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 .
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 (i.e., changing from AIA to pre-AIA ) 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.
FINAL ACTION
This action is in response to applicant’s claim amendment submitted on 06/30/2026. Claims 1, 8, 17 and 18 are amended. Claims 1-20 are pending.
Response to Arguments
Examiner’s Remarks - Specification (Title)
The examiner withdraws the objection in view of applicant’s title amendment.
Examiner’s Remarks - Specification (Abstract)
The examiner withdraws the objection in view of applicant’s amendment.
Examiner’s Remarks -35 USC § 103 – Claims 1, 17 and 18
The examiner notes that the applicant has amended each independent claim to include the new feature(s) of, “the live user image for one or more fraudulent image parameters related to liveness verification of a user related to the authentication event; wherein the one or more tasks are related to liveness verification and are different from obtaining the live user image”. The examiner notes that the applicant now alleges a deficiency on the part of the prior art. In view of the newly amended claim limitation(s), the examiner introduces the teachings of prior art reference Palmer III (US Patent Publication No. 2020/0327310) to the record. The examiner notes that Palmer teaches processing a liveness challenge image and a liveness control image. See rejection below.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, 15 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Genner (US Patent No. 11,288,530) in view of Machani (US Patent Publication No. 2020/0242232) and further in view of Palmer III (US Patent Publication No. 2020/0327310).
As to claims 1 and 18, Genner teaches a system for enhanced user authentication using image fraud detection during a liveness verification for determining access for a user account, the system comprising:
one or more memories (i.e., …teaches in col. 11 lines 50-65…“a system memory, and a system bus that couples various system components including the system memory to the processing unit.”);
and one or more processors, communicatively coupled to the one or more memories (i.e., …teaches in col. 11 lines 40-50 the following: “including personal computers, smartphones, tablets, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, networked PCs, minicomputers, mainframe computers,”), configured to:
detect an authentication event associated with an access attempt for the user account (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc., take a photo/video of his/her face for liveness verification (e.g., determining whether the photo/video has been spoofed or is of an actual live individual). Once liveness of the photo/video is confirmed…”),
the access attempt being initiated by an access requester (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc…),
obtain a live user image associated with the authentication event based on detecting the authentication event (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc., take a photo/video of his/her face for liveness verification…),
the live user image being a live image of the access requester (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc., take a photo/video of his/her face for liveness verification);
analyze, using a fraud detection machine learning model (i.e., …teaches in col. 2 lines 10-20 the following: “Once liveness of the photo/video is confirmed, in one embodiment, the user takes a photo of his/her photo identification document (e.g., government-issued photo identification, employer-issued photo identification, passport, driver's license, etc.) for comparison to the liveness-verified image to determine whether the individual on the photo identification document is the same person as in the liveness-verified image and whether that individual is authorized for the requested access.”);
obtain digital evidence of the access requester performing the one or more tasks, wherein the digital evidence includes at least one of live image data, live video data, or live audio data (i.e., … teaches in col. 6 lines 1-15 the following: “Once liveness of the photo/video is confirmed, in one embodiment, the phone 101 requests that the individual take a photo/video of a photo identification document (e.g., driver's license, passport, credit card with photo, etc.));
analyze, using the fraud detection machine learning model, the digital evidence to determine whether or not fraudulent activity associated with the authentication event is present (i.e., …teaches in col. 6 lines 5-20 the following: “the system 103 compares the name on the photo identification document to the name(s) associated with the email account and compares the individual in the received, liveness-verified photo to the individual in the photo on the photo identification document. In various embodiments, if the names and photos match, then the identity of the individual is verified, and the individual is given access to his/her email account. Generally, if the names and/or photos do not match, then the individual will not be permitted to access”);
and authenticate the access attempt based on the fraudulent activity not being present (i.e., …teaches in col. 2 lines 20-30 the following: “if the liveness and identity of the individual are verified, then the individual is provided access.”);
or deny the access attempt based on the fraudulent activity being present (i.e., …teaches in col. 2 lines 20-30 the following: “Generally, if the liveness and identity of the requesting individual cannot be verified, then the individual will not be permitted access.”).
The system of Genner does not expressly teach:
initiate, using the fraud detection machine learning model, a live identity verification challenge based on detecting the one or more fraudulent image parameters,
including generating one or more prompts indicating one or more tasks to be performed by the access requester,
execute the live identity verification challenge by prompting the access requester, with the one or more prompts, to perform the one or more tasks.
In this instance the examiner notes the teachings of prior art reference Machani.
With regards to applicant’s claim limitation element of, “initiate, using the fraud detection machine learning model, a live identity verification challenge based on detecting the one or more fraudulent image parameters”, teaches in par. 0051 the following: “If any of the verification operations performed in any of blocks 304, 305 or 306 provide a negative verification result, the account recovery system can terminate the account recovery process or otherwise provide notice of the verification failure and instruct the user on how to continue with the account recovery process (e.g., rescan the same or different identification card, upload a new live image of the user's face, etc.)”.
