Adaptively detecting an event of interest
First Claim
1. A method for detecting a likely event of interest, comprising:
- providing a prediction model M for a detection system, wherein when each of a plurality of data samples are input to M, said model M outputs a prediction related to a subsequent one of said data samples following said prediction;
first predicting, by M, two consecutive predictions P1 and P2 of said predictions, while said detection system does detect a likely event of interest, E1, such that E1 is detected using an output by M;
wherein for said two consecutive predictions P1 and P2 (a1) through (a3) following hold;
(a1) P1 is determined by M as a first function of a first multiplicity of said data samples that are provided to M prior to said P1, wherein for each data sample, DS1, from said first multiplicity of data samples, said detection system does not detect any likely event of interest, E1, such that E1 is detected using an output by M when DS1 is input to M;
(a2) P2 is determined by M as a second function of a second multiplicity of said data samples that are provided to M prior to said P2, wherein for each data sample, DS2, from said second multiplicity of data samples, said detection system does not detect any likely event of interest, E2, such that E2 is detected using an output by M when DS2 is input to M; and
(a3) said first multiplicity of said data samples and said second multiplicity of said data samples do not differ by any one of said data samples DS received by M between a determination of P1 and a determination of P2;
first determining whether a later one of P1 and P2 results in detecting an occurrence of a likely event of interest;
second predicting, by M, two consecutive predictions P3 and P4 of said predictions while said detection system does not detect a likely event of interest, E2, such that E2 is detected using an output by M;
wherein for said two consecutive predictions P3 and P4 (b1) through (b3) following hold;
(b1) P3 is determined by M as a third function of a third multiplicity of said data samples that are provided to M prior to said P3, wherein for each data sample, DS3, from said third multiplicity of data samples, said detection system does not detect any likely event of interest, E3, such that E3 is detected using an output by M when DS3 is input to M;
(b2) P4 is determined by M as a fourth function of a fourth multiplicity of said data samples that are provided to M prior to said P4, wherein for each data sample, DS4, from said fourth multiplicity of data samples, said detection system does not detect any likely event of interest, E4, such that E4 is detected using an output by M when DS4 is input to M; and
(b3) said third multiplicity of said data samples is different from said fourth multiplicity of said data samples by one of said data samples DS0 received by M between a determination of P3 and a determination of P4;
second determining whether a later one of P3 and P4 results in detecting an occurrence of a likely event of interest;
outputting, in response to a result from at least one of said steps of first and second determining, at least one of;
(c1) first data indicative of no occurrence of a likely event of interest being detected, and (c2) second data indicative of an occurrence of a likely event of interest being detected.
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Abstract
A detection system for detecting unusual or unexpected conditions in an environment monitored by one or more sensors generating a data samples for input to the detection system. The detection system includes a predictive signal processor that identifies unexpected data samples output by the sensors. The predictive signal processor includes at least one prediction model M for predicting subsequent data samples of a data stream S input to M from the sensors. M uses past sensor data samples of S that correspond anticipated environmental conditions for iteratively predicting a subsequent likely sensor data sample from S. If there is a sufficient variance between the actual subsequent sensor data of S, and it'"'"'s corresponding prediction, then a likely event of interest is identified. When the predictive signal processor is not detecting a likely event of interest due to a prediction by M, M iteratively adapts its predictions according to the most recent input data samples. When the predictive signal processor detects a likely event of interest due to a prediction by M, M does not use the data samples received during the detection for determining subsequent predictions. Thus, M processes its stream of data samples differently depending on a variance in its prediction from the corresponding actual data sample.
243 Citations
35 Claims
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1. A method for detecting a likely event of interest, comprising:
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providing a prediction model M for a detection system, wherein when each of a plurality of data samples are input to M, said model M outputs a prediction related to a subsequent one of said data samples following said prediction;
first predicting, by M, two consecutive predictions P1 and P2 of said predictions, while said detection system does detect a likely event of interest, E1, such that E1 is detected using an output by M;
wherein for said two consecutive predictions P1 and P2 (a1) through (a3) following hold;
(a1) P1 is determined by M as a first function of a first multiplicity of said data samples that are provided to M prior to said P1, wherein for each data sample, DS1, from said first multiplicity of data samples, said detection system does not detect any likely event of interest, E1, such that E1 is detected using an output by M when DS1 is input to M;
(a2) P2 is determined by M as a second function of a second multiplicity of said data samples that are provided to M prior to said P2, wherein for each data sample, DS2, from said second multiplicity of data samples, said detection system does not detect any likely event of interest, E2, such that E2 is detected using an output by M when DS2 is input to M; and
(a3) said first multiplicity of said data samples and said second multiplicity of said data samples do not differ by any one of said data samples DS received by M between a determination of P1 and a determination of P2;
first determining whether a later one of P1 and P2 results in detecting an occurrence of a likely event of interest;
second predicting, by M, two consecutive predictions P3 and P4 of said predictions while said detection system does not detect a likely event of interest, E2, such that E2 is detected using an output by M;
wherein for said two consecutive predictions P3 and P4 (b1) through (b3) following hold;
(b1) P3 is determined by M as a third function of a third multiplicity of said data samples that are provided to M prior to said P3, wherein for each data sample, DS3, from said third multiplicity of data samples, said detection system does not detect any likely event of interest, E3, such that E3 is detected using an output by M when DS3 is input to M;
