Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is the initial office action that has been issued in response to patent application, 19/212,444, filed on 05/19/2025. Claims 1-23 are currently pending and have been considered below. Claim 1, 11 and 19 are independent claim.
Information Disclosure Statement
The information disclosure statements (IDS's) submitted on 05/19/2025 are in compliance with provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
This application is a CON of 18/602,353 filed on 03/12/2024 and PAT 12328329. 18/602,353 is a CON of 16/161,110 filed on 10/16/2018 and PAT 11962606. 18/602,353 has PRO 62/652,320 filed on 04/04/2018.
Drawings
The drawings filed on 05/19/2025 are accepted by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 11 and 19 are non provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 of application No 18/602,353 (US Patent No. 12,328,329 B2). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the co-pending application contains every element of claims of the instant application. A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a 35 patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claims 1, 11 and 19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 of application 18/602,353 in view of Shimamura (US Patent Application No 2019/0166221 A1) in view of Gantman (US Patent Application Publication No 2016/0285897 A1). This is a nonstatutory obviousness type double patenting rejection.
This is a non provisional non-statutory double patenting rejection because the conflicting claims already got patented.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-6, 8-15 and 17-23 are rejected under 35 U.S.C. 103 as being unpatentable over Shimamura (US Patent Application No 2019/0166221 A1) in view of Gantman (US Patent Application Publication No 2016/0285897 A1).
Regarding Claim 1, Shimamura discloses a method comprising:
protecting a serverless application that uses a serverless function, wherein protecting the serverless application comprises (Shimamura, ¶[0013], Fig-1, the user devices are coupled to serverless computing environment),
monitoring inputs to the serverless application with a firewall in an environment of the serverless application (Shimamura, ¶[0041], Fig-3, each UDF is analyzed for characterizing information regarding the execution or provisioning required in order to enable the execution of the UDF. ¶[0042], a master node or scheduler receives a plurality of worker node tickets. These worker node tickets indicate the resource availability of a corresponding worker node);
Shimamura does not explicitly teach the following limitation that Gantman teaches:
monitoring serverless function behavior for anomalies or suspicious behavior based, at least in part, on a profile of allowed inputs and/or allowed behavior of the serverless function, wherein the profile is built from learned normative inputs and learned normative serverless function behavior (Gantman, ¶[0124]- ¶[0125], Fig-4, the computing device monitors behaviors of the software application to collect local behavior information and compares the received behavior information to the local behavior information structure. The comparison result is used to determine whether the observed behavior is within the range of expected behaviors. The computing device can increase its security in response to determining that the observed behavior is not within the range);
allowing the serverless function to execute with input allowed to the serverless function and allowing serverless function behavior that is normative based on the profile (Gantman, Fig-5, ¶[0130]- ¶[0132], the computing device may determine whether the results of this comparison are not suspicious, such as by determining that the behavior can be classified as either benign or non-benign with a high degree of confidence); and
blocking or denying suspicious or anomalous serverless function behavior based, at least in part, on the profile (Gantman, Fig-6, ¶[0138]- ¶[0140], the computing device may determine whether the suspicious behavior or potential problems can be identified and corrected based on the result of the deeper behavioral analysis).
Shimamura in view of Gantman are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “network security”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Shimamura in view of Gantman to include the idea of comparing a behavior information structure that characterizes a range of expected behaviors of a software application program to other behavior information structures to determine whether an observed behavior differs or deviates from the expected behavior (Gantman, ¶[0001]).
Regarding Claim 2, Shimamura in view of Gantman discloses the method of claim 1 further comprising learning the normative inputs and the normative serverless function behavior, wherein normative serverless function behavior at least comprises one or more of allowed access of files by the serverless function, allowed processes launched by the serverless function, and allowed connections to external network resources (Shimamura, ¶[0013], serverless computing environment. Gantman, ¶[0124]- ¶[0125], Fig-4, the computing device monitors behaviors of the software application to collect local behavior information and compares the received behavior information to the local behavior information structure. The comparison result is used to determine whether the observed behavior is within the range of expected behaviors. The computing device can increase its security in response to determining that the observed behavior is not within the range. Fig-5 & 6).
