Dell Products Inc.

Fnu Jasleen

Joseph Paul Marquardt

Vivek Viswanathan Iyer

Balasingh Ponraj Samuel

Michael S Gatson

Farzad Khosrowpour

Rocco Ancona

Karun Palicherla Reddy

Shailesh Jain

Gandali Patil

Systems and methods for managing workspaces using productivity profiles are described. In an embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: send a user's productivity profile to a remote service, where the productivity profile is created based, at least in part, upon a utilization of a peripheral device when the IHS is deployed in a first workspace; and receive, from the remote service, a recommendation to deploy the IHS in a selected workspace, where the recommendation is produced based, at least in part, upon a comparison between the user's productivity profile and a set of capabilities of the selected workspace.

Managing workspaces using productivity profiles

User responsiveness on an information handling system may be improved by classifying an application based on its importance and/or relevance for an individual user with the goal of prioritizing resource allocation to improve responsiveness and performance of applications. The classification may include analyzing telemetry data to determine the most important applications for a user, such as by determining an application's importance and/or relevance to a particular user, and determine the resource utilization of that application from a macro perspective. After classification, changing characteristics of an application may be monitored and used to dynamically allocate system resources to the application during runtime. In this manner, priority on resource allocations for certain resources may be adapted to fit the user and the application, and adapt to the changing requirements and scenarios. The determination of application importance and/or relevance and subsequent adaptation of system resource allocation may be performed using a model-based algorithm.

Model-based resource allocation for an information handling system

A peripheral device workspace cloud orchestrator executing at a cloud-based information handling system comprises a hardware processor to execute code instructions to determine a usage category from operational telemetry data for a current peripheral device workspace having an anchor node and current peripheral devices operatively coupled at a location, the usage category defined by a previous peripheral device operational telemetry data, previous manifest of previous peripheral devices within a previous peripheral device workspace, and at least one previous functional capability and previous adjustable operational configuration of a previous peripheral device. The hardware processor to determine a functional capability for a current peripheral device sufficiently matches the previous functional capability for the usage category and instructs the anchor node to automatically configure the current peripheral device according to the previous adjustable operational configuration in that usage category.

System and method for intelligent transference of peripheral device operational configurations across peripheral device workspaces based on shared usage patterns

An information handling system (IHS) may receive, from a first sensor of the information handling system, first sensor data. The IHS may detect an individual other than a user of the IHS in a field of view of the first sensor based on the first sensor data. The IHS may then, in response to the first sensor detecting the individual, receive, from a second sensor of the IHS, second sensor data. Based, at least in part, on the first sensor data and the second sensor data, the IHS may determine relevance of the individual to an application displayed on a display of the IHS. The IHS may then generate an intent score for the individual based at least in part on the determined relevance. The IHS may then determine whether a security profile is to be applied to the IHS based, at least in part, on the intent score.

Automated security profiles for an information handling system

An information handling system may receive, from a first sensor of the information handling system, first sensor data. The information handling system may receive, from a second sensor of the information handling system, second sensor data. Based, at least in part, on the first sensor data and the second sensor data, the information handling system may generate a plurality of security profiles for the information handling system. Based, at least in part, on the first sensor data and the second sensor data, the information handling system may apply a security profile of the plurality of security profiles to the information handling system.

Automated security profile management for an information handling system

Embodiments of systems and methods are provided to enhance network security by providing secure, multi-path user authentication, while also providing a more convenient login experience to the user. In the present disclosure, a cloud-based user authentication and threat detection system is provided with an artificial intelligence (AI) engine and a training dataset. Utilizing a cloud-based system enables the AI engine to collect data from multiple devices located within different physical locations or environments (such as, for example, the user's home and office). The collected data is stored within the training dataset and used to create a personalized user profile for each user. Each time a user initiates login to a system or network from a particular location, the AI engine collects data from multiple devices within that location and utilizes the user profile data previously stored within the training dataset to securely authenticate the user or detect potential security threats.

Multi-path user authentication and threat detection system and related methods

The present disclosure provides various embodiments of systems and methods to securely authenticate a user. More specifically, the present disclosure provides embodiments of multi-factor authentication methods that improve both security and user convenience by using trusted secondary devices or peripherals (hereinafter 'trusted devices') to provide additional authentication factor(s) for verifying user presence/identity after an initial authentication factor has been used to verify user presence/identity. Unlike conventional multi-factor authentication methods, the additional authentication factor(s) provided by the trusted devices do not require user input or intervention.

Growing community of inventors

Austin, TX, United States of America

Fnu Jasleen