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 of the Claims
Claims 1-5, 7, 13-17, 19, 20 and 22 are all the claims pending in the application.
Claims 1 and 16 are amended.
Claims 1-5, 7, 13-17, 19, 20 and 22 are rejected.
The following is a Final Office Action in response to amendments and remarks filed Jan. 2, 2026.
Response to Arguments
Regarding the 112(b) rejections, the rejections are withdrawn in light of the amendments to the claims.
Regarding the 112(a) rejections, the rejections are withdrawn in light of the amendments to the claims.
Regarding the 103 rejections, the rejections are maintained for the following reasons. First, Applicant asserts Wang does not teach the suspicious user's account being explicitly excluded from the non-suspicious user accounts. Examiner respectfully does not find this assertion persuasive because Examiner finds Teman teaches this limitation because Teman teaches banning fraudulent accounts.
Second, Applicant asserts Cheung does not teach determining the suspicious user is not found among images of non-suspicious users on multiple platforms. Examiner respectfully does not find this assertion persuasive because one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). That is, Cheung and Teman were used in combination to teach this concept and Examiner finds no reason the references could not be combined.
Third, Applicant asserts Teman’s teaching of whitelisting accounts does not teach the suspicious user in the verification request is not found among the plurality of non-suspicious user images. Examiner respectfully does not find this assertion persuasive because Teman explicitly teaches the whitelisted profiles are verified users. Thus, a fraudulent user would not be found among the plurality of non-suspicious (i.e. whitelisted) users because the whitelisted user are not suspicious.
Fourth, regarding claim 4, Applicant asserts Chan cannot be relied on to teach using a GIF format because Chan does not teach or suggest a GIF format would be selected. Examiner respectfully does not find this assertion persuasive because obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill would have recognized there are a finite number of identified, predictable potential options of file formats for storing images including the JPEGs, GIFs, TIFFs, RAW, and PNG formats discussed in Chan and one of ordinary skill in the art would have pursued any of the known potential options with a reasonable expectation of success, see MPEP 2143.I.E. Accordingly the 103 rejections are maintained, please see below for the complete rejections of the claims as amended.
In response to arguments in reference to any other depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 7, 13-17, 19, 20, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheung et al US Pub. No. 2009/0169062, herein referred to as "Cheung", in view of Teman, US Pub. No. 2016/0005050, herein referred to as “Teman”, further in view of Cohen, US Pub. No. 2012/0204225, herein referred to as "Cohen".
Regarding claim 1, Cheung teaches:
a processor; a memory attached to the processor; and a computer readable medium having instruction embedded therein, the instructions configured to cause the processor to perform the operations of (processor and instructions ¶[0078], memory, e.g. ¶¶[0063], [0064]):
and dynamically updated non-suspicious user data (profile of verified users includes a file, record, or other verification indicator, ¶[0075])
verifies user identity of a suspicious user in a verification request based on (verifies users of social networks in a first image based on a second image, e.g. ¶¶[0032], [0041]):
a verification image of a suspicious user comprising a plurality of facial features and hand gestures performed by the suspicious user in response to the verification request (users are requested to provide a second digital image for verification, e.g. ¶¶[0041]-[0042], in a specific pose, e.g. left hand downwards, open mouth smile, winking, ¶[0071] and Fig. 5);
biographical information associated with the suspicious user account (determines age of person in the first and second image, e.g. ¶¶[0020], [0042]-[0043]);
biographical information associated with the non-suspicious users of the plurality of non-suspicious user accounts in the plurality of social media platforms (determines age of person in the first and second image, e.g. ¶¶[0020], [0042]-[0043]);
and applying a likelihood determination model to generate a verification based on biometric consistency and historical verification patterns (determines whether the persons in the images are likely to be the same, ¶¶[0067], [0042]);
the verification image meets a threshold confidence score derived from anomaly detection and correlation analysis (determines whether the persons in the images are likely to be the same based on defined values, ¶¶[0067], [0042]);
and (d) transmitting a verification response upon verifying the verification image of the suspicious user (provides a file, record or other verification indicator, ¶[0075]; see also ¶[0077] discussing rejection of digital images and Fig. 4 showing overview of process),
and a timestamp confirming the real-time nature of the verification request (includes a stamp and date to indicate when the digital image was verified, ¶[0075]).
