Walgreen Company

International Business Machines Corporation

Adam Robert Snopek

Igor Gershfang

Mohsin Ovais Ansari

Nimesh S Jhaveri

Fauzia Somani

Oliver Derza

Renata Lurye

Rick A Hamilton, Ii

James W Seaman

Neil A Katz

Richard Young

Lawrence Salud

Nicholas Phillip Baldwin

John Yesko

Methods and systems may support dynamic, real-time or near-real-time processing, analysis, and processing of data to automatically identify and obtain coupons corresponding to consumer products such that, in response to receiving a request to purchase one or more products, qualifying coupons may be automatically identified, retrieved, and applied to the purchase.

Systems and methods for autonomous management of manufacturer coupons

To extract information from a customer's insurance card, an insurance policy recordation system obtains a scanned image of an insurance card and analyzes text in the insurance card image to identify labels describing insurance policy characteristics and insurance policy values corresponding to the insurance policy characteristics. The insurance policy recordation system compares the identified insurance policy values to a set of stored identifiers associated with various insurance plans. If the insurance policy values match one or more of the identifiers, the insurance policy recordation system may determine that the insurance policy values are valid and that the customer has the matching insurance plan. The insurance policy recordation system may obtain additional insurance policy information for the customer based on the stored information in association with the matching insurance plan. Then the insurance policy recordation system may update the customer's record with the insurance policy values and/or additional insurance policy information.

Systems and methods for automatic recording of insurance card information

Systems and methods may be provided that may enable an individual to retrieve one or more prescription products (e.g., product refills) from a self-service retrieval location such as a counter, drive-thru, drop box, or kiosk. A unique-machine readable code may be associated with a particular one or more prescription products (e.g., associated with a medical prescription of a patient). The unique machine-readable code may be provided to a computing device of the individual, along with an anticipated time of preparedness of the one or more products for retrieval at the retrieval location. Identification of the unique machine-readable code may result in dispensing of the one or more prescription products associated at the retrieval location.

Technology for processing prescription medications for pickup by individuals

Systems and methods may be provided that may enable an individual to retrieve one or more prescription products (e.g., product refills) from a self-service retrieval location such as a counter, drive-thru, drop box, or kiosk. The individual may identify a medical prescription, and a unique-machine readable code may be generated, wherein the code is associated with the medical prescription and a particular station at an identified retrieval location. The unique machine-readable code may be transmitted to a computing device of the individual, who may scan the code at particular station to retrieve one or more prescription products associated with the medical prescription.

Technology for managing priorities for on-site prescription pickup

Example methods, apparatus, and articles of manufacture to estimate waiting times of prescriptions are disclosed herein. An example computer-implemented method, executed by a processor, to estimate a waiting time of a prescription for a medication includes training a machine learning model using, for each of a plurality of previously filled prescriptions, a set of characteristics of the previously filled prescription, and a fill time for the previously filled prescription, receiving a prescription for a medication for a patient, receiving a request for an estimated waiting time for filling the prescription medication for the patient, identifying a set of characteristics of the prescription medication for the patient, applying the set of characteristics of the prescription medication to the machine learning model to determine the estimated waiting time for filling the prescription medication for the patient, and providing an indication of the estimated waiting time for display on a client device.

Methods and system to estimate retail prescription waiting time

Systems and methods may be provided that may enable an individual to retrieve one or more prescription products (e.g., product refills) from a self-service retrieval location such as a counter, drive-thru, drop box, or kiosk. A unique-machine readable code may be associated with a particular medical prescription of a patient, the medical prescription corresponding to one or more prescription products. The unique machine-readable code may be transmitted to a computing device of the individual, who may scan the code at the retrieval location. Identification of the unique machine-readable code may result in dispensing of the one or more prescription products associated with the medical prescription.

The present invention provides various approaches for disseminating emergency broadcasting messages or other messages of importance (hereinafter 'alerts') within virtual world environments such that interested virtual world residents can be made aware of matters of importance based on their physical world locations (e.g., without respect to the locale of their current virtual world presence).

Informing users of a virtual universe of real world events

Aspects of this invention are directed toward determining the effectiveness of advertising in a virtual universe. Specifically, under the present invention, a visit by an avatar to a region of the VU that includes targeted advertising content is registered. Then, the following avatar attributes are determined: a proximity to the target advertising content by the avatar; an avatar directionality of the avatar with respect to the target advertising content; a camera directionality of a camera object associated with the avatar; and a time with respect to which the avatar may be exposed to the target advertising content in the region. Thereafter, at least one score is assigned to the visit, the proximity, the directionality, and the time. An effectiveness score is then calculated based on the at least one score. The overall effectiveness of the target advertising content is then calculated based on the at least one score.

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Chicago, IL, United States of America

Adam Robert Snopek