With regards to applicant’s claim limitation element of, “including generating one or more prompts indicating one or more tasks to be performed by the access requester”, teaches in par. 0061 the following: “prompts the user to (i) take a real-time picture of the user's face and upload the picture to the account recovery system for verification using a liveness detection process and/or (ii) read a random phrase provided to the user and upload an audio file of the spoke phrase to the account recovery system for verification using a speaker recognition and verification process (block 402).”.
With regards to applicant’s claim limitation element of, “execute the live identity verification challenge by prompting the access requester, with the one or more prompts, to perform the one or more tasks”, teaches in par. 0048 the following: “The system prompts the user to scan an identification card and upload the scanned identification card to the account recovery system (block 303). The identification card can be, for example, a government issued identity card (e.g., driver's license, passport, etc.), an employer-issued identity card, etc. In addition, the system prompts the user to take a real-time picture of the user's face and upload the picture to the account recovery system for verification using a liveness detection process (block 304).”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner with the teachings of Machani by having their system comprise an enhanced authentication process. One would have been motivated to do so to provide a simple and effective means to control system access, wherein the enhanced authentication process helps facilitate security and makes it easier to fully verify a user.
The system of Genner and Machani do not expressly teach:
the live user image for one or more fraudulent image parameters related to liveness verification of a user related to the authentication event;
wherein the one or more tasks are related to liveness verification and are different from obtaining the live user image.
In this instance the examiner notes the teachings of prior art reference Palmer.
With regards to applicant’s claim limitation element of, “the live user image for one or more fraudulent image parameters related to liveness verification of a user related to the authentication event”, teaches in par. 0034 the following: “the system, via the device, will instruct the user to perform one or more liveness challenge images. This may be all or a subset of the Liveness Control Images. The result is one or more Liveness Challenge Images that can be compared to the Liveness Control Images to determine if the requesting person is wearing a mask or not.”. Teaches in par. 0038 the following: “system tests the Liveness Challenge Images against the Liveness Control Images.”. Teaches in par. 0038 the following: “system tests the Liveness Challenge Images against the Liveness Control Images. The techniques described above can be used to compare these images as well. The system may select certain frames from the Liveness Challenge Images to compare against the Liveness Control Images.”. The examiner notes that the comparison process will detect fraudulent image parameters in the frames if there is not a match during the comparison.
With regards to applicant’s claim limitation element of, “wherein the one or more tasks are related to liveness verification and are different from obtaining the live user image”, teaches in par. 0038 the following: “the system tests the Liveness Challenge Images against the Liveness Control Images. The techniques described above can be used to compare these images as well. The system may select certain frames from the Liveness Challenge Images to compare against the Liveness Control Images. In one embodiment, the system compares the video snippet of a Liveness Challenge Image against the video snippet of the Liveness Control Image. ”. Teaches in par. 0033 the following: “The system will instruct the user at step 307 to make certain facial expressions and movements (e.g. smile, head shake, frown, eye close, nod, and the like). There may be ten different expressions and movements that the system will request, although any number may be used. As the movements and expressions are performed by the user, the system will capture images (e.g. video, multi-photo sequence, and the like) for use in authorization procedures and stored as Liveness Control Images.”. Teaches in par. 0034 the following: “the system, via the device, will instruct the user to perform one or more liveness challenge images. This may be all or a subset of the Liveness Control Images. The result is one or more Liveness Challenge Images that can be compared to the Liveness Control Images to determine if the requesting person is wearing a mask or not.”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner and Machani with the teachings of Palmer by having their system comprise an enhanced user interactive instruction process. One would have been motivated to do so to provide a simple and effective means to obtain user identification related data, wherein the enhanced user interactive instruction process helps facilitate user authentication and makes it easier to provide system access control.
As to claim 2, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the fraud detection machine learning model includes at least one of a large language model (LLM), a convolution neural network (CNN), or a multimodal artificial intelligence (AI) model (i.e.,, …teaches in col. 8 lines 45-60 the following: “Generally, the system conducts a liveness verification process 301 on the received image/video to determine whether it is of a live individual (e.g., hasn't been spoofed) using various algorithms (e.g., machine learning algorithms, etc.).” …teaches in col. 9 lines 15-30 the following: “…using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.), then, in one embodiment, the system will extract the name from the photo identification document (e.g., using machine-learning algorithms, optical character recognition, etc.)”.).