Regarding Claim 3, Shimamura in view of Gantman discloses the method of claim 2, wherein learning at least one of the normative inputs and the normative serverless function behavior is during a deployment phase or a QA phase (Gantman, ¶[0073], to determine whether it is malware or to better determine the range of normal or expected behaviors of the software application. ¶[0122], by emulating the execution environment of the client computing device, the server computing device may generate behavior information structures that characterize all the normal or expected operations).
Regarding Claim 4, Shimamura in view of Gantman discloses the method of claim 2, wherein learning the normative serverless function behavior comprises collecting sample data of behavior and clustering to learn the normative serverless function behavior (Gantman, ¶[0073], to determine whether it is malware or to better determine the range of normal or expected behaviors of the software application. ¶[0122], by emulating the execution environment of the client computing device, the server computing device may generate behavior information structures that characterize all the normal or expected operations).
Regarding Claim 5, Shimamura in view of Gantman discloses the method of claim 4, wherein the sample data correspond to at least one of external data sources accessed, sequences of operations or interactions performed, and which operations or interactions are performed when accessing an external resource (Gantman, ¶[0099], generating the lean classifier model based on the full classifier model may include determining a number of unique test conditions that should be evaluated to classify an activity or behavior without consuming an excessive amount of processing, memory, or energy resources of the computing device, generating a list of test conditions by sequentially traversing the list of boosted decision stumps and inserting the test condition associated with each sequentially traversed boosted decision stump into the list of test conditions until the list of test conditions includes the determined number of unique test conditions, and generating the lean classifier model to include only those boosted decision stumps that test one of a plurality of test conditions included in the generated list of test conditions).
Regarding Claim 6, Shimamura in view of Gantman discloses the method of claim 1 further comprising generating a policy based on the learned normative inputs and learned normative serverless function behavior and enforcing the policy, wherein allowing, blocking, or denying is according to the policy (Gantman, ¶[0042], ¶[0090], the behavior observer module 202 may be configured to perform coarse observations by monitoring/observing an initial set of behaviors or factors that are a small subset of all factors that could con tribute to the computing device's degradation. Initial set of behaviors/factors may be specified in machine learning classifier models).
Regarding Claim 8, Shimamura in view of Gantman discloses the method of claim 1 further comprising learning a policy corresponding to the profile and instantiating in the environment protection logic that enforces the policy (Gantman, ¶[0099], generating the lean classifier model based on the full classifier model may include determining a number of unique test conditions that should be evaluated to classify an activity or behavior without consuming an excessive amount of processing, memory, or energy resources of the computing device, generating a list of test conditions by sequentially traversing the list of boosted decision stumps and inserting the test condition associated with each sequentially traversed boosted decision stump into the list of test conditions until the list of test conditions includes the determined number of unique test conditions, and generating the lean classifier model to include only those boosted decision stumps that test one of a plurality of test conditions included in the generated list of test conditions).
Regarding Claim 9, Shimamura in view of Gantman discloses the method of claim 1, wherein the learned normative inputs indicate a set of input attributes that at least include format and size/length (Gantman, ¶[0113], generate the behavior information structures to include information that may be input to a decision node in the machine learning classifier to generate an answer to a query regarding the monitored activity).
Regarding Claim 10, Shimamura in view of Gantman discloses the method of claim 1, wherein learning at least one of the normative inputs and the normative serverless function behavior is with machine learning (Gantman, ¶[0042], ¶[0090], the behavior observer module 202 may be configured to perform coarse observations by monitoring/observing an initial set of behaviors or factors that are a small subset of all factors that could con tribute to the computing device's degradation. Initial set of behaviors/factors may be specified in machine learning classifier models).
