Ca, Inc.

At+t Corp.

At&t Intellectual Property I, L.p.

Lucent Technologies Inc.

At&t Bell Laboratories

Avaya Inc.

Tao Technologies, LLC

Greenspan, Steven Lloyd

Steven Lloyd Greenspan

Serguei Mankovskii

Maria C Velez-Rojas

Carrie Elaine Gates

Gabriel M Silberman

Debra Jean Danielson

James Donald Reno

Prashant Parikh

David M Weimer

Evan Stephen Crandall

Andrea Basso

Serge Mankovskii

Richard H Janow

Stephen Whittaker

Bonnie A Nardi

John Patrick Kane

Anders Fernstedt

Maria Cecilia Velez Rojas

Eric Cosatto

Jin Zhang

Howard A Abrams

Wayzen Lin

Kenneth William Scott Morrison

Hongrui Gong

Victor Muntés Mulero

Ye Chen

Timothy Jerome Scale

Kenneth W S Morrison

Marc Solé Simó

Charley Chell

Connie Smallwood

Joel M Marks

Stefan Ionescu

Lee Begeja

Greg E Blonder

Eitan Hadar

Schuyler Reynier Quackenbush

Christopher John Stakutis

Geoffrey R Hird

Bruce Lowell Hanson

Peter Frank Driessen

Joseph Bernard Weinman, Jr

Kenneth Mervin Huber

Jocelyn Cloutier

Mathias R Kretschmer

Ronald J Brachman

Victor Muntés-Mulero

James Farber

Srikanth Mandava

Stanislav Mitranic

Binay Sugla

Preetdeep Kumar

Marc Sole Simo

Ralf Dieter Saborowski

Marvin Garold Waschke

Michael J Morgan

Victor Muntes

Vincent R Re

Shweta Tiwari

Esin Kiris

Barbara Anne Taylor

Michael C Roberts

Peter Brian Matthews

Rashmi Gupta

Langdon E Alger

Raymond Eugene Bright, Jr

Seymour Bloom

Brian George Beuning

Bruce Fat Wong

J Robert Mirville

Robert S Hisiger

Marc Solé Simo

Paul C Lipton

John J Harrison, Jr

Robert Biddle

Judith M Brown

Thomas M Boyle

Dirk Schlossecki

Wei-Jun Kong

Michael Grün

Nagavardhan Sogal

Stephan Stadler

Provided is a process of determining a future battery level of one or more battery-powered computing devices, the process including: accessing an event record in memory describing a scheduled event in which a user of a plurality of computing devices is scheduled to participate, inferring a subset of the plurality of computing devices to be used in that time period, determining present battery levels of the computing devices, the levels being values indicative of an amount of energy stored by batteries, determining present usage rates of battery energy, inferring battery outlooks corresponding to the scheduled event, a battery outlook being an estimated amount of energy consumption attributable to the scheduled event, and predicting future battery levels of computing devices based on at least a present battery level, a present usage rate, and a battery outlook corresponding to the scheduled event.

Battery management assistant

Techniques are disclosed relating to training a neural network using private training data. In some embodiments, a central computing system is configured to maintain an at least partially trained neural network and information that specifies data formats for inputs to the model and outputs from the model. In some embodiments, partner computing systems maintain subsections of the neural network model and may train them using data that is not shared with other partner computing systems or the central computing system. Parameters resulting from the training may be transmitted to the central computing system. In some embodiments, the central computing system processes the parameters to generate the updated complete version of the neural network model and transmits parameters from the updated complete version of the model to the partner computing systems. The partner computing systems may use the updated complete model to detect anomalies in input data.

Distributed secure training of neural network model

Systems and methods may include receiving performance data of components in a system. The performance data may include data for parameters for each of the components. The systems and methods may include determining aggregate data for each group of similar components of the components. The aggregate data for each group of similar components may include a group characteristic for each of the parameters. The systems and methods may include, for each group of similar components, determining whether the data for each of the parameters for each component is consistent with the group characteristic for the respective parameter. The systems and methods may include, for each component of the respective group determining that the component is anomalous in response to determining that the data for a parameter for the component is not consistent with the group characteristic for the parameter.

Assessing trust of components in systems

A digital currency driven channel assignment technique is disclosed. Each AP in a distributed network uses a channel selection manager and a distributed ledger to select channels according to a channel assignment and a digital currency associated with the distributed ledger. The digital currency incentivizes APs to make sacrifices in their channel selection for the benefit of the overall network while punishing APs that select channels selfishly and cause bandwidth interference.

Distributed ledger for wireless channel assignment

Provided is a computer system that includes a processor and a memory coupled to the processor, the memory including computer readable program code embodied therein that, when executed by the processor, causes the processor to generate a catalog that identifies a plurality of tasks that a plurality of network resources are available to perform, the network resources including Internet-of-things devices and human network resources and to generate, in response to receiving a request to perform a complex project, a solution path that includes an ordered list corresponding to selected ones of the plurality of tasks that are capable of aggregately performing the complex project, wherein the selected ones of the plurality of tasks define the solution path in an edge graph that include the plurality of tasks represented as edges therein.