(b2) P4 is determined by M as a fourth function of a fourth multiplicity of said data samples that are provided to M prior to said P4, wherein for each data sample, DS4, from said fourth multiplicity of data samples, said detection system does not detect any likely event of interest, E4, such that E4 is detected using an output by M when DS4 is input to M; and
(b3) said third multiplicity of said data samples is different from said fourth multiplicity of said data samples by one of said data samples DS0 received by M between a determination of P3 and a determination of P4;
second determining whether a later one of P3 and P4 results in detecting an occurrence of a likely event of interest;
outputting, in response to a result from at least one of said steps of first and second determining, at least one of;
(c1) first data indicative of no occurrence of a likely event of interest being detected, and (c2) second data indicative of an occurrence of a likely event of interest being detected. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A detection system for detecting a likely event of interest, comprising:
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a prediction model M, wherein when each data sample of a plurality of data samples, C, are input to M, said model M outputs a prediction related to a subsequent one of said data samples following said prediction;
wherein M predicts predictions P1, P2, P3, and P4 of said predictions, such that (a1) through (a5) following hold;
(a1) P1 and P2 are consecutive predictions obtained while said detection system does detect a likely event of interest, E1, such that E1 is detected using an output by M;
(a2) P3 and P4 are consecutive predictions, obtained while said detection system is-not detecting any likely event of interest, E2, such that E2 is detected using an output by M,;
(a3) for each prediction P of predictions P1, P2, P3, and P4, P is determined by M as a function of a corresponding multiplicity of said data samples C that are provided to M prior to a determination of P, such that for each data sample, DS, from said corresponding multiplicity of data samples, said detection system does not detect any likely event of interest, E, such that E is detected using an output by M when DS is input to M;
(a4) said corresponding multiplicity of said data samples for P1 and said corresponding multiplicity of said data samples for P2 do not differ by any one of said data samples DS used by M between a determination of P1 and a determination of P2;
(a5) said corresponding multiplicity of said data samples for P3 is different from said corresponding multiplicity of said data samples for P4 by one of said data samples DSo used by M between a determination of P1 and a determination of P2;
a prediction engine for receiving said predictions and determining whether a likely event of interest is detected, wherein said prediction engine includes one or more programmatic elements for comparing (c1) and (c2) following;
(b1) a measurement of a discrepancy between (i) and (ii) following;
(i) P1, and (ii) said subsequent data sample related to P1; and
(b2) a threshold obtained using a variance that is a function of other measurements, wherein each of said other measurements measures a discrepancy between one of said predictions prior to P1, and said subsequent data sample related to said one prediction. - View Dependent Claims (22, 23)
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24. A method for detecting a likely event of interest, comprising:
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providing one or more of computational models so that for each of said models M, when M receives a corresponding one or more data samples DS, said model M outputs a prediction PM related to a subsequent data sample DSP of said corresponding one or more data samples;
for each of said models M, and for a corresponding collection CM of a plurality of said predictions PM by M, perform the following steps (A) through (C);
(A) first determining a value V of a first threshold, V being dependent upon, for each PM of CM, a measurement of a variance between;
(a1) the PM of CM, and (a2) the subsequent data sample DSP related to PM of (i);
(B) comparing, for a prediction P0 output by M;
(b1) a variance between P0 and its related subsequent data sample DS0 with (b2) said first threshold value V;
(C) second determining, using a result from said step of comparing, whether there is a change between;
(c1) an instance of a likely event of interest occurring, and (c2) an instance of a likely event of interest not occurring;
wherein for at least one of said models, M0, there is a prediction P1 by M0 that is dependent on one of said data samples, DS, and an immediately previous predication P2 by M0 is independent of DS; and
wherein there are consecutive predictions P3 and P4 by M0 that do not differ by any one of said data samples DS used by M0 between a determination of P1 and a determination of P2. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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35. A method for determining a likely event of interest, comprising:
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supplying, to each of one or more adaptive models, a corresponding series of data samples, for each of said adaptive models M, and for each data sample dsA of said corresponding series SM, perform the following steps (a) and (b);
(a) generating a prediction, by M, when dsA is input to M, wherein said prediction includes a value v which is expected to correspond to a data sample dsB of SM wherein dsB is subsequent to dsA in SM;
(b) inputting information to M obtained from one or more errors in said predictions by M in order to reduce at least one of;
(i) subsequent instances of said prediction errors by M, and (ii) a variance in the subsequent instances of said prediction errors,for at least one of said adaptive models, M0, said step of inputting is performed substantially only when corresponding series is not indicative of a likely event of interest, and for said M0, performing the following steps;
(c) obtaining a measurement V of variance of a plurality of prediction errors between said values v and their corresponding values vB for M0;
(d) determining a further instance of one of said prediction errors for M0;
(e) determining a relationship between said variance V and said further instance for determining whether a likely event of interest has likely occurred; and
(f) when the likely event of interest is detected, M0 determines at least two consecutive predictions during said likely event of interest, wherein said predictions are only dependent on the predictions errors of M0 obtained prior to an earlier of said consecutive prediction errors.
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Specification