Verizon Patent and Licensing Inc.

Sensity Systems Inc.

Swim.it Inc.

Christopher David Sachs

Kent W Ryhorchuk

Peter Raymond Florence

In various examples there is a computer-implemented method performed by a digital twin at a computing device in a communications network. The method comprises: receiving at least one stream of event data observed from the environment. Computing at least one schema from the stream of event data, the schema being a concise representation of the stream of event data. Participating in a distributed inference process by sending information about the schema or the received event stream to at least one other digital twin in the communications network and receiving information about schemas or received event streams from the other digital twin. Computing comparisons of the sent and received information. Aggregating the digital twin and the other digital twin, or defining a relationship between the digital twin and the other digital twin on the basis of the comparison.

Inferring digital twins from captured data

In various example embodiments, a system and method are provided for a service data platform. The service data platform includes an application management API configured to receive software uploaded by a third party using a management application API. The service data platform also includes a plurality of graph servers configured to identify a group of lighting nodes to distribute the uploaded software and determine the uploaded software is safe for deployment to the identified group of lighting nodes. The service data platform further includes a device manager configured to distribute, using an administrative API, the uploaded software to the identified group of lighting nodes.

Application environment for sensory networks

Systems and methods for probabilistic semantic sensing in a sensory network are disclosed. The system receives raw sensor data from a plurality of sensors and generates semantic data including sensed events. The system correlates the semantic data based on classifiers to generate aggregations of semantic data. Further, the system analyzes the aggregations of semantic data with a probabilistic engine to produce a corresponding plurality of derived events each of which includes a derived probability. The system generates a first derived event, including a first derived probability, that is generated based on a plurality of probabilities that respectively represent a confidence of an associated semantic datum to enable at least one application to perform a service based on the plurality of derived events.

Systems and methods for probabilistic semantic sensing in a sensory network

A multiplexed demand signaled distributed messaging (MDSDM) system and method enables high capacity real-time messaging between application services by generating and utilizing innovative techniques for message management such as lanes, links and message distribution-related functionality.

Multiplexed demand signaled distributed messaging

Application environment for lighting sensory networks

A privacy awareness method and system observes data collection by third parties on websites and applications and determines what privacy metadata is retrieved and/or sent and where the privacy metadata is sent. The privacy awareness method and system informs users about the privacy information collection, so that users are able to better understand what information is being retrieved and navigate the Internet accordingly.

Method of and system for privacy awareness

In one example, a method of protecting customer data in a networked system comprises collecting sensor data available at sensor nodes within a sensor network in communication with a service data platform over a network. The method includes encrypting the sensor data using a certified public key associated with a customer key-pair, the sensor data representing the customer data associated with sensitive identification information. The sensor data is cryptographically signed with a device private key. The method includes transporting the encrypted sensor data to the service data platform for storage, and decrypting at the service data platform, the encrypted sensor data using a private key sharing scheme that reconstructs the private key associated with the customer key-pair using a first share and a password encrypted second share, the first share assigned to the service data platform and the password encrypted second share assigned to a customer of the customer key-pair.

Security and data privacy for lighting sensory networks

Methods, devices, systems, and non-transitory process-readable storage media for controlling lighting nodes of a lighting system associated with a lighting infrastructure based on composited lighting models. An embodiment method performed by a processor of a computing device may include operations for obtaining a plurality of lighting model outputs generated by lighting control algorithms that utilize sensor data obtained from one or more sensor nodes within the lighting infrastructure, combining the plurality of lighting model outputs in an additive fashion to generate a composited lighting model, calculating lighting parameters for a lighting node within the lighting infrastructure based on the composited lighting model and other factors, and generating a lighting control command for configuring the lighting node within the lighting infrastructure using the calculated lighting parameters. The method may be performed by any combination of lighting node(s), sensor node(s), a remote server, and/or other devices within the lighting infrastructure.

Dynamic spatially-resolved lighting using composited lighting models

In various example embodiments, a system and method are provided for protection customer data collected at sensor nodes within a networked system. A key recovery module determines the encrypted sensor data in a request was encrypted with a certified public key associated with a first customer key-pair. The first customer key-pair represents a recovered private key. The key recovery module determines the private key associated with the first customer key-pair is encrypted with the private key associated with a second customer key-pair. The private key associated with the first customer key-pair is decrypted by using the private key associated with the second customer key-pair. The encrypted sensor data in the request is decrypted using the decrypted private key associated with the first customer key-pair.

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Sunnyvale, CA, United States of America

Christopher David Sachs