IP Library › Granted Patent US 12,282,863
Granted Patent B2
US 12,282,863 · App. 17/171,087 · Granted Apr 22, 2025

Method and system of user identification by a sequence of opened user interface windows

Inventor: Pavel Vladimirovich Slipenchuk (Moscow, RU)
Assignee: F.A.C.C.T. ANTIFRAUD LLC
G06N5/04G06F21/31G06N20/00
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Quick Facts
Patent No.
US 12,282,863
App. No.
17/171,087
Granted
Apr 22, 2025
Kind
B2
Abstract

A method and a system for user identification of a user in a computer system are provided. The method comprising: obtaining a user identifier associated with the user; assigning a respective window identifier to each user interface window opened by the user on the computing device; for a given working session of a pre-determined number of working sessions: storing a sequence of user interface windows opened by the user; identify, within user interface windows opened over the pre-determined number of working sessions, at least one pattern including a pre-determined number of repetitive sequences of user interface windows; generating a set of parameters characterizing a time elapsed between a transition from a first user interface window to an other user interface window within the at least one pattern; using the set of parameters associated with the at least one pattern and the user identifier to train at least one classifier.

Claims (57)

1. A method of user identification of a user of a computing device in a computer system, the computing device being associated with the computer system, the method comprising:

obtaining a user identifier associated with the user in the computer system;

assigning a respective window identifier to each user interface window opened by the user on the computing device;

during a pre-determined number of working sessions of the user on the computing device, for a given working session of the pre-determined number of working sessions: storing a sequence of user interface windows opened by the user, the storing including, for a given user interface window of the sequence of user interface windows:

storing a respective window identifier of the given user interface window and a respective timestamp indicative of an opening time of the given user interface window;

identifying, within user interface windows opened over the pre-determined number of working sessions, at least one pattern including a pre-determined number of repetitive sequences of user interface windows opened by the user over the pre-determined number of working sessions, the identifying comprising:

receiving a pre-determined pattern length representative of a number of repetitive sequences of user interface windows in a given pattern;

identifying, over the pre-determined number of working sessions, the given pattern having the pre-determined pattern length;

determining whether a frequency of occurrence of the given pattern in the pre-determined number of working sessions is no greater than a predetermined frequency threshold;

in response to the frequency of occurrence of the given pattern being no greater than the predetermined frequency threshold, including the given pattern in the at least one pattern;

in response to the frequency of occurrence of the given pattern being greater than the predetermined frequency threshold:

increasing the pre-determined pattern length for the given pattern, thereby determining another pattern; and

determining whether the frequency of occurrence of the other pattern in the pre-determined number of working sessions is no greater than the predetermined frequency threshold;

generating a set of parameters characterizing a time elapsed between a transition from a first user interface window to another user interface window within the given one pattern;

using the set of parameters associated with the at least one pattern and the user identifier to train at least one classifier to identify the user by an in-use sequence of user interface windows opened therein.

2. The method of claim 1 , further comprising using the at least one classifier to identify the user in the computer system during an in-use session, the using comprising:

receiving data of the in-use sequence of user interface windows including in-use user interface windows having been opened during the in-use session, wherein:

each one of the in-use user interface windows is associated with a respective timestamp indicative of a time of opening thereof;

applying the at least one classifier to the in-use sequence of the user interface windows to determine a likelihood parameter indicative of each of the in-use user interface windows having been opened by the user;

in response to the likelihood parameter being lower than a pre-determined likelihood threshold, executing one or more pre-determined actions.

3. The method of claim 1 , wherein the given user interface window is a web page.

4. The method of claim 1 , wherein the at least one pattern has been selected as meeting pre-determined criteria.

5. The method of claim 1 , further comprising assigning a weight value to the at least one pattern indicative of an effect of the at least one pattern on a final decision of the at least one classifier.

6. The method of claim 1 , wherein the set of parameters further includes an averaged time elapsed between transitions from the first user interface window to the other user interface window over the pre-determined number of working sessions.

7. The method of claim 6 , wherein the set of parameters further includes a variance value of time intervals elapsed between the transitions from the first user interface window to the other user interface window over the pre-determined number of working sessions.