Regarding Claim 11, Shimamura discloses a non-transitory, machine-readable medium having program code stored thereon, the program code comprising instructions to:
monitor inputs to a serverless application with a firewall in an environment of the serverless application (Shimamura, ¶[0041], Fig-3, each UDF is analyzed for characterizing information regarding the execution or provisioning required in order to enable the execution of the UDF. ¶[0042], a master node or scheduler receives a plurality of worker node tickets. These worker node tickets indicate the resource availability of a corresponding worker node);
Shimamura does not explicitly teach the following limitation that Gantman teaches:
monitor serverless function behavior for anomalies or suspicious behavior based at least in part, on a profile of allowed inputs and/or allowed behavior of a serverless function of the serverless application, wherein the profile is built from learned normative inputs and learned normative serverless function behavior (Gantman, ¶[0124]- ¶[0125], Fig-4, the computing device monitors behaviors of the software application to collect local behavior information and compares the received behavior information to the local behavior information structure. The comparison result is used to determine whether the observed behavior is within the range of expected behaviors. The computing device can increase its security in response to determining that the observed behavior is not within the range);
allow the serverless function to execute with input allowed to the serverless function and allow serverless function behavior that is normative based on the profile (Gantman, Fig-5, ¶[0130]- ¶[0132], the computing device may determine whether the results of this comparison are not suspicious, such as by determining that the behavior can be classified as either benign or non-benign with a high degree of confidence); and
block or deny suspicious or anomalous serverless function behavior based, at least in part, on the profile (Gantman, Fig-6, ¶[0138]- ¶[0140], the computing device may determine whether the suspicious behavior or potential problems can be identified and corrected based on the result of the deeper behavioral analysis).
Shimamura in view of Gantman are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “network security”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Shimamura in view of Gantman to include the idea of comparing a behavior information structure that characterizes a range of expected behaviors of a software application program to other behavior information structures to determine whether an observed behavior differs or deviates from the expected behavior (Gantman, ¶[0001]).
Regarding Claim 12, Shimamura in view of Gantman discloses the non-transitory, machine-readable medium of claim 11, wherein the program code further comprises instructions to learn the normative inputs and the normative serverless function behavior, wherein normative serverless function behavior at least comprises one or more of allowed access of files by the serverless function, allowed processes launched by the serverless function, and allowed connections to external network resources (Shimamura, ¶[0013], serverless computing environment. Gantman, ¶[0124]- ¶[0125], Fig-4, the computing device monitors behaviors of the software application to collect local behavior information and compares the received behavior information to the local behavior information structure. The comparison result is used to determine whether the observed behavior is within the range of expected behaviors. The computing device can increase its security in response to determining that the observed behavior is not within the range. Fig-5 & 6).
Regarding Claim 13, Shimamura in view of Gantman discloses the non-transitory, machine-readable medium of claim 12, wherein the instructions to learn at least one of the normative inputs and the normative serverless function behavior comprise the instructions to learn during a deployment phase or a QA phase (Gantman, ¶[0073], to determine whether it is malware or to better determine the range of normal or expected behaviors of the software application. ¶[0122], by emulating the execution environment of the client computing device, the server computing device may generate behavior information structures that characterize all the normal or expected operations).
Regarding Claim 14, Shimamura in view of Gantman discloses the non-transitory, machine-readable medium of claim 12, wherein the instructions to learn the normative serverless function behavior comprise instructions to collect sample data of behavior and use one of statistical based analysis and machine learning to learn the normative serverless function behavior, wherein the sample data correspond to at least one of external data sources accessed, sequences of operations or interactions performed, and which operations or interactions are performed when accessing an external resource (Gantman, ¶[0099], generating the lean classifier model based on the full classifier model may include determining a number of unique test conditions that should be evaluated to classify an activity or behavior without consuming an excessive amount of processing, memory, or energy resources of the computing device, generating a list of test conditions by sequentially traversing the list of boosted decision stumps and inserting the test condition associated with each sequentially traversed boosted decision stump into the list of test conditions until the list of test conditions includes the determined number of unique test conditions, and generating the lean classifier model to include only those boosted decision stumps that test one of a plurality of test conditions included in the generated list of test conditions).
