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Detecting events from features derived from multiple ingested signals

  • US 10,474,733 B2
  • Filed: 04/09/2019
  • Issued: 11/12/2019
  • Est. Priority Date: 02/09/2018
  • Status: Expired due to Fees
First Claim
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1. A method implemented at a computing system comprising a processor:

  • receiving a first normalized signal including a first location dimension and a first context dimension from a first hardware computing component associated with a signal source, 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 normalized signal including from the first single source probability;

    determining that the first one or more features, including the first single source probability, are below a first threshold to be identified on their own as the real-world event of the specified event type;

    receiving a second normalized signal including a second location dimension and a second context dimension from a second hardware computing component associated with a second signal source, the second context dimension including a second single source probability representing at least a second approximate probability of the real-world event of the specified event type;

    calculating a multisource probability by at least aggregating the first single source probability that is below the first threshold to be identified as the real-world event and the second single source probability; and

    detecting the real-world event from evidence provided by the multisource probability, including determining that the multisource probability exceeds a second threshold probability associated with the specified event type.

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