However Cheung does not teach but Teman does teach:
(a) training a verification algorithm comprising a machine learning algorithm (system is trained to recognize faces and people, ¶¶[0142]-[0142]; see also e.g., Abstract noting system is for identifying fraudulent profiles);
wherein the machine learning algorithm is trained to identify and exclude accounts determined to be suspicious (ban users with suspicious photo, ¶[0151] and Fig. 4-2)
based on anomaly detection across multiple social media platforms (analysis is based on multiple social networks, i.e., detecting a fraudulent account based on photos stolen from on social network and posted in another, ¶[0070]);
(b) executing the trained verification algorithm that verifies user identity of a suspicious user in a verification request based on (system is trained to recognize faces and people, ¶¶[0142]-[0142]):
a plurality of non-suspicious user images associated with a plurality of non-suspicious user accounts in the plurality of social media platforms (identifies fraudulent accounts by identifying stolen images on various platforms, ¶¶[0070]-[0071]; see also ¶¶[0189]-[0191] discussing identifying stolen photos);
wherein the suspicious user's account is explicitly excluded from the plurality of non-suspicious user accounts (bans fraudulent users, ¶[0198] and Fig. 4-2; see also ¶[0151] discussing banning user from social media; see also ¶[0098] discussing whitelisting accounts)
and applying a likelihood determination model to generate a verification confidence score (creates fraud profile score based on various sub scores, ¶[0198] and Fig. 4-2).
(c) verifying the user identity of the suspicious user by determining that: the suspicious user in the verification request is not found among the plurality of non-suspicious user images associated with the plurality of non- suspicious user accounts in the plurality of social media platforms (whitelists accounts that have been verified, ¶¶[0098], [0113]);
the biographical data linked to the suspicious user exhibits discrepancies across multiple social media accounts (determines if accounts are fake based on user information, e.g., identifying an 18-year-old male is New York City does not likely earn $10 million dollars a year, ¶¶[0077]-[0078], [0199])
wherein the verification response includes metadata indicating: whether the suspicious user has multiple active accounts across social media platforms (provides notification user of accounts with stolen photos, ¶¶[0191]-[0192]);
a verification confidence score exceeding a predetermined threshold (provides alert if sub scores are above a threshold, ¶¶[0198]-[0199]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the user verification process of Cheung with the machine learning based verification of Teman because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Cheung teaches using various recognition applications, ¶[0043]. One of ordinary skill would have recognized the process in Cheung would likely be improved by using a machine learning based recognition application, e.g. as taught by Teman.
However the combination of Cheung and Teman does not teach but Cohen does teach:
(a) training a verification algorithm comprising a machine learning algorithm based on at least (validation system is trained, ¶[0147]):
historic user verification images comprising a plurality of facial features and hand gestures performed by users in response to prior verification requests; historic user biographical information associated with users depicted in the user verification images (based on captured data of the user and is used for improving facial recognition and gesture recognition,¶[0147]; see also e.g. ¶[0108] discussing capturing images of the user);
a plurality of user images associated with a plurality of user accounts in a plurality of social media platforms; and biographical information associated with the plurality of user accounts in the plurality of social media platforms (a user profile is associated with the first image ¶[0066]; see also e.g. ¶¶[0032], [0069] discussing profiles in social networking sites)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based user verification process of Cheung and Teman with the training process of Cohen because Teman suggests doing so, see MPEP 2142.I.G. That is, Teman teaches training a neural network to recognize people, ¶¶[0142]-[0143]. One of ordinary skill would have recognized the training data would likely use previously captured image data.
Regarding claim 2, the combination of Cheung, Teman and Cohen and teaches all the limitations of claim 1, and Cheung further teaches:
wherein the verification image of the suspicious user comprises a head shot of the suspicious user (Fig. 5 shows a head shot).
Regarding claim 3, the combination of Cheung, Teman and Cohen teaches all the limitations of claim 2, as discussed above and Cheung further teaches:
wherein the verification image of the suspicious user comprises a body shot of the user (the second image shows a body shot because the image includes the user's hand and fingers ¶[0019] which are a part of the body Fig. 5).
Regarding claim 5, the combination of Cheung, Teman and Cohen teaches all the limitations of claim 1, and Cheung further teaches:
wherein the verification requests comprises a specified pose comprising at least one of a hand gesture, facial expression, and body motion (three fingers pointing down (hand gesture) ¶[0073], open mouth smile (facial expression) ¶[0071], holding a specified object (body motion) ¶[0017]).
Regarding claim 7, the combination of Cheung, Teman and Cohen teaches all the limitations of claim 5 and Cheung further teaches:
wherein user verification image of the suspicious user comprises the suspicious user in a plurality of poses performed by the suspicious user in response to the verification request (users are requested to provide a second digital image for verification, e.g. ¶¶[0041]-[0042], in a specific pose, e.g. left hand downwards, open mouth smile, winking, ¶[0071] and Fig. 5).
