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 .
1. This action is in response to the communication filed on February 6, 2025. Claims 1-20 were originally received for consideration. No preliminary amendments for the claims have been received.
2. Claims 1-20 are currently pending consideration.
Information Disclosure Statement
3. An initialed and dated copy of Applicant’s IDS (form 1449), received on April 2, 2026, is attached to this Office Action.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
4. Claim(s) 1-3, 5-6, 15-17 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bowditch et al. (U.S. Patent Pub. No. US 2020/0358819).
Regarding claim 1, Bowditch discloses:
A computing apparatus comprising:
a computer-readable storage media (paragraph 0041: storage device);
a phishing detection engine (paragraph 0024: detection and extraction processor) comprising processor-executable instructions stored on the computer-readable storage media; and
a processor coupled to the computer-readable storage media and configured to execute the processor-executable instructions, wherein the processor-executable instructions, when executed by the processor, direct the computing apparatus, to at least:
determine focal content displayed on a user interface of a client device (paragraphs 0025-0026, 0029, 0039: a computer vision model is configured to extract or obtain one or more screenshots or images of an email, webpage, login page);
extract a plurality of features from the focal content (paragraphs 0026, 0030, 0039: the extraction processor can include one or more feature extractors to extract a domain or URL associated with the request, keywords in an email, or other features indicative of an attack);
detect potential phishing activity by processing the plurality of features using a machine-learning model (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity); and
perform a security action to limit execution of the potential phishing activity responsive to detection (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues).
Claim 2 is rejected as applied above in rejecting claim 1. Furthermore, Bowditch discloses:
The computing apparatus of claim 1, wherein the processor-executable instructions to detect the potential phishing activity by processing the plurality of features using the machine- learning model, when executed by the processor, further direct the computing apparatus to:
generate a prompt comprising the plurality of features and a request to detect phishing activity from the plurality of features (paragraph 30: can provide the retrieved screenshot as one or more inputs to the machine learning models);
submit the prompt as an input into the machine-learning model (Fig. 1, paragraph 0030: the input is submitted to the classifier);
receive a score for the plurality of features as an output from the machine-learning model (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity);
determine a risk level that the plurality of features correspond to phishing activity based on the score (paragraphs 0037-0039: determining that a probability exceeds a threshold to allow a user to determine whether or not to proceed to a webpage); and
detect the potential phishing activity based on the risk level (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity).
Claim 3 is rejected as applied above in rejecting claim 1. Furthermore, Bowditch discloses:
The computing apparatus of claim 1, wherein the processor-executable instructions to extract a plurality of features from the focal content, when executed by the processor, further direct the computing apparatus to:
detect an application displaying active content on the user interface, wherein the active content comprises the focal content (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page);
capture an image of the active content displayed by the application content (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page); and
extract textual elements from the image (paragraphs 0026, 0030, 0039: the extraction processor can include one or more feature extractors to extract a domain or URL associated with the request, keywords in an email, or other features indicative of an attack).
Claim 5 is rejected as applied above in rejecting claim 1. Furthermore, Bowditch discloses:
The computing apparatus of claim 1, wherein the processor-executable instructions to perform the security action to limit execution of the potential phishing activity responsive to detection, when executed by the processor, further direct the computing apparatus to:
block execution of an activation step for the potential phishing activity (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues).
Claim 6 is rejected as applied above in rejecting claim 1. Furthermore, Bowditch discloses:
The computing apparatus of claim 1, wherein the phishing detection engine is executed locally on the client device (paragraph 0041: the system can be part of an information handling system which can be on a personal computer or mobile device).
Regarding claim 15, Bowditch discloses:
A computer readable storage media comprising processor-executable instructions configured to cause a processor to:
determine, by a phishing detection engine, first content displayed via a user interface on a client device (paragraphs 0025-0026, 0029, 0039: a computer vision model is configured to extract or obtain one or more screenshots or images of an email, webpage, login page);
extract, by the phishing detection engine, a plurality of features from the first content (paragraphs 0026, 0030, 0039: the extraction processor can include one or more feature extractors to extract a domain or URL associated with the request, keywords in an email, or other features indicative of an attack);
detect, by the phishing detection engine, potential phishing activity from the plurality of features (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity);
determine, by the phishing detection engine, a risk level of the potential phishing activity (paragraphs 0037-0039: determining that a probability exceeds a threshold to allow a user to determine whether or not to proceed to a webpage); and
perform, by the phishing detection engine, a security action to limit execution of the potential phishing activity based on the risk level (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues).