As to claim 3, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the live user image depicts a face of the access requester (i.e., …teaches in col. 2 lines 20-35 the following: “Once liveness of the photo/video is confirmed, in one embodiment, the user takes a photo of his/her photo identification document (e.g., government-issued photo identification, employer-issued photo identification, passport, driver's license, etc.) for comparison to the liveness-verified image to determine whether the individual on the photo identification document is the same person as in the liveness-verified image and whether that individual is authorized for the requested access. In one embodiment, if the liveness and identity of the individual are verified, then the individual is provided access. Generally, if the liveness and identity of the requesting individual cannot be verified, then the individual will not be permitted access.”).
As to claim 4, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the one or more fraudulent image parameters include at least one of a deepfake image parameter, an image filter parameter, an image obfuscation parameter, a duress parameter, an unnatural image artefact, an unnatural facial feature, a distressed facial feature, an abnormal background feature, or a low lighting condition (i.e., …teaches in col. 9 lines 25-40 the following: “the system may also compare other attributes of the photo identification document (e.g., watermarks, holograms, color, other security features, etc.) to determine whether the photo identification document is genuine (e.g., based on characteristics and/or templates of photo identification documents stored in the system database 105, etc.).”. …teaches in col. 9 lines 55-60 the following: “, to determine whether that data is within expected parameters.”. …teaches in col. 8 lines 15-25 the following: “determining that it meets the expected format, contains the appropriate information, has a valid date/time stamp”.).
As to claims 5 and 19, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the one or more processors, to analyze the live user image for the one or more fraudulent image parameters, are configured to: determine a confidence score based on detecting the one or more fraudulent image parameters or not detecting the one or more fraudulent image parameters (i.e., …teaches in col. 9 lines 20-25 the following: “If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”),
and wherein the one or more processors, to initiate the live identity verification challenge (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc., take a photo/video of his/her face for liveness verification (e.g., determining whether the photo/video has been spoofed or is of an actual live individual).).
The system of Genner does not express teach:
are configured to: trigger the live identity verification challenge based on the confidence score satisfying a threshold.
In this instance the examiner notes the teachings of prior art reference Machani.
Machani teaches in par. 0062 the following: “If any of the verification operations performed in any of blocks 401 and 402 provide a negative verification result, the account recovery system can terminate the account recovery process or otherwise provide notice of the verification failure and instruct the user on how to continue with the account recovery process (e.g., resubmit an authentication factor, upload a new live image of the user's face, upload a new voice file, etc.)”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner with the teachings of Machani by having their system comprise an enhanced authentication process. One would have been motivated to do so to provide a simple and effective means to control system access, wherein the enhanced authentication process helps facilitate security and makes it easier to fully verify a user.
As to claim 15, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the one or more processors, to analyze the digital evidence, are configured to: analyze, using the fraud detection machine learning model,
an environment of the access requester for one or more fraud indicators to determine a confidence score that indicates a likelihood that the fraudulent activity associated with the authentication event is present (i.e., …teaches in col. 9 lines 15-30 the following: “the system will compare the image of the individual on the photo identification document (received at step 314) to the individual in the photo received at step 306 using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”);
and detect that the fraudulent activity associated with the authentication event is present based on the confidence score satisfying a threshold (i.e., …teaches in col. 9 lines 30-45 the following: “If the user is not authorized, then, in various embodiments, a rejection message is sent to the device, the system logs the rejection, the system otherwise denies access to the device, or the system takes another action, and the exemplary identity authentication process 300 ends thereafter.”).
As to claim 17, Genner teaches a system for enhanced user authentication using image fraud detection during a liveness verification for determining access for a user account, the system comprising:
one or more memories (i.e., …teaches in col. 11 lines 50-65…“a system memory, and a system bus that couples various system components including the system memory to the processing unit.”);
and one or more processors, communicatively coupled to the one or more memories (i.e., …teaches in col. 11 lines 50-65…“a system memory, and a system bus that couples various system components including the system memory to the processing unit.”), configured to:
detect an authentication event associated with an access attempt for the user account, the access attempt being initiated by an access requester (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc., take a photo/video of his/her face for liveness verification (e.g., determining whether the photo/video has been spoofed or is of an actual live individual). Once liveness of the photo/video is confirmed…”);
obtain a live user image associated with the authentication event based on detecting the authentication event (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc., take a photo/video of his/her face for liveness verification (e.g., determining whether the photo/video has been spoofed or is of an actual live individual).),
the live user image being a live image of the access requester (i.e., … teaches in col. 2 lines 10-20 the following: “the system requests that an individual, who desires access to a system/transaction/physical asset/etc., take a photo/video of his/her face for liveness verification (e.g., determining whether the photo/video has been spoofed or is of an actual live individual).);
analyze, using a fraud detection machine learning model (i.e., …teaches in col. 2 lines 10-20 the following: “Once liveness of the photo/video is confirmed, in one embodiment, the user takes a photo of his/her photo identification document (e.g., government-issued photo identification, employer-issued photo identification, passport, driver's license, etc.) for comparison to the liveness-verified image to determine whether the individual on the photo identification document is the same person as in the liveness-verified image and whether that individual is authorized for the requested access.”);
and authenticate the access attempt based on the fraudulent activity not being present (i.e., …teaches in col. 2 lines 20-30 the following: “if the liveness and identity of the individual are verified, then the individual is provided access. Generally, if the liveness and identity of the requesting individual cannot be verified, then the individual will not be permitted access.”);
or deny the access attempt based on the fraudulent activity being present (i.e., …teaches in col. 2 lines 20-30 the following: “if the liveness and identity of the individual are verified, then the individual is provided access. Generally, if the liveness and identity of the requesting individual cannot be verified, then the individual will not be permitted access.”).
