Google Patentsopen access

Cloud-based fleet and asset management for edge computing of machine learning … — Armada Systems Inc. (US12111744B1)

ABSTRACT

Abstract

An apparatus can be configured to receive monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 18/461,461 filed Sep. 5, 2023, which is hereby incorporated by reference, it its entirety and for all purposes.

TECHNICAL FIELD

The present disclosure pertains to edge computing, and more specifically pertains to systems and techniques for high-performance edge computing and management thereof.

BACKGROUND

Edge computing is a distributed computing paradigm that can be used to decentralize data processing and other computational operations by bringing compute capability and data storage closer to the edge (e.g., the location where the compute and/or data storage is needed, often at the “edge” of a network such as the internet). Edge computing systems are often provided in the same location where input data is generated and/or in the same location where an output result of the computational operations is needed. The use of edge computing systems can reduce latency and bandwidth usage, as data is ingested and processed locally at the edge and rather than being transmitted to a more centralized location for processing.

In many existing cloud computing architectures, data generated at endpoints (e.g., mobile devices, Internet of Things (IoT) sensors, robots, industrial automation systems, security cameras, etc., among various other edge devices and sensors) is transmitted to centralized data centers for processing. The processed results are then transmitted from the centralized data centers to the endpoints requesting the processed results. The centralized processing approach may present challenges for growing use cases, such as for real-time applications and/or artificial intelligence (AI) and machine learning (ML) workloads. For instance, centralized processing models and conventional cloud computing architectures can face constraints in the areas of latency, availability, bandwidth usage, data privacy, network security, and the capacity to process large volumes of data in a timely manner.

In the context of edge computing, the “edge” refers to the edge of the network, close to the endpoint devices and the sources of data. In an edge computing architecture, computation and data storage are distributed across a network of edge nodes that are near the endpoint devices and sources of data. The edge nodes can be configured to perform various tasks relating to data processing, storage, analysis, etc. Based on using the edge nodes to process data locally, the amount of data that is transferred from the edge to the cloud (or other centralized data center) can be significantly reduced. Accordingly, the use of edge computing has become increasingly popular for implementing a diverse range of AI and ML applications, as well as for serving other use cases that demand real-time processing, minimal latency, high availability, and high reliability. In general, such applications and use cases may rely on high-bandwidth sensors that have the ability to generate data at massive rates (e.g., on the order of 50 Gbit/sec or 22 TB/hr).

BRIEF SUMMARY

In some examples, systems and techniques are described for implementing fleet management (e.g., a fleet of edge compute units) and/or asset management (e.g., connected sensors and other assets at the edge) for high-performance edge computing, including edge computing for machine learning (ML) and/or artificial intelligence (AI) deployments and/or workloads.

According to at least one illustrative example, an apparatus is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory and configured to: receive monitoring information from each respective edge compute unit of a plurality of edge compute units, wherein the monitoring information includes information associated with one or more machine learning (ML) or artificial intelligence (AI) workloads implemented by the respective edge compute unit; receive respective status information corresponding to a plurality of connected edge assets, wherein each connected edge asset is associated with one or more edge compute units of the plurality of edge compute units, and wherein the plurality of edge compute units and the plurality of connected edge assets are included in a fleet of edge devices; display, using a remote fleet management graphical user interface (GUI), at least a portion of the monitoring information or the status information corresponding to a selected subset of the fleet of edge devices, wherein the selected subset is determined based on one or more user selection inputs to the remote fleet management GUI; receive, using the remote fleet management GUI, one or more user configuration inputs indicative of an updated configuration for at least one workload of at least one edge compute unit of the selected subset of the fleet of edge devices, the at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit; and transmit, from a cloud computing environment associated with the remote fleet management GUI, control information corresponding to the updated configuration, wherein the control information is transmitted to the at least one edge compute unit of the selected subset.

As used herein, the terms “user equipment” (UE) and “network entity” are not intended to be specific or otherwise limited to any particular radio access technology (RAT), unless otherwise noted. In general, a UE may be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, and/or tracking device, etc.), wearable (e.g., smartwatch, smart-glasses, wearable ring, and/or an extended reality (XR) device such as a virtual reality (VR) headset, an augmented reality (AR) headset or glasses, or a mixed reality (MR) headset), vehicle (e.g., automobile, motorcycle, bicycle, etc.), robotic system (e.g., autonomous passenger vehicle, unmanned aircraft system (UAS), uncrewed ground vehicle (UGV), mobile robotic platform, uncrewed submersible, biped or multi-legged robot, cobot, industrial automation, articulated arm, etc.), and/or Internet of Things (IoT) device, etc., used by a user to communicate over a wireless communications network. A UE may be mobile or may (e.g., at certain times) be stationary, and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT,” a “client device,” a “wireless device,” a “subscriber device,” a “connected device,” a “subscriber terminal,” a “subscriber station,” a “user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a “mobile station,” or variations thereof. Generally, UEs can communicate with a core network via a RAN, and through the core network the UEs can be connected with external networks such as the Internet and with other UEs. Of course, other mechanisms of connecting to the core network and/or the Internet are also possible for the UEs, such as over wired access networks, wireless local area network (WLAN) networks (e.g., based on IEEE 802.11 communication standards, etc.) and so on.

The term “network entity” or “base station” may refer to a single physical Transmission-Reception Point (TRP) or to multiple physical Transmission-Reception Points (TRPs) that may or may not be co-located. For example, where the term “network entity” or “base station” refers to a single physical TRP, the physical TRP may be an antenna of a base station (e.g., satellite constellation ground station/internet gateway) corresponding to a cell (or several cell sectors) of the base station. Where the term “network entity” or “base station” refers to multiple co-located physical TRPs, the physical TRPs may be an array of antennas (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming) of the base station. Where the term “base station” refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.

An RF signal comprises an electromagnetic wave of a given frequency that transports information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, the receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multipath channels. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where it is clear from the context that the term “signal” refers to a wireless signal or an RF signal.

This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

The foregoing, together with other features and embodiments, will become more apparent upon referring to the following specification, claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. The use of a same reference numbers in different drawings indicates similar or identical items or features. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 depicts an example design of a base station and a user equipment (UE) for transmission and processing of signals exchanged between the UE and the base station, in accordance with some examples;

FIG. 2 is a diagram illustrating an example configuration of a Non-Terrestrial Network (NTN) for providing data network connectivity to terrestrial (ground-based) devices, in accordance with some examples;

FIG. 3 is a diagram illustrating an example of a satellite internet constellation network that can be used to provide low latency satellite internet connectivity, in accordance with some examples;

FIG. 4 is a diagram illustrating an example of an edge computing system for machine learning (ML) and/or artificial intelligence (AI) workloads, where the edge computing system includes one or more local sites each having one or more edge compute units, in accordance with some examples;

FIG. 5 is a diagram illustrating an example software stack associated with implementing an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 6 is a diagram illustrating an example architecture for implementing global services and edge compute services of an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 7 is a diagram illustrating an example infrastructure and architecture for implementing an edge compute unit of an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 8 is a diagram illustrating an example graphical user interface (GUI) of a global management console associated with asset management and telemetry observation for a fleet of edge compute units of an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 9 is a diagram illustrating another example GUI of a global management console associated with asset management and telemetry observation for a fleet of edge compute units of an edge computing system for ML and/or AI workloads, in accordance with some examples; and

FIG. 10 is a block diagram illustrating an example of a computing system architecture that can be used to implement one or more aspects described herein, in accordance with some examples.

