DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 10, 2026 has been entered.
Response to Arguments
Claims 1-20 are currently pending. Claims 1 and 12 were amended.
Re: 35 U.S.C. 103 Rejections
Applicant argues on REMARKS filed on February 10, 2026 that the amended features to the independent claims are distinct from the teachings of US 2021/0160247 to GADDAM et al and US 2023/0344842 to AKHTAR. Applicant primarily argues that:
GADDAM’s focus is on classification of the request via a model trained across entities and events, not on computing a deviation of a specific access relative to a typical access representation of the same user. Specifically, the feature vector in GADDAM is an input to a classifier. See pg. 8 of the REMARKS.
GADDAM does not disclose a “typical feature vector” comparison, rather maintains an entity profile, including behavioral characteristics. Rather, the feature vector in GADDM is applied to a trained model. See pg. 9 of the REMARKS.
GADDM applies the current feature vector to a trained model to output a trust score, which answers the question: “How does this access score under a learned model?” In contrast, the claimed invention is directed to a different question of “How far does this access deviate from how this same user typically performs such access?” See pg. 9 of the REMARKS.
While GADDAM and AKHTAR operate near real time, neither reference discloses real-time computation of a deviation score relative to a per-user typical feature vector. See pp. 9-10 of the REMARKS.
The combination of the GADDAM and AKHTAR is deficient for the reasons listed on pg. 10 of the REMARKS.
In response to argument [1], the Examiner respectfully disagrees. Although it is true that GADDAM performs classification, but the classification is based on a trust score which is computed in the same manner as being claimed in the invention. The trust score is a quantitative likelihood representation of an output. According to [GADDAM, ¶0035], a trust score is described as:
A “trust score” can include a score used to indicate trust in something. For example, a trust score can be the output of an ensemble classifier model that is trained to determine whether requesting entity behavior is anomalous or not. A high trust score (for example, 100 on a 0-100 scale) can indicate total trust that the requesting entity is behaving normally, while a low trust score (for example, 0 on a 0-100 scale) can indicate no trust that the requesting entity is behaving normally. A trust score can be further evaluated to determine or enact a policy, such as a resource access policy.
While the trust score is used for classification based on thresholds, it also represents a degree of deviation of a feature vector. For example, [GADDAM, ¶0108] discloses:
The production model 202E can produce a trust score corresponding to the feature vector. The trust score can be a measure of the normality or abnormality of the request to access the resource, or alternatively, a measure of the maliciousness or non-maliciousness of the request. For example, on a 0-100 scale, a trust score of 100 can correspond to complete trust, indicating that the request to access the resource is completely normal or completely benign, and a trust score of zero can correspond to a completely abnormal or completely malicious request. As such, the production model is able to determine the trust score corresponding to the input feature vector based on knowledge accrued through training.
Thus, as evident in [GADDAM, ¶0108], one having ordinary skill in the art would have been able to interpret deviation in the claims as “a measure of normality or abnormality”, or the trust score, from GADDAM. A measurement close to normality would indicate a low similarity distance value, or a small deviation; whereas a measurement further from normality (abnormality) would indicate a high similarity distance value, or a large deviation. Hence, the computation of the trust score in GADDAM is not distinct from the computation of the access score in the claimed invention.
Furthermore, the claimed computing step is not distinct from how the trust score is computed in GADDAM. The computing step is silent on any specific step-by-step process of how the access score is computed that would otherwise distinguish any techniques in GADDAM, rather it is merely “based on a deviation”. Hence, the computing step is subjected to broadest reasonable interpretation (BRI). For example, the computing step is understood further in view of dependent claims 3 and 14. Dependent claims 3 and 14 further applies “at least one trained machine learning model” to the feature vector to compute the access score. These limitations provide features that the claimed invention utilizes a trained model to produce a “score”, similar to how a production model is applied to produce the trust score in GADDAM.
