Detecting events from features derived from multiple ingested signals
First Claim
1. A method comprising:
- receiving a first Time, Location, Context (TLC) normalized signal including a first time dimension, a first location dimension, and a first context dimension, the first context dimension including a first single source probability representing at least a first approximate probability of a real-world event of a specified event type;
deriving first one or more features from the first TLC normalized signal including from the first single source probability;
determining that the first one or more features, including the first single source probability, provide insufficient evidence to be identified as the real-world event of the specified event type;
receiving a second Time, Location, Context (TLC) normalized signal including a second time dimension, a second location dimension, and a second context dimension, the second context dimension including a second single source probability representing at least a second approximate probability that the real-world event of the specified event type;
deriving second one or more features from the second TLC normalized signal including from the second signal source probability;
aggregating the first single source probability and the second single source probability into a multisource probability; and
detecting the real-world event from evidence provided by the multisource probability, including the multisource probability exceeding a threshold probability associated with the event type.
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Abstract
The present invention extends to methods, systems, and computer program products for detecting events from features derived from multiple signals. In one aspect, an event detection infrastructure determines that characteristics of multiple signals, when considered collectively, indicate an event of interest to one or more parties. In another aspect, an evaluation module determines that characteristics of one or more signals indicate a possible event of interest to one or more parties. A validator then determines that characteristics of one or more other signals validate the possible event as an actual event of interest to the one or more parties. Signal features can be used to compute probabilities of events occurring.
89 Citations
20 Claims
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1. A method comprising:
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receiving a first Time, Location, Context (TLC) normalized signal including a first time dimension, a first location dimension, and a first context dimension, the first context dimension including a first single source probability representing at least a first approximate probability of a real-world event of a specified event type; deriving first one or more features from the first TLC normalized signal including from the first single source probability; determining that the first one or more features, including the first single source probability, provide insufficient evidence to be identified as the real-world event of the specified event type; receiving a second Time, Location, Context (TLC) normalized signal including a second time dimension, a second location dimension, and a second context dimension, the second context dimension including a second single source probability representing at least a second approximate probability that the real-world event of the specified event type; deriving second one or more features from the second TLC normalized signal including from the second signal source probability; aggregating the first single source probability and the second single source probability into a multisource probability; and detecting the real-world event from evidence provided by the multisource probability, including the multisource probability exceeding a threshold probability associated with the event type. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system comprising:
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a processor; system memory coupled to the processor and storing instructions configured to cause the processor to; receive a first Time, Location, Context (TLC) normalized signal including a first time dimension, a first location dimension, and a first context dimension, the first context dimension including a first single source probability representing at least a first approximate probability of a real-world event of a specified event type; derive first one or more features from the first TLC normalized signal including from the first single source probability; determine that the first one or more features, including the first single source probability, provide insufficient evidence to be identified as the real-world event of the specified event type; receive a second Time, Location, Context (TLC) normalized signal including a second time dimension, a second location dimension, and a second context dimension, the second context dimension including a second single source probability representing at least a second approximate probability that the real-world event of the specified event type; derive second one or more features from the second TLC normalized signal including from the second signal source probability; aggregate the first single source probability and the second single source probability into a multisource probability; and detect the real-world event from evidence provided by the multisource probability, including the multisource probability exceeding a threshold probability associated with the event type. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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Specification