System, method, and computer program for detecting regular and irregular events associated with various entities
A system, method, and computer program product are provided for detecting regular and irregular events associated with various entities. In operation, an event detection system detects at least one event associated with at least one entity. The event detection system identifies the at least one entity. Additionally, the event detection system identifies one or more historical patterns associated with the at least one event. Further, the event detection system determines whether the at least one event is anomalous based on the one or more historical patterns and details associated with the at least one event. Moreover, the event detection system performs at least one action based on whether the at least one event is determined to be anomalous.
1. A method, comprising:
learning, by an event detection system, patterns of events across a plurality of entities, utilizing historical events identified for the plurality of entities;
storing, by the event detection system, the patterns of events including storing for each pattern of events:
key features of the pattern of events, and
an indicator of one or more entities associated with the pattern of events;
building, by the event detection system, a scoring model for the patterns of events, the scoring model indicating scores for variances to the patterns of events;
detecting, by the event detection system, a sequence of events including an event to authenticate;
identifying, by the event detection system, one or more of the stored patterns associated with the event to authenticate;
using the scoring model to score, by the event detection system, the event to authenticate based on the detected sequence of events and the one or more of the stored patterns associated with the event to authenticate;
determining, by the event detection system, whether the event to authenticate is anomalous based on the score of the event; and
performing, by the event detection system, at least one first action responsive to determining that the event is anomalous; and
performing, by the event detection system, at least one second action responsive to determining that the event is not anomalous.
2. The method of claim 1 , wherein the plurality of entities include a person, a vehicle, and a device.
3. The method of claim 1 , wherein the plurality of entities are identified utilizing at least one of a biometric recognition algorithm, a facial recognition algorithm, an object recognition algorithm, and device identification information.
4. The method of claim 1 , wherein detecting the sequence of events includes receiving at least one request from an external system including the sequence of events with the event to authenticate.
5. The method of claim 4 , wherein the at least one request from the external system further includes a time limit indication representing a maximum time that the external system is willing to wait to receive a response to the at least one request.
6. The method of claim 4 , wherein the at least one request from the external system further includes an amount of money that the external system will pay for receiving a response to the at least one request.
7. The method of claim 4 , wherein the event detection system requests additional information from the external system to score event.
8. The method of claim 1 , wherein the event detection system utilizes information associated with the one or more patterns to generate insights.
9. The method of claim 1 , wherein the event is authenticated when it is determined that the event is not anomalous.
10. The method of claim 1 , wherein the event to authenticate is an event included in at least one event template that has been labeled an important event template.
11. The method of claim 1 , wherein the event to authenticate is scored based on key features identified from the sequence of events and the key features stored for the one or more of the stored patterns associated with the event to authenticate.
12. The method of claim 1 , wherein the at least one first action includes requesting active identification required to perform a requested action, and wherein the at least one second action includes performing the requested action without requiring the active identification.
13. A computer program product embodied on a non-transitory computer readable medium, comprising computer code for:
learning, by an event detection system, patterns of events across a plurality of entities, utilizing historical events identified for the plurality of entities;
storing, by the event detection system, the patterns of events including storing for each pattern of events:
key features of the pattern of events, and
an indicator of one or more entities associated with the pattern of events;
building, by the event detection system, a scoring model for the patterns of events, the scoring model indicating scores for variances to the patterns of events;
detecting, by the event detection system, a sequence of events including an event to authenticate;
identifying, by the event detection system, one or more of the stored patterns associated with the event to authenticate;
using the scoring model to score, by the event detection system, the event to authenticate based on the detected sequence of events and the one or more of the stored patterns associated with the event to authenticate;
determining, by the event detection system, whether the event to authenticate is anomalous based on the score of the event; and
performing, by the event detection system, at least one first action responsive to determining that the event is anomalous; and
performing, by the event detection system, at least one second action responsive to determining that the event is not anomalous.
14. The computer program product of claim 13 , wherein the plurality of entities include a person, a vehicle, and a device.
15. The computer program product of claim 13 , wherein the plurality of entities are identified utilizing at least one of a biometric recognition algorithm, a facial recognition algorithm, an object recognition algorithm, and device identification information.
16. An event detection system comprising one or more processors operable for:
learning, by an event detection system, patterns of events across a plurality of entities, utilizing historical events identified for the plurality of entities;
storing, by the event detection system, the patterns of events including storing for each pattern of events:
key features of the pattern of events, and
an indicator of one or more entities associated with the pattern of events;
building, by the event detection system, a scoring model for the patterns of events, the scoring model indicating scores for variances to the patterns of events;
detecting, by the event detection system, a sequence of events including an event to authenticate;
identifying, by the event detection system, one or more of the stored patterns associated with the event to authenticate;
using the scoring model to score, by the event detection system, the event to authenticate based on the detected sequence of events and the one or more of the stored patterns associated with the event to authenticate;
determining, by the event detection system, whether the event to authenticate is anomalous based on the score of the event; and
performing, by the event detection system, at least one first action responsive to determining that the event is anomalous; and
performing, by the event detection system, at least one second action responsive to determining that the event is not anomalous.