Therefore, the above discussed features of GADDAM read upon “computing…an access score based on a deviation of the feature vector” as recited in independent claims 1 and 12.
In response to argument [2], the Examiner agrees that GADDAM does not disclose typical feature vector(s). However, such features were taught by AKHTAR. One cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). See Claim Rejections - 35 USC § 103 for details.
In response to argument [3], the Examiner respectfully disagrees. GADDAM is directed to how the access scored in terms of normality and/or abnormality to enforce an access policy. See [GADDAM, ¶0110]: “Policy engine 202F can determine a resource access policy based on the trust score. The resource access policy controls the requesting entity's access to the resource.” As previously discussed, the score in GADDAM is based on a numerical scale (e.g., 0-100) that represents a deviation from expected behavior. Therefore, the scores in both the claimed invention and GADDAM are indicative of how normal or abnormal (i.e., how much deviation) a particular access request by an entity is.
In response to argument [4], the Examiner respectfully disagrees. As provided in the responses to [1] and [3], the score in GADDAM can reasonably be interpreted as a measure of deviation – e.g., a measurement of normality and abnormality from a baseline.
In response to argument [5], the Examiner respectfully disagrees.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, both GADDAM and AKHTAR are directed to analyzing characteristics of activities/events to identify unusual or anomalous behavior. See [GADDAM, Abstract, ¶0006] and [AKHTAR, ¶0024-0026]. A person having ordinary skill in the art (PHOSITA) of machine learning techniques would have applied AKHTAR’s per-user specific model training technique for the models, profiles, etc. in GADDAM. A PHOSITA would have recognized the advantage of having a user-specific model, which would have been more accurate than a generic user model, since every user behaves differently on a network.
In response to applicant's argument that both GADDAM and AKHTAR are architectural incompatibility, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Herein, a PHOSITA would not have attempted to structurally combined the two different models in GADDAM and AKHTAR, rather apply the concept of training models on a per-user basis from AKHTAR to GADDAM, rather than train/create a generic entity profile.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 12 recite the limitation "said typical feature vector". There is insufficient antecedent basis for this limitation in the claim. It is unclear to what typical feature vector is referenced for the deviation. The remaining claims are dependent on claims 1 and 12, and are similarly rejected.
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.
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 1-3, 6, 7, 9, 12-14, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0160247 to GADDAM et al. (hereinafter, “GADDAM”) in view of US 2023/0344842 to AKHTAR (hereinafter, “AKHTAR”).
As per claim 1: GADDAM discloses: A method of detecting potential fraudulent privileged user accesses (system and associated methods to controlling accesses to resources, including determining whether a request to access a resource is legitimate [GADDAM, ¶0058]), comprising: using at least one processor configured for (performed with a computer system including one or more processors [GADDAM, ¶0242]): collecting a plurality of access attributes identified during at least one privileged access conducted by at least one user using at least one client device (a resource access system receives a request from a requesting entity to access a resource, wherein the request includes request data (“access attributes”) that is stored before processing [GADDAM, ¶0122; Fig. 4(402 & 404)]), the plurality of access attributes relate to at least some of: a location of the at least one user, the at least one client device, a network environment, and interaction of the at least one user with at least one user input interface of the at least one client device (the request data includes “…a credential used to identify or authenticate the requesting entity (for example, a username and password), the time the request was made, the location the request originated from (either geographical or relative to a computer network, such as an IP address), data relating to a computer system that generated the request (for example, whether the requesting entity made the request via a laptop or a smartphone), whether or not there is a human user associated with the requesting entity, and input characteristics (for example, keystroke rates), among others.” [GADDAM, ¶0122]); creating a feature vector for the at least one privileged access based on a combination of at least some of the plurality of access attributes (“At step 406, the resource access system can process the request data to determine a feature vector.” [GADDAM, ¶0124; Fig. 4(406)]); computing, in real-time, an access score based on a deviation of the feature vector(“At step 414, the resource access system can determine a trust score by applying the feature vector as an input to the production model. The trust score can be used by the resource access system to determine whether the request is malicious or benign.” [GADDAM, ¶0128; Fig. 4(414)]; the score is generated in real-time [GADDAM, ¶0061]; furthermore, the production model is an ensemble classifier model corresponding to an entity profile [GADDAM, ¶0069, 0073]); and initiating at least one fraudulent access mitigation action responsive to determining that the access score exceeds a certain threshold indicative of potential fraudulent privileged accesses (“At step 418, the resource access system, via the policy engine of the online subsystem, can compare the trust scores against predetermined thresholds.” [GADDAM, ¶0130; Fig. 4(418)]; in steps 420 and 422, a resource access policy is applied when the trust score exceeds a maximum threshold [GADDAM, ¶0131-0132]).
