IP Library › Granted Patent US 9,552,470

Granted Patent B2

US 9,552,470 · App. 14/675,765 · Granted Jan 24, 2017

Method, device, and system of generating fraud-alerts for cyber-attacks

Inventors: Avi Turgeman (Cambridge, MA); Oren Kedem (Tel Aviv, IL); Uri Rivner (Mazkeret Batya, IL)

Assignee: BioCatch Ltd.

G06F21/32 G06F3/041 G06F21/31 G06F21/316 G06F21/554 H04L63/08 H04L63/1408 G06F2221/2133

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Quick Facts

Patent No.: US 9,552,470
App. No.: 14/675,765
Granted: Jan 24, 2017
Kind: B2

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USPTO Patent Center↗ Full Patent PDF↗

Abstract

Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. An end-user device (a desktop computer, a laptop computer, a smartphone, a tablet, or the like) interacts and communicates with a server of a computerized server (a banking website, an electronic commerce website, or the like). The interactions are monitored, tracked and logged. User Interface (UI) interferences are intentionally introduced to the communication session; and the server tracks the response or the reaction of the end-user to such communication interferences. The system determines whether the user is a legitimate human user; or a cyber-attacker posing as the legitimate human user. The system displays gauges indicating cyber fraud scores or cyber-attack threat-levels. The system extrapolates from observed fraud incidents and utilizes a rules engine to automatically search for similar fraud events and to automatically detect fraud events or cyber-attackers.

Claims (292)

1. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

calculating a current number of occurrences of utilization of said particular type of data entry method during said usage session, and generating output corresponding to said current number of occurrences during said usage session;

calculating a ratio between (A) said current number of occurrences of utilization of said particular type of data entry method during said usage session, and (B) an average number of occurrences of utilization of said particular type of data entry method during previous usage sessions of said user; and generating output corresponding to said ratio;

based on (i) said current number of occurrences, and (ii) said average number of occurrences during previous usage sessions of said user, determining whether said user is an authorized user or a cyber-attacker;

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(b1) a first-method current-session occurrence value, indicating the number of times that said user utilized the first data-entry method to engage with said user-interface element during said particular usage session being reviewed; and

(b2) a second-method current-session occurrence value, indicating the number of times that said user utilized the second data-entry method to engage with said user-interface element during said particular usage session being reviewed;

(c) for all previous usage sessions of said user, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(c1) a first-method aggregated occurrence value, indicating the number of times that said user utilized the first data-entry method to engage with said user-interface element during said previous usage sessions; and

(c2) a second-method aggregated occurrence value, indicating the number of times that said user utilized the second data-entry method to engage with said user-interface element during said previous sage sessions;

(d) generating a table comprising:

(d1) the first data-entry method, the first-method current-session occurrence value, and the first-method aggregated occurrence value; and

(d2) the second data-entry method, the second-method current-session occurrence value, and the second-method aggregated occurrence value.

2. The method of claim 1 , comprising:

based on (i) said current number of occurrences, and (ii) said average number of occurrences during previous usage sessions of said user, generating a fraud-score value indicating a likelihood that said user is a cyber-attacker and is not an authorized user.

3. The method of claim 1 , comprising:

calculating a ratio between (a) said current number of occurrences of utilization of said particular type of data entry method during said usage session, and (b) an average number of occurrences of utilization of said particular type of data entry method during previous usage sessions of a general population of users; and generating output corresponding to said ratio;

based on (i) said current number of occurrences, and (ii) said average number of occurrences during previous usage sessions of said general population of users, determining whether said user is an authorized user or a cyber-attacker.

4. The method of claim 3 , comprising:

based on (i) said current number of occurrences, and (ii) said average number of occurrences during previous usage sessions of said general population of users, generating a fraud-score value indicating a likelihood that said user is a cyber-attacker and is not an authorized user.

5. The method of claim 1 , comprising:

calculating a ratio between (a) said current number of occurrences of utilization of said particular type of data entry method during said usage session, and (b) an average number of occurrences of utilization of said particular type of data entry method during previous usage sessions of a group of users that excludes said user; and generating output corresponding to said ratio;

based on (i) said current number of occurrences, and (ii) said average number of occurrences during previous usage sessions of said group of users that excludes said user, determining whether said user is an authorized user or a cyber-attacker.

