System and method for remote activity detection
First Claim
1. A method for detecting the presence of an activity, the method comprising the steps of:
- (a) calculating, using a processor, for each entity in a plurality of entities;
(i) a peer comparison score for the activity for the entity;
(ii) a self comparison score for the activity for the entity;
(iii) a truth comparison score for the activity for the entity; and
(iv) an activity confidence score for the entity;
and(b) selecting, using the processor, a first entity from the plurality of entities, wherein the selecting is based at least in part on the activity confidence score for the first entity,wherein the calculation of the self comparison score includes the steps of;
(A) generating, using the processor, an individual model for the first entity wherein the individual model is based on at least one of a data stream for the first entity and an impact variable for the first entity;
(B) calculating, using the processor, a forecast data stream for the first entity, wherein the forecast data stream is based at least in part on the individual model for the first entity;
(C) calculating a difference, using the processor, between the individual model for the first entity and the forecast data stream for the first entity;
(D) calculating, using the processor, a time period for which the calculated difference is greater than a predetermined threshold; and
(E) assigning, using the processor, the self comparison score to the first entity based on at least one of the calculated difference and the time period;
and wherein the calculation of the truth comparison score includes the steps of;
(F) providing a ground truth data stream for the activity;
(G) comparing, using the processor, the ground truth data stream with a data stream model for the first entity; and
(H) assigning a truth comparison score to the first entity based at least in part on a result of the comparison of the ground truth data stream with the data stream model for the first entity.
1 Assignment
0 Petitions
Accused Products
Abstract
A system and method is disclosed for a remote activity detection process using an analysis of data streams of an entity such as an end user and/or a customer. In an embodiment, the detection process uses the data stream analysis to evaluate an entity'"'"'s potential involvement in an activity based on individual measures for the entity such as comparison of the entity'"'"'s data stream to the entity'"'"'s peers, comparison of the entity'"'"'s data stream to historical information for the entity, and/or comparison of the entity'"'"'s data stream to data streams for a known second entity involved in the activity. The detection process may also use other information available which may impact the data points in a data stream, such as premises attributes associated with an entity, demographic attributes for the entity, financial attributes for the entity, and system alerts.
-
Citations
19 Claims
-
1. A method for detecting the presence of an activity, the method comprising the steps of:
-
(a) calculating, using a processor, for each entity in a plurality of entities; (i) a peer comparison score for the activity for the entity; (ii) a self comparison score for the activity for the entity; (iii) a truth comparison score for the activity for the entity; and (iv) an activity confidence score for the entity; and (b) selecting, using the processor, a first entity from the plurality of entities, wherein the selecting is based at least in part on the activity confidence score for the first entity, wherein the calculation of the self comparison score includes the steps of; (A) generating, using the processor, an individual model for the first entity wherein the individual model is based on at least one of a data stream for the first entity and an impact variable for the first entity; (B) calculating, using the processor, a forecast data stream for the first entity, wherein the forecast data stream is based at least in part on the individual model for the first entity; (C) calculating a difference, using the processor, between the individual model for the first entity and the forecast data stream for the first entity; (D) calculating, using the processor, a time period for which the calculated difference is greater than a predetermined threshold; and (E) assigning, using the processor, the self comparison score to the first entity based on at least one of the calculated difference and the time period; and wherein the calculation of the truth comparison score includes the steps of; (F) providing a ground truth data stream for the activity; (G) comparing, using the processor, the ground truth data stream with a data stream model for the first entity; and (H) assigning a truth comparison score to the first entity based at least in part on a result of the comparison of the ground truth data stream with the data stream model for the first entity. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A system for detecting the presence of an activity, the system comprising:
-
a memory device for storing entity information for each of a plurality of entities; a processor for calculating for each entity in the plurality of entities; (i) a peer comparison score for the activity for the entity; (ii) a self comparison score for the activity for the entity; (iii) a truth comparison score for the activity for the entity; and (iv) an activity confidence score for the entity; and said processor for selecting a first entity from the plurality of entities, wherein the selecting is based at least in part on the activity confidence score for the first entity, wherein the processor for the calculation of the self comparison score includes; (A) circuitry for generating an individual model for the first entity wherein the individual model is based on at least one of a data stream for the first entity and an impact variable for the first entity; (B) circuitry for calculating a forecast data stream for the first entity, wherein the forecast data stream is based at least in part on the individual model for the first entity; (C) circuitry for calculating a difference between the individual model for the first entity and the forecast data stream for the first entity; (D) circuitry for calculating a time period for which the calculated difference is greater than a first predetermined threshold; and (E) circuitry for assigning the self comparison score to the first entity based on at least one of the calculated difference and the time period; and wherein the processor for the calculation of the truth comparison score includes; (F) circuitry for comparing a ground truth data stream for the activity with a data stream model for the first entity; and (G) circuitry for assigning a truth comparison score to the first entity based at least in part on a result of the comparison of the ground truth data stream with the data stream model for the first entity. - View Dependent Claims (11, 12, 13, 14)
-
-
15. A machine-readable medium having stored thereon a plurality of executable instructions to be executed by a processor, the plurality of executable instructions comprising instructions to:
-
(a) calculate for each entity in a plurality of entities; (i) a peer comparison score for the activity for the entity; (ii) a self comparison score for the activity for the entity; (iii) a truth comparison score for the activity for the entity; and (iv) an activity confidence score for the entity; and (b) select a first entity from the plurality of entities, wherein the selecting is based at least in part on the activity confidence score for the first entity, wherein the executable instructions further comprise instructions to calculate the self comparison score by; (A) generating an individual model for the first entity wherein the individual model is based on at least one of a data stream for the first entity and an impact variable for the first entity; (B) calculating a forecast data stream for the first entity, wherein the forecast data stream is based at least in part on the individual model for the first entity; (C) calculating a difference between the individual model for the first entity and the forecast data stream for the first entity; (D) calculating a time period for which the calculated difference is greater than a first predetermined threshold; and (E) assigning the self comparison score to the first entity based on at least one of the calculated difference and the time period; and wherein the executable instructions further comprise instructions to calculate the truth comparison score by; (F) comparing a ground truth data stream for the activity with a data stream model for the first entity; and (G) assigning a truth comparison score to the first entity based at least in part on a result of the comparison of the ground truth data stream with the data stream model for the first entity. - View Dependent Claims (16, 17, 18, 19)
-
Specification