Emc IP Holding Company LLC

Dell Products Inc.

Amy N Seibel

Eric Joseph Bruno

Victor Fong

Zhen Jia

Danqing Sha

Kathleen Moriarty

Xuebin He

Megan A Murawski

Alex Robbins

James Robert King

Michael Robillard

Shary Beshara

Benjamin Santaus

Zijia Wang

Nicole Reineke

Himanshu Arora

Joel Evans Christner

Seth Jacob Rothschild

Farida Shafik

Kranti Uppala

Malini Bhattacharjee

Akshaya Khare

Rana Afifi

Megan Hyland

Andrew Veitch

One example method includes generating sound information regarding sound sources in an environment. The sound information is generated by separating and localizing the sound sources. The sound information is then presented as guidance to a user. The guidance may be presented graphically in a user interface, haptically, or in another manner.

Visual guidance of audio direction

Approximating energy use of software executing on virtual machines is presented herein. A system determines a pair of energy consumption baselines of a host computing device representing respective energy being utilized by the host computing device; determines, at a sampling time within a job execution period during which a job has been executing on a virtual machine (VM) of the host computing device, an energy consumption of the host computing device; and based on the pair of energy consumption baselines and the energy consumption of the host computing device, and further based on respective estimated portions of processing resources of a central processing unit of the host computing device being utilized, at the sampling time, by the VM and by the job within the VM, determines an approximate amount of energy use of the job that has executed on the VM.

Approximating energy use of software executing on virtual machines

One example method includes determining representation bias in a data set. A bias detection engine is trained using a data set that is sufficiently diversified and/or unbiased. Once trained, test data sets can be evaluated by the bias detection engine to determine an amount of representation bias in the test data sets. The representation bias can be visually conveyed to a user and suggestions on how to reduce the representation bias may be provided and/or implemented to reduce the representation bias in the test data set. Suggestions can be implemented by adding or removing data from the test data that will reduce the representation bias.

Detecting representative bias within image data sets

Techniques are disclosed for management of edge resources. For example, a method comprises receiving energy consumption data corresponding to operation of a plurality of edge devices from a plurality of edge service providers. In the method, a plurality of energy efficiency scores are computed based, at least in part, on the energy consumption data. The energy efficiency scores correspond to operation of one or more of the edge devices associated with respective ones of the edge service providers. The method further comprises receiving one or more energy consumption parameters from at least one user device for the operation of the one or more edge devices, and identifying based, at least in part, on the energy efficiency scores, a subset of the edge devices corresponding to the energy consumption parameters. Data corresponding to the subset of the edge devices is transmitted to the at least one user device.

Management of energy efficiency parameters for resources in edge computing system

Techniques are provided that employ haptic feedback to influence an engagement level of a remote user. One method comprises obtaining sensor data associated with a remote user interacting with educational content items; applying the sensor data to an analytics engine to obtain an engagement status indicating an engagement level of the remote user with the educational content items; and automatically initiating a provision of a haptic feedback signal to the remote user based on the engagement status. The engagement status may comprise an engagement score, and the haptic feedback signal may be sent to the remote user based on an evaluation of the engagement score relative to one or more threshold-based criteria. A type and/or an intensity of the haptic feedback signal can be based on one or more user preferences of the remote user.

Haptic feedback for influencing user engagement level with remote educational content

Methods, apparatus, and processor-readable storage media for artificial intelligence-driven avatar-based personalized learning techniques are provided herein. An example computer-implemented method includes obtaining multiple forms of input data from user devices associated with a user in a virtual learning environment; determining status information for user variables by processing at least a portion of the multiple forms of input data using a first set of artificial intelligence techniques; determining instruction-related modifications for the user by processing, using a second set of artificial intelligence techniques, at least a portion of the multiple forms of input data and at least a portion of the determined status information; implementing, based on the determined instruction-related modifications, modifications to an instructor avatar with respect to the user in the virtual learning environment; and performing one or more automated actions based on user response to the implemented modifications to the instructor avatar.

