DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status
This Office Action is responsive to communications filed on 13 June 2025; Claim(s) 1-20 is/are pending in the application and have been presented for examination.
Continuation
This application is a continuation application of U.S. Application No. 18/462,483 filed 7 September 2023, now U.S. Patent 12,361,447, ("Parent Application"). In accordance with MPEP §609.02(II)(A)(2) and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also, in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered 'of record' in the Parent Application are now considered cited or 'of record' in this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Eligibility Step 1 analysis, it is determined that Claims 1-20 are directed to a system and method.
Under Eligibility Step 2A, Prong 1 analysis, Claim 1 recites, "A method for detecting click farm fraud, comprising: receiving network data and sensor data from a plurality of computing devices; extracting one or more features from the sensor data and the network data, wherein the one or more features are indicative of a physical or network environment associated with each of the plurality of computing devices; identifying, based on the one or more features, a subset of the plurality of computing devices that are co-located; and responsive to determining that the subset includes at least a threshold number of co-located computing devices, performing an action to mitigate click farm fraudulent activities”, the underlined limitations indicate additional elements that are to be further analyzed at Step 2A-2.
Independent Claim 10 and Claim 16 is similar to Claim 1 except for reciting, A system for detecting click farm fraud, comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor (Claim 10), and A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform a method for detecting click farm fraud, grouping, based on the environmental features, a set of the plurality of computing devices that are co-located (Claim 16), therefore Claim 10 and Claim 16 is analyzed similarly as Claim 1.
The Claim(s) are found to be within the enumerated group(s) of Certain Methods of Organizing Human Activity, specifically as it relates to a mental process and/or advertising/marketing or sales activities. Although the Examiner has provided this summary of the claims, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination).
Under Eligibility Step 2A, Prong 2 analysis, the limitations of - A method for detecting click farm fraud, comprising: receiving network data and sensor data from a plurality of computing devices; extracting one or more features (Claim 1, 10 and 16), a processor; and a memory storing instructions that, when executed by the processor, cause the processor (Claim 10), and A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform a method for detecting click farm fraud, grouping, based on the environmental features, a set of the plurality of computing devices that are co-located (Claim 16), confirming co-location of the subset of computing devices by actively probing, wherein actively probing includes instructing at least one device in the subset to emit a signal and detecting a response from other devices in the subset (Claim 8), and wherein the signal includes one or more of an audio signal, a light signal, or a wireless communication signal, and wherein the response is detected using one or more of a microphone, a light sensor, or a wireless receiver (Claim 9), causing at least one device in the group to generate a detectable signal and monitoring responses from other devices in the group (Claim 15), instructing a first device in the set to emit a signal and detecting a response from at least one other device in the set using a sensor (Claim 20) - does not integrate the judicial exception into practical application because the claims recite generic computer components (i.e., processor, memory), performing generic computer functions (i.e., sending, receiving etc.,) which amounts to nothing more than mere instructions to implement the abstract idea in a computer environment. The computer elements are claimed at a high level of generality which can be regarded as being at an “apply it” level.
The limitations of “extracting one or more features”, is considered by the courts to be well-used, routine and conventional activities when recited as a high level of generality, see MPEP 2106.05(d) (II) - The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity - v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition).
Furthermore, the steps of “receiving network data and sensor data from a plurality of computing devices”, can be regarded as insignificant pre-solution activity (i.e., mere data gathering) – see MPEP 2106.05(g).
The limitations of, “confirming co-location of the subset of computing devices by actively probing, wherein actively probing includes instructing at least one device in the subset to emit a signal and detecting a response from other devices in the subset” (Claim 8), and “wherein the signal includes one or more of an audio signal, a light signal, or a wireless communication signal, and wherein the response is detected using one or more of a microphone, a light sensor, or a wireless receiver” (Claim 9), indicates sending and receiving signal data.
Dependent Claims 2-7, 11-14, and 17-19 are also considered to be encompassed by the abstract idea by reciting, the type of sensor data collected (Claim 2), the type of features (Claim 3, 17), clustering the computing devices based on similarity of features (Claim 4, 5, 18), the type of action performed to mitigate click fraud (Claim 6, 13, 19), the type of influencer activities (Claim 7, 14), the type of characteristics (Claim 11), and clustering the computing devices based on shared characteristics (Claim 12).