6. The method of claim 5 , comprising:

based on (i) said current number of occurrences, and (ii) said average number of occurrences during previous usage sessions of said group of users that excludes said user, generating a fraud-score value indicating a likelihood that said user is a cyber-attacker and is not an authorized user.

7. The method of claim 1 , wherein said determining comprises:

checking whether (A) said current number of occurrences of utilization of said particular type of data entry method during said usage session of said user, is different by at least a pre-defined number of percent points, from (B) a threshold value of occurrences that characterizes on average a batch of previous usage sessions of said user.

8. The method of claim 1 , wherein said determining comprises:

9. The method of claim 1 , wherein said determining comprises:

10. The method of claim 1 , further comprising:

generating an automated machine-readable fraud-detection rule that detects a fraudulent transaction in an account of another user, wherein the automated machine-readable fraud-detection rule indicates that: if said other user exhibits (A) a current number of occurrences of utilization of said particular type of data entry method during a usage session of said other user, which is different by at least a pre-defined number of percent points, from (B) a threshold value of occurrences that characterizes on average a batch of previous usage sessions of said other user, then generate a possible-fraud alert with regard to the account of said other user.

11. The method of claim 1 , further comprising:

generating an automated machine-readable fraud-detection rule that detects a fraudulent transaction in an account of another user, wherein the automated machine-readable fraud-detection rule indicates that: if said other user exhibits (A) a current number of occurrences of utilization of said particular type of data entry method during a usage session of said other user, which is different by at least a pre-defined number of percent points, from (B) a threshold value of occurrences that characterizes on average a general population of users of said computerized service, then generate a possible-fraud alert with regard to the account of said other user.

12. The method of claim 1 , further comprising:

generating an automated machine-readable fraud-detection rule that detects a fraudulent transaction in an account of another user, wherein the automated machine-readable fraud-detection rule indicates that: if said other user exhibits (A) a current number of occurrences of utilization of said particular type of data entry method during a usage session of said other user, which is different by at least a pre-defined number of percent points, from (B) a threshold value of occurrences that characterizes on average a group of users of said computerized service that excludes said other user, then generate a possible-fraud alert with regard to the account of said other user.

13. The method of claim 1 , further comprising:

based on said determining step,

generating an automated machine-readable fraud-detection rule that detects a fraudulent transaction in an account of another user at said computerized service;

automatically scanning data reflecting user interactions of said other user with the computerized service;

automatically applying said machine-readable fraud-detection rule to said data to automatically determine whether said other user is a cyber-attacker.

14. The method of claim 1 , further comprising:

based on said determining step,

generating an automated machine-readable fraud-detection rule that detects a fraudulent transaction in an account of another user at said computerized service;

receiving and parsing a fraud-file comprising data reflecting user interactions of one or more users with said computerized service;

automatically applying said machine-readable fraud-detection rule to said data to automatically detect at least one other user that is possibly a cyber-attacker.

15. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for usage sessions of other users, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(c1) a first-method aggregated occurrence value, indicating the number of times that said other users utilized the first data-entry method to engage with said user-interface element during their previous usage sessions;

(c2) a second-method aggregated occurrence value, indicating the number of times that said other users utilized the second data-entry method to engage with said user-interface element during their previous usage sessions;

(c3) a first-ratio value indicating a ratio between (A) the first-method aggregated occurrence value of other users as calculated in step (c1), and (B) the first-method current-session occurrence value as calculated in step (b1);

(c4) a second-ratio value indicating a ratio between (A) the second-method aggregated occurrence value of other users as calculated in step (c2), and (B) the second-method current-session occurrence value as calculated in step (b2);

(d) generating and displaying a table comprising at least:

(d1) the first data-entry method, and the first-ratio value; and

(d2) the second data-entry method, and the second-ratio value.

16. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for all previous usage sessions of said user, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(d) if it is determined that the first-method current-session occurrence value as calculated in step (b1), is larger by at least a threshold percentage value relative to the first-method aggregated occurrence value as calculated in step (c1), then determining that said user is a cyber-attacker posing as said user.

17. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for all previous usage sessions of said user, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(d) if it is determined that the first-method current-session occurrence value as calculated in step (b1), is smaller by at least a threshold percentage value relative to the first-method aggregated occurrence value as calculated in step (c1), then determining that said user is a cyber-attacker posing as said user.

18. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for usage sessions of other users, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(d) if the first-ratio value is greater than a pre-defined threshold ratio value, then determining that said user is a cyber-attacker posing as said user.

19. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize via a mouse-unit to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize via a keyboard to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for all previous usage sessions of said user, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(d) generating a table comprising:

(d1) the first data-entry method, the first-method current-session occurrence value, and the first-method aggregated occurrence value; and

(d2) the second data-entry method, the second-method current-session occurrence value, and the second-method aggregated occurrence value.

20. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize via a mouse-unit to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize via a keyboard to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for usage sessions of other users, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(d) generating and displaying a table comprising at least:

(d1) the first data-entry method, and the first-ratio value; and

(d2) the second data-entry method, and the second-ratio value.

21. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize via a mouse-unit to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize via a keyboard to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for all previous usage sessions of said user, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

22. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize via a mouse-unit to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize via a keyboard to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for all previous usage sessions of said user, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

23. A method comprising:

monitoring user interactions of a user, who utilizes a computing device to interact with a computerized service during a usage session;

for each particular type of data entry method that the user utilizes during said usage session:

wherein the method comprises:

(a) for a particular type of user interaction with a user-interface element of said computerized service, defining at least:

(a1) a first data-entry method that users can utilize via a mouse-unit to engage with said user-interface element, and

(a2) a second data-entry method that users can utilize via a keyboard to engage with said user-interface element;

(b) for a particular usage session of said user, which is being reviewed for possible fraud, calculating:

(c) for usage sessions of other users, that occurred within a pre-defined time period prior to the particular usage session being reviewed, calculating:

(d) if the first-ratio value is greater than a pre-defined threshold ratio value, then determining that said user is a cyber-attacker posing as said user.

24. The method of claim 23 , comprising:

(A) automatically scanning the user interactions performed in usage sessions of a batch of other users of said computerized service;

(B) calculating separately for each account owner of said batch of other users,

(B1) a first-method current-session occurrence value, indicating the number of times that said account owner utilized the first data-entry method to engage with said user-interface element during a particular usage session being reviewed;

(B2) a first-method aggregated occurrence value, indicating the number of times that said other users except said account owner utilized the first data-entry method to engage with said user-interface element during their previous usage sessions;

(B3) a first-ratio value indicating a ratio between (x) the first-method aggregated occurrence value calculated in step (B2), and (y) the first-method current-session occurrence value as calculated in step (B1);

(C) automatically generating a list of account owners, out of said other users of the computerized service, for which: the first-ratio value is greater than a pre-defined threshold ratio value.

25. The method of claim 18 , comprising:

(A) automatically scanning the user interactions performed in usage sessions of a batch of other users of said computerized service;

(B) calculating separately for each account owner of said batch of other users,

(C) if the first-ratio value of said account owner is greater than a pre-defined threshold ratio value, then automatically determining that the account of said account user was exposed to a cyber-attack.

26. The method of claim 1 , further comprising:

based on analysis of said user interactions, calculating a fraud-score value indicating a fraud-risk associated with said usage session;

generating and displaying on a screen of a computer located remotely from said user, a graphical user interface (GUI) element which comprises: (A) an indication of said fraud-score value; (B) an indication of a lowest-limit fraud-score value; (C) an indication of an upper-limit fraud-score value.

27. The method of claim 1 , wherein the generating and displaying comprise:

generating and displaying on said screen of said computer located remotely from said user, an on-screen gauge representation which comprises: an on-screen needle representation pointing to a particular location along a circumference of said on-screen gauge representation, the on-screen needle representation indicating a value between said lowest-limit fraud-score value and said upper-limit fraud-score value.

