Cohesity, Inc.

Paypal, Inc.

Prashant Gaurav

Anubhav Gupta

Nagapramod S Mandagere

Apurv Gupta

Karandeep Singh Chawla

Sudhir Srinivas

Sidharth Mishra

Kevin Ward

Fransisco Kurniadi

Dinesh Kumar

Yue Xin

Krishnakumar Govindarajalu

Tao Sun

Yuanyuan Pan

Kimberly Kidney

An amount of data change associated with a version of a content file with respect to one or more previous versions of the content file is determined. The amount of change associated with the version of the content file is determined using a tree data structure associated with the content file that is stored on a storage cluster. One or more statistics associated with backup snapshot are provided to a server. The server is configured to determine that the amount of data change associated with the version of the content file is anomalous based in part on the one or more statistics associated with the backup snapshot. A notification that data associated with the backup snapshot is potentially infected by malicious software is received from the server. The version of the content file is indicated as being potentially infected by malicious software.

Malicious software detection

Aspects of the present disclosure involve systems, methods, devices, and the like for presenting a recommendation. In one embodiment, a system is introduced that includes a plurality of models for obtaining a recommendation score. The recommendation score may be obtained using one or more models which can include supervised and unsupervised learning as well as a combination of user information and transactions. In another embodiment, the system is introduced that can provide a total recommendation score and recommendation generated by an ensemble model whose input can include the one or more recommendation scores previously obtained.

System and method for obtaining recommendations using scalable cross-domain collaborative filtering

Aspects of the present disclosure involve systems, methods, devices, and the like for presenting a recommendation. In one embodiment, a system is introduced that includes a plurality of models for obtaining a recommendation score. The recommendation score may be obtained using one or more recommendation models and a recommendation made based on the recommendation score determined. In another embodiment, the system is introduced that can re-train the recommendation model based on a feedback received in response to a recommendation made using on the recommendation score obtained.

Growing community of inventors

Fremont, CA, United States of America

Prashant Gaurav