Google Inc.

Logistics Management Institute (lmi)

John D Westbrook

Leon G Palm

Colby Dee Ranger

Daniel J Berlin

Doug Coker

Tovey C Bachman

Karl Kruse

Joel Lepak

Helen V Hunt

Ethan C Ambabo

A computer-implemented method includes receiving (i) a request for ratable content, and (ii) information specifying a geographic location of a computing device; accessing a data repository configured to store items of ratable content, with an item of ratable content associated with a geographic location; obtaining a plurality of items of ratable content related to the geographic location of the computing device; sending, from the obtained items of ratable content, an item of ratable content to the computing device; receiving a request for ratable content associated with a level of granularity relative that is higher than the level of granularity of the sent item of ratable content; sending, to the computing device, another item of ratable content, from the obtained items of ratable content, that is associated with a level of granularity that is higher than the level of granularity of the previously sent item of ratable content.

Retrieving ratable content based on a geographic location

Method, computer-readable media, and systems for ranking content submissions and scheduling content submissions for additional voting in a distributed online community are disclosed. A statistical confidence interval is calculated for the approval ratio of each content submission based on one or more scaling formulae. A popularity ranking of the content submissions is generated based on the lower bounds of the confidence intervals calculated for the content submissions. Top-ranked content submissions in the popularity ranking are presented to users as the most popular content submissions. A voting priority ranking of the content submissions is generated based on the upper bounds of the confidence intervals calculated for the content submissions. Top-ranked content submissions in the voting priority ranking are selected as featured content submissions for additional voting. The selection of the top-ranked content submissions for additional voting is randomized based on a selected probability distribution.

Ranking and vote scheduling using statistical confidence intervals

A method or a machine determines minimum (s) and maximum (S) inventory control levels (s, S) for inventory items based upon a demand history of the inventory items, such as demand inter-arrival times (IA) and demand requisition sizes (RS). An expected cost is computed including two or more inventory item cost components of expected cost of ordering, expected cost of carrying inventory, or expected cost of outstanding backorders for the inventory control levels (s, S), directly from frequency statistics of different demand IA and demand RS on an inventory item basis. An optimal inventory control level (s, S) of an inventory item is searched that minimizes the cost of ordering, carrying inventory, and/or backorders, based upon the generated expected cost.

Method and computer system for setting inventory control levels from demand inter-arrival time, demand size statistics

Growing community of inventors

Reston, VA, United States of America

John D Westbrook