International Business Machines Corporation

Plantronics, Inc.

Raghavendra Balavalikar Krishnamurthy

Michael Mallo

Rajen Bhatt

Kui Zhang

Stephen Thomas Cochran

Vinod Thankappan Nair

Stephen Paul Schaefer

Varun Ajay Kulkarni

Kishore Venkat Rao Goka

Daniel Acevedo

Nhu Quynh Pham Nguyen

Andrew Pornthep Pechin

The number of persons in a still image acquired during a videoconference may change over time due to movement or persons entering or exiting a conference room. Minimizing the number of person identifiers that are tracked is beneficial for framing purposes. Typically, a stream of frames is sent to a far side, such as another endpoint device, for viewing. If the number of persons identified keeps changing, the composition of the stream of frames will be constantly changing as well. By using person identification and the use of timers, movement by the same person or a temporary change in a number or set of persons may be detected without making changes to the number of identifiers and persons being framed. It is only when a change has persisted past a period of time that changes are made to the frames, thereby improving the overall viewing experience.

Multi-person tracking and identification for robust framing experience

A system for preventing private image data captured at an endpoint from being shared during a videoconference is provided. A user can select three-dimensional regions which will not be seen during a videoconference while areas in front of the designated regions remain viewable.

Securing image data from unintended disclosure at a videoconferencing endpoint

A method implementing dynamic camera presets. An image is received. A list of bounding boxes and a list of classification values are generated from the image. A set of tags, for the image, are generated from the list of bounding boxes and the list of classification values. A tagged image incorporating the set of tags into the image is presented. In response to a user input, a tag selection identifying a tag of the set of tags is received. View settings are adjusted to focus on a bounding box, from the list of bounding boxes, corresponding to the tag selection. An adjusted image is presented using the view settings.

Dynamic camera presets

A collector for distributed data collection includes input and output queues employed for priority based queuing and dispatch of data received from endpoints and downstream collector nodes. Collection Table of Contents (CTOC) data structures for collection data are received by the collector from the endpoints or downstream collectors and are placed in the input queue, then sorted by the priority within the CTOC. Within a given priority level, collection of the data is scheduled based on the activation time window within the CTOC, which specifies the period during which the endpoint or downstream collector node will be available to service data transfer requests. The collected data, in the form of data packs and constituent data segments, is stored in persistent storage (depot). A CTOC is then transmitted to the next upstream collector node. Network bandwidth utilization is managed by adjusting the activation time window specified within a CTOC and the route employed between source and recipient.

Data collector for use in a scalable, distributed, asynchronous data collection mechanism

Scheduling in a distributed data collection process is performed locally, within collectors. Scheduling of data transfers from endpoints or downstream collectors or to upstream collectors is based on local queues, without global management. Additionally, scheduling for the collector input queue, which manages data collection from endpoints or downstream collectors, is bifurcated from scheduling for the output queue, which manages notifications to upstream collector(s) regarding the availability of collection data for pickup. Such bifurcation permits simpler scheduling logic and different functional responses to similar events, and further localizes scheduling. Scheduling of collection data transfer is controlled, within parameters specified by the output scheduler for the endpoint or downstream collector, by the input queue for the upstream collector. Scheduling is thus based primarily on the portion of the data transfer mechanism mostly likely to comprise a bottle-neck, the upstream collector, but accommodates large numbers of fully parallel data generation endpoints as well as nondeterministic endpoint availability.

Scheduler for use in a scalable, distributed, asynchronous data collection mechanism

The &ldquo;scan&rdquo; phase of a distributed data collection process is decoupled from upload of the return collection data, with the &ldquo;scan&rdquo; consisting merely of an infrequent profile push to configure autonomous scanners at the data collection endpoints. Distributed data collection is initiated by endpoints within the distributed network, which autonomously perform a scan and transmit a Collection Table of Contents (CTOC) data structure to a nearest available collector, then await a ready message from the collector. When ready to receive the return collection data, the collector signals the endpoint, which transfer the data collection in small packets to the collector. The collector stores the received data collection in persistent storage, then initiates collection to a higher collector or recipient in substantially the same manner as the endpoint. A routing manager controls the routing of data from endpoints through one or more collectors to the recipient. Scans for the data collection may thus be performed fully parallel, and upload of the collection data proceeds by direct channel under the control of the collectors. Bandwidth utilization for the data collection may thus be optimized for network loading by blackout periods and cooperation of the collectors with other distributed applications. The resulting distributed data collection mechanism is scalable, with large numbers of endpoints and large return collection data sizes being efficiently supported.

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Austin, TX, United States of America

Raghavendra Balavalikar Krishnamurthy