DETAILED DESCRIPTION

Certain aspects of this disclosure are provided below for illustration purposes. Alternate aspects may be devised without departing from the scope of the disclosure. Additionally, well-known elements of the disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the disclosure. Some of the aspects described herein may be applied independently and some of them may be applied in combination as would be apparent to those of skill in the art. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of aspects of the application. However, it will be apparent that various aspects may be practiced without these specific details. The figures and description are not intended to be restrictive.

The ensuing description provides example aspects only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the example aspects will provide those skilled in the art with an enabling description for implementing an example aspect. It should be understood that various changes may be made in the function and arrangement of elements without departing from the scope of the application as set forth in the appended claims.

Overview

Systems, apparatuses, methods (also referred to as processes), and computer-readable media (collectively referred to herein as “systems and techniques”) are described herein for high-performance edge computing for machine learning (ML) and artificial intelligence (AI) workloads. As used herein, reference to an “ML workload” includes both workloads implemented using a trained machine learning model and workloads implemented using a trained artificial intelligence model. Similarly, reference to an “AI workload” includes both workloads implemented using a trained artificial intelligence model and workloads implemented using a trained machine learning model.

In some aspects, one or more edge compute units (also referred to as a “fleet” of edge compute units) can be used to implement high-performance edge computing for ML and AI workloads. The edge compute unit can include modular and/or configurable compute hardware units (e.g., CPUs, GPUs, TPUs, NPUs, accelerators, memory, storage, etc.) for running the trained ML or AI models. In some cases, the edge compute unit can be a data center unit that is deployable to the edge. An edge compute unit (e.g., containerized data center or containerized compute unit) can be configured according to an intended use case and/or according to one or more deployment site location characteristics. For example, the edge compute unit can be a containerized data center having various hardware configurations (e.g., CPU-centric or GPU-centric compute hardware configuration; urban location or remote location configuration; private electrical/data network or utility electrical/data network connectivity configuration; etc.).

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CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 18/461,461 filed Sep. 5, 2023, which is hereby incorporated by reference, it its entirety and for all purposes.

TECHNICAL FIELD

The present disclosure pertains to edge computing, and more specifically pertains to systems and techniques for high-performance edge computing and management thereof.

BACKGROUND

Edge computing is a distributed computing paradigm that can be used to decentralize data processing and other computational operations by bringing compute capability and data storage closer to the edge (e.g., the location where the compute and/or data storage is needed, often at the “edge” of a network such as the internet). Edge computing systems are often provided in the same location where input data is generated and/or in the same location where an output result of the computational operations is needed. The use of edge computing systems can reduce latency and bandwidth usage, as data is ingested and processed locally at the edge and rather than being transmitted to a more centralized location for processing.

In many existing cloud computing architectures, data generated at endpoints (e.g., mobile devices, Internet of Things (IoT) sensors, robots, industrial automation systems, security cameras, etc., among various other edge devices and sensors) is transmitted to centralized data centers for processing. The processed results are then transmitted from the centralized data centers to the endpoints requesting the processed results. The centralized processing approach may present challenges for growing use cases, such as for real-time applications and/or artificial intelligence (AI) and machine learning (ML) workloads. For instance, centralized processing models and conventional cloud computing architectures can face constraints in the areas of latency, availability, bandwidth usage, data privacy, network security, and the capacity to process large volumes of data in a timely manner.

In the context of edge computing, the “edge” refers to the edge of the network, close to the endpoint devices and the sources of data. In an edge computing architecture, computation and data storage are distributed across a network of edge nodes that are near the endpoint devices and sources of data. The edge nodes can be configured to perform various tasks relating to data processing, storage, analysis, etc. Based on using the edge nodes to process data locally, the amount of data that is transferred from the edge to the cloud (or other centralized data center) can be significantly reduced. Accordingly, the use of edge computing has become increasingly popular for implementing a diverse range of AI and ML applications, as well as for serving other use cases that demand real-time processing, minimal latency, high availability, and high reliability. In general, such applications and use cases may rely on high-bandwidth sensors that have the ability to generate data at massive rates (e.g., on the order of 50 Gbit/sec or 22 TB/hr).

BRIEF SUMMARY

In some examples, systems and techniques are described for implementing fleet management (e.g., a fleet of edge compute units) and/or asset management (e.g., connected sensors and other assets at the edge) for high-performance edge computing, including edge computing for machine learning (ML) and/or artificial intelligence (AI) deployments and/or workloads.

According to at least one illustrative example, an apparatus is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory and configured to: receive monitoring information from each respective edge compute unit of a plurality of edge compute units, wherein the monitoring information includes information associated with one or more machine learning (ML) or artificial intelligence (AI) workloads implemented by the respective edge compute unit; receive respective status information corresponding to a plurality of connected edge assets, wherein each connected edge asset is associated with one or more edge compute units of the plurality of edge compute units, and wherein the plurality of edge compute units and the plurality of connected edge assets are included in a fleet of edge devices; display, using a remote fleet management graphical user interface (GUI), at least a portion of the monitoring information or the status information corresponding to a selected subset of the fleet of edge devices, wherein the selected subset is determined based on one or more user selection inputs to the remote fleet management GUI; receive, using the remote fleet management GUI, one or more user configuration inputs indicative of an updated configuration for at least one workload of at least one edge compute unit of the selected subset of the fleet of edge devices, the at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit; and transmit, from a cloud computing environment associated with the remote fleet management GUI, control information corresponding to the updated configuration, wherein the control information is transmitted to the at least one edge compute unit of the selected subset.

As used herein, the terms “user equipment” (UE) and “network entity” are not intended to be specific or otherwise limited to any particular radio access technology (RAT), unless otherwise noted. In general, a UE may be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, and/or tracking device, etc.), wearable (e.g., smartwatch, smart-glasses, wearable ring, and/or an extended reality (XR) device such as a virtual reality (VR) headset, an augmented reality (AR) headset or glasses, or a mixed reality (MR) headset), vehicle (e.g., automobile, motorcycle, bicycle, etc.), robotic system (e.g., autonomous passenger vehicle, unmanned aircraft system (UAS), uncrewed ground vehicle (UGV), mobile robotic platform, uncrewed submersible, biped or multi-legged robot, cobot, industrial automation, articulated arm, etc.), and/or Internet of Things (IoT) device, etc., used by a user to communicate over a wireless communications network. A UE may be mobile or may (e.g., at certain times) be stationary, and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT,” a “client device,” a “wireless device,” a “subscriber device,” a “connected device,” a “subscriber terminal,” a “subscriber station,” a “user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a “mobile station,” or variations thereof. Generally, UEs can communicate with a core network via a RAN, and through the core network the UEs can be connected with external networks such as the Internet and with other UEs. Of course, other mechanisms of connecting to the core network and/or the Internet are also possible for the UEs, such as over wired access networks, wireless local area network (WLAN) networks (e.g., based on IEEE 802.11 communication standards, etc.) and so on.

The term “network entity” or “base station” may refer to a single physical Transmission-Reception Point (TRP) or to multiple physical Transmission-Reception Points (TRPs) that may or may not be co-located. For example, where the term “network entity” or “base station” refers to a single physical TRP, the physical TRP may be an antenna of a base station (e.g., satellite constellation ground station/internet gateway) corresponding to a cell (or several cell sectors) of the base station. Where the term “network entity” or “base station” refers to multiple co-located physical TRPs, the physical TRPs may be an array of antennas (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming) of the base station. Where the term “base station” refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.

