At&t Intellectual Property I, L.p.

At&t Intellectual Property Ii, L.p.

At&t Corp.

Hyundai Motor Company

Kia Corporation

Lucent Technologies Inc.

Nuance Communications, Inc.

Comcast Cable Holdings, LLC

Other

Comcast Cable Communications, LLC

At&t Mobility Ii LLC

Shopify Inc.

At&t Intellectual Property I,l.p.

At&t Ipm Corp.

At&t Labs, Inc.

David Crawford Gibbon

Behzad Shahraray

Zhu Liu

Lee Begeja

Bernard S Renger

Eric Martin Zavesky

Andrea Basso

Raghuraman Gopalan

Tan Xu

Mazin E Gilbert

Amy Ruth Reibman

Yadong Mu

Michael J Johnston

Patrick Guy Haffner

Paul Triantafyllou

Rittwik Jana

Jean-Francois Paiement

Harris Drucker

Shan Liu

Srinivas Bangalore

Eric Cosatto

Hans Peter Graf

Yih-Farn Robin Chen

Bin Wei

Paul Gausman

Jianxiong Dong

Sumit Kumar

Horst Juergen Schroeter

Mazin G Rahim

Timothy Innes

Robert Edward Markowitz

Allen Louis Gorin

Giuseppe Di Fabbrizio

Steven Hart Lewis

Patrick John Ehlen

Kenneth H Rosen

Richard Vandervoort Cox

Qian Huang

Laurence W Ruedisueli

Mitchell J Weinraub

Stephanie A Morrison

James H Pratt

Nigel Bradley

Paul Van Vleck

Charles Douglas Blewett

Yeon-Jun Kim

Dilek Zeynep Hakkani-Tur

Gokhan Tur

John F Murray

Benjamin J Stern

Aaron Edward Rosenberg

Serban Jora

Richard Palazzo

Thomas Kirk

Samuel Seljan

Mohammed Abdel-Wahab

Ashutosh Sanzgiri

Steven Neil Tischer

Gustavo De Los Reyes

Mehmet Reha Civanlar

Jay Gordon Wilpon

Andrea Giuseppe Forte

Alicia Abella

Juan A Garay

Christopher Volinsky

Evgene Vahlis

Edward G Amoroso

Ann Eileen Skudlark

Roberto Pieraccini

Wen-Ling Hsu

Lien K Tran

Jakub Segen

Esther Levin

Deirdre Paul

Robert J Andres

Barbara Laing

Oliver Spatscheck

Bo Han

Roy Philip Weber

Han Q Nguyen

Douglas M Nortz

Jerald Robert Howcroft

Howard Paul Katseff

Bethany Scott Robinson

Karrie Jo Hanson

Gerald Michael Karam

Peter H Stuntebeck

Sunil Maloo

Richard Frank Bruno

Vishwa M Prasad

Kenneth Mervin Huber

Murat Saraclar

Ping-Fai Yang

Terrel Lecesne

Mark W Altom

Herani Soewarto Brotman

Zhengyi Zhou

Subhabrata Majumdar

Russell L Andersson

Hailong Sun

Concepts and technologies disclosed herein are directed to machine learning model understanding as-a-service. According to one aspect of the concepts and technologies disclosed herein, a model understanding as-a-service system can receive, from a user system, a service request that includes a machine learning model created for a user associated with the user system. The model understanding as-a-service system can conduct an analysis of the machine learning model in accordance with the service request. The model understanding as-a-service system can compile, for the user, results of the analysis of the machine learning model in accordance with the service request. The model understanding as-a-service system can create a service response that includes the results of the analysis. The model understanding as-a-service system can provide the service response to the user system.

Machine learning model understanding as-a-service

In one example, a method performed by a processing system including at least one processor includes establishing a communication group including at least three users of an extended reality environment as members, tracking locations and directional positions of the members of the communication group within the extended reality environment and within physical environments of the members, determining that a second user of the at least three users is an intended recipient of a first utterance made by a first user of the at least three users, and presenting the first utterance to the second user, where a directionality associated with a presentation of the first utterance is based on a location and a directional position of the first user relative to the second user, and presenting a spatial awareness warning to the second user, based on the location and the directional position of the second user.

Managing directed personal immersions in a communication group via awareness warning based on location and directional position

Aspects of the subject disclosure may include, for example, a system and method for selecting media content for a group of persons located at a venue. The system and method identify a plurality of viewers in a vicinity of a venue that has one or more display devices from location data and extracts a content viewing preference of each viewer from their profile data. The process includes aggregating the content viewing preference of each of the plurality of viewers to generate an aggregated content profile. Based on the aggregated content profile, a list of content is generated. Next, a first group of viewers approaching a viewing range of a display device are sensed by imaging data. Any conflicts between content viewing preferences of the first group of viewers are detected and resolved based on weighting the viewing preferences of the group. Other embodiments are disclosed.

