At&t Intellectual Property I, L.p.

At&t Intellectual Property Ii, L.p.

At&t Mobility Ii LLC

Venturi Wireless, Inc.

Intel Corporation

Other

Apple Inc.

Ringcentral, Inc.

At&t Intellecutal Property I, L.p.

At&t Mibility Ii LLC

Mostafa Tofighbakhsh

Rittwik Jana

David R Wolter

Deva-Datta Sharma

Shraboni Jana

Bogdan Ungureanu

Kaveh Hushyar

Shyam Parekh

Gaviphat Lekutai

Behzad Nadji

Hossein Eslambolchi

Mark D Austin

Mario Kosseifi

Rick Tipton

Cagatay Buyukkoc

Vinay Kundaje

Arvind Ramachandran Raghavan

Vivek G Gupta

Milap V Majmundar

Krishna Ramadas

Bryan L Sullivan

Waliur Rahman

Paul Gausman

Zhi Cui

Thomas Arnold Anschutz

Paul Edward Smith, Jr

Feng Chi Wang

Michael Q Iapalucci

Janette A Johnston

Hossein Besharatian

Vlad Vendrow

Yuang Lou

Yonatan Aharon Levy

Dhruv Gupta

Constance Jean Coty

Jacob Lahav

Homayoun Razavi

Stephen Blystone

Catagay Buyukkoc

A computer-implemented method includes receiving a plurality of data associated with a device, wherein the plurality of data includes a location data associated with the device; applying the plurality of data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the selected ML model; and sending the error correcting code to the device.

System and method for location based error correcting and video transcribing code selection

In one embodiment, a method includes receiving, by a storage device located at an edge of a network, user device data from a first user device and receiving, by the storage device, metadata associated with an environment of the first user device. The method also includes classifying, by the storage device, the metadata into a category to generate classified metadata and applying, by the storage device, a policy to the classified metadata to generate policy-based classified metadata. The method further includes assigning, by the storage device, one or more tokens to the policy-based classified metadata and assigning, by the storage device, one or more tags to the policy-based classified metadata. The method further includes transmitting, by the storage device, the one or more tags to a second user device in accordance with the one or more tokens.

Edge metadata

Methods, computer-readable media and devices are disclosed for selecting a plurality of network devices to perform a plurality of tasks in accordance with a set of functional network analytics instructions. For example, a processor deployed in a telecommunication network may receive a set of functional network analytics instructions compiled from a set of instructions in accordance with a functional network analytics platform application programming interface. The processor may further, in accordance with the set of functional network analytics instructions, select a plurality of network devices to perform a plurality of tasks, send the plurality of tasks to the plurality of network devices, receive control plane data from the plurality of network devices, correlate the control plane data in accordance with operations defined in the set of functional network analytics instructions to create resulting data, and forward the resulting data to at least one recipient device.

Telecommunication network analytics platform

The described technology is generally directed towards beam recovery for an antenna array. One or more recovery beams or recovery beam patterns can be defined for an antenna array, and in response to a failure, the antenna array can be restored to a defined recovery beam or recovery beam pattern. Techniques for defining recovery beams and recovery beam patterns for the antenna array, selecting a recovery beam or recovery beam pattern for the antenna array, and entering a selected recovery beam or recovery beam pattern by the antenna array are also disclosed.

Beam recovery for antenna array

Global Internet of things (IoT) quality of service (QoS) is provided through self-forming, self-healing, and/or collaborative edge IoT gateways. Moreover, global IoT services are provided by logically extending cellular networks with roaming partners to backhaul, track, and/or manage the globally deployed edge IoT gateways. In one aspect, real-time QoS and/or monitoring capabilities for the global IoT services can be provided through a communication between the edge IoT gateways and an edge gateway controller deployed within a cloud. The edge IoT gateways form a structured mesh network to coordinate workload execution under control of the edge gateway controller, which can facilitate a highly efficient QoS and/or SLA management for mobile IoT sensors, to provide a secure monitoring and/or diagnostic capability for the global IoT services.

Global internet of things (IoT) quality of service (QoS) realization through collaborative edge gateways

Deployment of Internet-of-things devices can comprise sensors deployed in remote and hard to reach areas and locations. Due to lack of access to reliable power, these sensors cannot always be connected to a network and also have limited computation power. Consequently, a mechanism can be established to periodically access these sensors and collect data from them. The mechanism can utilize a mobile radio unit device to serve as data collectors. The mobile radio unit device can make use of an adaptive beam scanning to perform sensory data collection via the beam scanning operation. Additionally, the platform can also comprise a radio access network intelligent controller to manage the data collecting radio units by providing specific instructions and data collection methodologies.