Regarding claim 13, the combination of Cheung, Teman and Cohen teaches the limitations of claim 1 and Teman further teaches:
generating a profile rating score for verification of the suspicious user image (determines fraud potential score (FPS) for profile, e.g., ¶, [0188])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the user verification process of Cheung with the machine learning based verification of Teman because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Cheung teaches using various recognition applications, ¶[0043]. One of ordinary skill would have recognized the process in Cheung would likely be improved by using a machine learning based recognition application, e.g. as taught by Teman.
Regarding claim 14, the combination of Cheung, Teman and Cohen teaches all the limitations of claim 1, and Cheung further teaches:
wherein the verification image of the suspicious user is associated with a user profile of the suspicious user account registered with an online platform representing an institution, the institution comprising at least one of banks, schools, social media companies, and retailers (images are verified for use in a social networks, e.g. ¶¶[0032], [0069]).
Regarding claim 15, the combination of Cheung, Teman and Cohen teaches all the limitations of claim 14, and Cheung further teaches:
wherein upon transmitting the verification response for verification of the suspicious user, the institution allows the suspicious user to access the online platform (Cheung teaches if IVM 355 determines that the images satisfy the defined criteria, then IVM 355 will enable the first image to be displayed at a defined social networking site which, under the broadest reasonable interpretation, is analogous to accessing the online account because displaying photos is an important aspect of using social networking sites like dating services, ¶[0066]).
Regarding claim 16, claim 16 recites similar limitations as claim 1 and accordingly is rejected for similar reasons as claim 1.
Regarding claim 17, the combination of Cheung, Teman and Cohen teaches all the limitations of claim 16 and Cheung further teaches:
wherein transmitting the verification response for verification of the suspicious user is completed within a set time frame of submission time of verification request (second image is provided with a defined time period to discourage faking the second image ¶[0019]).
Regarding claim 19, the combination of Cheung, Teman and Cohen teaches the limitations of claim 16, as discussed above and Teman further teaches:
generating a profile rating score for verification of the suspicious user image (determines fraud potential score (FPS) for profile, e.g., ¶, [0188])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the user verification process of Cheung with the machine learning based verification of Teman because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Cheung teaches using various recognition applications, ¶[0043]. One of ordinary skill would have recognized the process in Cheung would likely be improved by using a machine learning based recognition application, e.g. as taught by Teman.
Regarding claim 20, the combination of Cheung, Teman and Cohen teaches the limitations of claim 16 and Cheung further teaches:
wherein the verification image of the suspicious user is associated with a user profile of the suspicious user account registered with an online platform representing an institution, the institution comprising at least one of banks, schools, social media companies, and retailers (images are verified for use in a social networks, e.g. ¶¶[0032], [0069]).
Regarding claim 22, the combination of Cheung, Teman and Cohen teaches all the limitations of claim 20, and Cheung further teaches:
wherein upon transmitting the verification response for each verification of the suspicious user, the institution allows the suspicious user to access the online platform (Cheung teaches if IVM 355 determines that the images satisfy the defined criteria, then IVM 355 will enable the first image to be displayed at a defined social networking site which, under the broadest reasonable interpretation, is analogous to accessing the online account because displaying photos is an important aspect of using social networking sites like dating services, ¶[0066]).
Claim 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Cheung, Teman and Cohen and further in view of Chan et al US Pub. No. 2017/0124540, herein referred to as “Chan”.
Regarding claim 4, the combination of Cheung, Teman and Cohen teaches the limitations of claim 1 and does not explicitly teach, but Chan does teach:
wherein the verification photo is in a graphic interchange format file (Chan teaches user images, used for facial recognition services, can be in multiple different file formats, including the graphic interchange format (GIF), ¶[0035]).
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a GIF as the verification photo because choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success is obvious, see MPEP 2143.I.E. Chan teaches selecting file format for a user image from among a limited number of options including JPEGs (Joint Photographic Experts Group), GIFs (Graphic Interchange Format), TIFFs (Tagged Image File Format), RAW (raw image formats), and PNG (Portable Network Graphics). Therefore, it would have been obvious to try, by one of ordinary skill in the art at the time of the invention was made, to pick the GIF and incorporate it into the method of identification in Cheung, Teman, Cohen and Wang since there are a finite number of identified, predictable potential options and one of ordinary skill in the art could have pursued the known potential options with a reasonable expectation of success.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRENDAN S O'SHEA/Examiner, Art Unit 3626
Prosecution Insights