DETAILED ACTION
Claims 1-20 are pending in this 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 .
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stergioudis (US PGPUB No. 2022/0201008) in view of FIltenborg et al. (US PGPUB No. 2022/0156502) [hereinafter “Filtenborg”] in further view of Wang et al. (CN-109284590-A) [hereinafter “Wang”].
As per claim 1, Stergioudis teaches a system for performing misappropriation detection and prevention using Siamese Neural Networks, the system comprising: at least one network communication interface; at least one non-transitory storage device; and at least one processing device coupled to the at least one non-transitory storage device and the at least one network communication interface, wherein the at least one processing device is configured to: identify initiation of a resource interaction by a user, via an interaction device ([0064], request to access a session by a client device); determine if the resource interaction meets criteria associated with an unauthorized interaction based on applying a filtering mechanism ([0064], looking up various types of data and credentials to determine if an access request by a client device is authorized or unauthorized see also [0065]-[0067]); with extracted features ([0008], tracking and comparing extracted features from user session activity) comprising Internet Protocol (IP) address associated with the initiation of the resource interaction ([0170], tracking IP address of a download), trend associated with historical resource interactions (Abstract, historical data of an entity interacting with files see [0007]), geolocation of the user ([0007], sequence of user locations), name of the user ([0064], tracking user ID and password), resource credentials information to associate and check type of resource credential used for the type of the resource interaction ([0124], checking credentials for specific accounts where each account is interpreted to gains access to specific resource types), and time of the initiation of the resource interaction ([0010], features can include timestamp of user interaction); perform a primary check to determine that the resource interaction meets the criteria associated with the unauthorized interaction and flag the resource interaction ([0078], determining based on machine learning if a request is not authorized and terminating if necessary see [0094]); passing the resource interaction to a Siamese Neural Network to determine if the resource interaction is unauthorized ([0076], machine learning can be based on a Siamese Neural Network); and approve or deny the resource interaction based on determining if the resource interaction is unauthorized ([0094] and [0098], terminating a session if not properly authorized).
Stergioudis does not explicitly teach wherein the filtering mechanism comprises one or more dynamically changing filters. Filtenborg teaches wherein the filtering mechanism comprises one or more dynamically changing filters ([0066], implementing dynamic filters used to make predictions based on features inputted into a Siamese Neural Network see [0061]).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Stergiodis with the teachings of Filtenborg, wherein the filtering mechanism comprises one or more dynamically changing filters, to provide on the fly training and determinations regarding user behavior and access characteristics.
The combination of Stergioudis and Filtenborg does not explicitly teach in response to determining that the primary check was unsatisfactory, initiate a secondary check by passing the resource interaction to another model to determine if the resource interaction is unauthorized. Wang teaches in response to determining that the primary check was unsatisfactory, initiate a secondary check by passing the resource interaction to another model to determine if the resource interaction is unauthorized (Abstract and claims 1 and 6, using a second or more models to detect malicious behavior of a user accessing a network resource when a first matching fails).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Stergiodis and Filtenborg with the teachings of Wang, in response to determining that the primary check was unsatisfactory, initiate a secondary check by passing the resource interaction to another model to determine if the resource interaction is unauthorized, to provide on the fly training and determinations regarding user behavior and access characteristics.
As per claim 2, the combination of Stergioudis, FIltenborg and Wang teaches the system of claim 1, wherein the at least one processing device is configured to in response to determining the initiation of the resource interaction, cause the interaction device to capture information associated with execution of the interaction ([0065]-[0066], capturing various analytical data regarding a user and client device requesting to access the system).
As per claim 3, Stergioudis, FIltenborg and Wang teaches the system of claim 2, wherein the at least one processing device is configured to: process the information associated with the execution of the interaction to calculate one or more execution related values ([0045], calculated personalized acceptable deviations to past user behavior); extract one or more patterns associated with the user from a data repository ([0045], storing user behavior regarding past events and interactions by the user); extract one or more previously established unauthorized patterns associated with historical unauthorized resource interactions ([0078], observing and recording data samples from users that are not authorized to access the session); and pass the one or more execution related values, the one or more patterns, and the one or more established unauthorized patterns to the Siamese Neural Network along with the resource interaction ([0076], machine learning engine that analyzes user behavior and other related data to access requests – fed to a Siamese Neural Network).
As per claim 4, Stergioudis, FIltenborg and Wang teaches the system of claim 3, wherein the at least one processing device is configured to cause the Siamese Neural Network to process the one or more execution related values, the one or more patterns, and the one or more established unauthorized patterns to determine if the resource interaction is unauthorized ([0078], using TNN’s including Siamese Neural Network see [0076], observing and recording data samples from users that are not authorized to access the session).
As per claim 5, Stergioudis, FIltenborg and Wang teaches the system of claim 3, wherein the one or more execution related values, the one or more patterns, and the one or more established unauthorized patterns are based on behavior data ([0076] and [0078], use of Siamese Neural network is to analyze user behavior among other data).
As per claim 6, Stergioudis, FIltenborg and Wang teaches the system of claim 1, wherein the at least one processing device is configured to train the Siamese Neural Network with historical authorized resource interaction data, historical unauthorized resource interaction data, and established unauthorized pattern data ([0076] and [0078], training Siamese Neural Network with historical data of users that are authorized and unauthorized with data sample points for each to identify patterns of behavior see [0007]).
As per claim 8, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 9, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 10, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 11, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
As per claim 12, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 13, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale.
As per claim 15, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 2 Accordingly, this claim is rejected under the same rationale.
As per claim 17, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 20, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. 102 and 103 have been fully considered. In light of the new amendments, Examiner has introduced and cited to a new prior art reference, Wang and included new citations to Stergioudis.
To expedite prosecution, Examiner is open to conducting an after-final interview to discuss claim amendments to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Phatak et al. (US PGPUB No. 2025/0259053), Mariella et al. (US PGPUB No. 2025/0053698), Parhi et al. (US PGPUB No. 2023/0236801), Chatterjee et al. ("Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks," in IEEE Access, vol. 13, pp. 70980-71006, 2025, doi: 10.1109/ACCESS.2025.3561375), Shaw et al. ("Siamese Networks and Adversarial Attacks: An Overview," 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), Spokane, WA, USA, 2024, pp. 377-384, doi: 10.1109/ICPHM61352.2024.10626656) and Freitas et al. ("D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks," arXiv:2001.11108, January 29, 2020) all disclose various aspects of the claimed invention including analyzing user behavior with a Siamese Neural Network.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached Max Flex.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carl Colin can be reached at 571-272-3862. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PETER C SHAW/Primary Examiner, Art Unit 2493 March 26, 2026
Prosecution Insights