International Business Machines Corporation

Other

Globalfoundries Inc.

James E Carey

Philip James Sanders

Bin Cao

Charles J Archer

John Matthew Santosuosso

Michael J Branson

Jay Symmes Bryant

Mathew Richard Odden

Lance Bragstad

Matthew W Markland

Jim C Chen

Brian E Smith

Eric L Barsness

Paul E Schardt

Lance Gordon Thompson

Joseph W Cropper

Michael A Blocksome

Kirubel Z Seifu

Mark G Atkins

Ryan L Rossiter

Bradley L Manning

John Stephen Mysak

Aditi Rajagopal

Kendall J Nelson

Lucas A Palm

Zachary A Hill

Bryan M Jones

Jenny S Li

Timothy James Graser

Corville O Allen

Rafal Przemyslaw Konik

Michael E Aho

Kristan D Davis

Gordon G Stewart

Anders Magnus Nilsson

Lynn Anne Boger

Jeffrey M Ceason

Brent A Carlson

Scott N Gerard

Bradley William Fawcett

Lin Cai

Brian Robert Muras

Timothy J Schimke

Ryan Kirk Cradick

Bradford Lee Cobb

Brent Allen Carlson

Cory J Kleinheksel

Curtis Howard Brobst

Matthew Ryan Weaver

Tore Magnus Dahl

Randall Paul Baartman

Jesse Lackey

Siegfried Wiesenhofer

Jason Allan Nikolai

Alexander Cook

Amy D Travis

Dean J Burdick

Gregory Howard Bellows

Kathryn Ann Bohrer

John M Vlissides

Jason J Illg

Michael Terry Payne

Torbjorn Harald Pernbeck

Vincent Edmund Price

Randy Dee Baxter

Charles Volzka

Dale Henry Manthei

Mark Alan Pasch

Anders Mangus Nilsson

Drew C Varner

Charles J Acher

Wilfried Lesnik

Marybeth Gurski

Peter Taube

Verena Kranzl

Stephen Porter

Charlie M Jonas

Slade Baumann

Jacob Gregor

An analytical recognition system includes a video camera configured to capture video data of a subject and an antenna configured to capture mobile communication device data relating to the subject's mobile device. The system further includes a data analytics module configured to: analyze the video data to determine one of a physical attribute or a movement attribute of the subject and generate a first certainty match value based on this attribute perform facial recognition analysis of the subject to obtain facial recognition data and generate a second certainty match value based on the facial recognition data; generate a third certainty match value based on the mobile device data; and combine the first, second, and third certainty match values to produce a combined certainty match value, which enhances the accuracy of identifying the subject by integrating multiple data sources, including physical attributes, facial features, and mobile device information.

Video identification and analytical recognition system

A self-healing video surveillance system is described. The self-healing video surveillance system includes one or more surveillance cameras that are configured to store configuration data of a network video recorder in operable communication with the surveillance cameras. The network video recorder includes configuration data, and a module configured to store the configuration data on the surveillance cameras and/or retrieve configuration data stored on the surveillance cameras. A new network video recorder introduced into the network retrieves the stored configuration data to self-configure with minimal or no human interaction. In embodiments, configuration data is distributed among a plurality of surveillance cameras, and may be stored in encrypted format.

Self-healing video surveillance system

A system for protecting personal data contained on an RFID-enabled device, suitable for use with an RFID system including an RFID reader configured to extract information from an RFID chip associated with the RFID-enabled device, includes a personal data protection system including a personal data protection device configured to prevent reading of the RFID chip associated with an RFID-enabled personal item.

Protection of personal data contained on an RFID-enabled device

An entertainment device safety system includes a video camera configured to capture video of an entertainment device and a user of the entertainment device and a video analytic module to perform real-time video processing of the captured video to generate non-video data from video. A computer receives the video and the non-video data from the video camera analyzes the video or the non-video data to determine a user position in relation to the entertainment device. The user position is compared to a user position rule to determine whether the user position violates the user position rule. A notification is transmitted in response to a determination that the user position violates the user position rule.

Entertainment device safety system and related methods of use

A method for configuring a computing device in a network of at least one remote device is disclosed. The method includes: storing, in a remote device, a configuration data archive relating to an existing computing device, wherein the remote device is at least one of a traffic camera or an aerial drone camera; determining, by a computing device to be configured, whether the remote device has stored therein a configuration data archive; and transferring data from the configuration data archive to the computing device to be configured in response to a determination that the remote device has stored therein a configuration data archive.