Claim 16 is rejected as applied above in rejecting claim 15. Furthermore, Bowditch discloses:
The computer readable storage media of claim 15, wherein the processor-executable instructions to detect, by the phishing detection engine, the potential phishing activity from the
plurality of features cause the processor to further execute processor-executable instructions stored in the computer readable storage media to:
generate, by the phishing detection engine, a prompt comprising the plurality of features (paragraph 30: can provide the retrieved screenshot as one or more inputs to the machine learning models);
submit, by the phishing detection engine, the prompt as input into a machine-learning model (Fig. 1, paragraph 0030: the input is submitted to the classifier); and
receive, by the phishing detection engine, an output from the machine-learning model comprising one or more phishing features from the plurality of features that indicate potential phishing activity (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity).
Claim 17 is rejected as applied above in rejecting claim 15. Furthermore, Bowditch discloses:
The computer readable storage media of claim 15, wherein:
the processor-executable instructions to determine, by the phishing detection engine, the risk level of the potential phishing activity cause the processor to further execute processor- executable instructions stored in the computer readable storage media to:
determine, by the phishing detection engine, that the potential phishing activity is high risk (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues); and
the processor-executable instructions to perform, by the phishing detection engine, the security action to limit execution of the potential phishing activity based on the risk level (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues) cause the processor to further execute processor-executable instructions stored in the computer readable storage media to:
block, by the phishing detection engine, an activation step of the potential phishing activity (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues).
Claim 20 is rejected as applied above in rejecting claim 15. Furthermore, Bowditch discloses:
The computer readable storage media of claim 15, wherein:
the processor-executable instructions to determine, by the phishing detection engine, the first content displayed via the user interface on the client device cause the processor to further execute processor-executable instructions stored in the computer readable storage media to:
detect, by the phishing detection engine, an application displaying active content on the user interface (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page); and
capture, by the phishing detection engine, a screenshot of the active content being displayed (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page); and
the processor-executable instructions to extract, by the phishing detection engine, the plurality of features from the first content (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page) cause the processor to further execute processor- executable instructions stored in the computer readable storage media to:
extract, by the phishing detection engine, the first content from the screenshot (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page), wherein the first content comprises one or more of:
textual elements; or
image elements (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page).
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 (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.
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.
5. Claim(s) 4, 7-14, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bowditch et al. (U.S. Patent Pub. No. US 2020/0358819) in view of O’Connell et al. (US 2007/0239604).
Claim 4 is rejected as applied above in rejecting claim 1. Furthermore, Bowditch discloses:
The computing apparatus of claim 1, wherein the processor-executable instructions detect the potential phishing activity by processing the plurality of features using the machine- learning model, when executed by the processor, further direct the computing apparatus to:
submit the plurality of features to the machine-learning model (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity);
receive an indeterminate score as output from the machine-learning model (paragraphs 0037-0039: determining that a probability exceeds a threshold to allow a user to determine whether or not to proceed to a webpage).
Bowditch does not explicitly disclose monitoring subsequent content interactions performed via the user interface with the focal content and detecting the potential phishing activity based on the subsequent content interactions. In an analogous art, O’Connell discloses a method for fraud detection where the user-browser interaction data is monitored, wherein the interaction data includes keyboard interactions, navigation/selection tendencies, or movement device interactions (paragraph 0027). Furthermore, O’Connell discloses that the fraud detection server may then determine fraud detection results based on the user-browser interaction during the current session and known fraudulent behavioral patterns and return the fraud detection results to the user (paragraph 0024). It would have been obvious to one of ordinary skill in the art to use O’Connell’s monitoring of subsequent interactions in order to provide more information to make a confident conjecture on a possible phishing attempt thereby increasing the confidence on properly identifying phishing attempts (O’Connell: paragraph 0024).
Regarding claim 7, Bowditch discloses:
A method comprising:
capturing, by a phishing detection engine, first content displayed via a user interface on a client device (paragraphs 0025-0026, 0029, 0039: a computer vision model is configured to extract or obtain one or more screenshots or images of an email, webpage, login page);
detecting, by the phishing detection engine, potential phishing activity from the first content (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity); and
performing, by the phishing detection engine, a security action to limit potential damage of the phishing activity (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues).