28. The method of claim 1 , wherein the generating and displaying comprise:

generating and displaying on said screen of said computer located remotely from said user, an on-screen partial-ring-shaped gauge representation which comprises: an on-screen needle representation pointing to a particular location along a circumference of said on-screen partial-ring-shaped gauge representation;

wherein a relative position of said particular location along the circumference of said on-screen partial-ring-shaped gauge representation, corresponds to the relative location of the fraud-score value along a number line that begins at the lowest-limit fraud-score value and ends with the upper-limit fraud-score value.

29. The method of claim 1 , wherein the generating and displaying comprise:

wherein

the circumference distance between said particular location and said indication of lowest-limit fraud-score value, divided by the circumference distance between said indication of lowest-limit fraud-score value and said indication of upper-limit fraud-score value,

is generally equal to

the fraud-score value minus the lowest-limit fraud-score value, divided by the difference between the upper-limit fraud-score value and the lowest-limit fraud-score value.

30. The method of claim 1 , comprising:

based on analysis of said user interactions performed via a mouse unit, calculating a mouse dynamics fraud-score value indicating a fraud-risk associated with said usage session;

generating and displaying on the screen of said computer located remotely from said user, an on-screen generally-circular gauge element which comprises: (A) an indication of said mouse dynamics fraud-score value; (B) an indication of a lowest-limit fraud-score value associated with mouse dynamics; (C) an indication of an upper-limit fraud-score value associated with mouse dynamics.

31. The method of claim 1 , comprising:

based on analysis of said user interactions performed via keyboard, calculating a keyboard dynamics fraud-score value indicating a fraud-risk associated with said usage session;

generating and displaying on the screen of said computer located remotely from said user, an on-screen generally-circular gauge element which comprises: (A) an indication of said keyboard dynamics fraud-score value; (B) an indication of a lowest-limit fraud-score value associated with keyboard dynamics; (C) an indication of an upper-limit fraud-score value associated with keyboard dynamics.

32. The method of claim 1 , comprising:

based on analysis of timings of said user interactions, calculating a timing-related fraud-score value indicating a fraud-risk associated with said usage session;

generating and displaying on the screen of said computer located remotely from said user, an on-screen generally-circular gauge element which comprises: (A) an indication of said timing-related fraud-score value; (B) an indication of a timing-related lowest-limit fraud-score value;

33. The method of claim 1 , comprising:

based on analysis of access time reflected in said user interactions, calculating an access time fraud-score value indicating a fraud-risk associated with said usage session;

generating and displaying on the screen of said computer located remotely from said user, an on-screen generally-circular gauge element which comprises: (A) an indication of said access time fraud-score value; (B) an indication of access time lowest-limit fraud-score value; (C) an indication of access time upper-limit fraud-score value.

34. The method of claim 1 , comprising:

based on analysis of said user interactions, calculating at least (A) a first fraud-score value associated with a first behavioral aspect reflected in said user interactions, and (B) a second fraud-score value associated with a second behavioral aspect reflected in said user interactions;

calculating a weighted fraud-score value based on said first fraud-score value and said second fraud-score value;

displaying, on the screen of said computer located remotely from said user, at least the weighted fraud-score value.

35. The method of claim 1 , comprising:

calculating a weighted fraud-score value based on said first fraud-score value and said second fraud-score value;

generating and displaying, on the screen of said computer located remotely from said user, an on-screen interface which comprises: (x) a first gauge indicating the first fraud-score value; (y) a second gauge indicating the second fraud-score value; (z) a third gauge indicating the weighted fraud-score value.

36. The method of claim 1 , comprising:

detecting and storing in a repository the Internet Protocol (IP) address of each usage session of said user with the computerized service;

converting each IP address of each usage session of said user, into a geo-spatial location on Earth;

displaying a graphical representation of a map of Earth;

displaying on said map of Earth multiple representations of pins,

wherein the number of pins displayed is equal to the number of usage sessions of said user with the computerized service,

wherein each pin is displayed at a map location that corresponds to the geo-spatial location on Earth from which a corresponding usage session of said user has occurred.