The limitations of the claim(s) does not appear to recite an improvement to another technology or technical field; does not provide any improvements to the functioning of the computer itself; does not apply the judicial exception with, or by use of, a particular machine; does not effect a transformation or reduction of a particular article to a different state or thing; it does not add a specific limitation, or add unconventional steps that confine the claim(s) to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Generic computer components performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. None of the limitations, considered alone or in an ordered combination provide eligibility, because taken as a whole, the claim(s) is/are merely instructions to implement the abstract idea in a computer environment.
Under Eligibility Step 2B analysis, the claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claim elements, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. The claim includes that, as stated above, it is implemented by a system which employs a processor is nothing more than “apply it” with instruction to a generic computer. The claimed computer components are recited at a high level of generality and are merely invoked to perform the abstract idea.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO Internet website contains terminal disclaimer forms which may be used.
Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-5, 7-10 and 15-20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over Claims 1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 18 and 20 of U.S. Patent No. 12,361,447. The broad claims in the pending application are rejected under obviousness type double patenting over previously patented narrow claims. Although the conflicting claims are not identical, they are not patentably distinct from each other because the claimed invention in the instant application is fully disclosed and is broader than the claimed invention in Patent No. 12,361,447.
See for example:
Instant Claims 19/237,165
Patent No. 12,361,447 (Parent Application)
1. A method for detecting click farm fraud, comprising:
receiving network data and sensor data from a plurality of computing devices;
extracting one or more features from the sensor data and the network data, wherein the one or more features are indicative of a physical or network environment associated with each of the plurality of computing devices;
identifying, based on the one or more features, a subset of the plurality of computing devices that are co-located;
and responsive to determining that the subset includes at least a threshold number of co-located computing devices, performing an action to mitigate click farm fraudulent activities.
6. The method of claim 1, wherein performing the action to mitigate click farm fraudulent
activities includes one or more of sending a session termination command to a server, notifying an advertising network, or flagging the co-located computing devices for further review.
1. A method for detecting and mitigating click farm fraud, comprising:
receiving, at a backend server via a communication network, network data and sensor data from a plurality of remote computing devices;
extracting, with a feature extraction/filtering module, one or more features from the sensor data and the network data for each of the plurality of computing devices, wherein the one or more features represent one or more of a remote physical environment and communication channel environment associated with a device of the plurality of remote computing devices;
filtering, with the feature extraction/filtering module, one or more features from the sensor data to generate filtered sensor data;
detecting, based on the filtered sensor data, stationary durations associated with one or more devices of the plurality of remote computing devices;
determining, based on the one or more features and the stationary durations, one or more subsets of the plurality of remote computing devices;
identifying, with a co-location determination module, and based on the one or more subsets and detected influencer activities, co-located computing devices;
and responsive to determining that a count of the co-located computing devices is greater than a predetermined count, sending a session terminating command to one or more servers in communication with the co-located computing devices to mitigate click farm fraudulent activities.
2. The method of claim 1, wherein the sensor data includes one or more of location information, device orientation, ambient light conditions, device battery status, or environmental sensor readings, and wherein the network data includes one or more of WiFi network information, cellular network information, or Bluetooth device information.
2. The method of claim 1, wherein the sensor data comprises one or more of: location information; device orientation; device movement characteristics; ambient light conditions; and device battery charge; and wherein network data comprises one or more of: telecom network information; associated WiFi network information; and associated BLE devices.
3. The method of claim 1, wherein the one or more features include at least one of WiFi Service Set Identifiers (SSIDs), Received Signal Strength Indicator (RSSI) values, cell tower identifiers, or ambient light time-series data.
7. The method of claim 1, wherein the features extracted further include one or more of nearby device WiFi data comprising Service Set Identifiers (SSIDs) and Received Signal Strength Indicator (RSSI) values of detected WiFi access points, a statistical distance between one or more of SSID values and RSSI values, cell tower identifiers, and a statistical distance between the cell tower identifiers.
4. The method of claim 1, wherein identifying the subset of co-located computing devices
comprises clustering the plurality of computing devices based on similarity of the one or more features.
4. The method of claim 1, wherein identifying the co-located computing devices is further based on clustering the plurality of the computing devices into one or more subsets based on equivalent features of the one or more features.
5. The method of claim 4, wherein the clustering is based on features exhibiting similarity over a
predetermined time period.
5. The method of claim 4, wherein the clustering is further based on substantially equivalent features of the one or more features over a predetermined time period.