The system of Genner does not expressly teach:
execute, using the fraud detection machine learning model, a liveness challenge based on detecting the one or more fraudulent image parameters to determine whether or not fraudulent activity associated with the authentication event is present.
In this instance the examiner notes the teachings of prior art reference Machani.
With regards to applicant’s claim limitation element of, “execute, using the fraud detection machine learning model, a liveness challenge based on detecting the one or more fraudulent image parameters to determine whether or not fraudulent activity associated with the authentication event is present”, teaches in par. 0051 the following: “If any of the verification operations performed in any of blocks 304, 305 or 306 provide a negative verification result, the account recovery system can terminate the account recovery process or otherwise provide notice of the verification failure and instruct the user on how to continue with the account recovery process (e.g., rescan the same or different identification card, upload a new live image of the user's face, etc.)”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner with the teachings of Machani by having their system comprise an enhanced authentication process. One would have been motivated to do so to provide a simple and effective means to control system access, wherein the enhanced authentication process helps facilitate security and makes it easier to fully verify a user.
The system of Genner and Machani do not expressly teach:
the live user image for one or more fraudulent image parameters related to liveness verification of a user related to the authentication event;
wherein the one or more tasks are related to liveness verification and are different from obtaining the live user image.
In this instance the examiner notes the teachings of prior art reference Palmer.
With regards to applicant’s claim limitation element of, “the live user image for one or more fraudulent image parameters related to liveness verification of a user related to the authentication event”, teaches in par. 0034 the following: “the system, via the device, will instruct the user to perform one or more liveness challenge images. This may be all or a subset of the Liveness Control Images. The result is one or more Liveness Challenge Images that can be compared to the Liveness Control Images to determine if the requesting person is wearing a mask or not.”. Teaches in par. 0038 the following: “system tests the Liveness Challenge Images against the Liveness Control Images.”. Teaches in par. 0038 the following: “system tests the Liveness Challenge Images against the Liveness Control Images. The techniques described above can be used to compare these images as well. The system may select certain frames from the Liveness Challenge Images to compare against the Liveness Control Images.”. The examiner notes that the comparison process will detect fraudulent image parameters in the frames if there is not a match during the comparison.
With regards to applicant’s claim limitation element of, “wherein the one or more tasks are related to liveness verification and are different from obtaining the live user image”, teaches in par. 0038 the following: “the system tests the Liveness Challenge Images against the Liveness Control Images. The techniques described above can be used to compare these images as well. The system may select certain frames from the Liveness Challenge Images to compare against the Liveness Control Images. In one embodiment, the system compares the video snippet of a Liveness Challenge Image against the video snippet of the Liveness Control Image. ”. Teaches in par. 0033 the following: “The system will instruct the user at step 307 to make certain facial expressions and movements (e.g. smile, head shake, frown, eye close, nod, and the like). There may be ten different expressions and movements that the system will request, although any number may be used. As the movements and expressions are performed by the user, the system will capture images (e.g. video, multi-photo sequence, and the like) for use in authorization procedures and stored as Liveness Control Images.”. Teaches in par. 0034 the following: “the system, via the device, will instruct the user to perform one or more liveness challenge images. This may be all or a subset of the Liveness Control Images. The result is one or more Liveness Challenge Images that can be compared to the Liveness Control Images to determine if the requesting person is wearing a mask or not.”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner and Machani with the teachings of Palmer by having their system comprise an enhanced user interactive instruction process. One would have been motivated to do so to provide a simple and effective means to obtain user identification related data, wherein the enhanced user interactive instruction process helps facilitate user authentication and makes it easier to provide system access control.
Claim(s) 6 and 16 rejected under 35 U.S.C. 103 as being unpatentable over Genner and Machani in view of Palmer as applied to claim 1 above and further in view of ROUH et al. (US Patent Publication No. 2018/0005009 and ROUH hereinafter).