A stream computing application may be configured to manage the flow of tuples through a section of an operator graph. A window may be generated over one or more stream operators. The window may include breakpoint thresholds that set the maximum flow of tuples within the window. The stream operators within the window may be monitored to determine the flow of tuples occurring within the window using tuple flow counts. The tuple flow counts may be compared to the breakpoint thresholds to determine whether a breakpoint condition has occurred. If a breakpoint condition has occurred, a tuple flow change may be implemented to reduce the flow of tuples within the window.

Windowing across operators in a streaming environment

The present disclosure is directed to systems and methods for generating investigations of user behavior. In an example embodiment, the system includes a video camera configured to capture video of user activity, a video analytic module to perform real-time video processing of the captured video to generate non-video data from video, and a computer configured to receive the video and the non-video data from the video camera. In some embodiments, the video camera is at least one of a traffic camera or an aerial drone camera. The computer includes a video analytics module configured to analyze one of video and non-video data to identify occurrences of particular user behavior, and an investigation generation module configured to generate an investigation containing at least one video sequence of the particular user behavior. In some embodiments, the investigation is generated in near real time. The particular user behavior may be defined as an action, an inaction, a movement, a plurality of event occurrences, a temporal event and/or an externally-generated event.

Investigation generation in an observation and surveillance system

An analytical recognition system includes a video camera, an antenna, and a data analytics module. The video camera is configured to capture video data. The antenna is configured to capture mobile communication device data. The data analytics module is configured to correlate the video data and the mobile communication device data to generate a profile of a person associated with the video data and the mobile communication device data. The profile has profile data including any one or a combination of the captured video data and the captured mobile communication data.

An accelerator deployment tool deploys multiple accelerators to multiple programmable devices, and detects when a new programmable device becomes available. When a first accelerator in a first programmable device is a better match to the new programmable device, the accelerator deployment tool automatically generates an image for the first accelerator for the new programmable device, deploys the image on the new programmable device to generate a second accelerator, changes references to the first accelerator to reference instead the second accelerator, and casts the first accelerator out of the first programmable device.

Dynamic accelerator generation and deployment

Text is received from a first user. The text is associated with an electronic communication tool for communication to a second user. Candidate answers are generated based on the text using a question answering system. At least one of the candidate answers is provided to the first user.

Generating answers to text input in an electronic communication tool with a question answering system

A computer program includes calls to a software library. A virtual function table is built that includes the calls to the software library in the computer program. A programmable device includes one or more currently-implemented accelerators. The available accelerators that are currently-implemented are determined. The calls in the software library that correspond to a currently-implemented accelerator are determined. One or more calls to the software library in the virtual function table are replaced with one or more corresponding calls to a corresponding currently-implemented accelerator. When a call in the software library could be implemented in a new accelerator, an accelerator image for the new accelerator is dynamically generated. The accelerator image is then deployed to create the new accelerator. One or more calls to the software library in the virtual function table are replaced with one or more corresponding calls to the new accelerator.

Dynamically replacing a call to a software library with a call to an accelerator

Relocation of virtual machines is facilitated by obtaining, by a first controller, first power-related information from a first power system set that provides power to a first set of powered hardware components, where the first set of powered hardware components are running a first plurality of virtual machines. The first controller generates a relocation protocol for migrating the first plurality of virtual machines based, at least in part, upon the first power-related information. The relocation protocol includes: a migration of a first subset of one or more virtual machines so that the first subset of virtual machine(s) is to be migrated to and run on a second set of powered hardware components in a manner such that the first subset of virtual machine(s) continues to operation in a substantially continuous manner through the migration; and a snapshotting of a second subset of one or more virtual machines.

Generating a virtual machines relocation protocol

An accelerator manager monitors and logs performance of multiple accelerators, analyzes the logged performance, determines from the logged performance of a selected accelerator a desired programmable device for the selected accelerator, and specifies the desired programmable device to one or more accelerator developers. The accelerator manager can further analyze the logged performance of the accelerators, and generate from the analyzed logged performance an ordered list of test cases, ordered from fastest to slowest. A test case is selected, and when the estimated simulation time for the selected test case is less than the estimated synthesis time for the test case, the test case is simulated and run. When the estimated simulation time for the selected test case is greater than the estimated synthesis time for the text case, the selected test case is synthesized and run.

Accelerator monitoring and testing

A cloud-based hardware accelerator is selected by deploying an accelerator image to first and second clouds to generate first and second cloud-based hardware accelerators, executing a first request on the first and second cloud-based hardware accelerators, monitoring characteristics of the first and second cloud-based hardware accelerators executing the first request, which may include execution time and monetary cost, and selecting one of the first and second hardware accelerators according to defined selection criteria. Subsequent requests are then routed to the selected cloud-based accelerator.

Selecting and using a cloud-based hardware accelerator

Growing community of inventors

Rochester, MN, United States of America

James E Carey