Analytics with shared traits
A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.
1. A method programmed in a non-transitory memory of a device comprising:
monitoring and analyzing user information with analytics during a period while a user is known by the device to learn identifying actions and features of the user;
adjusting the analytics based on shared traits of the analytics; and
filtering acquired user information to determine whether to use the acquired user information to learn additional identifying actions and features of the user.
2. The method of claim 1 further comprising activating tracking with the analytics.
3. The method of claim 1 wherein the analytics include related analytics and unrelated analytics.
4. The method of claim 3 wherein the related analytics share the shared traits of the analytics.
5. The method of claim 1 wherein adjusting the analytics includes providing feedback from one of the analytics to another of the analytics.
6. The method of claim 1 wherein filtering the acquired user information includes ignoring the acquired user information.
7. The method of claim 1 wherein filtering the acquired user information includes classifying the acquired user information in a separate classification.
8. A device comprising:
a non-transitory memory for storing an application, the application configured for:
monitoring and analyzing user information with analytics during a period while a user is known by the device to learn identifying actions and features of the user;
adjusting the analytics based on shared traits of the analytics; and
filtering acquired user information to determine whether to use the acquired user information to learn additional identifying actions and features of the user; and
a processor configured for processing the application.
9. The device of claim 8 wherein the application is further for activating tracking with the analytics.
10. The device of claim 8 wherein the analytics include related analytics and unrelated analytics.
11. The device of claim 10 wherein the related analytics share the shared traits of the analytics.
12. The device of claim 8 wherein adjusting the analytics includes providing feedback from one of the analytics to another of the analytics.
13. The device of claim 8 wherein filtering the acquired user information includes ignoring the acquired user information.
14. The device of claim 8 wherein filtering the acquired user information includes classifying the acquired user information in a separate classification.
15. A server device comprising:
a non-transitory memory for storing an application, the application configured for:
receiving user information from a user device;
monitoring and analyzing the user information with analytics during a period while a user is known by the user device to learn identifying actions and features of the user;
adjusting the analytics based on shared traits of the analytics; and
filtering acquired user information to determine whether to use the acquired user information to learn additional identifying actions and features of the user; and
a processor configured for processing the application.
16. The server device of claim 15 wherein the application is further for activating tracking with the analytics.
17. The server device of claim 15 wherein the analytics include related analytics and unrelated analytics.
18. The server device of claim 17 wherein the related analytics share the shared traits of the analytics.
19. The server device of claim 15 wherein adjusting the analytics includes providing feedback from one of the analytics to another of the analytics.
20. The server device of claim 15 wherein filtering the acquired user information includes ignoring the acquired user information.
21. The server device of claim 15 wherein filtering the acquired user information includes classifying the acquired user information in a separate classification.