Akamai Technologies, Inc.

Facebook, Inc.

Fortinet, Inc.

Lacework Inc.

Meta Platforms, Inc.

Jay Parikh

William Edward Weihl

Andrew Thomas Davis

Yijou Chen

Úlfar Erlingsson

Srikanth Thirumalai

Daniel M Lewin

Mark Tsimelzon

Daniel Stodolsky

Srinivasan Pichai

F Thomson Leighton

Marty Kagan

Samuel Dov Gendler

Hongzhong Jia

Anne E Lewin, Legal Representative

Nate Kushman

Ashis Tarafdar

Yael Gregory Maguire

Narsing Krishna Vijayrao

Ravi Sundaram

Matthew S Levine

Hariharan Rahul

Michael M Afergan

Adrian Soviani

Robert D Kleinberg

Bruce Maggs

Rizwan S Dhanidina

Eddie Ruvinsky

Yoav O Yerushalmi

Pamela Bhattacharya

Njall Skarphedinsson

Aditya Rajkumar Kalro

Timothy Chase

Lukasz Wesolowski

Mohamed Fawzi Mokhtar Abd El Aziz

Srinivasn Pichai

Initiating and utilizing pedigree for content, including: gathering information associated with a submission of content; determining, based on the information, a pedigree for the content; and performing one or more actions with respect to the submission of the content based on the pedigree for the content.

Initiating and utilizing pedigree for content

Guided interactions with a natural language interface, including: gathering data describing one or more user interactions with a system configured for monitoring a cloud deployment; generating, based on the data, a prompt comprising one or more natural language inputs for a workflow, wherein each of the one or more natural language inputs corresponds to a query for information related to the cloud deployment; and providing a selected natural language input to a natural language interface of the anomaly detection framework.

Guided interactions with a natural language interface

Providing a customizable generative artificial intelligence ('AI') assistant, including: identifying one or more customizations for the generative AI assistant, the generative AI assistant configured to receive information describing a monitored deployment and a natural language input, the generative AI assistant further configured to generate a response to the natural language input; and modifying, based on the one or more customizations, the generative AI assistant.

Customizable generative artificial intelligence ('AI') assistant

Using user feedback for attack path analysis in an anomaly detection framework, including: performing an attack path analysis for a cloud deployment; receiving, from a user, user feedback for an attack vector of the attack path analysis; and initiating, based on the user feedback, a workflow for modifying one or more parameters for generating the attack path analysis.

Using user feedback for attack path analysis in an anomaly detection framework

Leveraging generative artificial intelligence ('AI') for securing a monitored deployment, including: receiving natural language input associated with the monitored deployment, the monitored deployment monitored by a monitoring tool; and receiving, from a generative AI application, a response to the natural language input, wherein: the generative AI application accesses publicly available information as well as data sources associated with the monitoring tool; and the response is generated based at least in part on information contained in the data sources associated with the monitoring tool.

Leveraging generative artificial intelligence ('AI') for securing a monitored deployment

Integrating a natural language interface into an anomaly detection framework, including: detecting, by an anomaly detection framework, an occurrence of an event associated with one or more assets that are being monitored by an anomaly detection framework; generating, based on information associated with the detected event, one or more natural language inputs; and submitting, to a natural language interface, the one or more natural language inputs.

Generating computer code for remediating detected events

A natural language interface for an anomaly detection framework, including: receiving a natural language input associated with a cloud deployment; generating a query corresponding to the natural language input by disambiguating at least a portion of the natural language input based on data describing activity associated with an anomaly detection framework monitoring the cloud deployment; and providing, based on a response to the query, a response to the natural language input.

Natural language interface for an anomaly detection framework

The disclosed apparatus may include unmanned aerial vehicle having (1) a flight system that causes movement and hovering of the unmanned aerial vehicle, (2) a coupling mechanism that interacts with a corresponding coupling mechanism of an electronic device for carrying the electronic device from a remote location to a mount of an infrastructure component, and (3) an extension mechanism connecting the coupling mechanism to the flight system, wherein the extension mechanism dynamically extends the coupling mechanism from the flight system to facilitate installation of the electronic device to the mount while the unmanned aerial vehicle hovers. Various other apparatuses, devices, and methods are also disclosed.

Automated installation of electronic devices on infrastructure components

Providing a data lake-enabled security platform, including: storing security data associated with a customer in a data lake comprising a plurality of storage environments implemented in different cloud environments of a plurality of cloud environments; generating, based on a plurality of records in the security data, an abstracted security record describing one or more derived insights of the security data; and providing access to the abstracted security record to one or more users associated with the customer.

Data lake-enabled security platform

A guided anomaly detection framework, including: gathering data describing activity associated with an anomaly detection framework monitoring a cloud deployment; generating, based on the data, a prompt describing one or more natural language inputs for a security workflow, wherein each of the one or more natural language inputs corresponds to a query for information related to the cloud deployment; and providing a selected natural language input to a natural language interface.

Guided anomaly detection framework

A machine learning model is trained. A feature importance metric is determined for each machine learning feature of a plurality of machine learning features of the machine learning model. Based on the feature importance metrics, one or more machine learning features of the plurality of machine learning features of the machine learning model are managed.

Managing machine learning features

The disclosed apparatus may include (1) a mount of an infrastructure component, where the mount includes an installation surface that contacts an installed electronic device, and (2) a coupling mechanism that (a) provides a force causing a corresponding installation surface of the installed electronic device to maintain contact with the installation device, and (b) delivers electrical power via the installation surface to the installed electronic device. Various other apparatuses, devices, and methods are also disclosed.

A system and method for multi-channel communication with dual in-line memory modules ('DIMMs') is disclosed. The system retrieves information characterizing a plurality of memory channels, each of each is configurable to facilitate data communication between a DIMM and a memory controller with associated memory channel interfaces. Based on the retrieved information, one of the memory channels is designated as the active memory channel, granting the designated memory channel the ability to issue memory requests or transactions to the DIMM. On a periodic or as-needed basis (e.g., when the active memory channel is stalled or nearly stalled), the system determines whether to designate a different of the memory channels as the active memory channel, thereby enabling the newly-designated active memory channel the ability to issue memory requests or transactions to the DIMM. In some embodiments, only one of the memory channels is active at a time for communication with each DIMM.

Multi-channel DIMMs

In one embodiment, a method includes establishing access to first and second different computing systems. A machine learning model is assigned for training to the first computing system, and the first computing system creates a check-point during training in response to a first predefined triggering event. The check-point may be a record of an execution state in the training of the machine learning model by the first computing system. In response to a second predefined triggering event, the training of the machine learning model on the first computing system is halted, and in response to a third predefined triggering event, the training of the machine learning model is transferred to the second computing system, which continues training the machine learning model starting from the execution state recorded by the check-point.

Growing community of inventors

Redwood City, CA, United States of America

Jay Parikh