Bowditch does not explicitly disclose monitoring, by the phishing detection engine, subsequent content interactions performed via the user interface and determining, by the phishing detection engine, a high-risk of phishing activity based on the subsequent content interactions. Bowditch does not explicitly disclose monitoring subsequent content interactions performed via the user interface with the focal content and detecting the potential phishing activity based on the subsequent content interactions. In an analogous art, O’Connell discloses a method for fraud detection where the user-browser interaction data is monitored, wherein the interaction data includes keyboard interactions, navigation/selection tendencies, or movement device interactions (paragraph 0027). Furthermore, O’Connell discloses that the fraud detection server may then determine fraud detection results based on the user-browser interaction during the current session and known fraudulent behavioral patterns and return the fraud detection results to the user (paragraph 0024). It would have been obvious to one of ordinary skill in the art to use O’Connell’s monitoring of subsequent interactions in order to provide more information to make a confident conjecture on a possible phishing attempt thereby increasing the confidence on properly identifying phishing attempts (O’Connell: paragraph 0024).
Claim 8 is rejected as applied above in rejecting claim 7. Furthermore, Bowditch discloses:
The method of claim 7, wherein detecting, by the phishing detection engine, the potential phishing activity from the first content comprises:
submitting, by the phishing detection engine, the first content as input into a machine- learning model (Fig. 1, paragraph 0030: the input is submitted to the classifier); and
identifying, by the phishing detection engine, the potential phishing activity from an output of the machine-learning model (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity).
Claim 9 is rejected as applied above in rejecting claim 7. Furthermore, Bowditch discloses:
The method of claim 7, wherein monitoring, by the phishing detection engine, the subsequent content interactions performed via the user interface comprises:
capturing, by the phishing detection engine, focal content displayed via the user interface at predefined time intervals after detecting the potential phishing activity from the first content (paragraphs 0025-0026, 0029, 0039: a computer vision model is configured to extract or obtain one or more screenshots or images of an email, webpage, login page); and
processing, by the phishing detection engine, the focal content captured at the predefined time intervals for additional phishing activity (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity).
Claim 10 is rejected as applied above in rejecting claim 7. Furthermore, O’Connell discloses:
The method of claim 7, wherein: monitoring, by the phishing detection engine, the subsequent content interactions performed via the user interface comprises:
detecting, by the phishing detection engine, a user interaction with the first content, wherein the user interaction causes second content to be displayed via the user interface (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior); and
detecting, by the phishing detection engine, one or more phishing features present in the second content (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior); and
determining, by the phishing detection engine, the high-risk of phishing activity based on the subsequent content interactions comprises:
determining, by the phishing detection engine, the high-risk of phishing activity based on the one or more phishing features present in the second content (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior).
Claim 11 is rejected as applied above in rejecting claim 7. Furthermore, O’Connell discloses:
The method of claim 7, wherein detecting, by the phishing detection engine, the potential phishing activity from the first content comprises:
detecting, by the phishing detection engine, one or more phishing features present in the first content (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
identifying, by the phishing detection engine, a plurality of historical user interactions associated with the client device (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
comparing, by the phishing detection engine, the one or more phishing features to the plurality of historical user interactions (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior); and
determining, by the phishing detection engine, that the first content comprises the potential phishing activity based on the comparison of the one or more phishing features to the plurality of historical user interaction (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior).
Claim 12 is rejected as applied above in rejecting claim 7. Furthermore, O’Connell discloses:
The method of claim 7, wherein the method further comprises:
capturing, by the phishing detection engine, second content displayed via the user interface on the client device (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
detecting, by the phishing detection engine, potential phishing activity from the second content (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
detecting, by the phishing detection engine, one or more phishing features present in the second content (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
identifying, by the phishing detection engine, a plurality of historical user interactions associated with the client device (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
comparing, by the phishing detection engine, the one or more phishing features to the plurality of historical user interactions (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior); and
determining, by the phishing detection engine, a low risk of phishing activity for the second content based on the comparison of the one or more phishing features to the plurality of historical user interactions (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior).
Claim 13 is rejected as applied above in rejecting claim 7. Furthermore, Bowditch discloses:
The method of claim 7, wherein capturing, by the phishing detection engine, the first content displayed via the user interface on the client device comprises:
detecting, by the phishing detection engine, an application displaying active content on the user interface (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page);
capturing, by the phishing detection engine, a screenshot of the active content being displayed (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page); and
extracting, by the phishing detection engine, the first content from the screenshot (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page), wherein the first content comprises one or more of:
textual elements; or
image elements (paragraphs 0029-0031: the extraction processor extracts or obtains screenshots or images of an email, webpage, or login page).
