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 .
Preliminary Amendment, received on 20 March 2025, has been entered into record. In this amendment, claims 1, 2, 11, 12, and 20 have been amended.
Claims 1-20 are presented for examination.
Priority
The claim for priority from US Provisional 63/607,261 filed on 7 December 2023 is duly noted.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because:
they do not include the following reference sign(s) mentioned in the description: 104 (0022).
they include the following reference character(s) not mentioned in the description: 202, 206, 210 (Figure 2); 312, 316, 318 (Figure 3); 506 (Figure 5); 612 (Figure 6).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaddam et al. (US 2021/0160247 A1 and Gaddam hereinafter) in view of Lefèvre et al. (US 2023/0308453 A1 and Lefevre hereinafter).
As to claims 1 and 20, Gaddam discloses a system and method for real-time entity anomaly detection, the system and method having:
storing data in memory regarding current features and historical features associated with one or more devices (0027, lines 1-11; 0066, lines 1-7);
receiving a request sent over a communication network, wherein the request concerns performance of a requested action within a device application (0085, lines 1-16);
identifying a role associated with the request based on at least one of a device or user account associated with the request, wherein the role is identified by a policy engine (0021, lines 1-10; 0026, lines 8-14);
determining one of a plurality of sets of policies associated with the identified role (0026, lines 1-4; 0068, lines 1-7);
assigning a trust score in real-time based on the current features and the historical features associated with the device or the user account, wherein the real-time trust score is assigned by a trust scoring engine (0061, lines 1-4; 0067, lines 1-7);
identifying a required trust level in accordance with the determined set of policies (0068, lines 1-7);
so as to require a higher trust level that is not met (0068, lines 1-7); and
denying access to the requested action within the device application (0006, lines 12-15).
Gaddam fails to specifically disclose:
determining that one or more external factors not associated with the device override the identified trust level.
Nonetheless, this feature is well known in the art and would have been an obvious modification of the teachings disclosed by Gaddam, as taught by Lefevre.
Lefevre discloses a system and method for adaptive trust recovery in mixed environment communications, the system and method having:
determining that one or more external factors (functions defined/altered by user) not associated with the device override the identified trust level (0024, lines 15-27; 0028, lines 2-5).
Given the teaching of Lefevre, a person having ordinary skill in the art before the effective filing date of the claimed invention would have readily recognized the desirability and advantages of modifying the teachings of Gaddam with the teachings of Lefevre by determining an external trust level override. Lefevre recites motivation by disclosing that determining an external override of trust level allows for adjustments to the trust level to be made according to changes made by the user, providing flexibility to implementation of trust-based controls (0028). It is obvious that the teachings of Lefevre would have improved the teachings of Gaddam by determining an external override of trust level in order to provide flexibility to trust-based controls.
As to claim 11, Gaddam discloses:
one or more databases in memory, the databases storing data regarding current features and historical features associated with one or more devices (0027, lines 1-3, 6-11; 0066, lines 1-7);
a communication interface that communicates over a communication network to receive a request to determine that at least one of a device of the one or more devices and a user account passes one or more policies to perform an action in an application (0026, lines 8-14; 0085, lines 1-3); and
one or more processors that execute instructions stored in the memory, wherein the processors execute the instructions for (0013, lines 1-5):
identifying a role associated with the request based on at least one of a device or user account associated with the request, wherein the role is identified by a policy engine (0021, lines 1-10; 0026, lines 8-14);
determining one of a plurality of sets of policies associated with the identified role (0026, lines 1-4; 0068, lines 1-7);
assigning a trust score in real-time based on the current features and the historical features associated with the device or the user account, wherein the real- time trust score is assigned by a trust scoring engine (0061, lines 1-4; 0067, lines 1-7);
identifying a required trust level in accordance with the determined set of policies (0068, lines 1-7);
so as to require a higher trust level that is not met (0068, lines 1-7); and
denying access to the requested action within the device application (0006, lines 12-15).
Gaddam fails to specifically disclose:
determining that one or more external factors not associated with the device override the identified trust level.
Nonetheless, this feature is well known in the art and would have been an obvious modification of the teachings disclosed by Gaddam, as taught by Lefevre.
Lefevre discloses:
determining that one or more external factors (functions defined/altered by user) not associated with the device override the identified trust level (0024, lines 15-27; 0028, lines 2-5).
Given the teaching of Lefevre, a person having ordinary skill in the art before the effective filing date of the claimed invention would have readily recognized the desirability and advantages of modifying the teachings of Gaddam with the teachings of Lefevre by determining an external trust level override. Please refer to the motivation recited above with respect to claims 1 and 20 as to why it is obvious to apply the teachings of Lefevre to the teachings of Gaddam.
As to claims 2 and 12, Gaddam fails to specifically disclose:
initially identifying an initial required trust level; and
receiving information regarding the one or more external factors associated with the initial required trust level.
Nonetheless, these features are well known in the art and would have been an obvious modification of the teachings disclosed by Gaddam, as taught by Lefevre.
Lefevre discloses:
initially identifying an initial required trust level (0018, lines 1-10); and
receiving information regarding the one or more external factors associated with the initial required trust level (0024, lines 1-14).
Given the teaching of Lefevre, a person having ordinary skill in the art before the effective filing date of the claimed invention would have readily recognized the desirability and advantages of modifying the teachings of Gaddam with the teachings of Lefevre by receiving external factors. Please refer to the motivation recited above with respect to claims 1 and 20 as to why it is obvious to apply the teachings of Lefevre to the teachings of Gaddam.
