Excalibur Ip, LLC

Yahoo! Inc.

Harenss Inc.

Shivakumar Ningappa

Supreeth Rao

Yu-Chin Tai

Sundeep Narravula

Katrina Kimball Clark Tempero

Chris Chan

Shatakshi Goyal

Ameya Barsode

Clarence Lok Him Leung

Brian Bouta

Srinivasa Rao Gurubelli

Uri Scheiner

Frank Teoh

Srinivas Bandi Ramesh Babu

A system that automatically reduces test cycle time to save resources and developer time. The present system selects a subset of tests from a full test plan that should be selected for a particular test cycle, rather than running the entire test plan. The subset of tests is intelligently selected using metrics such as tests associated with changed code and new and modified tests.

Test cycle time reduction and optimization

In one embodiment, data received from one or more streaming data sources may be monitored by one or more devices. A rate of change in flow of the data received from the one or more streaming data sources may be ascertained. It may be determined whether the rate of change in flow of the data received from the one or more streaming data sources exceeds a threshold rate. Transmission of an alert may be initiated according to a result of determining whether the rate of change in the flow of the data received from the one or more streaming data sources exceeds the threshold rate.

Rainbow event drop detection system

A method and/or technique for geolocation safety awareness is provided herein. When a user, having a client device, travels to a location, threats associated with that location may not be known to the user. To determine a safety of the location, the location of a client device may be determined. A search for safety information about the location may be performed, and a threat level may be determined based upon the safety information. When the threat level exceeds a threat threshold, a security operation may be performed. The security operation may comprise presenting a warning notice to the user and/or activating a security timer.

Geolocation safety awareness

Software on a content-aggregation website obtains a resource associated with a podcast from a website publishing the podcast and stores it e resource on the content-aggregation website. The software adds the resource as a leaf node to a taxonomy generated by the content-aggregation website. The addition is based on data associated with the podcast. The non-leaf nodes in the taxonomy are categories of content. The software determines that a user of the content-aggregation website is qualified as to at least one category that includes the resource as a leaf node. The determination is based at least in part on feedback from the user that includes a viewing or listening history for the user. Then the software serves the resource to the user in a content stream published by the content-aggregation website, based at least in part on a personalization score associated with the resource.

Podcasts in personalized content streams

Growing community of inventors

Milpitas, CA, United States of America

Shivakumar Ningappa