Sri International

California Institute of Technology

Harvard College

Brown University

Sint Holdings Limited Liability Company

Patrick Denis Lincoln

Rukman Senanayake

Grit Denker

Phillip Andrew Porras

Kenneth C Nitz

Roy D Kornbluh

Harsha Prahlad

Karen L Myers

Steven Mark Dawson

Steven S Weiner

Steven Mark Eker

Natarajan Shankar

Ronald E Pelrine

Scott E Stanford

William Scott Mark

Norman D Winarsky

David W J Stringer-Calvert

Hung H Bui

Susmit Jha

Jeffrey Klaben

Ashish Tiwari

Nicholas Chapin

Peter D Karp

Charles M Lieber

Bhaskar S Ramamurthy

André M DeHon

Lawrence Robert Toll

John E Savage

Maneesh Yadav

Christopher Connolly

Shalini Ghosh

Kemal Sonmez

Joseph S Eckerle

Charles M Patton

Richard P Heydt

John Murray

Martin W Fong

Melinda Gervasio

Thomas J Lee

Eric M Pearson

Daniel McConnell Aukes

Bruno Dutertre

Huascar Sanchez

Pedro Romero

Sierra J Lincoln

Pierangela Samarati

Sabrina De Capitani Di Vimercati

Karl D Van Dyk

Geoffrey A Mangus

John Cornwell

Ian A Mason

Richard Dean

Processing circuitry is configured to obtain a data structure that defines a plurality of conversions of data between pairs of fields; perform a search to identify a plurality of paths from a source node of the data structure to a destination node of the data structure, wherein the source node corresponds to a first field of the fields and the destination node corresponds to a second field of the fields; convert, for each path of the plurality of paths, transforms represented by corresponding edges of the path to a sequence of transforms that conform to a solver format; process the sequence of transforms for each path to determine whether all paths of the plurality of paths are equivalent up to an equivalence relation; and output an indication of whether all paths of the plurality of paths are equivalent up to an equivalence relation.

Multipath verification of data transforms in a system of systems

Systems and methods of generating and applying a synthetic neuromodulatory signal are described. A subject may be put under a particular condition that causes an effect in the subject. While the subject is under the condition, a recording of neurogram signals derived from the condition can be made from the subject. For example, neuronal signals traveling on the vagus nerve of the subject may be monitored and recorded. The neurogram may then be used to create a synthetic neuromodulatory signal that can be administered to a user. When the synthetic neuromodulatory signal is administered to the user, the user may experience the same effect as the subject that had been placed in the condition, even though the user was never put under the same condition.

Methods and systems for stimulating nerve signals

In general, the disclosure describes techniques for facilitating trust in neural networks using a trusted neural network system. For example, described herein are multi-headed, trusted neural network systems that can be trained to satisfy one or more constraints as part of the training process, where such constraints may take the form of one or more logical rules and cause the objective function of at least one the heads of the trusted neural network system to steer, during machine learning model training, the overall objective function for the system toward an optimal solution that satisfies the constraints. The constraints may be non-temporal, temporal, or a combination of non-temporal and temporal. The constraints may be directly compiled to a neural network or otherwise used to train the machine learning model.

Trusted neural network system

The activities of multiple virtual personal assistant (VPA) applications are coordinated. For example, different portions of a conversational natural language dialog involving a user and a computing device may be handled by different VPAs.

Providing virtual personal assistance with multiple VPA applications

Techniques are disclosed for identifying fixed bits of a bitstring format. One or more processors are configured to generate a first bitstring having respective first bit values that have a first satisfiability state and generate a second bitstring having respective second bit values that have a second satisfiability state. The one or more processors are configured to identify first potential free bits having respective first common values and generate a third bitstring having first potential free bits with the respective first common values and third remaining bits. The one or more processors are configured to identify second potential free bits having respective second common values and identify a fixed bit that is not included in the first potential free bits and is not included in the second potential free bits.

Identifying fixed bits of a bitstring format

A method, apparatus and system for enabling the searchable storage of sequential application programs includes normalizing the sequential application programs, canonicalizing the sequential application programs, determining a unique identifier for each of the sequential application programs, and storing the unique identifier for each of the sequential application programs. The stored, unique identifier for the canonicalized sequential application programs can then be searched to, for example, determine if a sequential application program is stored and/or to certify at least one of the stored, canonicalized sequential application programs.

Searchable storage of sequential application programs

Methods and systems of using machine learning to create a trusted model that improves the operation of a computer system controller are provided herein. In some embodiments, a machine learning method includes training a model using input data, extracting the model, and determining whether the model satisfies the trust-related constraints. If the model does not satisfy the trust-related constraint, modifying at least one of: the model using one or more model repair algorithms, the input data using one or more data repair algorithms, or a reward function of the model using one or more reward repair algorithms, and re-training the model using at least one of the modified model, the modified input data, or the modified reward function. If the model satisfies the trust-related constraints, providing the model as a trusted model that enables a computer system controller to perform system actions within predetermined guarantees.

Systems and methods for machine learning using a trusted model

Network security management technology as disclosed herein generates and dynamically updates an intuitive, interactive visualization of a computer network in live operation. The network security management technology interprets human user interactions, such as gestures, as network directives, and updates the interactive visualization in response to the network directives.

Visually intuitive interactive network management

Network management technology as disclosed herein conducts conversational natural language dialog with a user to facilitate the user's analysis of network activity and the implementation of network security measures and other actions in furtherance of network operations, management, or security.

Natural language dialog-based security help agent for network administrator

Network management technology as disclosed herein performs an impact analysis of actual or hypothetical network commands, and presents the impact analysis results to facilitate the user's understanding of the predicted consequences of the actual or hypothetical commands on network operations, management, or security.

Impact analyzer for a computer network

A device, method and system for automatically inferring a mobile user's current context includes applying a user activity knowledge base to real-time inputs and stored user-specific information to determine a current situation. Automated reasoning is used to infer a user-specific context of the current situation. Automated candidate actions may be generated and performed in accordance with the current situation and user-specific context.

Method, system and device for inferring a mobile user's current context and proactively providing assistance

Network management technology as disclosed herein generates and dynamically updates an intuitive, interactive visualization of a computer network in live operation. The network management technology interprets human user interactions, such as gestures, conversational natural language dialog, and combinations of gestures and natural language dialog, as network directives. The technology can implement the network directives to, for example, facilitate analysis of network activity or to respond to network security events.

Multimodal help agent for network administrator

Network security management technology as disclosed herein generates and dynamically updates an intuitive, interactive visualization of a computer network in live operation. The network security management technology interprets human user interactions, such as gestures, as network directives. The network directives may be implemented by the network in response to security events.

Visually intuitive interactive network cyber defense

The present invention relates to an adaptable input/output device. One embodiment of a hardware device for facilitating an interaction between a computing system and a user includes: an interaction surface for supporting the interaction, a single actuator capable of driving a first region of the interaction surface, and a first selective clamping mechanism capable of restricting movement of one or more second regions of the interaction surface that partly intersect the first region, wherein a displacement of one or more desired portions of the interaction surface is dynamically controllable.

Adaptable input/output device

Growing community of inventors

Woodside, CA, United States of America

Patrick Denis Lincoln