GADDAM does not explicitly disclose but AKHTAR discloses: the at least one privileged access from user-specific typical access behavior learned for the at least one user from a plurality of the user’s previous privileged accesses (a behavior of a user is tracked over time using a profile to model a baseline [AKHTAR, ¶0014, 0055]).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to track normal behavior (e.g., access activities) of the entity in GADDAM to produce a baseline model. AKHTAR is directed to analogous art of detecting anomalous user behavior, such as application usage or access [AKHTAR, ¶0012, 0014]. Furthermore, AKHTAR discloses per-user feature vectors to train profiles to establish a baseline for a specific user [AKHTAR, ¶0022]. User-specific behavior profiles would have improved the accuracy of trust scores as every user’s behavior is unique.
As per claim 2: GADDAM in view of AKHTAR disclose all limitations of claim 1. Furthermore, GADDAM discloses: wherein the at least one privileged access is subject to user authentication, the at least one privileged access relates to at least one member of a group comprising: a login to an account, a login to a device, a login to a secure service, a transaction, and an account creation (the request to the resource includes credentials for authenticating the requesting entity [GADDAM, ¶0122]; a resource can include a service that can be provided to an entity [GADDAM, ¶0016]; the resource can be an account tied to financial information [GADDAM, ¶0093]; a new requesting entity and generating a new entity profile [GADDAM, ¶0137]).
As per claim 3: GADDAM in view of AKHTAR disclose all limitations of claim 1. Furthermore, GADDAM discloses: wherein the access score is computed by applying at least one trained machine learning model to the feature vector, the at least one machine learning model is trained to learn at least one typical access pattern for the at least one user based on a plurality of training feature vectors created based on a plurality of access attributes collected during a respective one of the plurality of previous privileged accesses (the trust score is determined by applying the feature vector as an input to a model, wherein the model is trained on verified feature vectors and trust scores [GADDAM, ¶0071, 0128]; entity profiles can be used to determine a model corresponding to the entity [GADDAM, ¶0067]).
As per claim 6: GADDAM in view of AKHTAR disclose all limitations of claim 1. Furthermore, GADDAM discloses: further comprising adjusting the certain threshold to reduce false positive detection of privileged accesses potentially conducted by at least one fraudulent party emulating privileged accesses of the at least one user (the thresholds corresponding to each policy can take on any appropriate value, and the policy engine can modify the thresholds: “For example, incoming data that indicates that one known entity may be attempting to impersonate another known entity may increase the threshold corresponding to the no action resource access policy 308D…” [GADDAM, ¶0120]).
As per claim 7: GADDAM in view of AKHTAR disclose all limitations of claim 1. Furthermore, GADDAM discloses: wherein the access attributes relating to interaction of the at least one user with the at least one user input interface comprise a plurality of movement parameters of at least one pointing device used by the at least one user during the at least one privileged access (the feature vector contains data related to mouse movements involved in generating the request to access a resource [GADDAM, ¶0106]).
