Broadcom Corporation

Avago Technologies International Sales Pte. Limited

Avago Technologies General IP (singapore) Pte. Ltd.

General Instrument Corporation

Other

Motorola Corporation

General Instrument Corporation of Delaware

Boadcom Corporation

Nxp B.v.

Motorola Mobility LLC

Avago Technologies General IP Pte, Ltd.

Google Technology Holdings LLC

Xuemin Chen

Jeyhan Karaoguz

Wael William Diab

Marcus Christopher Kellerman

Victor Hou

Thomas J Quigley

Stephen Elliott Gordon

Michael Dove

David Rosmann

David Garrett

David Albert Lundgren

John S Walley

Nicholas Ilyadis

Rich Prodan

Yongbum Kim

Nariman Yousefi

Rajesh Mamidwar

Gordon Li

Vinko Erceg

Charles Abraham

Mark Kent

Iue-Shuenn Chen

Yong Li

Jason C Demas

Wade Keith Wan

Stephane Walter Rodgers

Brian Allan Heng

Vladimir Silyaev

Samir N Hulyalkar

Sandeep Bhatia

David Erickson

Ilya Klebanov

Zhijie Yang

Jiang Fu

Philippe Klein

Ajay K Luthra

Andrew R Dellow

Robert Owen Eifrig

Arlen J Kirchoff, Jr

Alexander Garland MacInnis

Steve Rodgers

Srinivasa Mogathala Prabhakara Reddy

Qiang Ye

Chris Boross

Minhua Zhou

Krit Panusopone

Francis Cheung

Bradley Donald Blanche

Pramod Chandraiah

Nambi Seshadri

Stephen Ray Palm

Frederick George Walls

Guangcai Zhou

Sam Anderson

James D Bennett

Bruce Joseph Currivan

Yasantha N Rajakarunanayake

Girish Raghunath Hulmani

Vijayanand Aralaguppe

Shee-Yen Tan

Francis Chi-Wai Cheung

Brett Tischler

Avi Kliger

Michael David Johas Teener

Vivian T Hsiun

Ramanujan K Valmiki

Anand Tongle

Glen A Grover

Nambirajan Seshadri

Peisong Chen

Weimin Zhang

Stephen Edward Krafft

Jason William Herrick

Vincent C Liu

Daniel A Simon

Prashant Katre

Bhaskar Mala Sherigar

Sunkwang Hong

Joshua Stults

Fabian Alberto Russo

Richard Schwartz

Gheorghe Berbecel

Kumaran David Siva

Limin Wang

Mohan V Kalkunte

Brian Francis Schoner

Ariel Hendel

Michael Glik

Mandayam A Narasimhan

Robert Hulvey

Patricia Ann Thaler

Richard Stephen Prodan

Glen Kramer

Christopher Pasqualino

Daniel William English

Hongtao Zhu

Binfan Liu

Shawn Val Johnson

Arun Gopalakrishna Rao

Victor Liang

Queue management for latency control is provided. A method includes receiving packets of multiple flows, each of the packets including an arrival time stamp. The method can include classifying, based on a type of application, the flows for transmission as higher priority via a higher priority channel, or lower priority via a lower priority channel. The method can include determining, for packets for the flows classified as higher priority, a queue of one or more second queues to use for transmission and a departure time stamp for each of the packets according to a latency budget for the type of application. The method can include communicating the higher priority packets with the departure time stamp to a corresponding queue of the one or more second queues based on the type of application. A transmission scheduler can prioritize transmission of packets from the second queues based on the departure time stamp.

Systems for and methods for queue management for latency control

A method includes accessing a video comprising a set of frames; receiving a command to identify a target displayed in a first frame; generating a first target data based on the target; generating a first confidence level between the first target data and a second target data stored in a remote database, wherein the second target data comprises identity data; in response to the first confidence level being at or above a confidence level threshold, outputting the identity data of the second target data; otherwise, generating a third target data based on the target displayed in a second frame of the set of frames; generating a second confidence level of similarity between the third target data and the second target data stored in the remote database; in response to the second confidence level being at or above the confidence level threshold, outputting the identity data of the second target data.

Face recognition systems and methods for media playback devices

A method is provided that includes receiving a data request from a requesting device requesting data from a data source, looking up, in a blockchain ledger, a transaction corresponding to the data request to validate the data request, forwarding the validated data request to a data server, receiving a data response from the data server in response to the data request, wherein the data response comprises the requested data from the data source, and forwarding the data response to the requesting device.

Blockchain-enforced data access control

A device is provided that includes computer-readable storage media storing one or more sequences of instructions and processing circuitry configured to execute the one or more sequences of instructions. Upon executing the instructions, the processing circuitry may receive a plurality of network packets containing content encapsulated in a plurality of layers; process the received plurality of network packets to extract the content for presentation; generate a predicted presentation quality indicator for the extracted content using a plurality of machine learning models in a hierarchical order with data generated during processing of the received plurality of network packets used as inputs to the plurality of machine learning models; and provide the predicted presentation quality indicator for the extracted content to a server via a network, wherein the data generated during processing of the received plurality of network packets is correlated across the plurality of layers to generate the predicted presentation quality indicator.

