Apparatuses, Methods and Systems for a Trackable Virtual Currencies Platform

• US 20120016796A1
• Filed: 09/26/2011
• Published: 01/19/2012
• Est. Priority Date: 09/30/2009
• Status: Active Grant

• Current Assignee: Zynga Incorporated
• Original Assignee: Zynga Incorporated
• Inventors: Ocko, Matthew, Driemeyer, Justin
• Granted Patent: US 8,315,944 B2
• Time in Patent Office: Days
• US Class Current: 705/39
• CPC Class Codes: A63F 13/00 Video games, i.e. games using an electronically generated display having two or more dimensions gaming systems which provide a financial reward G07F17/32
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 20/384 using social networks
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 50/10 Services

Abstract

Methods for detecting fraud using virtual currency transaction include receiving information regarding a virtual currency transaction from a user. The information is analyzed to determine whether the transaction involves a user that is being tracked, wherein the user being tracked is associated with an elevated level of risk of fraud. When a user associated with the transaction is being tracked, the virtual currency transaction is monitored using tracked virtual currency. The virtual currency transaction is identified as potentially fraudulent based on the monitoring.

Claims

1. A fraud detection method implemented by a processor, the method comprising:

receiving information regarding a virtual currency transaction from a user;

analyzing the information to determine whether the virtual currency transaction involves the user that is being tracked, wherein the user being tracked is associated with an elevated level of risk of fraud;

when the virtual currency transaction involves the user that is being tracked, monitoring the virtual currency transaction using tracked virtual currency;

identifying the virtual currency transaction as potentially fraudulent as a result of the monitoring.

2. The method of claim 1, wherein the virtual currency transaction is identified as potentially fraudulent based on one or a combination of a nature of virtual currency transaction, previous historical data of the user being tracked and social graph information associated with virtual currency transaction participants.

3. The method of claim 1, wherein the virtual currency transaction involves one of tracked virtual currency, untracked virtual currency or a combination of tracked virtual currency and untracked virtual currency.

4. The method of claim 1, wherein when the virtual currency transaction does not involve tracked virtual currency, adding a new batch of virtual currency involved in the transaction to the tracked virtual currency.

5. The method of claim 4, wherein adding the virtual currency includes associating a unique identifier to the virtual currency in the new batch so as to enable tracking of virtual currency transactions involving the virtual currency in the new batch.

6. The method of claim 1, wherein monitoring the virtual currency transaction further includes,

retrieving prior game play patterns of the user;

and

analyzing the prior game play patterns to determine the virtual currency transaction as potentially fraudulent.

7. The method of claim 1, wherein monitoring the virtual currency transaction further includes,

obtaining social graph data from social graphs associated with the user and friends of the user;

and

analyzing the social graph data according to predetermined rules to determine the virtual currency transaction as potentially fraudulent, wherein the predetermined rules accord different weights for different links in the social graph.

8. The method of claim 1, wherein monitoring the virtual currency transaction further includes,

obtaining previous actual fraud data and potential fraud events data associated with the user and friends of the user;

and

analyzing the previous actual fraud data and the potential fraud events data according to predetermined rules to determine the virtual currency transaction as potentially fraudulent.

9. The method of claim 1, further includes,

when the virtual currency transaction is identified as potentially fraudulent,

recording an event associated with the virtual currency transaction for future auditing, wherein the recording includes updating a fraud event table with information associated with the virtual currency transaction for subsequent retrieval and analysis.

10. A fraud detection method implemented by a processor, the method comprising:

receiving a fraud alert;

investigating the fraud alert by analyzing social graph information and other available information of users associated with the fraud alert to determine likelihood of users being involved in fraud;

when it is determined that a fraud was committed based on the analysis,

reversing a chain of fraudulent transactions related to the fraud; and

updating fraud events data related to the fraud into a fraud event database for subsequent analysis.

11. The method of claim 10, wherein the fraud alert is initiated by one of a user or an algorithm executing the method.

12. The method of claim 11, wherein analyzing further includes,

determining different types of links between the user initiating the fraud alert and other users in the social graph, each type of link accorded a distinct weight;

and

examining the weights associated with each of the different types of links to determine level of linkage of the user with the other users in the social graph, the type of links and level of linkage determining a likelihood that a fraud was committed.

13. The method of claim 10, wherein analyzing further includes,

analyzing social graph information of users associated with the fraud alert according to predetermined rules;

calculating fraud score for each user according to each predetermined rule;

computing a weighted average of the fraud score associated with each of the users using the fraud score calculated according to each predetermined rule, the weighted average computed based on established predetermined weights;

comparing the weighted average of the fraud score associated with each of the users against a predetermined threshold value; and

establishing that a fraud was committed when the weighted average exceeds the predetermined threshold value.

14. A method for optimizing virtual currency tracking for a user implemented by a processor, the method comprising:

receiving a request to optimize virtual currency tracking;

selecting a batch of tracked virtual currency from a plurality of batches of tracked virtual currency associated with the user;

examining attributes of the selected batch of tracked virtual currency; and

performing one of consolidation of or pruning of the selected batch from the plurality of batches of tracked virtual currency associated with the user based on the examined attributes.

15. The method of claim 14, wherein examining attributes further includes,

determining expiration date of the selected batch;

comparing the expiration date of the selected batch with a current date;

declaring the selected batch as expired when the current date exceeds the expiration date of the selected batch; and

wherein performing further includes,

when the selected batch has expired, performing pruning of the selected batch from the batches of tracked virtual currency associated with the user.

16. The method of claim 15, wherein examining attributes further includes,

when the selected batch has not expired, comparing an user identifier (UID) of the selected batch with the UID of each of the plurality of batches of tracked virtual currency associated with the user to determine a match, wherein each of the plurality of batches are distinct from the selected batch, and

wherein performing further includes,

when the UID of the selected batch matches the UID of a particular tracked batch within the plurality of batches of virtual currency associated with the user, performing consolidation of the selected batch with the particular tracked batch with matching UID.

17. The method of claim 16, wherein performing consolidation further includes,

incrementing a size of the particular tracked batch by a size of the selected batch;

and

removing the selected batch from the plurality of batches from a user table group associated with the user.

18. The method of claim 16, wherein performing consolidation further includes,

determining a size of the selected batch;

identifying a threshold size; and

removing the selected batch from the plurality of batches associated with the user when the size of the selected batch is less than the threshold size, wherein the threshold size is one of a predetermined value or is dynamically calculated based on a metric.

19. The method of claim 14, wherein when the number of tracked batches within the plurality of batches associated with the user exceeds a threshold value, pruning the tracked batches so as to reduce the number of tracked batches below the threshold value, wherein the threshold value is a predetermined value or is dynamically calculated based on a metric.

20. The method of claim 19, wherein the pruning further includes,

determining a size of each of the tracked batches within the plurality of tracked batches associated with the user;

and

pruning the number of tracked batches within the plurality of tracked batches based on the size.