As to claim 6, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the one or more processors, to analyze the live user image for the one or more fraudulent image parameters, are configured to:
determine a confidence score that indicates a likelihood that the one or more fraudulent image parameters are present in the live user image;
and detect the one or more fraudulent image parameters based on the confidence score satisfying a threshold.
In this instance the examiner notes the teachings of prior art reference ROUH.
With regards to applicant’s claim limitation element of, “determine a confidence score that indicates a likelihood that the one or more fraudulent image parameters are present in the live user image”, ROUH teaches in par. 0018 the following: “wherein the first value corresponds to a high degree of likeness between the first and second images and the second value corresponds to a low degree of likeness between these first and second images, wherein the signal representative of suspected detection of fraud is issued to indicate a suspected instance of fraud when said similarity parameter adopts a value located between said intermediate threshold value and said first value which corresponds to a high degree of likeness according to said at least one given likeness criterion.”.
With regards to applicant’s claim limitation element of, “and detect the one or more fraudulent image parameters based on the confidence score satisfying a threshold”, teaches in par. 0018 the following: “wherein the first value corresponds to a high degree of likeness between the first and second images and the second value corresponds to a low degree of likeness between these first and second images, wherein the signal representative of suspected detection of fraud is issued to indicate a suspected instance of fraud when said similarity parameter adopts a value located between said intermediate threshold value and said first value which corresponds to a high degree of likeness according to said at least one given likeness criterion.”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of ROUH by having their system comprise an enhanced image analysis process. One would have been motivated to do so to provide a simple and effective means to identify users, wherein the enhanced image analysis process helps facilitate strong access control and makes it easier to process user image data.
As to claim 16, the system of Genner, Machani and Palmer as applied to claim 6 above teaches fraud detection, specifically Genner does not expressly teach a system of claim 6, wherein the one or more processors are further configured to transmit fraud alert information, corresponding to the fraudulent activity, to one or more investigator networks.
In this instance the examiner notes the teachings of prior at art reference Machani.
Machani teaches in par. 0063 the following: “notification message can be sent via a text message to the registered mobile phone number of the selected trusted referee.”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner with the teachings of Machani by having their system comprise an enhanced authentication process. One would have been motivated to do so to provide a simple and effective means to control system access, wherein the enhanced authentication process helps facilitate security and makes it easier to fully verify a user.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Genner and Machani in view of Palmer as applied to claim 1 above and further in view of Altaf et al. (US Patent Publication No. 2024/0355337 and Altaf hereinafter).
As to claim 7, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the one or more tasks include providing a verbal response (i.e., …teaches in col. 5 lines 35-45 the following: “Liveness-verified biometric data may, in various embodiments, be derived from facial features, fingerprints, voice recognition, DNA, or any other individual-specific/unique biometric data.”),
and determine that the fraudulent activity associated with the authentication event is not present based on the confidence score satisfying a threshold (i.e., …teaches in col. 9 lines 15-30 the following: “the system will compare the image of the individual on the photo identification document (received at step 314) to the individual in the photo received at step 306 using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”)
or determine that the fraudulent activity associated with the authentication event is present based on the confidence score not satisfying the threshold (i.e., …teaches in col. 9 lines 15-30 the following: “the system will compare the image of the individual on the photo identification document (received at step 314) to the individual in the photo received at step 306 using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”).
The system of Genner and Machani do not expressly teach:
wherein the digital evidence includes a voice recording of the verbal response,
and wherein the one or more processors, to analyze the digital evidence, are configured to: compare the voice recording to a voice biometric template of an authorized user of the user account to determine a confidence score that indicates a likelihood that the voice recording of the access requester is associated with the authorized user.
In this instance the examiner notes the teachings of prior art reference Altaf.
With regards to applicant’s claim limitation element of, “wherein the digital evidence includes a voice recording of the verbal response”, Altaf teaches in par. 0053 the following: “computer may obtain a raw audio signal from a calling device including a speech signal for a speaker.”.
With regards to applicant’s claim limitation element of, “and wherein the one or more processors, to analyze the digital evidence, are configured to: compare the voice recording to a voice biometric template of an authorized user of the user account to determine a confidence score that indicates a likelihood that the voice recording of the access requester is associated with the authorized user”, teaches in par. 245 the following: “The computer may compare the liveness score with a threshold to classify the speaker in the sample audio signal as one of the human speaker or a machine spoof attack.”. Teaches in par. 0271 the following: “the computer may determine the machine likelihood score based on a comparison among the quality metrics…”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Altaf by having their system comprise an enhanced biometric voice analysis process. One would have been motivated to do so to provide a simple and effective means to identify users, wherein the enhanced biometric voice analysis process helps facilitate strong system security and makes it easier to authenticate a user.
