Abstract: Systems and methods for enabling contextual privacy controls in heterogenous computing platforms are described. In a non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, the memory including a plurality of sets of firmware instructions that, upon execution by a respective device among the plurality of devices, enable the respective device to provide a corresponding firmware service, where at least one of the plurality of devices operates as an orchestrator configured to: execute or instruct a device to execute an Artificial Intelligence model usable to identify one of a plurality of privacy modes to be applied to the IHS based, at least in part, upon context data received from at least a subset of the plurality of devices; and, based upon the identified privacy mode, modify one or more IHS settings.

Abstract: Systems and methods for battery management in heterogenous computing platforms. In a non-limiting embodiment, an Information Handling System may include a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, where the memory includes a plurality of sets of firmware instructions, each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices; and execute or instruct one or more selected devices among the plurality of devices to execute one or more Artificial Intelligence models usable to manage a charging or discharging operation of a battery based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for managing consumption of alternating-current (AC) power by an Information Handling System (IHS). In a non-limiting embodiment, an IHS may include: a heterogeneous computing platform comprising a plurality of devices and a memory, the memory having a plurality of sets of firmware instructions, where each of the sets, upon execution by a respective device, enables that device to provide a corresponding firmware service, and where at least one of the devices operates as an orchestrator configured to: execute or instruct one or more devices to execute one or more Artificial Intelligence models usable to characterize usage profiles based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices; and, based upon the usage profiles, determine whether to allow a battery to power the IHS at least partially while the IHS is coupled to an AC power source.

Abstract: Systems and methods for firmware-based switching between user profiles in a heterogenous computing platform. In an illustrative, non-limiting embodiment, an Information Handling System may include: a heterogeneous computing platform comprising a plurality of devices, and a memory coupled to the platform having a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a firmware service, and at least one of the devices operates as an orchestrator configured to: execute or instruct a device to execute an Artificial Intelligence model configured to produce an inference result based, at least in part, upon context or telemetry data received from a subset of the plurality of devices; select one of a plurality of user profiles based upon the inference result; and apply a setting associated with the selected user profile to a selected device.

Abstract: Systems and methods for firmware-based user persona discovery in a heterogenous computing platform. In a non-limiting embodiment, an Information Handling System may include: a heterogeneous computing platform having a plurality of devices, and a memory coupled to the heterogeneous computing platform, where the memory includes a plurality of sets of firmware instructions, each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct one or more selected devices to execute one or more Artificial Intelligence models configured to discover a user persona associated with a user of the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for detecting video gaming sessions in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the heterogeneous computing platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct a device among the plurality of devices to execute an Artificial Intelligence (AI) model configured to detect a video gaming session based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for enabling localized image enhancements in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System may include a heterogeneous computing platform having a plurality of devices and a memory coupled to the heterogeneous computing platform, where the memory includes a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from a subset of one or more of the plurality of devices; and instruct a selected device among the plurality of devices to execute an Artificial Intelligence model configured to produce a localized image based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for enabling collaboration session migration in heterogenous computing platforms. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform including a plurality of devices and a memory coupled to the platform, the memory having a plurality of sets of firmware instructions that, upon execution by a respective device among the plurality of devices, enable the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: execute or instruct a device selected among the plurality of devices to execute an AI model usable to identify a migration opportunity for an ongoing collaboration session based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices, and notify another IHS to handle at least a portion of the ongoing collaboration session.

Abstract: Systems and methods for managing embedded Subscriber Identity Module (eSIM) credentials in heterogenous computing platforms. In a non-limiting embodiment, an Information Handling System may include: a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, where the memory includes firmware instructions that, upon execution by a respective device among the plurality of devices, enable a respective device to provide a corresponding firmware service, and at least one of the devices operates as an orchestrator configured to: deploy or instruct another device to deploy an AI model configured to produce an inference result based, at least in part, upon context or telemetry data received from a subset of the devices; select an eSIM profile based upon the inference result; and transmit a credential within the selected eSIM profile to a wireless controller configured to authenticate the credential.