7. The method of claim 1, further comprising detecting influencer activities associated with the subset of co-located computing devices, wherein the influencer activities include one or more of advertisement clicks, content sharing, or posting reviews.
3. The method of claim 1, wherein the detected influencer activities comprise one or more of clicking on an advertisement, sharing a link, promoting content, and leaving a review.
8. The method of claim 1, further comprising confirming co-location of the subset of computing devices by actively probing, wherein actively probing includes instructing at least one device in the subset to emit a signal and detecting a response from other devices in the subset.
9. The method of claim 1, further comprising confirming a proximal arrangement of the one or more subsets of devices via actively probing, the probing comprising instructing a first device of the co-located computing devices of the one or more subsets to emit a signal and measuring an associated response from other devices of the co-located computing devices of the one or more subsets.
9. The method of claim 8, wherein the signal includes one or more of an audio signal, a light signal, or a wireless communication signal, and wherein the response is detected using one or more of a microphone, a light sensor, or a wireless receiver.
20. The system of claim 13, further comprising confirming a proximal arrangement of the one or more subsets of devices via actively probing, the probing comprising instructing a first device of the co-located computing devices of the one or more subsets to emit a signal and measuring an associated response from other devices of the co-located computing devices of the one or more subsets, wherein measuring the associated response from the other devices comprises measuring using one or more of a microphone and a magnetometer sensor.
10. A system for detecting click farm fraud, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, cause the processor
to: collect network data and sensor data from a plurality of computing devices;
process the sensor data and the network data to identify one or more
characteristics associated with a physical or network environment of each of the plurality
of computing devices;
determine, based on the one or more characteristics, a group of co-located
computing devices; and
initiate a fraud mitigation action when a number of devices in the group exceeds a
predetermined threshold.
13. The system of claim 10, wherein the fraud mitigation action includes one or more of
terminating a communication session, alerting a third-party server, or restricting activities of the group of co-located computing devices.
13. A system, comprising:
a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, cause the processor to:
receive, at a backend server via a communication network, network data and sensor data from a plurality of remote computing devices;
extract, with a feature extraction/filtering module, one or more features from the sensor data and the network data for each of the plurality of remote computing devices, wherein the one or more features represent one or more of a remote physical environment and communication channel environment associated with a device of the plurality of remote computing devices; filter, with the feature extraction/filtering module, one or more features from the sensor data to generate filtered sensor data; detect, based on the filtered sensor data, stationary durations associated with one or more devices of the plurality of remote computing devices; determine, based on the one or more features and the stationary durations, one or more subsets of the plurality of remote computing devices; identify, with a co-location determination module, and based on the one or more subsets and detected influencer activities, co-located computing devices; and send a session terminating command to one or more servers in communication with the co-located computing devices to mitigate click farm fraudulent activities responsive to determining that a count of the co-located computing devices is greater than a predetermined count.
12. The system of claim 10, wherein the instructions further cause the processor to cluster the plurality of computing devices into the group based on shared characteristics.
15. The system of claim 13, wherein identifying the co-located computing devices is further based on clustering the plurality of the computing devices into one or more subsets based on equivalent features of the one or more features.
14. The system of claim 10, wherein the instructions further cause the processor to monitor
influencer activities performed by the group of co-located computing devices, wherein the
influencer activities include one or more of advertisement interactions, link sharing, or content promotion.
14. The system of claim 13, wherein the influencer activities comprise one or more of clicking on an advertisement, sharing a link, promoting content, and leaving a review.
15. The system of claim 10, wherein the instructions further cause the processor to perform
active verification of co-location by causing at least one device in the group to generate a
detectable signal and monitoring responses from other devices in the group.
20. The system of claim 13, further comprising confirming a proximal arrangement of the one or more subsets of devices via actively probing, the probing comprising instructing a first device of the co-located computing devices of the one or more subsets to emit a signal and measuring an associated response from other devices of the co-located computing devices of the one or more subsets, wherein measuring the associated response from the other devices comprises measuring using one or more of a microphone and a magnetometer sensor.
16. A non-transitory computer-readable medium storing instructions that, when executed by a
processor, cause the processor to perform a method for detecting click farm fraud, the method
comprising: obtaining network data and sensor data from a plurality of computing devices;
analyzing the network data and sensor data to extract environmental features associated
with each of the plurality of computing devices; grouping, based on the environmental features, a set of the plurality of computing devices that are co-located; and
executing a mitigation action when the set of co-located computing devices exceeds a
predefined size.