Automated collaboration workflow generation in thing-sourcing environments

Systems and methods may include receiving, by an expert system, performance data for a monitored system. The systems and methods may include generating a prediction for the monitored system in response to determining that the performance data satisfies a condition. The prediction may identify an anomaly that is predicted to occur. The systems and methods may include receiving, by a filter system, the prediction, information identifying the condition, and user information. The user information may include user preference information and user feedback information. The systems and methods may include determining a filter criteria based on the user information. The filter criteria may be based on the preferences for predictions to be provided to the user and on the historical user feedback regarding the historical predictions. The systems and methods may include providing the prediction to the user in response to determining that the particular prediction satisfies the filter criteria.

Feedback and customization in expert systems for anomaly prediction

In an embodiment, a password risk evaluator may receive a request including a user identifier (ID) and a password. The password risk evaluator may retrieve a password preference model associated with the user ID, and may determine a risk score indicating a likelihood that the password is associated with the user ID. For example, the password preference model may be based on previous passwords used by the user, and may identify one or more characteristics, formulas, rules, or other indicia typically employed by the user in creating passwords. If the password supplied in the request matches or is similar to one or more elements of the password preference model, it may be more likely that the password in the request is a password supplied by the user. That is, the risk score may be an authentication of the user, or part of the authentication of the user, in some embodiments.

Authenticating identity for password changes

Provided is a process of configuring a dashboard of a graphical user interface, the process including: obtaining identifiers of metrics; obtaining features that are properties of the metrics; accessing in a graph-effectiveness matrix effectiveness scores corresponding to the features; selecting a plurality of graphs to graphically represent the metrics in a dashboard; and instructing a computing device to display the dashboard.

Predictive graph selection

A thing-sourcing project request including requirements for a thing-sourcing task that requires data input by a thing-sourcing device is received from a requestor device. A determination is made if real-time data is needed in order to complete the thing-sourcing task. In response to determining that real-time data is not needed, a determination is made if a similar thing-sourcing task has been previously completed. If not, the method determines if the thing-sourcing task can be completed using pre-existing data. If so, a data archive is searched for relevant pre-existing data that can be used to complete the thing-sourcing task. The thing-sourcing task is completed using the relevant pre-existing data, and a response to the thing-sourcing project request is transmitted to the requestor device.

Data collection and estimation using an internet of things

A computing device has a camera and tracks the movement of a user's eye as the user looks at a display monitor. Upon detecting that a user is unable to locate the position of a cursor on the display monitor, the computing device displays the cursor at a conspicuous location on the display monitor so that the user is likely to find the cursor. The location at which the cursor is conspicuously displayed is selected by the computing device based on an analysis of the user's eye movements.

Positioning a cursor on a display monitor based on a user's eye-gaze position

A method is described for receiving a plurality of node data streams through a data network from a plurality of source nodes, respectively, each of the plurality of node data streams comprising a plurality of node data. The method further comprises determining a respective risk assessment for each of the plurality of node data streams based on a plurality of elements, wherein the respective risk assessment indicates a level of trustworthiness of each of the plurality of node data streams. Moreover, the method comprises determining a plurality of respective actions for each of the plurality of source nodes, based on the respective risk assessment of the plurality of node data streams. The method further comprises instructing each of the plurality of source nodes to perform the respective action.

Automated data risk assessment

Provided is a process of conducting a collaborative session between two analytics graphical user interfaces (GUI), the process including: instructing a first computing device associated with a first user to display a first GUI having a first graph depicting a first set of values of a first metric; determining that the first graph is to be shared on a second computing device associated with a second user in a second GUI; inferring that the second user prefers to view the first metric in a second graph based on a record of previous interactions in which the second graph was selected to view the first metric; and in response, instructing the second computing device to display in the second GUI the second graph depicting at least some of the first set of values of the first metric.

Collaborative data analytics application

In some embodiments, a method includes receiving an identification of an intended recipient of a physical package and a personalized message having content based on the identity of the intended recipient. The method further includes identifying a uniform resource locator (URL) comprising a reference to a webpage that displays the personalized message and one or more advertisements regarding products or services related to the physical package. A Quick Response (QR) code encoding the URL comprising the reference to the webpage that displays the personalized message and the one or more advertisements may be generated. The method further includes transmitting the personalized message and the one or more advertisements to the intended recipient via the webpage in response to the intended recipient accessing the URL encoded by the QR code.

Communicating personalized messages using quick response (QR) codes

Systems and methods may include receiving real-time data about a real component operating in a real-world environment. The systems and methods may further include generating a virtual representation of the real component based on the real-time data about the real component and historical data associated with the real component. In addition, the systems and methods may include receiving injected data from a lab. The injected data may provide data about a lab component operating in the lab. The systems and methods may also include simulating, in a virtual environment, a real-time interaction in the real-world environment between the real component and the lab component using the virtual representation of the real component and the injected data. Moreover, the systems and methods may include determining a real-time performance characteristic of at least one of the lab component and the real component based on the simulated real-time interaction in the real-world environment.

Multi-component and mixed-reality simulation environments

A user selects an appliance or other device to control using a wearable computing device, such as an augmented eyewear device, for example. Based on the selection, data regarding a current operating state and one or more subsequent operating states are provided for display to the user. Using the wearable computing device, the user selects one of the subsequent states and controls the appliance to transition from its current state to the selected subsequent state. The commands for controlling the transition may be proxied for the wearable computing device by a mobile device or a network server, for example, via one or more established communication links.

Initiating actions on wearable devices

Growing community of inventors

Scotch Plains, NJ, United States of America

Steven Lloyd Greenspan