Plume Design, Inc.

Uno.ai, Inc.

Murugesan Guruswamy

William John McFarland

Adam R Hotchkiss

Paul White

Balaji Rengarajan

Evan Jeng

Aman Singla

Sameer Vaidya

William Mcfarland

Griffin Maxwell Brome

Kaixin Huang

Sahaj Tushar Gandhi

Shashank Tiwari

Stephanie Yuen-kwan Knill

Sukhvir Singh

Adam Hotchkiss

A computer-implemented system and method for generating time-based contextual graph in cloud computing environment is disclosed. The system receives, from computing devices, requests corresponding to security anomaly analysis of cloud infrastructure-based resources and applications. The system identifies parent node and node attributes corresponding to parent node in cloud infrastructure-based resources and/or applications, based on requests. Further, system determines hierarchical node relationships between parent node and child nodes, based on identified node attributes. Furthermore, system analyses risks in context of security anomaly associated with cloud infrastructure-based resources and/or applications, based on hierarchical node relationships between parent node and child nodes. Further, system generates time-based contextual graph corresponding to analyzed risks in context of security anomaly, based on determined hierarchical node relationships. Furthermore, system outputs on display associated with the computing devices, result corresponding to analyzed risks in context of security anomaly using generated time-based contextual graph.

System and method for generating time-based contextual graph in cloud computing environment

Systems and methods include obtaining measurements from a Wi-Fi network that includes i) a plurality of access points interconnected to one another and at least one connected to a gateway, and ii) a plurality of Wi-Fi client devices that each connect to an access point of the plurality of access points; learning usage patterns in the Wi-Fi network through analysis with one or more machine learning algorithms; and providing a configuration to the Wi-Fi network based on the learned usage pattern where the configuration includes a topology of the plurality of access points.

Cloud-based control of a Wi-Fi network

Systems and methods include obtaining measurements from a Wi-Fi network that includes one or more access points; determining a configuration of the Wi-Fi network based on the measurements, wherein the configuration includes one or more of i) steering a client device to associate with a specific access point of the one or more access points and ii) steering the client device to connect to a specific frequency band of the one or more access points; and providing the configuration to the Wi-Fi network for implementation thereof.

Systems and methods implemented by a cloud-based controller to control a Wi-Fi network with a plurality of access points include obtaining measurements from the Wi-Fi network; determining a configuration of the Wi-Fi network based on the measurements, wherein the configuration comprises a topology of the Wi-Fi network and the topology comprises the plurality of access points selectively interconnected to one another via backhaul links; and providing the configuration to the Wi-Fi network for implementation thereof.

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San Jose, CA, United States of America

Murugesan Guruswamy