Artificial intelligence-driven avatar-based personalized learning techniques

One example method includes performing sound quality operations. Microphone arrays are used to cancel background noise and to enhance speech. With arrays at each environment of each user participating in a call, a first microphone array can cancel or suppress background noise and a second array can generate enhanced speech for transmission to other users. Thus, for user, the audio signal output by the user's device includes an anti-noise signal to cancel background noise present in the user's environment and enhanced speech from other users.

Adaptive noise suppression for virtual meeting/remote education

One example method includes creating an image definition file, using the image definition file to create an image that is deployable to an edge device, copying an agent into the image, stripping any user passwords out of the image, and removing any unnecessary packages from the image, and after the stripping and the removing, the image becomes an immutable image. The immutable image may then be deployed to a group of edge devices, and one or more layers of the immutable image may be updated by the agent.

Immutable image for deployment to edge devices

A method includes aggregating edge resources of a plurality of edge computing sites of an edge utility system, with the aggregated edge resources including edge resources provided by at least one user for utilization by one or more other users. The method further includes utilizing one or more portions of the aggregated edge resources to execute one or more workloads of the one or more other users, metering the utilization of the one or more portions of the aggregated edge resources in conjunction with the execution of the one or more workloads, and committing information characterizing at least the metered utilization of the aggregated edge resources to a distributed ledger.

Edge utility system with distributed ledger verification for dynamic aggregation of edge resources

One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

Edge function bursting

Techniques are provided for machine learning-based educational content adaptation based on user personal characteristics. One method comprises obtaining personal characteristics of at least one user; applying the personal characteristics of the at least one user to at least one machine learning model to automatically adapt at least one educational content item for the at least one user using one or more of the applied personal characteristics of the at least one user; and initiating a provision of the at least one automatically adapted educational content item to the at least one user. Technology-related characteristics of the at least one user may also be applied to the at least one machine learning model to further adapt the at least one educational content item for the at least one user using the applied technology-related characteristics.

Machine learning-based educational content adaptation based on user personal characteristics

Methods, apparatus, and processor-readable storage media for automatically generating context-based alternative text using artificial intelligence techniques are provided herein. An example computer-implemented method includes generating text captions for an image derived from a web page by processing the image using an artificial intelligence-based image captioning model; determining context information pertaining to the image by processing the image using an artificial intelligence-based context and emotion recognition library; generating context-based alternative text for at least a portion of the image by processing, using at least one artificial intelligence-based alternative text generation model, at least a portion of one or more of the generated text caption(s) for the image and the determined context information pertaining to at least a portion of the image; and performing one or more automated actions based on the generated context-based alternative text.

Automatically generating context-based alternative text using artificial intelligence techniques

One example method includes injecting processing into sessions including IO sessions. Events in a file system are intercepted and processed. During processing, policies may be applied to the events. Some of the policies are triggered such that external actions or processing is applied to the event. Once the actions have been performed, the event may be processed by the file system.

System and method for proxying IO sessions to inject external processing

A method comprises executing a workload acquiring algorithm at a first edge node of a plurality of edge nodes to pull at least one workload of a plurality of workloads from a workload queue corresponding to a second edge node of the plurality of edge nodes. In one example, the first edge node may be a component of a first edge location, and the method may further comprise providing the first edge node with access to one or more workloads from one or more workload queues corresponding to one or more edge nodes from a second edge location different from the first edge location.

Edge node workload acquisition for distribution of edge workloads

A method includes receiving inputs for respective users in an edge utility system comprising edge and core computing sites, with a first one of the inputs for a first user characterizing edge resources requested by that user for executing at least a portion of a workload of that user, and a second one of the inputs for a second user characterizing edge resources available from that user for executing at least a portion of a workload of another user. The method includes populating one or more data structures based at least in part on the received inputs, aggregating edge resources of multiple ones of the edge computing sites into an edge network based at least in part on the populated data structures, and utilizing at least a portion of the aggregated edge resources of the edge network to execute at least a portion of a workload of a particular user.

Growing community of inventors

Newton, MA, United States of America

Amy N Seibel