Claim 14 is rejected as applied above in rejecting claim 7. Furthermore, Bowditch discloses:
The method of claim 7, wherein the method further comprises:
detecting, by the phishing detection engine, execution of the phishing activity (paragraphs 0030-0032, 0039: based on the extracted screenshot or image information, the machine learning model determines the probability that the screen or image are indicative of malicious activity/phishing activity); and
performing, by the phishing detection engine, the security action to limit potential damage of the phishing activity (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues) comprises:
generating, by the phishing detection engine, a summary of the phishing activity (paragraph 0051: generating a log or report in response to detecting phishing activity), wherein the summary comprises:
screenshots of the subsequent content interactions (paragraph 0051: generating a log or report in response to detecting phishing activity); and
identification of one or more phishing features within the first content that correspond to the phishing activity (paragraph 0051: generating a log or report in response to detecting phishing activity); and
providing, by the phishing detection engine, the summary to a security system associated with the phishing detection engine (paragraph 0051: generating a log or report in response to detecting phishing activity).
Claim 18 is rejected as applied above in rejecting claim 15. Furthermore, Bowditch discloses:
The computer readable storage media of claim 15, wherein:
the processor-executable instructions to determine, by the phishing detection engine, the risk level of the potential phishing activity cause the processor to further execute processor- executable instructions stored in the computer readable storage media to:
determine, by the phishing detection engine, that the risk level of the potential phishing activity is indeterminate (paragraphs 0037-0039: determining that a probability exceeds a threshold to allow a user to determine whether or not to proceed to a webpage); and
prevent, by the phishing detection engine, further content interactions with the first content on the user interface based on the potential phishing activity being high risk (paragraphs 0037-0039: the processor can provide an alert, alarm or other notification of a phishing attack including a pop-up window which will allow the user to prevent future communications with the webpage/domain such as to prevent the webpage from creating additional dialogues).
Bowditch does not explicitly disclose monitoring subsequent content interactions with the first content performed via the user interface, reevaluating, by the phishing detection engine, the risk level of the potential phishing activity in view of the subsequent content interactions and determining, by the phishing detection engine, that the potential phishing activity is high risk based on the subsequent content interaction. In an analogous art, O’Connell discloses a method for fraud detection where the user-browser interaction data is monitored, wherein the interaction data includes keyboard interactions, navigation/selection tendencies, or movement device interactions (paragraph 0027). Furthermore, O’Connell discloses that the fraud detection server may then determine fraud detection results based on the user-browser interaction during the current session and known fraudulent behavioral patterns and return the fraud detection results to the user (paragraph 0024). It would have been obvious to one of ordinary skill in the art to use O’Connell’s monitoring of subsequent interactions in order to provide more information to make a confident conjecture on a possible phishing attempt thereby increasing the confidence on properly identifying phishing attempts (O’Connell: paragraph 0024).
Claim 19 is rejected as applied above in rejecting claim 15. Furthermore, Bowditch does not explicitly discloses instructions configured to: capture, by the phishing detection engine, second content displayed via the user interface on the client device; detect, by the phishing detection engine, potential phishing activity from the second content; detect, by the phishing detection engine, one or more phishing features present in the second content; identify, by the phishing detection engine, a plurality of historical user interactions associated with the client device; and determine, by the phishing detection engine, a low risk of phishing activity for the second content based on the plurality of historical user interactions and the one or more phishing features of the second content. In an analogous art, O’Connell discloses capturing, by the phishing detection engine, second content displayed via the user interface on the client device (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
detecting, by the phishing detection engine, potential phishing activity from the second content (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
detecting, by the phishing detection engine, one or more phishing features present in the second content (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
identifying, by the phishing detection engine, a plurality of historical user interactions associated with the client device (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior);
comparing, by the phishing detection engine, the one or more phishing features to the plurality of historical user interactions (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior); and
determining, by the phishing detection engine, a low risk of phishing activity for the second content based on the comparison of the one or more phishing features to the plurality of historical user interactions (paragraph 0047-0049: automated program detector may compare the user-browser interaction data with known fraudulent behavior). It would have been obvious to one of ordinary skill in the art to use O’Connell’s monitoring of subsequent interactions in order to provide more information to make a confident conjecture on a possible phishing attempt thereby increasing the confidence on properly identifying phishing attempts (O’Connell: paragraph 0024).
Conclusion
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/KAVEH ABRISHAMKAR/
07/30/2026Primary Examiner, Art Unit 2494
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