pharmacies.

Growing community of inventors

San Francisco, CA, United States of America

Frédéric Médous