Automated content selection for groups

Aspects of the subject disclosure may include, for example, a machine-readable medium with executable instructions that facilitate performance of operations by a processor including receiving a registration for a trajectory of a satellite; estimating a zone of occupancy at a future time for the satellite; determining an interference of the satellite from the zone of occupancy at the future time; determining a deconfliction for minimizing or eliminating the interference; and sending the deconfliction to a controller of the satellite. Other embodiments are disclosed.

System for satellite discovery and decommission of orbital obstructions

Aspects of the subject disclosure may include, for example, a processing system including a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving immersive media content; creating foreground information for rendering foreground video content of the immersive media content; receiving a first point-of-view (PoV) of a first viewer operating a first display device; generating a background video content from the immersive media content; sending the foreground information to the first display device; and sending a first portion of the background video content to the first display device, wherein the first portion is based on the first PoV. Other embodiments are disclosed.

System and method for social immersive content rendering

Aspects of the subject disclosure may include, for example, receiving first image data for a first image from an electronic device, the first image data including first information associated with a first object identified by the electronic device as being in the first image. Second image data for a second image is received from the electronic device subsequent to receiving the first image data. The second image data includes second information associated with a second object identified by the electronic device as being in the second image. Location information is determined based on the first information and the second information and sent to the electronic device. Other embodiments are disclosed.

Dynamic image recognition model updates

Devices, computer-readable media, and methods for providing an enhanced indication of an object that is located via a visual feed in accordance with a user context are disclosed. For instance, in one example, a processing system including at least one processor may detect a user context from a visual feed, locate an object via the visual feed in accordance with the user context, and provide an enhanced indication of the object via an augmented reality display.

Context-based object location via augmented reality device

The present disclosure describes a device, computer-readable medium, and method for providing logistical support for robots. In one example, the method includes receiving, at a centralized support center that is in communication with a plurality of robots, a query from a first robot of the plurality of robots that has been deployed to perform a task, wherein the query indicates an error encountered by the first robot and evidence of the error collected by the first robot, formulating, at the centralized support center, a proposed solution to resolve the error, wherein the formulating comprises soliciting an analysis of the evidence by a party other than the first robot, and delivering, by the centralized support center, the proposed solution to the first robot.

Providing logistical support for robots

An intelligent agent monitors operation of at least one software virtualized network (VN). Context information associated with the VN is used to analyze a state of the VN. At least one configuration change is caused to the VN in response to analysis of the state of the VN. A change is identified to the state of the VN caused by the configuration change. A determination is made as to whether or not the change to the state of the VN is an improvement to operation of the VN. A response to the determination is made by causing at least one other configuration change to the VN.

Systems and methods to control operation of virtualized networks

In one example, a method performed by a processing system including at least one processor includes acquiring a first item of media content from a user, where the first item of media content depicts a subject, acquiring a second item of media content, where the second item of media content depicts the subject, compositing the first item of media content and the second item of media content to create, within a metaverse of immersive content, an item of immersive content that depicts the subject, presenting the item of immersive content on a device operated by the user, and adapting the presenting of the item of immersive content in response to a choice made by the user.

Compositing non-immersive media content to generate an adaptable immersive content metaverse

A method for validating objects appearing in volumetric video presentations includes obtaining a volumetric video presentation depicting a scene, wherein the volumetric video presentation is associated with a metadata file containing identifying information for the scene, identifying user-generated content that depicts the scene, by matching metadata associated with the user-generated content to the metadata file associated with the volumetric video presentation, comparing a first object appearing in the volumetric video presentation to a corresponding second object appearing in the user-generated content, assigning a score to the first object based on the comparing, wherein the score indicates a probability that the first object has not been manipulated, and altering the volumetric video presentation to filter the first object from the volumetric video presentation when the score falls below a threshold.

Validating objects in volumetric video presentations

A processing system having at least one processor may establish a communication session between a first communication system of a first user and a second communication system of a second user, the communication session including first video content of a first physical environment of the first user and second video content of a second physical environment of the second user, determine a first visualization action for a first physical object in the first physical environment in accordance with a first configuration setting of the first user for the communication session, obtain the first video content from a first camera of the first communication system, detect the first physical object in the first video content, and perform the first visualization action to modify the first video content. The processing system may then transmit first visualization information including the first video content that is modified to the second communication system.

Physical object-based visual workspace configuration system

One example of a method includes receiving a plurality of video streams depicting a scene, wherein the plurality of video streams provides images of the scene from a plurality of different viewpoints, identifying a target that is present in the scene, wherein the target is identified based on a determination of a likelihood of being of interest to a viewer of the scene, determining a trajectory of the target through the plurality of video streams, wherein the determining is based in part on an automated visual analysis of the plurality of video streams, wherein the determining is based in part on a visual analysis of the plurality of video streams, rendering a volumetric video traversal that follows the target through the scene, wherein the rendering comprises compositing the plurality of video streams, receiving viewer feedback regarding the volumetric video traversal, and adjusting the rendering in response to the viewer feedback.

Selecting viewpoints for rendering in volumetric video presentations

Growing community of inventors

Lincroft, NJ, United States of America

David Crawford Gibbon