An RF signal comprises an electromagnetic wave of a given frequency that transports information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, the receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multipath channels. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where it is clear from the context that the term “signal” refers to a wireless signal or an RF signal.

This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

The foregoing, together with other features and embodiments, will become more apparent upon referring to the following specification, claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. The use of a same reference numbers in different drawings indicates similar or identical items or features. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 depicts an example design of a base station and a user equipment (UE) for transmission and processing of signals exchanged between the UE and the base station, in accordance with some examples;

FIG. 2 is a diagram illustrating an example configuration of a Non-Terrestrial Network (NTN) for providing data network connectivity to terrestrial (ground-based) devices, in accordance with some examples;

FIG. 3 is a diagram illustrating an example of a satellite internet constellation network that can be used to provide low latency satellite internet connectivity, in accordance with some examples;

FIG. 4 is a diagram illustrating an example of an edge computing system for machine learning (ML) and/or artificial intelligence (AI) workloads, where the edge computing system includes one or more local sites each having one or more edge compute units, in accordance with some examples;

FIG. 5 is a diagram illustrating an example software stack associated with implementing an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 6 is a diagram illustrating an example architecture for implementing global services and edge compute services of an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 7 is a diagram illustrating an example infrastructure and architecture for implementing an edge compute unit of an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 8 is a diagram illustrating an example graphical user interface (GUI) of a global management console associated with asset management and telemetry observation for a fleet of edge compute units of an edge computing system for ML and/or AI workloads, in accordance with some examples;

FIG. 9 is a diagram illustrating another example GUI of a global management console associated with asset management and telemetry observation for a fleet of edge compute units of an edge computing system for ML and/or AI workloads, in accordance with some examples; and

FIG. 10 is a block diagram illustrating an example of a computing system architecture that can be used to implement one or more aspects described herein, in accordance with some examples.

DETAILED DESCRIPTION

Certain aspects of this disclosure are provided below for illustration purposes. Alternate aspects may be devised without departing from the scope of the disclosure. Additionally, well-known elements of the disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the disclosure. Some of the aspects described herein may be applied independently and some of them may be applied in combination as would be apparent to those of skill in the art. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of aspects of the application. However, it will be apparent that various aspects may be practiced without these specific details. The figures and description are not intended to be restrictive.

The ensuing description provides example aspects only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the example aspects will provide those skilled in the art with an enabling description for implementing an example aspect. It should be understood that various changes may be made in the function and arrangement of elements without departing from the scope of the application as set forth in the appended claims.

Overview

Systems, apparatuses, methods (also referred to as processes), and computer-readable media (collectively referred to herein as “systems and techniques”) are described herein for high-performance edge computing for machine learning (ML) and artificial intelligence (AI) workloads. As used herein, reference to an “ML workload” includes both workloads implemented using a trained machine learning model and workloads implemented using a trained artificial intelligence model. Similarly, reference to an “AI workload” includes both workloads implemented using a trained artificial intelligence model and workloads implemented using a trained machine learning model.

In some aspects, one or more edge compute units (also referred to as a “fleet” of edge compute units) can be used to implement high-performance edge computing for ML and AI workloads. The edge compute unit can include modular and/or configurable compute hardware units (e.g., CPUs, GPUs, TPUs, NPUs, accelerators, memory, storage, etc.) for running the trained ML or AI models. In some cases, the edge compute unit can be a data center unit that is deployable to the edge. An edge compute unit (e.g., containerized data center or containerized compute unit) can be configured according to an intended use case and/or according to one or more deployment site location characteristics. For example, the edge compute unit can be a containerized data center having various hardware configurations (e.g., CPU-centric or GPU-centric compute hardware configuration; urban location or remote location configuration; private electrical/data network or utility electrical/data network connectivity configuration; etc.).

The containerized edge compute unit can be deployed to a user-determined (e.g., enterprise-determined) site location. The enterprise site location may have varying levels of existing infrastructure availability, based upon which the containerized edge compute unit can be correspondingly configured. For example, the containerized edge compute unit can be configured based at least in part on the electrical infrastructure and data connectivity infrastructure availability at the enterprise site location. The containerized edge compute unit can be pre-configured (at the time of deployment to the enterprise site location) with hardware infrastructure, data connectivity, and critical environmental systems fully or partially integrated.

In one illustrative example, the containerized edge compute unit can be pre-configured (e.g., pre-loaded) with a fleet management, knowledge bases, predetermined datasets and trained ML/AI models and application software stack for edge computing AI and ML workloads, such as the fleet management and application software stack that is the subject of this disclosure and will be described in greater depth below.

The presently disclosed fleet management and application software stack is also referred to herein as an “edge ML/AI platform” or an “edge ML/AI management system.” In some embodiments, the edge ML/AI platform can include at least a remote fleet management engine, a telemetry and monitoring observation engine, a platform application orchestration engine, and a deployable application repository, each of which are described in greater detail with respect to the figures, and with particular reference to the examples of FIGS. 5 - 7 .

In one illustrative example, the remote fleet management (e.g., command) engine, telemetry and monitoring (e.g., observation) engine, platform application orchestration engine, and deployable application repository can be implemented using a single, global management console of the presently disclosed edge ML/AI platform. For instance, the global management console can be configured to provide a single pane of glass interface that provides a unified data presentation view and bidirectional interaction across the various sources and constituent engines included in or otherwise associated with the presently disclosed edge ML/AI platform. As used herein, the global management “console” can also be referred to as a global management “portal.” Further details of the global management console are described below with respect to the figures, and with particular reference to the examples of FIGS. 8 and 9 .

In some embodiments, the presently disclosed edge ML/AI platform can be used to implement a connected edge and cloud for ML and AI workloads. For instance, many ML and AI workloads, applications, and/or models are data-intensive and benefit from continual (or relatively frequent) retraining to account for data drift and model degradation. In some cases, the ML and AI workloads may require monitoring of model degradation in conjunction with regular training (e.g., retraining, fine tuning, instruction tuning, continual learning, etc.) with new data, model parameters, model hyperparameters, etc., as appropriate. In one illustrative example, the systems and techniques described herein can be used to provide an edge ML/AI monitoring and management platform configured to provide streamlined and efficient operations for deploying, maintaining, and updating ML and AI workloads or models to the edge.

As described in greater detail herein, the edge ML/AI monitoring and management platform can be implemented for a fleet of edge compute units (e.g., the containerized edge compute units described above) and a plurality of connected sensors and/or edge assets associated with at least one edge compute unit of the fleet. The containerized edge compute units of the fleet can be used to provide local (e.g., edge) storage, inference, prediction, response, etc. using trained ML and/or AI models from a centralized or cloud distribution source, where the trained models are trained or fine-tuned remotely from the edge compute unit. For instance, the edge ML/AI monitoring and management platform can be used to implement or otherwise interface to ML/AI training clusters running in the cloud. The edge compute units can subsequently run or implement their respective trained models locally (e.g., onboard the edge compute unit), using as input thee data obtained from local sensors or other endpoint assets associated with the edge compute unit.