As per claim 9: GADDAM in view of AKHTAR disclose all limitations of claim 1. Furthermore, GADDAM discloses: wherein the access attributes relating to interaction of the at least one user with the at least one user input interface comprise at least one stroke parameter captured for at least one keyboard device used by the at least one user during the at least one privileged access (the feature vector includes keystroke rates [GADDAM, ¶0128]).
As per claim 12: Claim 12 is different in overall scope from claim 1 but recites substantially similar subject matter as claim 1. Claim 12 is directed to a system performing functions corresponding to the steps of the method in claim 1. GADDAM is also directed to a system [GADDAM, ¶0006]. Thus, the response provided herein and above for claim 1 is equally applicable to claim 12.
As per claim 13: Claim 13 incorporates all limitations of claim 12 and is a system corresponding to the method of claim 2. Therefore, the arguments set forth above with respect to claims 2 and 12 are equally applicable to claim 13 and rejected for the same reasons.
As per claim 14: Claim 14 incorporates all limitations of claim 12 and is a system corresponding to the method of claim 3. Therefore, the arguments set forth above with respect to claims 3 and 12 are equally applicable to claim 14 and rejected for the same reasons.
As per claim 17: Claim 17 incorporates all limitations of claim 12 and is a system corresponding to the method of claim 6. Therefore, the arguments set forth above with respect to claims 6 and 12 are equally applicable to claim 17 and rejected for the same reasons.
As per claim 18: Claim 18 incorporates all limitations of claim 12 and is a system corresponding to the method of claim 7. Therefore, the arguments set forth above with respect to claims 7 and 12 are equally applicable to claim 18 and rejected for the same reasons.
Claims 4, 5, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over GADDAM in view of AKHTAR and in further view of US 2019/0197236 to NICULESCU-MIZIL et al. (hereinafter, “NICULESCU-MIZIL”).
As per claim 4: GADDAM in view of AKHTAR disclose all limitations of claim 3. GADDAM and AKHTAR do not explicitly disclose, but NICULESCU-MIZIL discloses: wherein the at least one machine learning model is configured to apply dimension reduction and dimension reconstruction to the feature vectors (in an anomaly detection system, an autoencoder is implemented that uses an encoder to reduce an input to an N-dimensional vector and a decoder that reconstructs the N-dimensional vector [NICULESCU-MIZIL, ¶0021]).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement any well-known machine learning model in GADDAM, such as an autoencoder disclosed by NICULESCU-MIZIL. According to [GADDAM, ¶0073], any machine learning model that best fits the application is determined. Conventional autoencoders would have provided advantages of reducing the dimensionality of data to reduce the computational difficulty and provide effective anomaly detection by measuring reconstruction loss.
As per claim 5: GADDAM in view of AKHTAR and NICULESCU-MIZIL disclose all limitations of claim 4. Furthermore, the motivation for incorporating NICULESCU-MIZIL in claim 4 is also applicable in claim 5. Therefore, GADDAM in view of NICULESCU-MIZIL disclose: wherein the access score is computed for the at least one privileged access based on a reconstruction error of the at least one machine learning model applied to the feature vector of the at least one privileged access (a loss function is applied to reflect the difference between input measurements and the reconstructed measurements, wherein the loss function is used to obtain an error in measure of a reconstructed sample – i.e., this corresponds to the “trust score” from [GADDAM, ¶0035], which is similarly used to indicate a degree of difference from normal behavior [NICULESCU-MIZIL, ¶0021]).
As per claim 15: Claim 15 incorporates all limitations of claim 14 and is a system corresponding to the method of claim 4. Therefore, the arguments set forth above with respect to claims 4 and 14 are equally applicable to claim 15 and rejected for the same reasons.
As per claim 16: Claim 16 incorporates all limitations of claim 15 and is a system corresponding to the method of claim 5. Therefore, the arguments set forth above with respect to claims 5 and 15 are equally applicable to claim 16 and rejected for the same reasons.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over GADDAM in view of AKHTAR and in further view of Lei Ma, et al. (hereinafter, “MA”) "A kind of mouse behavior authentication method on dynamic soft keyboard," (2016), pp. 000211-000216.
