Walgreen Company

Sears Brands, LLC

Kartik Subramanian

Andrew David Schweinfurth

Raymond J Kim

Lindsey Kanefsky

Bryan Anderson

Benjamin Weiss

Nicholas L Eby

Dimple Rao

Garima Pokharel

Joe Rago

Shubham Agarwal

Vijan Bhaumik

Robert C Hudson

Richard Kevin Lyons

Todd Schmeling

Giuseppe Mario Di Prizio

Abhishek Ramani

Viral Brahmbhatt

G Mercedes DeLuca

Shivakumar Chandramouli

Techniques for automatically securing receptacles of a retailer via an electronic lock include receiving a user request to reserve a receptacle, selecting a suitable receptacle based on one or more conditions, some of which may be specified by the user, and transmitting an electronic locking signal to secure, and thereby reserve, the selected receptacle. An association between the reserved receptacle and the user is stored, and the user is electronically notified. Subsequently, an electronic signal indicative of the identification of the user (or of the user's agent) is locally received. Upon determination that the user is expected and associated with the reserved receptacle, the techniques include causing an electronic unlocking signal to be transmitted to the electronic lock, thereby unlocking the receptacle. Further subsequently, upon receiving an electronic locking instruction, the receptacle may automatically be secured via its corresponding electronic lock, and a retailer agent may be electronically notified.

Remote trigger for security system

Systems and methods for automatically processing prescription refills and transfers are disclosed. According to certain aspects, a server associated with a pharmacy may receive, from an electronic device, a message including a digital image depicting a container of a prescription. The server may analyze the digital image to identify a set of visual identifiers depicted in the digital image. Based on the set of visual identifiers, the server may determine whether the prescription is associated with the pharmacy and, based on the determination, may facilitate either a refill of the prescription or a transfer of the prescription to the pharmacy.

Technology for automatically processing prescription functionalities based on transmitted digital images

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

Systems and methods are provided for identifying and communicating with a user through the user's mobile device. One example method includes receiving, from the mobile device, a user identifier associated with the user and an entity identifier associated with an entity location, the entity identifier being obtained by the mobile device from a wireless transmitter located adjacent to an entrance of the entity location; retrieving user profile data associated with the user identifier from a user database, the user profile data including a user qualifier; providing the user profile data to a display device at the entity location, the display device configured to display the user profile data, including the user qualifier; and sending at least one notification to the mobile device for display thereon, the at least one notification comprising at least one of information related to the entity location and a portion of the user profile data.

System and method for identifying mobile device users using a wireless communications device at an entity location

The method and system may provide a seamless handoff of user information from a drugstore to a call agent. When a customer communicates with a drugstore device regarding a drugstore-related inquiry, the drugstore device attempts to identify an answer to the drugstore-related inquiry. When the drugstore device does not identify an answer to the drugstore-related inquiry, the drugstore device initiates communication between the customer and a contact center. A transcribed version of the communication may be stored in a database accessible by the contact center along with additional user information for the customer related to the customer's experiences with the drugstore. The user information may be provided to a call agent's contact center device for display and in this manner, the call agent may be made aware of the communication to avoid asking repeat questions and to quickly and efficiently answer the customer's drugstore-related inquiry.

Method and system for providing a seamless handoff from a voice channel to a call agent

Techniques are disclosed for leveraging user shopping habits to deliver store-relevant materials in a more efficient manner. User location and other relevant information may be analyzed to predict an in-store shopping path for future visits, which may include different store areas each having different store-relevant materials (e.g., coupons) associated therewith. Upon a user visiting the store on a future visit, the predicted path may be used to upload data to the user's portable computing device for store-relevant materials in the order in which they will likely be needed. As the user moves within the store, the portable computing device may purge data associated with store areas that have already been visited while pre-fetching the next store area likely to be visited next.

Leveraging predictive modeling for application optimization

A computer-implemented method includes receiving a message of a pharmacy customer wherein the message includes a text string, and generating a set of one or more intents corresponding to the pharmacy customer by analyzing the text string using a trained machine learning model, wherein each of the one or more intents correspond to a respective desired action pertaining to the pharmacy order. The machine learning model may be a classification model. The method further includes generating at least one response message based on the set of intents corresponding to the pharmacy customer and transmitting the at least one response message to a mobile device associated with the pharmacy customer. The set of one or more intents may be chosen from a list of commonplace pharmacy customer intents.

Systems and methods are provided for anticipating needs of a user through a mobile device of the user. One example method includes receiving, from the mobile device, a location identifier associated with a position within an entity location, the mobile device receiving the location identifier from a wireless transmitter upon entering a predetermined transmission range of the wireless transmitter; starting a first timer upon receiving the location identifier; and initiating a predefined action associated with the position within the entity location upon determining that a time on the first timer exceeds a predetermined threshold.

Growing community of inventors

Chicago, IL, United States of America

Kartik Subramanian