Video quality monitoring system

Novel tools and techniques are provided for implementing encoding or decoding of adaptive bitrate streams. In various embodiments, one or more first computing systems may divide a live media content stream into one or more segments, each segment might include a starting segment boundary and an ending segment boundary. The one or more first computing systems might encode the one or more segments into one or more primary adaptive bitrate streams. The one or more first computing systems might also divide the one or more segments of the live media content stream into one or more subsegments. Each subsegment might be less than a length of a corresponding segment of the one or more segments. The one or more first computing systems might the encode and/or a second computing system might decode the one or more subsegments into or from one or more secondary adaptive bitrate streams.

Gapped and/or subsegmented adaptive bitrate streams

A method is provided that includes setting, by a controller, a first bit-rate level for a next video segment, and comparing a fill level of a playback buffer to a first threshold. If the fill level of the playback buffer satisfies the first threshold, the first bit-rate level for the next video segment is replaced by setting a second bit-rate level for the next video. A first request is issued to a server for the next video segment encoded at the first bit-rate level or, if the fill level of the playback buffer satisfies the first threshold, encoded at the second bit-rate level and downloading of the requested next video segment and storing the requested video segment in the playback buffer. A decoder decodes the next video segment from the playback buffer for playback on a display device after the next video segment has been downloaded and stored in the playback buffer.

Rebuffering reduction in adaptive bit-rate video streaming

A data analytics device for application monitoring includes an application-monitor manager engine to receive configuration files from a cloud server and to generate monitoring rules for a number of monitors. A data-processing engine is coupled to the application-monitor manager engine and the monitors and coordinates activities of the plurality of monitors. The monitors are embedded in a user space and a kernel of a host operating system, and the monitoring rules for the monitors are generated based on the configuration files.

Secure monitoring of system-on-chip applications

A semiconductor product being convertible or converted from a customizable configuration into a selectable or selected one of a plurality of different customized configurations, wherein the semiconductor product comprises a customizing unit configured for customizing the semiconductor product into one of the customized configurations selected by a received customizing data structure specifying a selected application of the semiconductor product, and a plurality of functional blocks each configured for providing an assigned functionality and all being deactivated when the semiconductor product is not in one of the customized configurations, wherein the customizing unit is configured for activating only a subgroup of the functional blocks based on the received customizing data structure.

Customizing a semiconductor product

A system for multimedia content recognition includes a cloud server and a media client including a silicon-on-chip (SoC) device to communicate with the cloud server via a network. The SoC device includes a local area network (LAN) interface to receive media content from a media source and a media monitor to analyze the received media content and to generate signature information for transmission to the cloud server or for a local analysis. The SoC device further includes an inference engine to locally analyze the signature information to detect an unauthorized access.

Multimedia content recognition with local and cloud-assisted machine learning

Systems, methods and apparatus for processing video can include a processor. The processor can be configured to perform object detection to detect visual indications of potential objects of interest in a video scene, to receive a selection of an object of interest from the potential objects of interest, and to provide enhanced video content within the video scene for the object of interest indicated by the selection.

Object or region of interest video processing system and method

Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Distributed machine-learning resource sharing and request routing

A vehicle control module includes a vehicle device and a vehicle network interface. The vehicle device is operable to perform a vehicle function. The vehicle network interface facilitates communication regarding the vehicle function between the vehicle device and a vehicle network fabric in accordance with a global vehicle network communication protocol.

Unified vehicle network frame protocol

A network may include multiple allocations. The allocations may include: a first allocation encompassing central infrastructure, such as central office servers, data centers, or other core infrastructure; an second allocation encompassing gateway elements or other central consumer premises network infrastructure; and a third allocation encompassing nodes, such as client devices, terminals, or other nodes. A virtualization management engine may coordinate resources from the various allocations to support virtual functions distributed over multiple allocations of the network. The virtualization management engine may determine the distribution across the allocations for the virtual functions. The virtualization management engine may be implemented as a virtual function and be distributed across the allocations of the network.

System for network-based reallocation of functions

A system for integrating video content and data provision includes an upstream signal path to process upstream data traffic received from a first transmission network for transmission to a second transmission network, and a downstream signal path to process downstream data traffic and downstream video traffic for transmission from the second transmission network to the first transmission network. The upstream signal path includes an analog-to-digital converter (ADC) and an upstream demodulator, and the downstream signal path includes an Ethernet processor, a digital-signal-processor (DSP) and a digital-to-analog converter (DAC).

Integrated video and data system

Growing community of inventors

Rancho Santa Fe, CA, United States of America

Xuemin Chen