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1. WO2020112383 - PREDICTIVE MODEL BASED ON DIGITAL FOOTPRINTS OF WEB APPLICATIONS

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

THAT WHICH IS CLAIMED:

1. A web server comprising:

a memory comprising a web application stored therein; and a processor coupled to said memory and configured to perform the following based on the web application being accessed by a plurality of users:

log application inputs and outputs during a respective user session for each user,

create a state diagram for each user during the respective user session based on the logged application inputs and outputs, with each state diagram representing states and transitions between the states,

translate each state diagram into vector space constructed by a sum of transition sequences defined by the transitions between the states,

cluster similar transition sequences in each state diagram, reduce each cluster of similar transition sequences in each state diagram into a single transition sequence,

generate a reduced state diagram for each state diagram based on the single transition sequences, and

analyze the reduced state diagrams to generate a predictive model representing a probability of state transitions between the different states.

2. The web server according to Claim 1 wherein said processor is further configured to use the predictive model to predict behavior of a subsequent user when accessing the web application.

3. The web server according to Claim 2 wherein the subsequent user accesses the web application via a client computing device comprising a cache memory, and wherein said processor pro-actively pushes, based on the predicted

behavior, relevant components of the web application to the cache memory before being requested by the web application.

4. The web server according to Claim 2 wherein said processor compares actual behavior of the subsequent user to the predicted behavior to determine efficiency of the subsequent user when accessing the web application.

5. The web server according to Claim 2 wherein said processor compares actual behavior of the subsequent user to the predicted behavior to determine anomalies of the subsequent user when accessing the web application.

6. The web server according to Claim 1 wherein said processor is configured to cluster the similar transition sequences in each state diagram using cosine distance calculations between equal events.

7. The web server according to Claim 1 wherein the predictive model comprises a Markov model.

8. The web server according to Claim 1 wherein the application inputs being logged include at least one of keyboard events, mouse events, onload events and document object management (DOM) events; and wherein the application outputs being logged include at least one of hypertext transfer protocol (http) responses and user interface function calls.

9. The web server according to Claim 1 wherein said processor is further configured to eliminate transition sequences that are not based on user input and were generated based on system events.

10. The web server according to Claim 1 wherein the web application comprises a Software as a Service (SaaS) application.

11. A method for operating a web server comprising a web application to be accessed by a plurality of users, and comprising:

logging application inputs and outputs during a respective user session for each user;

creating a state diagram for each user during the respective user session based on the logged application inputs and outputs, with each state diagram

representing states and transitions between the states;

translating each state diagram into vector space constructed by a sum of transition sequences defined by the transitions between the states;

clustering similar transition sequences in each state diagram; reducing each cluster of similar transition sequences in each state diagram into a single transition sequence;

generating a reduced state diagram for each state diagram based on the single transition sequences; and

analyzing the reduced state diagrams to generate a predictive model representing a probability of state transitions between the different states.

12. The method according to Claim 11 further comprising using the predictive model to predict behavior of a subsequent user when accessing the web application.

13. The method according to Claim 12 wherein the subsequent user accesses the web application via a client computing device comprising a cache memory, and the method further comprising pro-actively pushing, based on the predicted behavior, relevant components of the web application to the cache memory before being requested by the web application.

14. The method according to Claim 12 further comprising comparing actual behavior of the subsequent user to the predicted behavior to determine efficiency of the subsequent user when accessing the web application.

15. The method according to Claim 12 further comprising comparing actual behavior of the subsequent user to the predicted behavior to determine anomalies of the subsequent user when accessing the web application.

16. The method according to Claim 11 wherein the predictive model comprises a Markov model.

17. A non-transitory computer readable medium for operating a web server comprising a web application to be accessed by a plurality of users, and with the non-transitory computer readable medium having a plurality of computer executable instructions for causing the web server to perform steps comprising:

logging application inputs and outputs during a respective user session for each user;

creating a state diagram for each user during the respective user session based on the logged application inputs and outputs, with each state diagram

representing states and transitions between the states;

translating each state diagram into vector space

constructed by a sum of transition sequences defined by the transitions between the states;

clustering similar transition sequences in each state diagram; reducing each cluster of similar transition sequences in each state diagram into a single transition sequence;

generating a reduced state diagram for each state diagram based on the single transition sequences; and

analyzing the reduced state diagrams to generate a predictive model representing a probability of state transitions between the different states.

18. The non-transitory computer readable medium according to Claim 17 further comprising using the predictive model to predict behavior of a subsequent user when accessing the web application.

19. The non-transitory computer readable medium according to Claim 18 wherein the subsequent user accesses the web application via a client computing device comprising a cache memory, and further comprising the step of pro-actively

pushing, based on the predicted behavior, relevant components of the web application to the cache memory before being requested by the web application.

20. The non-transitory computer readable medium according to Claim 17 wherein the predictive model comprises a Markov model.