Walgreen Company

Plantronics, Inc.

Gunjan Dhanesh Bhow

Julija Alegra Petkus

Andrew David Schweinfurth

Ajumobi O Udechukwu

Brian C Tyrrell

Ryan M Snedden

John H Hart

Ken Kannappan

Kenneth A Kannappan

Methods and systems may incorporate voice interaction and other audio interaction to facilitate access to prescription related information and processes. Particularly, voice/audio interactions may be utilized to achieve authentication to access prescription-related information and action capabilities. Additionally, voice/audio interactions may be utilized in performance of processes such as obtaining prescription refills and receiving reminders to consume prescription products.

Communication mode selection based upon user context for prescription processes

Voice authentication systems and methods for prescription processes

Customizable audio in prescription reminders

Communication mode selection based upon device context for prescription processes

Audio authentication systems and methods for prescription processes

An offer decay generation model determines, for a particular customer, a personalized optimal offer decay curve of an incentive corresponding to a product provided by an enterprise, where the offer decay curve defines a set of decreasing incentive values and respective time intervals during which each incentive value is valid. The offer decay generation model is trained on historical data indicative of customers, customer interactions, offered incentives, resulting outcomes of the incentives, and time intervals elapsing between incentives and resulting outcomes. As such, the optimized offer decay curve is structured to maximize a probability that the particular customer is motivated to accept the incentive offer, purchase a product, and/or further interact with the enterprise during the lifetime of the offer decay curve. The offer decay curve may unique to the individual customer, and may be further customized based on other parameters such as location, time/day/date, inventories, etc.

Machine learning system for personally optimized offer decay curves

The following relates generally to merchandise pickup location and/or pharmacy selection. In some embodiments, factors are used to determine a pharmacy and/or merchandise pickup location selection for an individual. In this regard, the factors may include: whether the pharmacy and/or merchandise pickup location has a merchandise item and/or medication in stock; wait time at the pharmacy and/or merchandise pickup location; geographic distance to the individual; travel time from the location of the individual; urgency of filling a prescription; price of a prescription; whether another product or class of products available at the pharmacy and/or merchandise pickup location; and/or whether a locker is available at the pharmacy and/or merchandise pickup location. In some embodiments, Artificial Intelligence (AI) is used to create a model of pharmacy and/or merchandise pickup location selection for the individual.

System and method for displaying dynamic pickup location information on a graphical user interface

The following relates generally to pharmacy and/or merchandise pickup location selection. In some embodiments, factors are used to determine a pharmacy and/or merchandise pickup location selection for an individual. In this regard, the factors may include: whether the pharmacy and/or merchandise pickup location has a medication in stock; wait time at the pharmacy and/or merchandise pickup location; geographic distance to the individual; travel time from the location of the individual; urgency of filling a prescription; price of a prescription; whether another product or class of products available at the pharmacy and/or merchandise pickup location; and/or whether a locker is available at the pharmacy and/or merchandise pickup location. In some embodiments, Artificial Intelligence (AI) is used to create a model of pharmacy and/or merchandise pickup location selection for the individual.

System for displaying dynamic pharmacy information on a graphical user interface

Methods and systems for auto-connection into conference calls are disclosed. An attendee receives a meeting request at a computing device. At the proposed date and time of the meeting, the conference call dial-in number is automatically dialed and a first call is established. The conference call meeting identification number is transmitted over the first call. A second call to a second device is initiated. Upon establishing the second call, the first and second calls are linked/conferenced.

Method and systems for connection into conference calls

Systems and methods for initiating telephony calls are presented. In one example, a phone call initiation is detected on a first communications network. The phone call initiation is redirected to a second communications network. The call is connected utilizing the second communications network.

Growing community of inventors

Menlo Park, CA, United States of America

Gunjan Dhanesh Bhow