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 peer 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) assigning, using the processor, the first entity to one or more clusters in a first set of clusters, wherein the assigning to the one or more clusters in the first set of clusters is based on the individual model for the first entity;
(C) assigning, using the processor, the first entity to one or more clusters in a second set of clusters, wherein the assigning to the one or more clusters in the second set of clusters is based on a set of external data for the first entity;
(D) correlating, using the processor, the assigning of the first entity to one or more clusters in the first set of clusters with the assigning of the first entity to one or more clusters in the second set of clusters, wherein the results of the correlation are used to refine the assigning of the first entity to the second set of clusters; and
(E) assigning, using the processor, the first entity to at least one peer group, wherein the assigning to the at least one peer group is based at least in part on the results of the correlation.
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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.
27 Citations
22 Claims
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1. A method for detecting the presence of an activity, the method comprising the steps of:
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(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 peer 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) assigning, using the processor, the first entity to one or more clusters in a first set of clusters, wherein the assigning to the one or more clusters in the first set of clusters is based on the individual model for the first entity; (C) assigning, using the processor, the first entity to one or more clusters in a second set of clusters, wherein the assigning to the one or more clusters in the second set of clusters is based on a set of external data for the first entity; (D) correlating, using the processor, the assigning of the first entity to one or more clusters in the first set of clusters with the assigning of the first entity to one or more clusters in the second set of clusters, wherein the results of the correlation are used to refine the assigning of the first entity to the second set of clusters; and (E) assigning, using the processor, the first entity to at least one peer group, wherein the assigning to the at least one peer group is based at least in part on the results of the correlation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for detecting the presence of an activity, the system comprising:
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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 peer 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 assigning the first entity to one or more clusters in a first set of clusters, wherein the assigning to the one or more clusters in the first set of clusters is based on the individual model for the first entity; (C) circuitry for assigning the first entity to one or more clusters in a second set of clusters, wherein the assigning to the one or more clusters in the second set of clusters is based on a set of external data for the first entity; (D) circuitry for correlating the assigning of the first entity to one or more clusters in the first set of clusters with the assigning of the first entity to one or more clusters in the second set of clusters, wherein the results of the correlation are used to refine the assigning of the first entity to the second set of clusters; and (E) circuitry for assigning the first entity to at least one peer group, wherein the assigning to the at least one peer group is based at least in part on the results of the correlation. - View Dependent Claims (12, 13, 14, 15, 16)
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17. 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:
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(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 peer 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) assigning the first entity to one or more clusters in a first set of clusters, wherein the assigning to the one or more clusters in the first set of clusters is based on the individual model for the first entity; (C) assigning the first entity to one or more clusters in a second set of clusters, wherein the assigning to the one or more clusters in the second set of clusters is based on a set of external data for the first entity; (D) correlating the assigning of the first entity to one or more clusters in the first set of clusters with the assigning of the first entity to one or more clusters in the second set of clusters, wherein the results of the correlation are used to refine the assigning of the first entity to the second set of clusters; and (E) assigning the first entity to at least one peer group, wherein the assigning to the at least one peer group is based at least in part on the results of the correlation. - View Dependent Claims (18, 19, 20, 21, 22)
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Specification