19. The non-transitory computer-readable medium of claim 16, wherein the mitigation action
includes one or more of sending a command to terminate a session, notifying an advertising
platform, or logging the set of co-located computing devices as potentially fraudulent.
13. A system, comprising:
a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, cause the processor to:
receive, at a backend server via a communication network, network data and sensor data from a plurality of remote computing devices;
extract, with a feature extraction/filtering module, one or more features from the sensor data and the network data for each of the plurality of remote computing devices, wherein the one or more features represent one or more of a remote physical environment and communication channel environment associated with a device of the plurality of remote computing devices; filter, with the feature extraction/filtering module, one or more features from the sensor data to generate filtered sensor data; detect, based on the filtered sensor data, stationary durations associated with one or more devices of the plurality of remote computing devices; determine, based on the one or more features and the stationary durations, one or more subsets of the plurality of remote computing devices; identify, with a co-location determination module, and based on the one or more subsets and detected influencer activities, co-located computing devices; and send a session terminating command to one or more servers in communication with the co-located computing devices to mitigate click farm fraudulent activities responsive to determining that a count of the co-located computing devices is greater than a predetermined count.
17. The non-transitory computer-readable medium of claim 16, wherein the environmental
features include one or more of WiFi network identifiers, signal strength measurements, cellular
network identifiers, or sensor-based environmental conditions.
18. The system of claim 13, wherein the features extracted further include one or more of nearby device WiFi data comprising Service Set Identifiers (SSIDs) and Received Signal Strength Indicator (RSSI) values of detected WiFi access points, a statistical distance between one or more of SSID values and RSSI values, cell tower identifiers, and a statistical distance between the cell tower identifiers.
18. The non-transitory computer-readable medium of claim 16, wherein grouping the set of co-located
computing devices includes applying a clustering algorithm to the environmental
features.
15. The system of claim 13, wherein identifying the co-located computing devices is further based on clustering the plurality of the computing devices into one or more subsets based on equivalent features of the one or more features.
20. The non-transitory computer-readable medium of claim 16, wherein the method further
comprises verifying co-location of the set of computing devices by instructing a first device in
the set to emit a signal and detecting a response from at least one other device in the set using a
sensor.
20. The system of claim 13, further comprising confirming a proximal arrangement of the one or more subsets of devices via actively probing, the probing comprising instructing a first device of the co-located computing devices of the one or more subsets to emit a signal and measuring an associated response from other devices of the co-located computing devices of the one or more subsets, wherein measuring the associated response from the other devices comprises measuring using one or more of a microphone and a magnetometer sensor.
The Applicant is now attempting to claim broadly that which had been previously described in more detail in the Claim(s) of Patent No. 12,361,447. Independent Claim 10 and 16 of the instant application is analyzed similarly, and found to be unpatentable over independent Claim 1 and 13 of the Parent Application for the same reasons.
Although the instant Claim(s) are directed to a method, system and non-transitory computer-readable medium, whereas the Parent Application are also directed to a method and system it would appear obvious to claim the non-transitory computer-readable medium to be executed on the computer, and especially so since the system as at instant Claim 10 recite a memory, instructions, and processor circuitry to perform the activities. The other differences between the claims appear to be either minor, necessarily required, and/or a difference in verbiage to convey the same basic activity.
It would have been obvious to use Claims 1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 18 and 20 of U.S. Patent No. 12,361,447 to reject Claims 1-5, 7-10 and 15-20 of the instant application since all of the limitations of Claims 1-5, 7-10, 12-20 of the instant application are present in the Claims 1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 18 and 20 of U.S. Patent No. 12,361,447. Therefore, 1-5, 7-10 and 15-20 of the instant claims are found to be double patenting based on the nonstatutory double patenting analysis.
Allowable Subject Matter
Claim 1-20 are allowed over the prior art.
The following is a statement of reasons for the indication of allowable subject matter:
The closest art of record, Aaron et al (US 2008/0201214 A1, indicates tracking and collecting geographic location and network data when it is detected that an internet advertisement has been activated by a source (i.e., clicked on by a user), calculating the number of activations (clicks) occurring in a particular geographic area or region, and comparing the number of activations to a predetermined threshold hold to identify click fraud.