Abstract: Systems and methods for voice-assisted wireless docking in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System may include a heterogeneous computing platform having a plurality of devices, and a memory coupled to the heterogeneous computing platform, where the memory includes a plurality of sets of firmware instructions, each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: detect a predetermined utterance or direct a first one of the plurality of devices to detect the predetermined utterance; and, in response to the detection, direct a second one of the plurality of devices to: (a) connect the IHS to a wireless dock, or (b) disconnect the IHS from the wireless dock.

Abstract: Systems and methods for usage detection and notification in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices and instruct one or more selected devices to execute one or more Artificial Intelligence (AI) models configured to characterize one or more workloads executed by the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for deploying instances of Artificial Intelligence (AI) models having different levels of complexity in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS), may include a heterogeneous computing platform comprising a plurality of devices and a memory having a plurality of sets of firmware instructions that, upon execution by a respective device, enable the respective device to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: select an instance of an AI model among a plurality of instances of the AI model based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices, where each of the plurality of instances of the AI model has a different level of complexity, and instruct a device to execute the selected instance of the AI model.

Abstract: Systems and methods for Artificial Intelligence (AI) model characterization and offloading in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform including a plurality of devices and a memory, where the memory includes a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables it to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: receive a characterization result from a first device, where the first device is configured to execute a first AI model configured to characterize a second AI model executed by a second device, and, prior to a subsequent execution of the second AI model, assign the second AI model to a third device based, at least in part, upon the characterization result.

Abstract: Systems and methods for runtime control of Artificial Intelligence (AI) model parameters in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct a device among the plurality of devices to modify a parameter of an AI model executed by the device based, at least in part, upon the context or telemetry data.

Abstract: A filter assembly comprises a housing defining an internal volume. An opening is defined at a bottom end of the housing. An adaptor is coupled to the bottom end of the housing. A filter element is disposed within the housing. The filter element comprises a filter media defining a central channel, and an endcap coupled to a filter media bottom end of the filter media proximate to the adaptor. A sealing member is disposed around the adaptor, the sealing member configured to form an axial seal between the endcap and the housing.

Abstract: Systems and methods are provided that may be implemented to automatically discover intended and/or unintended changes as they occur in an IT environment, such as a network that includes multiple endpoint information handling systems. Intended changes may include, but are not limited to, changes in user and device or endpoint system level experience metrics that occur on one or more of the endpoint information handling systems within the IT environment after a planned action occurring in the IT environment. Unintended changes to similar metrics may occur when no activities are planned or initiated for the IT environment. These IT environment changes may be discovered automatically based on individual device level telemetry data and/or fleetwide aggregations derived of benchmarking various telemetry metrics.

Abstract: Systems and methods for generating workspace distribution and/or configuration recommendations 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: receive data related to a plurality of workspaces; generate, based upon the data, a recommendation to modify at least one of the plurality of workspaces; and modify the at least one of the plurality of workspaces.

Abstract: Among other things, an equipment for use on board a first ground transportation entity has (a) a receiver for information generated by a sensor of the environment of the first ground transportation entity, (b) a processor, and (c) a memory storing instructions executable by the processor to generate and send safety message information to a second ground transportation entity based on the information generated by the sensor.

Abstract: An information handling system may include at least one processor, a physical storage resource, and a non-volatile memory other than the physical storage resource. The at least one processor may be configured to execute instructions for: storing learned profile data at the physical storage resource; in response to detection of a catastrophic event, copying the learned profile data from the physical storage resource to the non-volatile memory; and subsequent to a remediation event for the catastrophic event, restoring the learned profile data from the non-volatile memory.

Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive a first multiple telemetry data from multiple information handling systems (IHSs); determine first multiple performance health scores respectively associated with the IHSs; determine first multiple availability scores respectively associated with the IHSs; determine first multiple information handling system (IHS) update churn scores respectively associated with the IHSs; determine, via a machine learning process, a second distribution of the IHSs to the multiple IHS groups based at least on the first multiple IHS and performance health scores, the first multiple availability scores, and the first multiple IHS churn scores; and provide a first software update to IHSs of each IHS group.