In some embodiments, the edge ML/AI monitoring and management platform can be used to implement and/or orchestrate a hub-and-spoke architecture for efficient ML/AI inferencing at the edge. For instance, ML and AI model training often necessitates massive amounts of data and processing power, with the resultant trained model quality being highly correlated with the size and diversity of the training and test data. Training large models can require running thousands of GPUs and ingesting hundreds of terabytes of data, over the course of several weeks. Accordingly, large-scale ML and AI model training may be better suited for deployment on cloud and/or on-premises infrastructure (e.g., centralized large-scale compute infrastructure). By comparison, ML and AI inferencing (e.g., performing inference using a trained ML or AI model) utilizes a relatively smaller amount of compute resources (e.g., CPU, GPU, memory, etc.) and can be performed efficiently at the edge—which often is also the location where the input data for the ML or AI inferencing originates and is collected. Accordingly, performing inference at the edge provides the benefit of better latency (e.g., lower latency, higher frame rate, lower response time, larger sampling frequency), as the input data does not need to first transit over to a cloud region prior to inference. In some aspects, trained ML or AI models (generated in the cloud or on-premises) can be optimized and compressed significantly prior to delivery or distribution to some (or all) of the edge locations, ultimately enabling the trained model to be distributed to a greater quantity and range of edge locations, and in a more efficient manner.

In one illustrative example, the systems and techniques described herein can be used to implement a hub-and-spoke architecture for efficient ML/AI inferencing at the edge, based on training (or retraining/finetuning) performed at a centralized cloud or on-premises location. The hub-and-spoke architecture can be orchestrated and managed using the presently disclosed edge ML/AI monitoring and management platform. In some embodiments, a continuous feedback loop can be used to capture data locally (e.g., at the containerized edge compute units managed by the edge ML/AI monitoring and management platform), perform inference, and respond locally. In some aspects, inference results and/or features from the source data can be compressed and transmitted from the edge to the ML/AI monitoring platform. For instance, the containerized edge compute units can be used to compress and transmit inference results and/or source data features back to the presently disclosed ML/AI monitoring platform (and/or other centralized management location). Training and finetuning can be performed in the cloud or using a centralized on-premises infrastructure, in both cases with training or finetuning operations mediated and managed by the edge ML/AI monitoring platform. The edge ML/AI monitoring platform can subsequently transmit or otherwise distribute the new or updated ML and AI models back to the edge (e.g., back to some or all of the edge compute units included in a fleet of edge compute units managed by the presently disclosed edge ML/AI monitoring and management platform). In some aspects, the edge ML/AI monitoring and management platform can be configured to optimize the usage of cloud, edge, and bandwidth resources for performing ML and/or AI workloads at the edge. The edge ML/AI monitoring and management platform can be further configured to ensure privacy and security of data generated at the edge (e.g., financial records and transactions, personal identifiable information, protected health information, proprietary images and videos, etc.).

Further details regarding the systems and techniques described herein will be discussed below with respect to the figures.

FIG. 1 shows a block diagram of a design of a base station 102 and a UE 104 that enable transmission and processing of signals exchanged between the UE and the base station, in accordance with some aspects of the present disclosure. Design 100 includes components of a base station 102 and a UE 104 . In some examples, the architecture of base station 102 can be the same as or similar to an architecture used to implement a satellite constellation ground station (e.g., internet gateway for providing internet connectivity via a satellite constellation). In some examples, the architecture of base station 102 can be the same as or similar to an architecture used to implement a satellite of a satellite constellation and/or a network entity in communication with a satellite constellation (e.g., such as the satellite constellations and/or networks depicted in FIGS. 2 and 3 ).

As illustrated in FIG. 1 , base station 102 may be equipped with T antennas 134 a through 134 t , and UE 104 may be equipped with R antennas 152 a through 152 r , where in general T≥1 and R≥1. At base station 102 , a transmit processor 120 may receive data from a data source 112 for one or more UEs, select one or more modulation and coding schemes (MCS) for each UE based at least in part on channel quality indicators (CQIs) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS(s) selected for the UE, and provide data symbols for all UEs. Transmit processor 120 may also process system information (e.g., for semi-static resource partitioning information (SRPI) and/or the like) and control information (e.g., CQI requests, grants, upper layer signaling, and/or the like) and provide overhead symbols and control symbols. Transmit processor 120 may also generate reference symbols for reference signals (e.g., the cell-specific reference signal (CRS)) and synchronization signals (e.g., the primary synchronization signal (PSS) and secondary synchronization signal (SSS))). A transmit (TX) multiple-input multiple-output (MIMO) processor 130 may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, the overhead symbols, and/or the reference symbols, if applicable, and may provide T output symbol streams to T modulators (MODs) 132 a through 132 t . The modulators 132 a through 132 t are shown as a combined modulator-demodulator (MOD-DEMOD). In some cases, the modulators and demodulators can be separate components. Each modulator of the modulators 132 a to 132 t may process a respective output symbol stream, e.g., for an orthogonal frequency-division multiplexing (OFDM) scheme and/or the like, to obtain an output sample stream. Each modulator of the modulators 132 a to 132 t may further process (e.g., convert to analog, amplify, filter, and upconvert) the output sample stream to obtain a downlink signal. T downlink signals may be transmitted from modulators 132 a to 132 t via T antennas 134 a through 134 t , respectively. According to certain aspects described in more detail below, the synchronization signals can be generated with location encoding to convey additional information.

At UE 104 , antennas 152 a through 152 r may receive the downlink signals from base station 102 and/or other base stations and may provide received signals to demodulators (DEMODs) 154 a through 154 r , respectively. The demodulators 154 a through 154 r are shown as a combined modulator-demodulator (MOD-DEMOD). In some cases, the modulators and demodulators can be separate components. Each demodulator of the demodulators 154 a through 154 r may condition (e.g., filter, amplify, downconvert, and digitize) a received signal to obtain input samples. Each demodulator of the demodulators 154 a through 154 r may further process the input samples (e.g., for OFDM and/or the like) to obtain received symbols. A MIMO detector 156 may obtain received symbols from all R demodulators 154 a through 154 r , perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor 158 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for UE 104 to a data sink 160 , and provide decoded control information and system information to a controller/ processor 180 . A channel processor may determine reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), channel quality indicator (CQI), and/or the like.

On the uplink, at UE 104 , a transmit processor 164 may receive and process data from a data source 162 and control information (e.g., for reports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) from controller/ processor 180 . Transmit processor 164 may also generate reference symbols for one or more reference signals (e.g., based at least in part on a beta value or a set of beta values associated with the one or more reference signals). The symbols from transmit processor 164 may be precoded by a TX- MIMO processor 166 if application, further processed by modulators 154 a through 154 r (e.g., for DFT-s-OFDM, CP-OFDM, and/or the like), and transmitted to base station 102 . At base station 102 , the uplink signals from UE 104 and other UEs may be received by antennas 134 a through 134 t , processed by demodulators 132 a through 132 t , detected by a MIMO detector 136 if applicable, and further processed by a receive processor 138 to obtain decoded data and control information sent by UE 104 . Receive processor 138 may provide the decoded data to a data sink 139 and the decoded control information to controller (e.g., processor) 140 . Base station 102 may include communication unit 144 and communicate to a network controller 131 via communication unit 144 . Network controller 131 may include communication unit 194 , controller/ processor 190 , and memory 192 . In some aspects, one or more components of UE 104 may be included in a housing. Memories

142 and 182 may store data and program codes for the base station 102 and the UE 104 , respectively. A scheduler 146 may schedule UEs for data transmission on the downlink, uplink, and/or sidelink.