Regarding Claim 15, Shimamura in view of Gantman discloses the non-transitory, machine-readable medium of claim 11, wherein the program code further comprises instructions to generate a policy based on the learned normative inputs and learned normative serverless function behavior and to enforce the policy, wherein the instructions to enforce comprise the instructions to allow, block, or deny according to the policy (Gantman, ¶[0042], ¶[0090], the behavior observer module 202 may be configured to perform coarse observations by monitoring/observing an initial set of behaviors or factors that are a small subset of all factors that could con tribute to the computing device's degradation. Initial set of behaviors/factors may be specified in machine learning classifier models).
Regarding Claim 17, Shimamura in view of Gantman discloses the non-transitory, machine-readable medium of claim 11, wherein the program code further comprises instructions to learn a policy corresponding to the profile and instantiate in the environment protection logic that enforces the policy (Gantman, ¶[0099], generating the lean classifier model based on the full classifier model may include determining a number of unique test conditions that should be evaluated to classify an activity or behavior without consuming an excessive amount of processing, memory, or energy resources of the computing device, generating a list of test conditions by sequentially traversing the list of boosted decision stumps and inserting the test condition associated with each sequentially traversed boosted decision stump into the list of test conditions until the list of test conditions includes the determined number of unique test conditions, and generating the lean classifier model to include only those boosted decision stumps that test one of a plurality of test conditions included in the generated list of test conditions).
Regarding Claim 18, Shimamura in view of Gantman discloses the non-transitory, machine-readable medium of claim 11, wherein the learned normative inputs indicate a set of input attributes that at least include format and size/length (Gantman, ¶[0113], generate the behavior information structures to include information that may be input to a decision node in the machine learning classifier to generate an answer to a query regarding the monitored activity).
Regarding Claim 19, Shimamura discloses an apparatus comprising:
a processor (Shimamura, ¶[0013], Fig-1, the user devices are coupled to serverless computing environment); and
a machine-readable medium having instructions stored thereon that are executable by the processor to cause the apparatus to,
monitor inputs to a serverless application with a firewall in an environment of the serverless application (Shimamura, ¶[0041], Fig-3, each UDF is analyzed for characterizing information regarding the execution or provisioning required in order to enable the execution of the UDF. ¶[0042], a master node or scheduler receives a plurality of worker node tickets. These worker node tickets indicate the resource availability of a corresponding worker node);
Shimamura does not explicitly teach the following limitation that Gantman teaches:
monitor serverless function behavior for anomalies or suspicious behavior based,at least in part, on a profile of allowed inputs and/or allowed behavior of a serverless function of the serverless application, wherein the profile is built from learned normative inputs and learned normative serverless function behavior (Gantman, ¶[0124]- ¶[0125], Fig-4, the computing device monitors behaviors of the software application to collect local behavior information and compares the received behavior information to the local behavior information structure. The comparison result is used to determine whether the observed behavior is within the range of expected behaviors. The computing device can increase its security in response to determining that the observed behavior is not within the range);
allow the serverless function to execute with input allowed to the serverless function and allow serverless function behavior that is normative based on the profile (Gantman, Fig-5, ¶[0130]- ¶[0132], the computing device may determine whether the results of this comparison are not suspicious, such as by determining that the behavior can be classified as either benign or non-benign with a high degree of confidence); and
block or deny suspicious or anomalous serverless function behavior based, at least in part, on the profile (Gantman, Fig-6, ¶[0138]- ¶[0140], the computing device may determine whether the suspicious behavior or potential problems can be identified and corrected based on the result of the deeper behavioral analysis).
Shimamura in view of Gantman are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “network security”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Shimamura in view of Gantman to include the idea of comparing a behavior information structure that characterizes a range of expected behaviors of a software application program to other behavior information structures to determine whether an observed behavior differs or deviates from the expected behavior (Gantman, ¶[0001]).
Regarding Claim 20, Shimamura in view of Gantman discloses the apparatus of claim 19, wherein the machine-readable medium further has stored thereon instructions to learn the normative inputs and the normative serverless function behavior, wherein normative serverless function behavior at least comprises one or more of allowed access of files by the serverless function, allowed processes launched by the serverless function, and allowed connections to external network resources (Shimamura, ¶[0013], serverless computing environment. Gantman, ¶[0124]- ¶[0125], Fig-4, the computing device monitors behaviors of the software application to collect local behavior information and compares the received behavior information to the local behavior information structure. The comparison result is used to determine whether the observed behavior is within the range of expected behaviors. The computing device can increase its security in response to determining that the observed behavior is not within the range. Fig-5 & 6).
