Abstract: Systems and methods for managing settings based upon Information Handling System (IHS) motion using heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an IHS may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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: identify an IHS motion, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices; and trigger a modification of an IHS setting based, at least in part, upon the identification.

Abstract: Systems and methods for managing peripheral device settings based upon context using heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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 collected by at least one of the plurality of devices; and trigger a modification of a setting for a peripheral device based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods are provided for context-based docking of an IHS (Information Handling System) that includes a heterogeneous computing platform. In an illustrative, non-limiting embodiment, an IHS may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: detect a docking station in proximity to the IHS based on a wireless signal generated by the docking station; determine context information for the proximity of the IHS to the docking station, wherein the context information comprises a strength of the wireless signal of the docking station and further comprises movement of the IHS; infer whether the user intends to dock the IHS based on the determined context for the proximity of the IHS to the docking station; and couple the IHS to the docking station in response to the inferred intent.

Abstract: Systems and methods for managing user engagement, productivity, and wellness based upon contextual and telemetry data are described. In an illustrative, non-limiting embodiment, an Information Handling System may include a heterogeneous computing platform and a memory coupled to the heterogeneous computing platform, where the memory comprises a plurality of sets of firmware instructions, each of the sets of firmware instructions, upon execution by a respective device among a plurality of devices of the heterogeneous computing platform, 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: based at least in part upon context or telemetry data collected by one or more of the plurality of devices, determine a user ergonomic metric, or a user engagement metric, and trigger an engagement, productivity, or wellness action based, at least in part, upon the determination.

Abstract: Systems and methods for capturing, recording, storing, and transmitting images in response to Onlooker Detection events in heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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: identify a positive Onlooker Detection event; and in response to the identification, trigger at least one of: capture, recording, storage, or transmittal of an image via another one of the plurality of devices.

Abstract: Systems and methods for interrupted workflow notification and performance management in heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an IHS may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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 obtain and configure IHS user presence and/or engagement, and IHS usage mode inference, IHS telemetry data, and in response to the presence, engagement and/or usage mode telemetry data, enact interrupted user workflow notifications to one or more IHS components to enact one or more interrupted user workflow actions.

Abstract: Systems and methods for managing settings based upon user persona using heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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: determine a user persona associated with a user of the IHS based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices; and trigger a modification of an IHS setting based, at least in part, upon the determination.

Abstract: Systems and methods for managing settings based upon Information Handling System (IHS) posture and orientation using heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an IHS may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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 IHS posture or orientation information; and in response to the IHS posture or orientation information, trigger a modification of an IHS setting.

Abstract: Systems and methods for managing device lighting using heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) includes a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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 user presence or engagement information; and in response to the user presence or engagement information, trigger modification of a lighting setting of an IHS component.

Abstract: Systems and methods for managing system protection settings with heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an IHS may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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; and based, at least in part, upon the context or telemetry data, modify a setting of a feature selected from the group consisting of: Wake-on-Approach (WoA), and Walk away Lock (WaL).

Abstract: Systems and methods are provided for managing the use of background images used in streaming video sessions hosted using a heterogeneous computing platform of an IHS (Information Handling System). A streaming video session is detected that is conducted using resources of the heterogeneous computing platform, where the user of the IHS is a participant in the streaming video session. A context of the user's participation in the streaming video session is determined. A background image is selected for display behind the user based on the determined context of the user's participation in the streaming video session. The background image selected for display behind the user may visually convey information to other participants in the video, without requiring the participants to read any text displayed in support of the streaming video session.

Abstract: Systems and methods for remotely provisioning facial recognition data are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) shareable among a plurality of users in a hoteling environment may include: a heterogeneous computing platform; 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 a plurality of devices of the heterogeneous computing platform, 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: transmit a request to a server to receive facial recognition data associated with a given one of the plurality of users assigned to the IHS, and receive the facial recognition data.

Abstract: Systems and methods for managing notifications in response to multi-user detection in heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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 data; determine a user count based, at least in part, upon the context data; and instruct a selected firmware service of the user count, wherein the selected firmware service is configured to trigger a notification, to a user of the IHS, of the user count.

Abstract: Systems and methods for managing Onlooker Detection in heterogeneous computing platforms are described. In an illustrative, non-limiting embodiment, an IHS may include a heterogeneous computing platform 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 a plurality of devices of the heterogeneous computing platform, 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, and modify an Onlooker Detection setting of the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for detecting and configuring imaging optimization settings during a collaboration session in a heterogenous computing platform are described. In some embodiments, an Information Handling System (IHS) 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 set 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 telemetry data and change an imaging optimization setting during a collaboration session, at least in part, based upon the telemetry data.

Abstract: Systems and methods for firmware-based orchestration of Artificial Intelligence (AI) performance profiles 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 having 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: deploy or instruct another device among the plurality of devices to deploy an AI model configured to produce an inference based, at least in part, upon context or telemetry data received from a subset of the plurality of devices; and select an AI performance profile based, at least in part, upon the inference.

Abstract: Systems and methods for firmware-based diagnostics in heterogenous computing platforms are described. In an illustrative, 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, where the memory includes firmware instructions that, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and wherein at least one of the plurality of devices operates as an orchestrator configured to: execute or instruct a selected device among the plurality of devices to execute an Artificial Intelligence (AI) model configured to determine whether to trigger a diagnostics process; and, in response to the determination, trigger the diagnostics process.

Abstract: Systems and methods for Artificial Intelligence (AI) model dependency handling 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 platform, where the memory includes a plurality of sets of firmware instructions that, upon execution by a respective device, 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 an indication of a relationship between a first AI model and a second AI model; and in response to an instruction to update the first AI model and in the absence of another instruction to update the second AI model, trigger installation of an update to the second AI model.

Abstract: Systems and methods for firmware-based network management 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 platform and having firmware instructions that, upon execution, enables a respective device to provide a corresponding firmware service, where at least one of the devices operates as an orchestrator configured to: execute or instruct a selected device to execute an Artificial Intelligence (AI) model configured to identify or predict a network communication or performance issue and, in response to the determination, modify a configuration of a wireless controller based, at least in part, upon a policy received from an Information Technology Decision Maker (ITDM) or Original Equipment Manufacturer (OEM).

Abstract: Systems and methods for firmware-based Artificial Intelligence (AI) model authorization in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform having a plurality of devices and a memory including a plurality of sets of firmware instructions, where the firmware instructions, upon execution by a respective device among the plurality of devices, enable the respective device to provide a firmware service, and at least one of the devices operates as an orchestrator configured to: receive a request for deployment, loading, extraction, launch, or release of AI model data; and at least one of: in response to a successful authentication of the request, allow the deployment, extraction, launch, or release of the AI model data; or in response to a failed authentication of the request, deny the deployment, extraction, launch, or release of the AI model data.