Data Network Connectivity Using Satellite Constellations

As noted previously, low-orbit satellite constellation systems have been rapidly developed and deployed to provide wireless communications and data network connectivity. A fleet of discrete satellites (also referred to as “birds”) can be arranged as a global satellite constellation that provides at least periodic or intermittent coverage to a large portion of the Earth&#39;s surface. In many cases, at least certain areas of the Earth&#39;s service may have continuous or near-continuous coverage from at least one bird of the satellite constellation. For instance, a global satellite constellation can be formed based on a stable (and therefore predictable) space geometric configuration, in which the fleet of birds maintain fixed space-time relationships with one another. A satellite constellation be used to provide data network connectivity to ground-based devices and/or other terrestrial receivers. For example, a satellite constellation can be integrated with or otherwise provide connectivity to one or more terrestrial (e.g., on-ground) data networks, such as the internet, a 4G/LTE network, and/or a 5G/NR network, among various others. In one illustrative example, a satellite internet constellation system can include a plurality of discrete satellites arranged in a low-earth orbit and used to provide data network connectivity to the internet.

To implement an internet satellite constellation, the discrete satellites can be used as space-based communication nodes that couple terrestrial devices to terrestrial internet gateways. The terrestrial internet gateways may also be referred to as ground stations, and are used to provide connectivity to the internet backbone. For instance, a given satellite can provide a first communication link to a terrestrial device and a second communication link to a ground station that is connected to an internet service provider (ISP). The terrestrial device can transmit data and/or data requests to the satellite over the first communication link, with the satellite subsequently forwarding the transmission to the ground station internet gateway (from which point onward the transmission from the device is handled as a normal internet transmission). The terrestrial device can receive data and/or requests using the reverse process, in which the satellite receives a transmission from the ground station internet gateway via the second communication link and then forwards the transmission to the terrestrial device using the first communication link.

Although an internet satellite constellation includes a fleet of discrete satellites, in some cases terrestrial devices connected with a satellite may communicate with a ground station/internet gateway that is also able to communicate with the same satellite. In other words, it is typically the case that the first and second communication links described above must be established with the same satellite of the satellite constellation. A user connecting to any particular satellite is therefore limited by the ground station/internet gateways that are visible to that particular satellite. For instance, a user connected to a satellite that is unable to establish a communication link with a ground station/internet gateway is therefore unable to connect to the internet—although the fleet of satellites is a global network in terms of spatial diversity and arrangement, the individual satellites function as standalone internet relay nodes unless an inter-satellite link capability is provided.

In some cases, inter-satellite links can allow point to point communications between the individual satellites included in a satellite constellation. For instance, data can travel at the speed of light from one satellite to another, resulting in a fully interconnected global mesh network that allows access to the internet as long as the terrestrial device can establish communication with at least one satellite of the satellite internet constellation. In one illustrative example, a satellite internet constellation can implement inter-satellite links as optical communication links. For example, optical space lasers can be used to implement optical intersatellite links (ISLs) between some (or all) of the individual birds of a satellite constellation. In this manner, the satellite internet constellation can be used to transmit data without the use of local ground stations, and may be seen to provide truly global coverage.

For instance, optical laser links between individual satellites in a satellite constellation can reduce long-distance latency by as much as 50%. Additionally, optical laser links (e.g., ISLs) can enable the more efficient sharing of capacity by utilizing the otherwise wasted satellite capacity over regions without ground station internet gateways. Moreover, optical laser links allow the satellite constellation to provide internet service (or other data network connectivity) to areas where ground stations are not present and/or are impossible to install.

To implement a satellite constellation, one or more satellites may be integrated with the terrestrial infrastructure of a wireless communication system. In general, satellites may refer to Low Earth Orbit (LEO) devices, Medium Earth Orbit (MEO) devices, Geostationary Earth Orbit (GEO) devices, and/or Highly Elliptical Orbit (HEO) devices. In some aspects, a satellite constellation can be included in or used to implement a non-terrestrial network (NTN). A non-terrestrial network (NTN) may refer to a network, or a segment of a network, that uses an airborne or spaceborne vehicle for transmission. For instance, spaceborne vehicles can refer to various ones of the satellites described above. An airborne vehicle may refer to High Altitude Platforms (HAPs) including Unmanned Aircraft Systems (UAS). An NTN may be configured to help to provide wireless communication in un-served or underserved areas to upgrade the performance of terrestrial networks. For example, a communication satellite (e.g., of a satellite constellation) may provide coverage to a larger geographic region than a terrestrial network base station. The NTN may also reinforce service reliability by providing service continuity for UEs or for moving platforms (e.g., passenger vehicles-aircraft, ships, high speed trains, buses). The NTN may also increase service availability, including critical communications. The NTN may also enable network scalability through the provision of efficient multicast/broadcast resources for data delivery towards the network edges or even directly to the user equipment.

FIG. 2 is a diagram illustrating an example configuration 200 of an NTN for providing data network connectivity to terrestrial (ground-based) devices. In one illustrative example, the NTN can be a satellite internet constellation, although various other NTNs and/or satellite constellation data network connectivity types may also be utilized without departing from the scope of the present disclosure. As used herein, the terms “NTN” and “satellite constellation” may be used interchangeably.

An NTN may refer to a network, or a segment of a network, that uses RF resources on-board an NTN platform. The NTN platform may refer to a spaceborne vehicle or an airborne vehicle. Spaceborne vehicles include communication satellites that may be classified based on their orbits. For example, a communication satellite may include a GEO device that appears stationary with respect to the Earth. As such, a single GEO device may provide coverage to a geographic coverage area. In other examples, a communication satellite may include a non-GEO device, such as an LEO device, an MEO device, or an HEO device. Non-GEO devices do not appear stationary with respect to the Earth. As such, a satellite constellation (e.g., one or more satellites) may be configured to provide coverage to the geographic coverage area. An airborne vehicle may refer to a system encompassing Tethered UAS (TUA), Lighter Than Air UAS (LTA), Heavier Than Air UAS (HTA) (e.g., in altitudes typically between 8 and 50 km including High Altitude Platforms (HAPs)).

A satellite constellation can include a plurality of satellites, such as the

satellites

202 , 204 , and 206 depicted in FIG. 2 . The plurality of satellites can include satellites that are the same as one another and/or can include satellites that are different from one another. A terrestrial gateway 208 can be used to provide data connectivity to a data network 210 . For instance, the terrestrial gateway 208 can be a ground station (e.g., internet gateway) for providing data connectivity to the internet. Also depicted in FIG. 2 is a UE 230 located on the surface of the earth, within a cell coverage area of the first satellite 202 . In some aspects, the UE 230 can include various devices capable of connecting to the NTN 200 and/or the satellite constellation thereof for wireless communication.

The gateway 208 may be included in one or more terrestrial gateways that are used to connect the NTN 200 and/or satellite constellation thereof to a public data network such as the internet. In some examples, the gateway 208 may support functions to forward a signal from the satellite constellation to a Uu interface, such as an NR-Uu interface. In other examples, the gateway 208 may provide a transport network layer node, and may support various transport protocols, such as those associated with providing an IP router functionality. A satellite radio interface (SRI) may provide IP trunk connections between the gateway 208 and various satellites (e.g., satellites 202 - 206 ) to transport NG or FI interfaces, respectively.

Satellites within the satellite constellation that are within connection range of the gateway 208 (e.g., within line-of-sight of, etc.) may be fed by the gateway 208 . The individual satellites of the satellite constellation can be deployed across a satellite-targeted coverage area, which can correspond to regional, continental, or even global coverage. The satellites of the satellite constellation may be served successively by one or more gateways at a time. The NTN 200 associated with the satellite constellation can be configured to provide service and feeder link continuity between the successive serving gateways 208 with time duration to perform mobility anchoring and handover.

