Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement
Reexamination Certificate
2001-01-23
2004-01-13
Wachsman, Hal (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Statistical measurement
C702S181000, C702S176000, C345S215000
Reexamination Certificate
active
06678635
ABSTRACT:
RESERVATION OF COPYRIGHT
This patent document contains information subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent, as it appears in the U.S. Patent and Trademark Office files or records but otherwise reserves all copyright rights whatsoever.
BACKGROUND
1. Field of the Invention
Aspects of the present invention relate to the field of detecting semantics from temporal data. Other aspects of the present invention relate to a method and system that identifies meaningful events from temporal data based on event models.
2. General Background and Related Art
Recent technical advances are enabling more and more data being recorded, stored, and delivered over Internet Protocol (IP). Data acquisition devices such as cameras are becoming commodities with low cost yet high quality. Disk storage technology is riding a Moore's law curve and is currently at a dollar-per-megabyte point that makes huge digital content archive practical. Optical network and cable modems are bringing megabit bandwidth to offices and homes. Selective delivery of content is, however, less well established yet often necessary and desirable.
Selective delivery of content largely depends on whether the content is understood and properly indexed. When well understood content and its indexing become available, selective delivery can be accomplished by developing systems that use indices to select appropriate segments of content and to transmit such segments to where the content is requested. Conventionally, content indexing is performed manually. With the explosion of information, manual approach is no longer feasible.
Various automated methods emerged over the years to index content. For example, for text data, words can be detected automatically and then used for indexing purposes. With the advancement in multimedia, data is no longer limited to text. Video and audio data have nowadays become ubiquitous and preferred. Understanding the content embedded in such media data requires understanding both the intrinsic signal properties of different semantics as well as the high level knowledge (such as common sense) about various semantics. For example, a goal event in a soccer game may be simultaneously seen and heard from recorded video and audio data. To detect such a semantic event, common sense prompts us that a goal event is usually accompanied by crowd cheering. Yet automated recognition of crowd cheering from recorded digital data can be achieved only when the acoustic properties of crowd cheering can be understood and properly characterized.
Automatically establishing indices for such media data is difficult. Existing approaches for detecting semantic event usually hard-wire high level knowledge into a system. Most of such systems employ inference mechanisms but with a fixed set of inference methods. When semantic event models are used for detection, they are often built based on the snap-shots of the underlying events. For a temporal semantic event (which often is the case), such snap-shot based event models fail to capture the temporal properties of the events.
As a result of the above mentioned limitations of existing approaches, systems developed using such approaches can detect only a few special types of events. Detection of complex events often requires human intervention. The existing methods, therefore, can not meet the challenges of rapidly and automatically indexing huge volume of data.
What is needed is a semantic event detection method and system that is able to dynamically invoke high level domain knowledge from hierarchical event models and to automatically detect a wide range of complex temporal events and actions using pluggable probabilistic inference modules.
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Epshteyn Eugene
Qian Richard J.
Tovinkere Vasanth R.
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