Claim(s) 8 are rejected under 35 U.S.C. 103 as being unpatentable over Genner and Machani in view of Palmer as applied to claim 7 above and further in view of Jones (US Patent Publication No. 2015/0269946).
As to claim 8, the system of Genner, Machani and Palmer as applied to claim 7 above teaches fraud detection, specifically neither reference expressly teaches a system of claim 7, wherein the one or more processors are further configured to:
store the voice recording of the access requester in one or more memories based on determining that the fraudulent activity associated with the authentication event is present;
compare a voice recording associated with a different authentication event with the voice recording of the access requester to determine whether the voice recording associated the different authentication event is associated with the voice recording of the access requester;
and detect fraudulent activity associated with the different authentication event based on the voice recording associated with the different authentication event being associated with the voice recording of the access requester.
In this instance the examiner notes the teachings of prior art reference Jones.
With regards to applicant’s claim limitation element of,” store the voice recording of the access requester in one or more memories based on determining that the fraudulent activity associated with the authentication event is present”, teaches in par. 0007 the following: “In one or more embodiments, fraud detection may be implemented by utilizing voice biometrics, voice printing, etc. For example, a database may include a voice library which includes one or more voice signals from individuals deemed to be fraudsters. To this end, one or more of the voice signals from the communication may be compared against one or more of the voice signals from the voice library to determine whether or not a match exists. When a match exists, actions may be taken to mitigate losses, an account takeover (ATO), or potentially fraudulent actions.”.
With regards to applicant’s claim limitation element of, “compare a voice recording associated with a different authentication event with the voice recording of the access requester to determine whether the voice recording associated the different authentication event is associated with the voice recording of the access requester”, teaches in par. 0007 the following: “In one or more embodiments, fraud detection may be implemented by utilizing voice biometrics, voice printing, etc. For example, a database may include a voice library which includes one or more voice signals from individuals deemed to be fraudsters. To this end, one or more of the voice signals from the communication may be compared against one or more of the voice signals from the voice library to determine whether or not a match exists. When a match exists, actions may be taken to mitigate losses, an account takeover (ATO), or potentially fraudulent actions.”.
With regards to applicant’s claim limitation element of, “and detect fraudulent activity associated with the different authentication event based on the voice recording associated with the different authentication event being associated with the voice recording of the access requester”, teaches in par. 0007 the following: “In one or more embodiments, fraud detection may be implemented by utilizing voice biometrics, voice printing, etc. For example, a database may include a voice library which includes one or more voice signals from individuals deemed to be fraudsters. To this end, one or more of the voice signals from the communication may be compared against one or more of the voice signals from the voice library to determine whether or not a match exists. When a match exists, actions may be taken to mitigate losses, an account takeover (ATO), or potentially fraudulent actions.”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Jones by having their system comprise an enhanced biometric voice authentication process. One would have been motivated to do so to provide a simple and effective means to identify users, wherein the enhanced biometric voice authentication process helps facilitate strong user authentication and makes it easier to provide system security.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Genner and Machani in view of Palmer as applied to claim 1 above and further in view of Steelberg et al. (US Patent No. 12,306,918 and Steelberg hereinafter).
As to claim 9, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1, wherein the one or more tasks include providing a verbal response (i.e., …teaches in col. 5 lines 35-45 the following: “Liveness-verified biometric data may, in various embodiments, be derived from facial features, fingerprints, voice recognition, DNA, or any other individual-specific/unique biometric data.”),
and determine that the fraudulent activity associated with the authentication event is not present based on the confidence score satisfying a threshold (i.e., …teaches in col. 9 lines 15-30 the following: “the system will compare the image of the individual on the photo identification document (received at step 314) to the individual in the photo received at step 306 using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”),
or determine that the fraudulent activity associated with the authentication event is present based on the confidence score not satisfying the threshold (i.e., …teaches in col. 9 lines 15-30 the following: “the system will compare the image of the individual on the photo identification document (received at step 314) to the individual in the photo received at step 306 using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”).
The system of Genner and Machani does not expressly teach:
wherein the digital evidence includes a voice recording of the verbal response,
and wherein the one or more processors, to analyze the digital evidence, are configured to: compare the voice recording to a voice biometric template of an authorized user of the user account to determine a confidence score that indicates a likelihood that the access requester is under duress.
In this instance the examiner notes the teachings of prior art reference Steelberg.
With regards to applicant’s claim limitation element of, “wherein the digital evidence includes a voice recording of the verbal response”, Altaf teaches in par. 0053 the following: “computer may obtain a raw audio signal from a calling device including a speech signal for a speaker.”.