As per claim 8: GADDAM in view of AKHTAR disclose all limitations of claim 7. GADDAM and AKHTAR do not explicitly disclose, but MA discloses: wherein the plurality of movement parameters of the at least one pointing device are expressed by at least one log-normal cumulative distribution function (CDF) indicative of at least one movement pattern of the at least one pointing device (“Mouse behavior feature vector is obtained by using a combination of CDF and LRS.” (mouse behavior characteristics are used to authenticate a user – i.e., to detect anomalous user activity) [MA, pg. 000211]).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to collect mouse movement data in [GADDAM, ¶0128] as a cumulative distribution function (CDF). MA discloses collecting non-fixed tracks to model non-fixed mouse behavior pattern by obtaining new mouse characteristics using a combination of CDF and LRS (Plus-L Minus-R Selection). A distribution of these characteristics can be obtained via CDF to assist in preliminary selection for a feature vector [MA, pg. 000213].
As per claim 19: Claim 19 incorporates all limitations of claim 18 and is a system corresponding to the method of claim 8. Therefore, the arguments set forth above with respect to claims 8 and 18 are equally applicable to claim 19 and rejected for the same reasons.
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable over GADDAM in view AKHTAR and in further view of US 2024/0427862 to AGRAWAL (hereinafter, “AGRAWAL”).
As per claim 10: GADDAM in view of AKHTAR disclose all limitations of claim 1. GADDAM and AKHTAR do not explicitly disclose, but ARGAWAL discloses: wherein the access attributes relating to interaction of the user with the at least one user input interface comprise at least one voice and/o speech attribute of the at least one user captured for at least one audio input device used by the at least one user during the at least one privileged access (training data include historical speech patterns that are used to train one or more machine learning models for identifying unauthorized or anomalous activities [AGRAWAL, ¶0033]; for example a communication interface of a computing device receives cadence patterns, such as voice input [AGRAWAL, ¶0037]).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include input characteristics as speech/voice cadence of a user. Therefore, any known input characteristics corresponding to user behavior would have been included in GADDAM, such as voice/speech input characteristics, which were common means of user input due to the growth of voice-assisted devices with improvements to voice recognition machine learning technology.
Claims 11 are rejected under 35 U.S.C. 103 as being unpatentable over GADDAM in view of AKHTAR and in further view of US 2020/0380119 to CORREA BAHNSEN et al. (hereinafter, “CORREA”).
As per claim 11: GADDAM in view of AKHTAR disclose all limitations of claim 1. GADDAM and AKHTAR do not explicitly disclose, but CORREA discloses: wherein the access attributes relating to interaction of the at least one user with the at least one user input interface comprise at least one tactile attribute of the at least one user captured for at least one tactile input device used by the at least one user during the at least one privileged access (behavioral dynamics recorded to generate a feature vector for behavior information include variables related to user interaction with a touchscreen [CORREA, ¶0083]).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include input characteristics from a touchscreen. CORREA is directed to analogous art of detecting anomalous behavior or activities based on device context [CORREA, ¶0024]. Therefore, any known input characteristics corresponding to user behavior would have been included in GADDAM, such as touchscreen input behavior, which were increasingly becoming common due to the growth of mobile devices and limited space for hardware input devices.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over GADDAM in view of AKHTAR in view of AGRAWAL and in further view of CORREA.
As per claim 20: Claim 20 incorporates all limitations of claim 12 and is a system corresponding to the method of claims 9-11. Therefore, the arguments set forth above with respect to claims 9-12 are equally applicable to claim 20 and rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0308460: A risk score is calculated based on a distance between an event vector and the entity model in a vector space. Deviations from expected behavior can be identified based on the distance to identify unusual or suspicious behavior. See ¶0188, 0195, 0203.
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/ROBERT B LEUNG/Primary Examiner, Art Unit 2494