In one illustrative example, the first satellite 202 may communicate with the data network 210 (e.g., the internet) through a feeder link 212 established between the first satellite 202 and the gateway 208 . The feeder link 212 can be used to provide bidirectional communications between the first satellite 202 and the internet backbone coupled to or otherwise provided by gateway 208 . The first satellite 202 can communicate with the UE 230 using a service link 214 established within the cell coverage (e.g., field-of-view) area of an NTN cell 220 . The NTN cell 220 corresponds to the first satellite 202 . In particular, the first satellite 202 and/or service link 214 can be used to communicate with different devices or UEs that are located within the corresponding NTN cell 220 of <figure-callout id="202"

CLAIMS

Claims ( 20 )

What is claimed is:

1. A method comprising:

receiving monitoring information from each respective edge compute unit of a plurality of edge compute units, wherein the monitoring information includes information associated with one or more machine learning (ML) or artificial intelligence (AI) workloads implemented by the respective edge compute unit;

receiving respective status information corresponding to a plurality of connected edge assets, wherein each connected edge asset is associated with one or more edge compute units of the plurality of edge compute units, and wherein the plurality of edge compute units and the plurality of connected edge assets are included in a fleet of edge devices;

displaying, using a remote fleet management graphical user interface (GUI), at least a portion of the monitoring information or the status information corresponding to a selected subset of the fleet of edge devices, wherein the selected subset is determined based on one or more user selection inputs to the remote fleet management GUI;

receiving, using the remote fleet management GUI, one or more user configuration inputs indicative of an updated configuration for at least one workload of at least one edge compute unit of the selected subset of the fleet of edge devices, the at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit; and

transmitting, from a cloud computing environment associated with the remote fleet management GUI, control information corresponding to the updated configuration, wherein the control information is transmitted to the at least one edge compute unit of the selected subset.

2. The method of claim 1 , wherein the one or more user configuration inputs are indicative of an updated configuration for a respective ML or AI workload of the one or more ML or AI workloads.

3. The method of claim 1 , wherein the updated configuration for the respective ML or AI workload corresponds to a pre-trained ML or AI model associated with the respective ML or AI workload.

4. The method of claim 3 , wherein the updated configuration is configured to cause the at least one edge compute unit of the selected subset to perform local retraining of the pre-trained ML or AI model at an edge location of the at least one edge compute unit.

5. The method of claim 4 , wherein the updated configuration further includes retraining information for the local retraining of the pre-trained ML or AI model at the edge location.

6. The method of claim 5 , wherein the retraining information is generated by the cloud computing environment based on information obtained across the plurality of edge compute units included in the fleet of edge devices.

7. The method of claim 3 , wherein the updated configuration is configured to cause the at least one edge compute unit of the selected subset to perform local finetuning of the pre-trained ML or AI model at an edge location of the at least one edge compute unit.

8. The method of claim 7 , wherein the updated configuration further includes finetuning information for the local finetuning of the pre-trained ML or AI model at the edge location.

9. The method of claim 8 , wherein the finetuning information is generated by the cloud computing environment based on information obtained across the plurality of edge compute units included in the fleet of edge devices.

10. The method of claim 3 , wherein the updated configuration is configured to cause a subset of edge compute units of the plurality of edge compute units of the fleet of edge devices to perform distributed retraining of the pre-trained ML or AI model, and wherein the updated configuration information includes orchestration information for distributing a retraining workload across the respective edge compute units of the subset of edge compute units.

11. The method of claim 3 , wherein the updated configuration is configured to cause a subset of edge compute units of the plurality of edge compute units of the fleet of edge devices to perform distributed finetuning of the pre-trained ML or AI model, and wherein the updated configuration information includes orchestration information for distributing a finetuning workload across the respective edge compute units of the subset of edge compute units.

12. The method of claim 1 , wherein the one or more user configuration inputs are indicative of an updated network connectivity configuration applicable to at least a portion of the selected subset of the fleet of edge devices.

13. The method of claim 12 , wherein:

the updated network connectivity configuration corresponds to a local edge network implemented at an edge deployment location of a plurality of edge deployment locations for the fleet of edge devices; and

the local edge network is implemented at the edge deployment location by a corresponding edge compute unit of the fleet of edge devices.

14. The method of claim 13 , wherein the local edge network is configured for wireless communications between the corresponding edge compute unit and the respective connected edge assets associated with the corresponding edge compute unit.

15. The method of claim 14 , wherein the updated network connectivity configuration corresponds to updated provisioning information for deploying one or more additional connected edge assets to the fleet of edge devices and within the edge deployment location.

16. The method of claim 12 , wherein the one or more user configuration inputs are indicative of an updated network connectivity configuration applicable to one or more internet backhaul links between the fleet of edge devices and the cloud computing environment associated with the remote fleet management GUI.

17. The method of claim 16 , wherein each internet backhaul link of the one or more internet backhaul links is configured between a respective edge deployment location of a plurality of edge deployment locations for the fleet of edge devices and the cloud computing environment associated with the remote fleet management GUI.

18. The method of claim 16 , wherein each internet backhaul link of the one or more internet backhaul links comprises a satellite internet constellation backhaul link between at least one edge device of the fleet of edge devices and at least one satellite of a satellite internet constellation.

19. The method of claim 18 , wherein the one or more user configuration inputs are indicative of updated subscription information between the satellite internet constellation and a satellite internet constellation transceiver terminal associated with an edge compute unit of the fleet of edge devices.

20. The method of claim 12 , wherein the updated network connectivity configuration corresponds to a Software-Defined Networking (SDN) layer associated with the fleet of edge devices, and wherein the updated network connectivity configuration is indicative of one or more updated SDN layer configurations for at least a portion of the plurality of edge compute units of the fleet of edge devices.

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Cited By (4)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US12361220B1

( en )

2024-11-27

2025-07-15

Alpha Deal Llc

Customized integrated entity analysis using an artificial intelligence (AI) model

US12406084B1

( en )

2024-11-27

2025-09-02

Alpha Deal Llc

Providing access to composite AI-generated data

US12524809B1

( en )

2024-11-27

2026-01-13

Alpha Deal Llc

Evaluating tokenized entities using an artificial intelligence (AI) model

US12572551B1

( en )

2024-11-27

2026-03-10

Alpha Deal Llc

User interaction within a data analysis system

Families Citing this family (10)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US9397521B2

( en )

*

2012-01-20

2016-07-19

Salesforce.Com, Inc.

Site management in an on-demand system

US12571648B2

( en )

*

2023-08-31

2026-03-10

Adeia Guides Inc.

Guidance for collaborative map building and updating

US12596014B2

( en )

2023-08-31

2026-04-07

Adeia Guides Inc.

Guidance for collaborative map building and updating

US11876858B1

( en )

*

2023-09-05

2024-01-16

Armada Systems Inc.

Cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads

US12014219B1

( en )

2023-09-05

2024-06-18

Armada Systems Inc.

Cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads

US11907093B1

( en )

*

2023-09-05

2024-02-20

Armada Systems Inc.

Cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads

US11899671B1

( en )

2023-09-05

2024-02-13

Armada Systems Inc.

Real-time search and retrieval of streaming sensor data

US12131242B1

( en )

2023-09-05

2024-10-29

Armada Systems Inc.

Fleet and asset management for edge computing of machine learning and artificial intelligence workloads deployed from cloud to edge

US11935416B1

( en )

2023-10-24

2024-03-19

Armada Systems Inc.