With regards to applicant’s claim limitation element of, “and wherein the one or more processors, to analyze the digital evidence, are configured to: compare the voice recording to a voice biometric template of an authorized user of the user account to determine a confidence score that indicates a likelihood that the access requester is under duress”, Steelberg teaches in col. 4 lines 45-55 the following: “the CMFA system can also use an emotion detection neural network trained to detect duress, anxiety, and fear. If the emotion detection neural network detected a great amount of duress, anxiety, or fear on the user's face and/or voice,”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Steelberg by having their system comprise an enhanced biometric voice analysis process. One would have been motivated to do so to provide a simple and effective means to identify users, wherein the enhanced biometric voice analysis process helps facilitate strong system security and makes it easier to authenticate a user.
Claim(s) 10-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Genner and Machani in view of Palmer as applied to claim 1 above and further in view of Madzhunkov et al. (US Patent Publication No. 2019/0147676 and Madzhunkov hereinafter).
As to claim 10, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically neither reference expressly teaches a system of claim 1, wherein the one or more tasks include at least one of providing a live full-body image of the access requester, providing a live full-body video of the access requester, providing a live environment image of an environment of the access requester, providing a live environment video of the environment of the access requester, providing a live video stream of the access requester, or providing a live video stream of the environment of the access requester.
In this instance the examiner notes the teachings of prior art reference Madzhunkov.
Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Madzhunkov by having their system comprise an enhanced biometric verification process. One would have been motivated to do so to provide a simple and effective means to authenticate users, wherein the enhanced biometric verification process helps facilitate easier user verification and makes it easier to provide better system access control.
As to claim 11, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically neither reference expressly teaches a system of claim 1, wherein the digital evidence includes at least one of: a live verbal response of the access requester, a live full-body image of the access requester, a live full-body video of the access requester, a live environment image of an environment of the access requester, or a live environment video of the environment of the access requester.
In this instance the examiner notes the teachings of prior art reference Madzhunkov.
Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Madzhunkov by having their system comprise an enhanced biometric verification process. One would have been motivated to do so to provide a simple and effective means to authenticate users, wherein the enhanced biometric verification process helps facilitate easier user verification and makes it easier to provide better system access control.
As to claim 12, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically Genner teaches a system of claim 1,
and determine that the fraudulent activity associated with the authentication event is not present based on the confidence score satisfying a threshold (i.e., …teaches in col. 9 lines 15-30 the following: “the system will compare the image of the individual on the photo identification document (received at step 314) to the individual in the photo received at step 306 using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”);
or determine that the fraudulent activity associated with the authentication event is present based on the confidence score not satisfying the threshold (i.e., …teaches in col. 9 lines 15-30 the following: “the system will compare the image of the individual on the photo identification document (received at step 314) to the individual in the photo received at step 306 using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.),”).
The system of Genner, Machani and Palmer does not expressly teach:
wherein the one or more tasks include providing a live full-body image of the access requester, wherein the digital evidence includes the live full-body image,
and wherein the one or more processors, to analyze the digital evidence, are configured to:
analyze the live full-body image to estimate one or more biometric parameters of the access requester;
compare the one or more biometric parameters of the access requester with one or more known biometric parameters of an authorized user of the user account to determine a confidence score that indicates a likelihood that the access requester is the authorized user.
In this instance the examiner notes the teachings of prior art reference Madzhunkov.
With regards to applicant’s claim limitation element of, “wherein the one or more tasks include providing a live full-body image of the access requester, wherein the digital evidence includes the live full-body image”, Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
With regards to applicant’s claim limitation element of, “and wherein the one or more processors, to analyze the digital evidence, are configured to: analyze the live full-body image to estimate one or more biometric parameters of the access requester”, Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
With regards to applicant’s claim limitation element of, “compare the one or more biometric parameters of the access requester with one or more known biometric parameters of an authorized user of the user account to determine a confidence score that indicates a likelihood that the access requester is the authorized user”, Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Madzhunkov by having their system comprise an enhanced biometric verification process. One would have been motivated to do so to provide a simple and effective means to authenticate users, wherein the enhanced biometric verification process helps facilitate easier user verification and makes it easier to provide better system access control.
As to claim 13, the system of Genner, Machani and Palmer as applied to claim 12 above teaches fraud detection, specifically neither reference expressly teaches a system of claim 12, wherein the one or more biometric parameters include at least one of a weight or a height.
In this instance the examiner notes the teachings of prior art reference Madzhunkov.
Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Madzhunkov by having their system comprise an enhanced biometric verification process. One would have been motivated to do so to provide a simple and effective means to authenticate users, wherein the enhanced biometric verification process helps facilitate easier user verification and makes it easier to provide better system access control.
