Walgreen Company

Benjamin Weiss

Nicholas L Eby

Timothy P McCauley

Adam Crouch

Jon Turkington

Atul Singh

Kartik Subramanian

Richard S Majka

Andrew David Schweinfurth

Lindsey Kanefsky

Joseph Zizzo

Arindam Bhattacharjee

Tim P McCauley

Kate Louise Paduchowski

Ankur Jain

Shivakumar Chandramouli

Viral Brahmbhatt

Abraham Jeyaraj

Rich Majka

A method and system may provide a new prescription order interface which allows a customer to order one or more new prescription medications in a quick and easy manner from a remote location using a computer or mobile device. The customer provides a new paper prescription image that includes the prescription data for a pharmacy to fill the new prescription order. The new paper prescription image is received by a server and a default pickup store and a default pickup time are determined. The server also determines whether there is a high risk that the new prescription order is fraudulent by comparing a signature in the new paper prescription image to stored signatures associated with the prescriber, and comparing distances between the locations of the customer's web-enabled device, the pharmacy, and the prescriber. The system may be accessed through a series of web pages and/or via an application.

System and method for a new prescription

Systems and methods for managing and facilitating geofencing features associated with electronic devices are disclosed. According to certain aspects, an entity may own or manage a set of retail stores. A server associated with the entity may receive an estimated location of an electronic device, and may determine that the estimated location is in proximity to one of the set of retail stores. The server may further calculate a geofencing boundary for the retail store based on certain location-based data, and may determine whether the estimated location of the electronic device is within the geofencing boundary. The server may additionally facilitate certain geofencing features based on the determination.

Technology for managing location-based functionalities for electronic devices

Embodiments are disclosed to determine customer's prescription information and their position while waiting in a drive-thru lane. The drive-thru lane may have one or more transmitters installed at predetermined locations. By utilizing an application installed on each customer's mobile computing device, each mobile computing device may transmit customer information and ranging data when within range of one or more of the transmitters. The ranging data may include an indication of a range between each of the customer's mobile computing device and one or more proximate transmitters. By correlating the customer information to prescription information from recently filled and/or called in prescriptions, each customer's prescription order may be retrieved. Furthermore, by analyzing the ranging data, the position of each customer in the drive-thru lane may be determined. A drive-thru computing device is disclosed that may display each customer's position in the drive-thru lane along with their prescription order information.

Drive-thru system implementing location tracking

Systems and methods are disclosed for manipulating and accessing a blockchain containing coupon authentication information for coupon processing services to process a coupon maintained by a network of nodes including a first node and a second node. A method may include generating, by the first node, a first coupon transaction that includes a smart coupon contract and broadcasting the first coupon transaction to the second node communicatively coupled to the network. The second node is configured to apply a set of consensus rules to validate the smart coupon contract included in the first coupon transaction prior to adding the first coupon transaction onto the blockchain, causing any one of the network of nodes to execute the smart coupon contract against input data after the first coupon transaction is validated and added onto the blockchain.

Coupon clearinghouse with blockchain

A computer-implemented method includes downloading respective instances of an enterprise mobile application to a plurality of mobile devices. The instances of the enterprise mobile applications, while executing on respective mobile devices, capture, for each session, a session log that includes indications of ordered user actions occurring during the session, and optionally time intervals between user actions and/or user attributes. Captured session logs stored at and are mined by one or more servers to discover a particular pattern or sequence of user actions that occurred across multiple, different user sessions. If the number and/or rate of occurrences of the particular pattern is greater than a threshold, a new test case corresponding to the pattern is automatically generated and added to a suite of test cases for the UI functionality of the enterprise mobile application. The updated test suite may be automatically executed on a test version of the enterprise mobile application.

Crowd-sourced automatic generation of user interface tests for enterprise-specific mobile applications

Systems and methods are provided for diagnosing an issue associated with an enterprise application on a client device by initiating a communication from the client device to a CSR device to report the issue, generating a matter identifier associated with the reported issue, transmitting a communication containing a deep link to the client device, invoking the deep link to initiate mining of logging data corresponding to the use of the enterprise application and stored locally on the client device, formatting and/or tagging the mined logging data with the matter identifier, transmitting the formatted mined logging data from the client device to the CSR device and/or a proprietary server, analyzing the mined logging data to diagnose a source of the reported issue, and attempting to resolve the reported issue based on the analysis of the mined logging data.

Client side diagnostics for enhanced customer care

A computing system for adaptive prescription reminders includes a processor and a memory storing instructions that, when executed by the processor, cause the system to receive a fill event, generate an initial schedule, receive an medication consumption indication, generate an updated schedule using a trained machine learning model, cause the updated schedule to be displayed, and receive a patient confirmation. A computer-implemented method includes receiving a fill event, generating an initial schedule, receiving an medication consumption indication, generating an updated schedule using a trained machine learning model, causing the updated schedule to be displayed, and receiving a patient confirmation. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive a fill event, generate an initial schedule, receive an medication consumption indication, generate an updated schedule using a trained machine learning model, cause the updated schedule to be displayed, and receive a patient confirmation.

Cloud-based methods and systems for providing adaptive prescription reminders

Embodiments are disclosed to detect whether an interaction has occurred between a customer and one or more store employees. By analyzing a how long a communication device used by the customer was within a threshold range of one or more devices worn by store employees, an interaction between the customer and one or more store employees may be identified. Employees whom the customer interacted with may be identified by processing transmission parameters broadcasted by the devices worn by the store employees. Thus, all employees in which a customer interacted with while visiting a store may be identified. The customer may be asked for feedback via the communication device upon detection that a customer has completed a transaction and/or left the store. Using the solicited feedback provided by the customer, various types of reports may be generated.

Gathering in-store employee ratings using triggered feedback solicitations

A pharmacy order facilitation method includes receiving a message; training a machine learning model; generating an intent; generating a response message; and transmitting the response message. A computing system includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: receive a message; train a machine learning model; generate an intent; generate a response message; and transmit the response message. A computing system includes a mobile device; a server device configured to: train a machine learning model; analyze the inbound message; identify a customer intent; perform an action based on the customer intent; and transmit an outbound message.

Facilitating pharmacy customer orders through natural language processing

System and method for a new prescription scan

A method for facilitating location-based notification display by an application at a mobile device includes, while the application is in a launched state, receiving from the mobile device a location of the mobile device, determining one or more entity locations based on the location of the mobile device, and causing the mobile device to register the one or more entity locations. The method also includes, while the application is in a background state, causing the mobile device to register one or more location-triggered notifications, wherein each location-triggered notification of the one or more location-triggered notifications is associated with a corresponding entity location of the one or more entity locations.

Growing community of inventors

Chicago, IL, United States of America

Benjamin Weiss