Li et al (US 11,373,205 B2) indicates identifying abnormal advertisement place(s) by tracking abnormal behavior from user terminals, this is accomplished by determining if the terminal is detected to be in more than one region and determining a ratio of abnormal terminals to the total number of terminals, and when the ratio is greater than a predetermined threshold marking the region as “suspected abnormal”.
Wee et al (U.S. Patent Publication No. 20080126159), indicates detecting non-relevant traffic to an online advertisement using exposure and click patterns, notifying an advertiser of the click fraud and stopping an advertisement to prevent further click fraud.
Krasniqi et al (U.S. Patent Publication No. 20140169358), indicates analyzing SSIDs data to determines advertising messages to send to one or more users.
Shi et al (C. Shi, R. Song, X. Qi, Y. Song, B. Xiao and S. Lu, "ClickGuard: Exposing Hidden Click Fraud via Mobile Sensor Side-channel Analysis," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6), indicates using motion sensor data from mobile device(s) to identify click fraud, and using bandpass filtering to reduce noise when analyzing the sensor data to improve accuracy of the results.
Hegarty et al (U.S. Patent Publication No. 2014/0143304), similarly indicates analyzing clickstream data via device sensors and using a low pass filter.
Ge et al (U.S. Patent Publication No. 2007/0255821), indicates scoring click quality by comparing client side and server side data, if a fraud score is above a threshold the web request is blocked.
However, Aaron, Wee, Li, Shi, Krasniqi, Hegarty and/or Ge do not teach or disclose, “identifying, based on the one or more features, a subset of the plurality of computing devices that are co-located; and responsive to determining that the subset includes at least a threshold number of co-located computing devices, performing an action to mitigate click farm fraudulent activities”.
The Examiner has searched for and does not find art that teaches the claim limitations as indicated above. Furthermore, the Examiner finds that it would not be obvious to combine the numerous references to arrive at the claimed invention, therefore the Examiner has indicated allowability over the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Martino et al (U.S. Patent Publication US 2019/0102790 A1), indicates determining how many users are associated with a particular IP address, and if the number of users exceed a number of authorized users for the IP address the system analyzes phone sensor data to determine if ad fraud is occurring (see at least Abstract, 0008, 0010).
Aaron et al (U.S. Patent Publication No. 20080201214), indicates a system and method for detecting and identifying click fraud by collecting and analyzing GPS location and IP address data to determine large click events occurring in certain geographic regions (see at least 0033-0037, 0047).
Li et al (U.S. Patent Publication No. 11,373,205 B2), indicates identifying abnormal advertisement place(s) by tracking abnormal behavior from user terminals (see at least Fig. 2, col 7 lines 5-15).
C. Shi, R. Song, X. Qi, Y. Song, B. Xiao and S. Lu, "ClickGuard: Exposing Hidden Click Fraud via Mobile Sensor Side-channel Analysis," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, accessed at < https://ieeexplore.ieee.org/document/9149420?source=IQplus> (hereinafter, Shi et al), indicates using motion sensor data from mobile device(s) to identify click fraud, Shi also indicates using bandpass filtering to reduce noise when analyzing the sensor data.
Hegarty et al (U.S. Patent Publication No. 2014/0143304), indicates analyzing clickstream data via device sensors and using a low pass filter (see at least 0072-0075).
Wee et al (U.S. Patent Publication No. 2008/0126159), indicates detecting non-relevant traffic to an online advertisement using exposure and click patterns, notifying an advertiser of the click fraud and stopping an advertisement to prevent further click fraud (see at least Brief Summary).
Ge et al (U.S. Patent Publication No. 2007/0255821), indicates scoring click quality by comparing client side and server side data, if a fraud score is above a threshold the web request is blocked (see at least Summary).
Krasniqi et al (U.S. Patent Publication No. 2014/0169358), indicates analyzing SSIDs data to determines advertising messages to send to one or more users (see at least Abstract).
N. Li, S. Du, H. Zheng, M. Xue and H. Zhu, "Fake reviews tell no tales? dissecting click farming in content-generated social networks," in China Communications, vol. 15, no. 4, pp. 98-109, April 2018, accessed at < https://ieeexplore.ieee.org/document/8357744?source=IQplus>, indicates a year long study of click farming communities as it relates to fake reviews, where click farms were detected based on analyzing community and user features using a machine learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELINDA GIERINGER whose telephone number is (408)918-7593. The examiner can normally be reached Monday - Friday (11AM-6PM ET).
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/M.G./Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
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