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Bhavnish H. Lathia
Researcher at Amazon.com
Publications - 17
Citations - 798
Bhavnish H. Lathia is an academic researcher from Amazon.com. The author has contributed to research in topics: Mobile device & Geolocation. The author has an hindex of 7, co-authored 17 publications receiving 798 citations.
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Patent
User profile and geolocation for efficient transactions
TL;DR: In this article, a transaction between the user and a merchant may be completed with zero or minimal input from the user based on the geolocation of the mobile device and the user identifiers.
Patent
Location-based Coupons and Mobile Devices
Michael Carr,Bhavnish H. Lathia +1 more
TL;DR: In this paper, the authors describe a system for providing electronic coupons to users of mobile devices, where the relevance of the coupons may be determined based on the distance of the mobile device from a merchant location and/or a time period during which the coupon is valid.
Patent
Converged Web-identity and Mobile Device Based Shopping
TL;DR: In this paper, the authors proposed a method for providing information to a user of a mobile device based on an online or web-identity of the user and a geolocation of the mobile device.
Patent
Identifying early adopters and items adopted by them
David L. Selinger,Bhavnish H. Lathia,Hoi-Cheung Pang,David Liu,Shenghuo Zhu,Paat Rusmevichientong +5 more
TL;DR: In this paper, a computer process is disclosed for identifying and informing users of early adopter items, where scores are generated for particular items represented in an electronic catalog, with each score reflecting an extent to which a corresponding item has been ordered by customers who tend to order items promptly after they become available.
Patent
Scheduling data access jobs based on job priority and predicted execution time using historical execution data
Leon Robert Warman,Mark Austin Buckley,Bhavnish H. Lathia,Harsha Ramalingam,Erik W. Selberg,Robert Eicher Simmering +5 more
TL;DR: In this article, the authors proposed techniques for scheduling data access jobs based on a job dependency analysis, where a requested primary data access job is analyzed to determine one or more preliminary data access tasks on which it depends, and an execution duration of each data access task is predicted based on historical data or other factors.