As to claims 3 and 13, Gaddam discloses:
deriving one or more factors from the current features and the historical features wherein one or more of user factors are derived from user features, device factors are derived from the device features, and machine-learning factors are derived from a mix of the current features and the historical features (0027, lines 1-7; 0066, lines 1-7; 0084, lines 7-10).
As to claims 5 and 15, Gaddam discloses:
generating the machine-learning factors, based on a trained machine-learning model, wherein inputs include the current features and the historical features, and wherein the generated machine-learning factors include at least one or more clusters representing devices and users with similar trust levels, one or more alert flags indicating potential trustworthiness issues, one or more predicted trust scores, or one or more predicted labels indicating trustworthiness of a device or user, and wherein the machine-learning model is tuned to minimize a loss function that penalizes incorrect predictions (0126, lines 19-25; 0144, lines 7-14).
As to claim 6, Gaddam discloses:
training the machine-learning model based on the generated machine-learning factors to minimize the loss function that penalizes the incorrect predictions (0208, lines 1-6; 0212, lines 1-9; 0213, lines 1-5).
As to claim 7, Gaddam discloses:
wherein the current features and the historical features include at least one of device health, user behavior, network activity, previous scores, login attempts, or system updates that provide context about past events (0006, lines 8-12; 0066, lines 1-11).
As to claims 8 and 17, Gaddam fails to specifically disclose:
exposing the real-time trust score to one or more users; and
providing an interface with interactive elements that facilitate execution of actionable steps to improve the real-time trust score.
Nonetheless, these features are well known in the art and would have been an obvious modification of the teachings disclosed by Gaddam, as taught by Lefevre.
Lefevre discloses:
exposing the real-time trust score to one or more users (0014, lines 19-22); and
providing an interface with interactive elements that facilitate execution of actionable steps to improve the real-time trust score (0014, lines 17-19; 0015, lines 1-13).
Given the teaching of Lefevre, a person having ordinary skill in the art before the effective filing date of the claimed invention would have readily recognized the desirability and advantages of modifying the teachings of Gaddam with the teachings of Lefevre by exposing trust score to users. Please refer to the motivation recited above with respect to claims 1 and 20 as to why it is obvious to apply the teachings of Lefevre to the teachings of Gaddam.
As to claims 9 and 18, Gaddam discloses:
wherein receiving the request is based on an initiation of access to a resource exposed through an application programming interface (API) (0015, lines 1-10; 0042, lines 1-9).
As to claims 10 and 19, Gaddam discloses:
determining, by an access proxy, there is an application policy attached to the application, wherein the application score associated with the application, wherein the access proxy denies the access (0090, lines 1-9).
As to claim 16, Gaddam discloses:
wherein the current features and the historical features include at least one of device health, user behavior, network activity, previous scores, login attempts, or system updates that provide context about past events (0006, lines 8-12; 0066, lines 1-11), and wherein the processors execute the instructions for training the machine-learning model based on the generated machine-learning factors to minimize the loss function that penalizes the incorrect predictions (0208, lines 1-6; 0212, lines 1-9; 0213, lines 1-5).
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaddam in view of Lefevre as applied to claims 3 and 13 above, and further in view of O’Neill et al. (US 2023/0188527 A1 and O’Neill hereinafter).
As to claims 4 and 14, Gaddam in view of Lefevre fails to specifically disclose:
receiving a selection of active or inactive for at least one of the user factors, the device factors, or the machine-learning factors, wherein assigning the real-time trust score is further based on the active factors.
Nonetheless, this feature is well known in the art and would have been an obvious modification of the teachings disclosed by Gaddam in view of Lefevre, as taught by O’Neill.
O’Neill discloses a system and method for use of sentiment analysis to assess trust in a network, the system and method having:
receiving a selection of active or inactive (positive, negative) for at least one of the user factors, the device factors, or the machine-learning factors, wherein assigning the real-time trust score is further based on the active factors (0003, lines 1-19; 0004, lines 1-6).
Given the teaching of O’Neill, a person having ordinary skill in the art before the effective filing date of the claimed invention would have readily recognized the desirability and advantages of modifying the teachings of Gaddam in view of Lefevre with the teachings of O’Neill by assigned a trust score based on active factors. O’Neill recites motivation by disclosing that adjusting the trust score based on sentiment factors improves the accuracy of trust metrics (0004). It is obvious that the teachings of O’Neill would have improved the teachings of Gaddam in view of Lefevre by assigning a trust score based on active factors in order to improve the accuracy of trust metrics.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gershanov et al. (US 2024/0323216 A1) discloses a system and method for credential-based security posture engine in a security management.
Li et al. (CN 119538220 A) discloses a system and method for browser authentication based on user action.
Liang et al. (CN 120597287 A) discloses a system and method for managing vulnerability based on self-adaptive security platform.
Park (WO 2009/105540 A1) discloses a system and method for active access control.
Pratt et al. (WO 2021/133592 A1) discloses a system and method for malware and phishing detection and mediation platform.
Somasundaram et al. (US Patent 11,522,899 B2) discloses a system and method for vulnerability management for connected devices.
Soroush et al. (US Patent 12,101,357 B2) discloses a system and method for constructing a graph-based model for optimizing the security posture of a composed Internet of Things system.
Tseitlin et al. (US Patent 9,027,141 B2) discloses a system and method for improving security and reliability in a networked application environment.
Weizman et al. (WO 2024/238197 A1) discloses a system and method for security risk management engine in a security management system.
Xie et al. (CN 120597341 A) discloses a system and method for access protection of solid hard disk.
Zhang et al. (CN 120822954 A) discloses a system and method for mobile payment encryption based on data random algorithm.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH SU whose telephone number is (571)270-3835. The examiner can normally be reached 6:30 AM - 3:00 PM.
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/SARAH SU/Primary Examiner, Art Unit 2431