Regarding Claim 21, Shimamura in view of Gantman discloses the apparatus of claim 20, wherein the instructions to learn at least one of the normative inputs and the normative serverless function behavior comprise the instructions executable by the processor to cause the apparatus to learn during a deployment phase or a QA phase (Gantman, ¶[0073], to determine whether it is malware or to better determine the range of normal or expected behaviors of the software application. ¶[0122], by emulating the execution environment of the client computing device, the server computing device may generate behavior information structures that characterize all the normal or expected operations).
Regarding Claim 22, Shimamura in view of Gantman discloses the apparatus of claim 20, wherein the instructions to learn the normative serverless function behavior comprise instructions executable by the processor to cause the apparatus to collect sample data of behavior and use one of statistical based analysis and machine learning to learn the normative serverless function behavior, wherein the sample data correspond to at least one of external data sources accessed, sequences of operations or interactions performed, and which operations or interactions are performed when accessing an external resource (Gantman, ¶[0099], generating the lean classifier model based on the full classifier model may include determining a number of unique test conditions that should be evaluated to classify an activity or behavior without consuming an excessive amount of processing, memory, or energy resources of the computing device, generating a list of test conditions by sequentially traversing the list of boosted decision stumps and inserting the test condition associated with each sequentially traversed boosted decision stump into the list of test conditions until the list of test conditions includes the determined number of unique test conditions, and generating the lean classifier model to include only those boosted decision stumps that test one of a plurality of test conditions included in the generated list of test conditions).
Regarding Claim 23, Shimamura in view of Gantman discloses the apparatus of claim 19, wherein the machine-readable medium further has stored thereon instructions to generate a policy based on the learned normative inputs and learned normative serverless function behavior and to enforce the policy, wherein the instructions to enforce comprise the instructions executable by the processor to cause the apparatus to allow, block, or deny according to the policy (Gantman, ¶[0042], ¶[0090], the behavior observer module 202 may be configured to perform coarse observations by monitoring/observing an initial set of behaviors or factors that are a small subset of all factors that could con tribute to the computing device's degradation. Initial set of behaviors/factors may be specified in machine learning classifier models).
Claim 7and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shimamura (US Patent Application No 2019/0166221 A1) in view of Gantman (US Patent Application Publication No 2016/0285897 A1) and further in view of Jawa (US Patent Application Publication No 2014/0059573 A1).
Regarding Claim 7, Shimamura in view of Gantman does not disclose the following limitations that Jawa teaches:
the method of claim 1 further comprising wrapping the serverless function with wrapper code to monitor the serverless function behavior (Jawa, ¶[0047], in order to create wrapped applications, an enterprise or a developer of an enterprise application can utilize a wrapping utility program or tool to wrap generic applications with a policy-enforcement mechanism, which, involves injecting references to dynamically linked libraries).
Shimamura in view of Gantman and Jawa are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “network security”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Shimamura in view of Gantman and Jawa to include the idea of replacing or interposing certain system library calls with customized system calls for providing sufficient virtualization capabilities on mobile devices that remain challenging (Jawa, ¶[0005])
Regarding Claim 16, Shimamura in view of Gantman and Jawa discloses the non-transitory, machine-readable medium of claim 11, wherein further comprising wrapping the serverless function with wrapper code and dynamic hooking to monitor the serverless function behavior (Jawa, ¶[0048], a wrapped application includes a generic application wrapped with a policy enforcement mechanism (which in one embodiment manifests as a wrapper, comprising a set of injected dynamic libraries (e.g., an interposition library dynamically loaded by a hook and injection manager) that replace conventional system calls of generic application).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (see PTO-Form 892).
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/WASIKA NIPA/ Primary Examiner, Art Unit 2433