8. The method of claim 6 , wherein the set of parameters further includes a frequency of occurrence of the at least one pattern in the pre-determined number of working sessions of the user.

9. The method of claim 8 , wherein a greater value of the frequency of occurrence has a greater effect on a final decision of the at least one classifier.

10. The method of claim 1 , wherein training the at least one classifier is executed by applying one or more machine learning techniques.

11. The method of claim 1 , wherein the at least one classifier comprises at least one of a probabilistic graphical model and a Support Vector Machine (SVM) classifier.

12. A system for user identification of a user, the system including a computing device including:

at least one processor;

at least one non-transitory computer-readable medium comprising instructions storing executable instructions, which, when executed by the at least one processor, cause the system:

obtain a user identifier associated with the user in the computer system;

assign a respective window identifier to each user interface window opened by the user on the computing device;

during a pre-determined number of working sessions of the user on the computing device, for a given working session of the pre-determined number of working sessions: store a sequence of user interface windows opened by the user including, for a given user interface window of the sequence of user interface windows:

a respective window identifier of the given user interface window and a respective timestamp indicative of an opening time of the given user interface window;

identify, within user interface windows opened over the pre-determined number of working sessions, at least one pattern including a pre-determined number of repetitive sequences of user interface windows opened by the user over the pre-determined number of working sessions, by:

receiving a pre-determined pattern length representative of a number of repetitive sequences of user interface windows in a given pattern;

identifying, over the pre-determined number of working sessions, the given pattern having the pre-determined pattern length;

determining whether a frequency of occurrence of the given pattern in the pre-determined number of working sessions is no greater than a predetermined frequency threshold;

in response to the frequency of occurrence of the given pattern being no greater than the predetermined frequency threshold, including the given pattern in the at least one pattern;

in response to the frequency of occurrence of the given pattern being greater than the predetermined frequency threshold:

increasing the pre-determined pattern length for the given pattern, thereby determining another pattern; and

determining whether the frequency of occurrence of the other pattern in the pre-determined number of working sessions is no greater than the predetermined frequency threshold;

generate a set of parameters characterizing a time elapsed between a transition from a first user interface window to another user interface window within the at least one pattern;

use the set of parameters associated with the at least one pattern and the user identifier to train at least one classifier to identify the user by an in-use sequence of user interface windows opened therein.

13. The system of claim 12 , wherein the executable instructions further cause the system to use the at least one classifier to identify the user in the computer system during an in-use session, by executing:

receiving data of the in-use sequence of user interface windows including in-use user interface windows having been opened during the in-use session, wherein:

each one of the in-use user interface windows is associated with a respective timestamp indicative of a time of opening thereof;

applying the at least one classifier to the in-use sequence of the user interface windows to determine a likelihood parameter indicative of each of the in-use user interface windows having been opened by the user;

in response to the likelihood parameter being lower than a pre-determined likelihood threshold, executing one or more pre-determined actions.

14. The system of claim 12 , wherein the given user interface window is a web page.

15. The system of claim 12 , wherein the at least one pattern has been selected as meeting pre-determined criteria.

16. The system of claim 12 , wherein the computing device is an integral part of a remote banking service system.

17. The system of claim 12 , wherein the computing device is an integral part of a website including a user authorization feature.

18. The system of claim 12 , wherein the computing device is an integral part of a multiplayer computer game including a user authorization feature.

19. The system of claim 12 , wherein the computing device is an integral part of a software and hardware system for collaborative work including a user authorization feature.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 17, 2025
From: F.A.C.C.T. ANTIFRAUD LLC
To: GROUP-IB GLOBAL PRIVATE LIMITED
Reel/Frame 071438/0815 →
CHANGE OF NAME Recorded Jul 19, 2024
From: GROUP IB, LTD
To: F.A.C.C.T. ANTIFRAUD LLC
Reel/Frame 068462/0907 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 11, 2021
From: SLIPENCHUK, PAVEL VLADIMIROVICH
To: GROUP IB, LTD
Reel/Frame 055736/0661 →
Continuity (2)
Continuation PCTRU2019000232 · Apr 10, 2019
Related Publication 20210182710A1 · Jun 17, 2021
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