Fleet and asset management and interfaces thereof associated with edge computing deployments

US20250200034A1

( en )

*

2023-12-19

2025-06-19

Yahoo Ad Tech Llc

System and method for mining data using generative ai

Citations (83)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

WO2002007330A2

( en )

2000-07-18

2002-01-24

Motorola, Inc.

Wireless bidirectional interface

US20050156715A1

( en )

2004-01-16

2005-07-21

Jie Zou

Method and system for interfacing with mobile telemetry devices

US20090079555A1

( en )

2007-05-17

2009-03-26

Giadha Aguirre De Carcer

Systems and methods for remotely configuring vehicle alerts and/or controls

US20090249215A1

( en )

2008-04-01

2009-10-01

Samsung Electronics Co., Ltd.

System and method for remote application configuration management on multifunction peripherals

US20120123951A1

( en )

2010-11-17

2012-05-17

Decisiv Inc.

Service management platform for fleet of assets

US20150120749A1

( en )

2013-10-30

2015-04-30

Microsoft Corporation

Data management for connected devices

US20160093119A1

( en )

2014-09-26

2016-03-31

International Business Machines Corporation

Monitoring and Planning for Failures of Vehicular Components

US20160117059A1

( en )

2014-10-24

2016-04-28

Caterpillar Inc.

User Interface for Fleet Management

US20160266739A1

( en )

2015-03-13

2016-09-15

Caterpillar Inc.

User Interface for Fleet Management

US20170285623A1

( en )

2016-04-05

2017-10-05

Wellaware Holdings, Inc.

Device for monitoring and controlling industrial equipment

US20180025304A1

( en )

2016-07-20

2018-01-25

Fisher-Rosemount Systems, Inc.

Fleet management system for portable maintenance tools

US20180032915A1

( en )

*

2016-07-29

2018-02-01

Splunk Inc.

Transmitting machine learning models to edge devices for edge analytics

US20180082152A1

( en )

2016-09-21

2018-03-22

GumGum, Inc.

Training machine learning models to detect objects in video data

US20180341720A1

( en )

2017-05-24

2018-11-29

International Business Machines Corporation

Neural Bit Embeddings for Graphs

US20190156246A1

( en )

2017-11-21

2019-05-23

Amazon Technologies, Inc.

Generating and deploying packages for machine learning at edge devices

US20190212754A1

( en )

2018-01-09

2019-07-11

Robert Bosch Llc

Mass Evacuation Utilizing a Fleet of Autonomous Vehicles

US20190258747A1

( en )

2018-02-22

2019-08-22

General Electric Company

Interactive digital twin

US20190370686A1

( en )

2018-06-01

2019-12-05

Nami Ml Inc.

Machine learning model re-training based on distributed feedback

WO2020006160A1

( en )

2018-06-28

2020-01-02

Amazon Technologies, Inc.

Satellite antenna ground station service system

US20200027033A1

( en )

*

2018-07-19

2020-01-23

Adobe Inc.

Updating Machine Learning Models On Edge Servers

US20200112612A1

( en )

2018-10-08

2020-04-09

Ciambella Ltd.

System, apparatus and method for providing end to end solution for networks

US20200136994A1

( en )

2019-09-28

2020-04-30

Intel Corporation

Methods and apparatus to aggregate telemetry data in an edge environment

US20200153623A1

( en )

2018-11-09

2020-05-14

Microsoft Technology Licensing, Llc

Trusted key diversity on cloud edge devices

US20200160207A1

( en )

2018-11-15

2020-05-21

General Electric Company

Automated model update based on model deterioration

US20200267053A1

( en )

2019-02-15

2020-08-20

Samsung Electronics Co., Ltd.

Systems and methods for latency-aware edge computing

US20200286020A1

( en )

2019-03-08

2020-09-10

Toyota Jidosha Kabushiki Kaisha

Managing Vehicles Using Mobility Agent

US20200349852A1

( en )

2019-05-03

2020-11-05

Michele DiCosola

Smart drone rooftop and ground airport system

EP3737003A1

( en )

2019-05-07

2020-11-11

Contec Co., Ltd.

System, apparatus and method for managing satellite operation service

US20200374974A1

( en )

2019-05-23

2020-11-26

Verizon Patent And Licensing Inc.

System and method for sharing multi-access edge computing resources in a wireless network

US20200379805A1

( en )

2019-05-30

2020-12-03

Microsoft Technology Licensing, Llc

Automated cloud-edge streaming workload distribution and bidirectional migration with lossless, once-only processing

US20200401891A1

( en )

2020-09-04

2020-12-24

Intel Corporation

Methods and apparatus for hardware-aware machine learning model training

US20210108937A1

( en )

2020-12-21

2021-04-15

Maik Sven FOX

Fleet emission control, distribution, and limits adherence

US20210112441A1

( en )

2020-12-23

2021-04-15

Dario Sabella

Transportation operator collaboration system

CN112685069A

( en )

2019-10-20

2021-04-20

辉达公司

Real-time updating of machine learning models

US20210136869A1

( en )

2019-10-31

2021-05-06

Connectivia Labs, Inc.

Edge computing full-mesh internet of things gateway

US20210243232A1

( en )

2020-01-31

2021-08-05

Palo Alto Networks, Inc.

Multi-access edge computing services security in mobile networks by parsing application programming interfaces

US20210266225A1

( en )

2020-02-25

2021-08-26

International Business Machines Corporation

Personalized machine learning model management and deployment on edge devices

US20210304418A1

( en )

2020-03-31

2021-09-30

Nant Holdings Ip, Llc

Digital representation of multi-sensor data stream

US11145208B1

( en )

2021-03-15

2021-10-12

Samsara Networks Inc.

Customized route tracking

US20210360070A1

( en )

2019-01-13

2021-11-18

Strong Force Iot Portfolio 2016, Llc

Sensor kits for generating feature vectors for monitoring and managing industrial settings

US11201939B1

( en )

2019-06-28

2021-12-14

Amazon Technologies, Inc.

Content and compute delivery platform using satellites

US20210392049A1

( en )

2020-06-15

2021-12-16

Cisco Technology, Inc.

Machine-learning infused network topology generation and deployment

US20220015269A1

( en )

2020-07-10

2022-01-13

Shenzhen Attom Technology Co., Ltd.

Energy-saving container type data center

US20220019422A1

( en )

2020-07-17

2022-01-20

Sensia Llc

Systems and methods for edge device management

US11238849B1

( en )

*

2021-05-31

2022-02-01

Rockspoon, Inc.

System and method for federated, context-sensitive, adaptive language models

US20220035878A1

( en )

2021-10-19

2022-02-03

Intel Corporation

Framework for optimization of machine learning architectures

US20220060455A1

( en )

2020-08-21

2022-02-24

Microsoft Technology Licensing, Llc

Secure computing device

US20220121556A1

( en )

2021-12-23

2022-04-21

Intel Corporation

Systems, methods, articles of manufacture, and apparatus for end-to-end hardware tracing in an edge network

US20220150125A1

( en )

2021-12-22

2022-05-12

Intel Corporation

AI Named Function Infrastructure and Methods

US20220197304A1

( en )

2020-12-18

2022-06-23

Verizon Patent And Licensing Inc.

Systems and methods for centralized control of a fleet of robotic devices

US11405801B1

( en )

2021-03-18

2022-08-02

Amazon Technologies, Inc.

Managing radio-based network infrastructure using unmanned vehicles

US11410423B1

( en )

2021-02-12

2022-08-09

ShipIn Systems Inc.

