Silicon Image Gmbh

Scenera, Inc.

Vmtv, Inc.

Hewlett-packard Development Company, L.p.

Dual Aperture International Co. Ltd.

Azimuth Industrial Company, Inc.

Other

David D Lee

Andrew Augustine Wajs

Deog-Kyoon Jeong

Chien Lim

Seungoh Ryu

Yeshik Shin

Shing Kong

Dongyun Lee

Sungjoon Kim

Youngwoon Yoon

Jaegeun Lim

Dongcheol Jeong

Gyudong Kim

Michael Ken Minakami

Kyeongho Lee

Souvik Sen

Martin Robert Fink

Taekun Woo

Russel A Martin

Kyu-Han Kim

J Duane Northcutt

Kyu Han Kim

Seung Ho Hwang

John Hahn

Neil C Wilhelm

Bong-Joon Lee

Bruce Kim

Deog-kyoon Jeong

Victor M Da Costa

Jeremy Gummeson

Derek McAuley

Byoung-Woon Kim

Ho Young Song

Security and access control is provided for sensor devices, the data captured by sensor devices, and the results of processing and analyzing that data. In one aspect, SceneData related to a Scene is requested from a sensor-side technology stack and at least some of the SceneData is secured, for example by encryption. Different SceneData can be secured separately and at different levels of security, thus providing fine-grained security of the SceneData. In yet another aspect, data security is implemented by a separate privacy management system. In yet another aspect, sensor devices themselves are secured against external network threats. The sensor device includes an execution environment and a separate network management layer that secures the execution environment against threats from the external network. In one implementation, the sensor device is partitioned into a trusted region and a non-trusted region, and the network security stack is implemented in the trusted region.

Security for scene-based sensor networks, with access control

An approach to sensor data is based on scenes. One aspect concerns a computer-implemented method for specifying and obtaining a variety of sensor data and processed sensor data related to a scene. The method incorporates a Scene-based API that uses SceneModes and SceneData. An application requesting sensor data communicates a SceneMode to a group of one or more sensor devices and/or sensor modules via the Scene-based API. The SceneMode determines the SceneData to be captured or provided by the sensor group, which typically includes different types of sensor data related to the Scene and also further processed or analyzed data. The application receives the SceneData from the sensor group via the Scene-based API, with the SceneData organized into SceneShots which are samples of the Scene.

Scene-based sensor networks

A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. A method to curate different custom workflows for multiple applications include the following. Requirements for custom sets of data packages for the applications is received. The custom set of data packages include sensor data packages (e.g., SceneData) and contextual metadata packages that contextualize the sensor data packages (e.g., SceneMarks). Based on the received requirements and capabilities of components in the technology stack, the custom workflow for that application is deployed. This includes a selection, configuration and linking of components from the technology stack. The custom workflow is implemented in the components of the technology stack by transmitting workflow control packages directly and/or indirectly via the interfaces to the different layers.

Curation of custom workflows using multiple cameras

Security for scene-based sensor networks, with privacy management system

A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. Each layer contains multiple nodes. A method to develop contextual understanding of related events detected by the nodes includes the following. Sensor nodes capture sensor data, including multiple image sensors capturing images. Various nodes receive sensor data and/or metadata packages from other nodes, analyze the received data for events, and generate and/or augment metadata packages describing the detected events. The analysis includes image understanding. Events that are related are identified, based on the metadata packages describing the events. A contextual understanding of the related events is developed, based on analysis of the metadata packages describing the events.

Tracking sequences of events

Automated discovery of the relative positioning of a network of cameras that view a physical environment. The automated discovery is based on comparing TimeLines for the cameras. The TimeLines are time-stamped data relating to the camera's view, for example a sequence of time stamps and corresponding images captured by a camera at those time stamps. In one approach, the relative positioning is represented by a proximity graph of nodes connected by edges. The nodes represent spaces in the physical environment, and each edge between two nodes represents a pathway between the spaces represented by the two nodes.

Automated proximity discovery of networked cameras

A sensor device has a sensor base with a sensor mount, for which different sensor assemblies are attachable to the sensor mount. The sensor base transmits different settings to the sensor assembly via the sensor mount interface. The transmission preferably is wireless, rather than using mechanical connectors. The sensor assembly stores the settings in control registers that determine the settings for the capture of sensor data. In one approach, the sensor base includes an application processor and software executing on the application processor determines the settings. The control registers for the sensor assembly are thus software programmable, allowing different settings to be applied to different samples of sensor data.

Intelligent interface for interchangeable sensors

Described herein are techniques for beamforming a wireless signal to a harvesting device. In an example, a method to increase available energy to a harvesting device is performed by a WiFi transmitting device. The WiFi transmitting device determines a preferred wireless path to the harvesting device using input from a proxy device. The WiFi transmitting device then beamforms a WiFi signal to the harvesting device along the preferred wireless path.

Beamforming to a harvesting device

Security for scene-based sensor networks

Data about content of the videos is organized into threads for the videos. The threads also contain video bookmarks. Different data are associated with different video bookmarks. The video bookmarks include a frame of the video that marks a time point in the video corresponding to the associated data. The video bookmarks enabling playback of the video from the marked time point. The threads are stored and published to allow subsequent searching by third parties. The video bookmarks can be shared with the third parties.

Data associated with bookmarks to video content

Examples herein disclose capturing a wifi signal from a computing device corresponding to a computing accessory and harvesting energy from the captured wifi signal. The examples power the computing accessory based on the harvested energy.

Supplying power to a computer accessory from a captured wifi signal

Growing community of inventors

Palo Alto, CA, United States of America

David D Lee