As to claim 14, the system of Genner, Machani and Palmer as applied to claim 1 above teaches fraud detection, specifically neither reference expressly teaches a system of claim 1, wherein the one or more tasks include providing a live video of the access requester while the access requester performs a 360-degree spin,
wherein the digital evidence includes the live video,
and wherein the one or more processors, to analyze the digital evidence, are configured to:
analyze the live video for one or more fraud indicators to determine a confidence score that indicates a likelihood that the fraudulent activity associated with the authentication event is present;
and detect that the fraudulent activity associated with the authentication event is present based on the confidence score satisfying a threshold.
In this instance the examiner notes the teachings of prior art reference Madzhunkov.
With regards to applicant’s claim limitation element of, “wherein the one or more tasks include providing a live video of the access requester while the access requester performs a 360-degree spin”, Madzhunkov teaches in par. 164 the following: “process whole-body images.”. Teaches in par. 0159 the following: “analysis of whole-body data”.
With regards to applicant’s claim limitation element of “wherein the digital evidence includes the live video”, teaches in par. 0281 the following: “This module contains an RGB and depth cameras (or IR sensors) pointing to the user and live streaming the captured video”.
With regards to applicant’s claim limitation element of, “and wherein the one or more processors, to analyze the digital evidence, are configured to: analyze the live video for one or more fraud indicators to determine a confidence score that indicates a likelihood that the fraudulent activity associated with the authentication event is present”, Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
With regards to applicant’s claim limitation element of, “and detect that the fraudulent activity associated with the authentication event is present based on the confidence score satisfying a threshold”, Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Madzhunkov by having their system comprise an enhanced biometric verification process. One would have been motivated to do so to provide a simple and effective means to authenticate users, wherein the enhanced biometric verification process helps facilitate easier user verification and makes it easier to provide better system access control.
As to claim 20, the system of Genner, Machani and Palmer as applied to claim 18 above teaches fraud detection, specifically Genner teaches a method of claim 18, and detecting that the fraudulent activity associated with the authentication event is present based on the confidence score satisfying a threshold …teaches in col. 9 lines 15-30 the following: “…using one or more matching algorithms (e.g., machine-learning algorithms, face matching algorithms, etc.). If the individuals match within a predetermined tolerance (e.g., 50% likelihood, 60%, 70%, 80%, 90%, 95%, 99%, 100%, etc.), then, in one embodiment, the system will extract the name from the photo identification document (e.g., using machine-learning algorithms, optical character recognition, etc.)”.).
The system of Genner, Machani and Palmer does not expressly teach:
wherein the digital evidence includes the live image data or the live video data,
and wherein analyzing the digital evidence comprises: analyzing the live image data or the live video data for one or more fraud indicators to determine a confidence score that indicates a likelihood that the fraudulent activity associated with the authentication event is present;
In this instance the examiner notes the teachings of prior art reference Madzhunkov.
With regards to applicant’s claim limitation element of, “wherein the digital evidence includes the live image data or the live video data”, the examiner notes that applicant’s usage of the term “or” places the above limitation in alternative form. As such with regards to applicant’s alternative limitation of, “the live video data”, Madzhunkov teaches in par. 164 the following: “process whole-body images.”. Teaches in par. 0159 the following: “analysis of whole-body data”. Teaches in par. 0281 the following: “This module contains an RGB and depth cameras (or IR sensors) pointing to the user and live streaming the captured video”.
With regards to applicant’s claim limitation element of, “and wherein analyzing the digital evidence comprises: analyzing the live image data or the live video data for one or more fraud indicators to determine a confidence score that indicates a likelihood that the fraudulent activity associated with the authentication event is present”, the examiner notes that applicant’s usage of the term “or” places the above limitation in alternative form. As such with regards to applicant’s alternative limitation of, “the live video data”, Madzhunkov teaches in par. 0170 the following: “In step 1620, the IR-based portion of the principal facial recognition process is performed. In step 1622, the RGB-based portion of the principal facial recognition process is performed. In step 1624, the principal portion of the body recognition process is performed. This process may include analysis of the person's body shape, clothing, height, stride, and other factors. In step 1626, the results of IR image facial processing, RGB image facial processing and body image processing are combined and weighted in so that a single profile of the person seeking admission is ready for evaluation. In general, face recognition will be weighted most heavily, followed by height, followed by other characteristics. In step 1628, the output of step 1626 is evaluated against the database of recognized users to determine if the person is recognized. If the person is recognized with a sufficient confidence level”. Teaches in par. 0281 the following: “This module contains an RGB and depth cameras (or IR sensors) pointing to the user and live streaming the captured video”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the of the claimed invention was made to implement the teachings of Genner, Machani and Palmer with the teachings of Madzhunkov by having their system comprise an enhanced biometric verification process. One would have been motivated to do so to provide a simple and effective means to authenticate users, wherein the enhanced biometric verification process helps facilitate easier user verification and makes it easier to provide better system access control.
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.
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/BRYAN F WRIGHT/Examiner, Art Unit 2497
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