37. The method of claim 1 , comprising:

detecting and storing in a repository the Internet Protocol (IP) address of each usage session of said user with the computerized service;

converting each IP address of each usage session of said user, into a geo-spatial location on Earth;

displaying a graphical representation of a map of Earth;

displaying on said map of Earth multiple representations of pins,

wherein the number of pins displayed is smaller than the number of usage sessions of said user with the computerized service,

wherein each pin is displayed at a map location that corresponds to the geo-spatial location on Earth from which at least one corresponding usage session of said user has occurred,

wherein each pin further displays a numeric value that indicates the aggregate number of usage sessions of said user that occurred from multiple geo-spatial locations that are within a pre-defined radius around the map location to which said pin points.

38. The method of claim 1 , comprising:

detecting and storing in a repository the Internet Protocol (IP) address of each usage session of said user with the computerized service;

converting each IP address of each usage session of said user, into an IP-based geo-spatial location on Earth;

determining an actual geo-spatial location on Earth for each one of said usage sessions of said user, based on a combination of both: (A) said IP-based geo-spatial location, and (B) at least one more parameter selected from the group consisting of: a language in which said user interacts with the computerized service, a language in which a keyboard of said user is able to type, a language that said user selected for user interface of said computerized service;

displaying a graphical representation of a map of Earth;

displaying on said map of Earth multiple representations of pins,

wherein the number of pins displayed is equal to the number of usage sessions of said user with the computerized service,

wherein each pin is displayed at a map location that corresponds to the actual geo-spatial location on Earth from which a corresponding usage session of said user has occurred.

Assignments (6)

INTELLECTUAL PROPERTY SECURITY AGREEMENT TERMINATION UNDER REEL/FRAME: 049480/0823 Recorded Sep 14, 2020

From: KREOS CAPITAL VI (EXPERT FUND) LP

To: BIOCATCH LTD.

Reel/Frame 053769/0729 →

INTELLECTUAL PROPERTY SECURITY AGREEMENT TERMINATION UNDER REEL/FRAME: 040233/0426 Recorded Sep 14, 2020

From: KREOS CAPITAL V (EXPERT FUND) L.P.

To: BIOCATCH LTD.

Reel/Frame 053770/0145 →

SECURITY INTEREST Recorded Jun 16, 2019

From: BIOCATCH LTD.

To: KREOS CAPITAL VI (EXPERT FUND) L.P.

Reel/Frame 049480/0823 →

CHANGE OF ADDRESS Recorded Jun 13, 2019

From: BIOCATCH LTD.

To: BIOCATCH LTD.

Reel/Frame 049459/0302 →

SECURITY INTEREST Recorded Nov 6, 2016

From: BIOCATCH LTD.

To: KREOS CAPITAL V (EXPERT FUND) L.P.

Reel/Frame 040233/0426 →

ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 6, 2015

From: TURGEMAN, AVI; KEDEM, OREN; RIVNER, URI

To: BIOCATCH LTD.

Reel/Frame 035978/0127 →

Continuity (29)

Continuation In Part 14566723 · Dec 11, 2014

Continuation 13922271 · Jun 20, 2013

Continuation In Part 13877676

Continuation In Part 14675765

Continuation In Part 14320653 · Jul 1, 2014

Continuation In Part 14675765

Continuation In Part 14320656 · Jul 1, 2014

Continuation In Part 14675765

Continuation In Part 14325393 · Jul 8, 2014

Continuation In Part 14675765 · Apr 1, 2015

Continuation In Part 14325394 · Jul 8, 2014

Continuation In Part 14675765 · Apr 1, 2015

Continuation In Part 14325395 · Jul 8, 2014

Continuation In Part 14675765

Continuation In Part 14325396 · Jul 8, 2014

Continuation In Part 14675765

Continuation In Part 14325397 · Jul 8, 2014

Continuation In Part 14675765

Continuation In Part 14325398 · Jul 8, 2014

Provisional Application 61973855 · Apr 2, 2014

Provisional Application 61417479 · Nov 29, 2010

Provisional Application 61843915 · Jul 9, 2013

Related Publication 20150213246A1 · Jul 30, 2015