System and method for fleet management and communication of visual events and collaboration with the same

US20220345518A1

( en )

2021-04-19

2022-10-27

International Business Machines Corporation

Machine learning based application deployment

US20220353732A1

( en )

2019-10-04

2022-11-03

Intel Corporation

Edge computing technologies for transport layer congestion control and point-of-presence optimizations based on extended inadvance quality of service notifications

US20220360957A1

( en )

2021-05-06

2022-11-10

Numurus LLC

Edge devices and remote services interfacing framework and related systems and methods

US20220383859A1

( en )

*

2021-05-31

2022-12-01

Rockspoon, Inc.

System and method for federated, context-sensitive, acoustic model refinement

US20220405157A1

( en )

2019-11-27

2022-12-22

Siemens Aktiengesellschaft

System, device, method and datastack for managing applications that manage operation of assets

US20220410750A1

( en )

2021-06-09

2022-12-29

MOEV, Inc.

System and method for smart charging management of electric vehicle fleets

US20230021216A1

( en )

2021-07-09

2023-01-19

Mantech International Corporation

Systems and methods for deploying secure edge platforms

US20230028338A1

( en )

2019-12-18

2023-01-26

Volvo Construction Equipment Ab

A method of operating a fleet of autonomous vehicles

US20230044620A1

( en )

2020-04-19

2023-02-09

Xact Robotics Ltd.

Algorithm-based methods for predicting and/or detecting a clinical condition related to insertion of a medical instrument toward an internal target

US20230066765A1

( en )

2021-08-26

2023-03-02

JBT AeroTech Corporation

Controlling devices in operations areas based on environmental states detected using machine learning

US20230064472A1

( en )

2021-08-24

2023-03-02

Honeywell International Inc.

Automated setpoint generation for an asset via cloud-based supervisory control

US20230101308A1

( en )

2021-09-30

2023-03-30

Konica Minolta Business Solutions U.S.A., Inc.

Imaging iot platform utilizing federated learning mechanism

US20230147814A1

( en )

2021-11-05

2023-05-11

At&amp;T Intellectual Property I, L.P.

Method and system for routing/orchestration/management of aerial vehicles or data on a network

US20230141055A1

( en )

2020-07-22

2023-05-11

Samsung Electronics Co., Ltd.

Edge computing system and method for recommending connection device

US20230164033A1

( en )

2020-07-22

2023-05-25

Samsung Electronics Co., Ltd.

Edge computing system and method

US20230171235A1

( en )

2021-12-01

2023-06-01

Paypal, Inc.

Edge Device Representation Learning

US20230205994A1

( en )

2021-12-23

2023-06-29

Google Llc

Performing machine learning tasks using instruction-tuned neural networks

US20230205606A1

( en )

2020-05-29

2023-06-29

Intel Corporation

Systems, apparatus, and methods to workload optimize hardware

US11695632B1

( en )

2022-06-30

2023-07-04

Amazon Technologies, Inc.

Management and control across heterogeneous edge devices of a client network using device abstractions

US20230244470A1

( en )

2021-05-24

2023-08-03

Israel Aerospace Industries Ltd.

Database updates induced communication in a communication network

US11741760B1

( en )

2022-04-15

2023-08-29

Samsara Inc.

Managing a plurality of physical assets for real time visualizations

US20230280059A1

( en )

2022-03-01

2023-09-07

Johnson Controls Tyco IP Holdings LLP

Building automation system with edge device local configuration

US20230300195A1

( en )

2019-04-09

2023-09-21

Johnson Controls Tyco IP Holdings LLP

Intelligent edge computing platform with machine learning capability

US20230316927A1

( en )

2021-10-08

2023-10-05

Gatik Ai Inc.

Method and system for operation of fleet vehicles

US20230325725A1

( en )

2022-04-12

2023-10-12

Google Llc

Parameter Efficient Prompt Tuning for Efficient Models at Scale

US20230360679A1

( en )

2009-03-02

2023-11-09

Rovi Technologies Corporation

Application tune manifests and tune state recovery

US20230359600A1

( en )

2022-05-09

2023-11-09

T-Mobile Usa, Inc.

Database provisioning and management systems and methods

US20230368281A1

( en )

2022-05-11

2023-11-16

Oshkosh Corporation

Equipment rental system and method

US20230368637A1

( en )

2022-05-10

2023-11-16

Western Digital Technologies, Inc.

Adaptive automated alarm response system

US11876858B1

( en )

*

2023-09-05

2024-01-16

Armada Systems Inc.

Cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads

US11907093B1

( en )

*

2023-09-05

2024-02-20

Armada Systems Inc.

Cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads

2023

2023-09-05

US

US18/461,461

patent/US11907093B1/en

active

Active

2024

2024-01-08

US

US18/406,471

patent/US12111744B1/en

active

Active

2024-05-03

WO

PCT/US2024/027797

patent/WO2025053879A1/en

active

Pending

2024-05-03

AU

AU2024337641A

patent/AU2024337641A1/en

active

Pending

Patent Citations (84)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

WO2002007330A2

( en )

2000-07-18

2002-01-24

Motorola, Inc.

Wireless bidirectional interface

US20050156715A1

( en )

2004-01-16

2005-07-21

Jie Zou

Method and system for interfacing with mobile telemetry devices

US20090079555A1

( en )

2007-05-17

2009-03-26

Giadha Aguirre De Carcer

Systems and methods for remotely configuring vehicle alerts and/or controls

US20090249215A1

( en )

2008-04-01

2009-10-01

Samsung Electronics Co., Ltd.

System and method for remote application configuration management on multifunction peripherals

US20230360679A1

( en )

2009-03-02

2023-11-09

Rovi Technologies Corporation

Application tune manifests and tune state recovery

US20120123951A1

( en )

2010-11-17

2012-05-17

Decisiv Inc.

Service management platform for fleet of assets

US20150120749A1

( en )

2013-10-30

2015-04-30

Microsoft Corporation

Data management for connected devices

US20160093119A1

( en )

2014-09-26

2016-03-31

International Business Machines Corporation

Monitoring and Planning for Failures of Vehicular Components

US20160117059A1

( en )

2014-10-24

2016-04-28

Caterpillar Inc.

User Interface for Fleet Management

US20160266739A1

( en )

2015-03-13

2016-09-15

Caterpillar Inc.

User Interface for Fleet Management

US20170285623A1

( en )

2016-04-05

2017-10-05

Wellaware Holdings, Inc.

Device for monitoring and controlling industrial equipment

US20180025304A1

( en )

2016-07-20

2018-01-25

Fisher-Rosemount Systems, Inc.

Fleet management system for portable maintenance tools

US20180032915A1

( en )

*

2016-07-29

2018-02-01

Splunk Inc.

Transmitting machine learning models to edge devices for edge analytics

US20180082152A1

( en )

2016-09-21

2018-03-22

GumGum, Inc.

Training machine learning models to detect objects in video data

US20180341720A1

( en )

2017-05-24

2018-11-29

International Business Machines Corporation

Neural Bit Embeddings for Graphs

US20190156246A1

( en )

2017-11-21

2019-05-23

Amazon Technologies, Inc.

Generating and deploying packages for machine learning at edge devices

US20190212754A1

( en )

2018-01-09

2019-07-11

Robert Bosch Llc

Mass Evacuation Utilizing a Fleet of Autonomous Vehicles

US20190258747A1

( en )

2018-02-22

2019-08-22

General Electric Company

Interactive digital twin

US20190370686A1

( en )

2018-06-01

2019-12-05

Nami Ml Inc.

Machine learning model re-training based on distributed feedback

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