Beam provisioning for sensory data collection for 5G or other next generation networks

Given real-time user equipment (UE) measurements from an open radio access network (O-RAN) infrastructure, a radio access network intelligent controller (RIC) can compute a UE distribution context space. The O-RAN can comprise gNode-Bs, centralized units, and distributed units. The UE distribution context space computations can be performed by a UE context correlator module of the RIC. The UE context correlator module can also utilize a pre-defined UE context model, which contains definitions and values for various UE context attributes to generate adaptive beam-forming patterns.

Adaptive beam sweeping for 5G or other next generation network

Aspects of the subject disclosure may include, for example, determining content information associated with participant information from a plurality of data channels associated with a plurality of participants in a communications session, modifying the participant information responsive to identifying non-conforming participant information, generating categorized participant information according to the content information associated with the participant information from the plurality of data channels, aggregating, by the processing system, the categorized participant information from the plurality of data channels to generate group information, sending the group information to a common data channel, wherein the common data channel is presented to each participant of the plurality of participants in the communications session, and sending a first notification to a first participant via a first data channel of the plurality of data channels responsive to identifying an involvement level associated with the first participant of the plurality of participants according to a plurality of participation metrics. Other embodiments are disclosed.

Method and apparatus for enhanced conferencing

Aspects of the subject disclosure may include, for example, a method in which a processing system obtains a sample of a content stream directed to a user device, identifies a type of the content stream, and selects a model for recognizing objects appearing in the content stream. The system analyzes the content stream in accordance with the model to recognize the object, generates a label for the object, and associates the label with the object in the content stream. The system also delivers the content stream for presentation at the user device; the label is delivered in-line with respect to the content stream and is generated in real time with respect to the presentation. The method further includes training the model in accordance with a machine learning procedure; the model is refined based on the analyzing of the content stream. Other embodiments are disclosed.

Video object tagging based on machine learning

An intelligent determination of a set of candidate handover-beams for a radio access network (RAN) can be facilitated by a RAN intelligent controller (RIC). The RIC can obtain data associated with user equipment devices and their current state, historical patterns, and a current state of serving network node devices and neighboring network node devices. Based on the obtained data, a handover procedure can be optimized by the user equipment by estimating the signal quality from a source cell and neighboring cells. Additionally, the user equipment can send measurement reports to the network to identify and quantify the quality of reference signals transmitted by the network node devices.

Candidate beam selection and control for 5G or other next generation network

Precoding matrix computations for a large number of antenna arrays can be used to generate efficiencies within a wireless network. Utilizing network topology and context data in conjunction with known available network resources and mobile device measurements can facilitate gains in power and spectral efficiency and reduction in computation complexity posed by current procedures. To take advantage of multiple paths, the precoding matrix can be known at the radio units for each mobile device.

Context-based precoding matrix computations for radio access network for 5G or other next generation network

A network access credential can be shared among devices based on location information for a device. Location information can include timed fingerprint location information. In an aspect, location information can be associated with a location of user equipment. This location information can be correlated with network access credentials. Location information can be used to access a relevant network access credential. The relevant network access credential can be shared with other devices. In an embodiment, sharing a network access credential can be between mobile devices. In another embodiment, sharing a network access credential can be between a remote computing device and a mobile device. Sharing a credential can allow for access to a network without having to generate or input new credentials.

Location based sharing of a network access credential

Global internet of things (IOT) quality of service (QOS) realization through collaborative edge gateways

A radio access network intelligent controller (RIC) platform can enable various highly secure, reliable, fast communications for first responders during emergency situations. Self-organizing service chaining of public safety edge applications, can be enabled in both time and space when triggered by an emergency situation. The RIC platform can perform reassignment of resources and network slices according to historical data and situational analysis. The RIC platform can select and provide the best frequencies and resources to ensure that first responders have communication services that do not get affected by anomalies such as network load, congestion, and/or related degradations.

Beam as a service for 5G or other next generation network

The described technology is generally directed towards an intent design tool for communication networks. A graphical user interface provided by the intent design tool can include a collection of communication network topology elements and a communication network intent topology design area. Selected communication network topology elements can be placed into the communication network intent topology design area, and connection types between the selected communication network topology elements can also be specified in the communication network intent topology design area, in order to define custom communication network intent topologies.

Intent design tool for wireless communication networks

Growing community of inventors

Cupertino, CA, United States of America

Mostafa Tofighbakhsh