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 .
Response to Amendments / Arguments
Regarding the rejection(s) of claims under 35 USC 103:
Applicant’s arguments, filed 11/26/2025, in view of the amended claims, have been fully considered and are not persuasive.
Applicant's arguments have been fully considered but they are not persuasive.
Applicant argues that Rieke does not teach the claimed feature of "iteratively processing, each invariance identifying property in a set of invariance identifying properties by: determining whether values of the invariance identifying property associated with each resource included in the plurality of resources in the resource group satisfies a second equivalence rule." Specifically, Applicant contends that "at best, Rieke describes a mapping of attributes to a specific evaluator" and that "the feature of mapping attributes to an evaluator does not correspond to the above amended feature."
In response, it is noted that the cited portions of Rieke explicitly describe iterative evaluation processes. Paragraph [0115]-[0116] of Rieke recites "each event is evaluated one by one, consistent with method 2450 and each event is evaluated based on the evaluators in the relationship." Additionally, paragraphs [0115-0116] state "The events are evaluated one by one...When the event at time T4 is evaluated, the Dest Asset and Dest Port attributes match the evaluations, but Source Asset does not." This demonstrates that Rieke teaches evaluating multiple attributes ("invariance identifying properties") for each asset/resource in an iterative manner.
Furthermore, Rieke explicitly teaches evaluation of multiple properties per resource. Paragraph [0024] and Figure 11 describe how "the event in this case fails multiple evaluations in the relationship and can be said to deviate in multiple dimensions," while paragraph [0126] states "each event in the stream is evaluated against both Relationship2 and Relationship3." This teaches the claimed iterative processing of multiple invariance identifying properties for each resource.
Applicant further argues that their invention requires determining "that each resource included in the plurality of resources in the resource group satisfies the second equivalence rule" before incorporating an invariant. However, Rieke teaches this concept through its baseline evaluation system. Paragraph [0122] recites "All events up to the event at time T7 evaluate positively with respect to Relationship 4," demonstrating that the system determines when resources satisfy evaluation criteria before detecting deviations. The baseline creation process inherently requires confirming compliance across all selected assets before establishing the baseline for anomaly detection.
Additionally, Rieke teaches equivalence rule evaluation. Paragraph [0116] explicitly states "The symbol '=' or equal sign here is evaluated like 'is in the set'" and paragraph [0135]-[0136] describes the "evaluator 'Detected Ports[]=Asset Ports[]'" - both demonstrating equivalence-based evaluation rules as claimed.
Therefore, the identified claim language is considered to be taught by the Rieke reference when combined with Allen and Ahad as originally cited. Rieke's iterative evaluation of multiple attributes across assets within logical groups, combined with baseline creation contingent on successful evaluation, teaches the claimed invention. Therefore, the identified claim language is considered to be taught by the combined references, and the rejection is maintained. Further, since Applicant has not presented additional arguments concerning the dependent claims, their rejections are likewise maintained
DETAILED ACTION
This is a reply to the arguments filed on 11/26/2025, in which, claims 1-6, 8, and 10-21 are pending. Claims 1, 11, and 19 are independent. Claim 7 and 9 has been canceled.
When making claim amendments, the applicant is encouraged to consider the references in their entireties, including those portions that have not been cited by the examiner and their equivalents as they may most broadly and appropriately apply to any particular anticipated claim amendments.
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.
Claims 1-6, 8 and 10-21 are rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. (US 20170078315 A1, referred to as Allen), in view of Rieke (US 20180248901 A1, referred to as Rieke) in further view of Ahad et al. (US 20190042353 A1, referred to as Ahad).
In reference to claim 1, A method comprising: generating a resource group including a plurality of resources of a environment based on a grouping property, wherein values of the grouping property associated with the plurality of resources satisfy a first condition (Allen: [0006] provides for defining assets as entities capable of generating or receiving information, which encompasses resources in a monitored environment. Allen paragraph [0032] further provides for a security system that generates cluster maps in which it groups together similar assets based on having similar state and event information. Allen paragraph [0087] even further provides that assets are grouped together into a cluster when their state and event information values are sufficiently similar to each other.) Incorporating a value in a baseline (Allen: paragraph [0088] provides for establishing baseline values for attributes of devices within a cluster. Allen paragraphs [0032] and [0087] (mentioned above) provides that the attributes used to group devices into a cluster include state information and event information. The baseline values are defined based on the normal/expected values of that same state and event information for the clustered devices.) Performing, by an anomaly detector, anomaly detection of the environment using the baseline, wherein the anomaly detector modifies a configuration of the environment upon detection of an anomaly (Allen: paragraph [0041] provides for using the baseline activities to detect when a user performs an action that is not included in the baseline, such as accessing a database at an unusual time. Allen paragraph [0104] provides for automated responses to detected anomalies.)
Allen does not explicitly disclose that the incorporating step is a response of a successful determination of whether values of the invariance identifying property associated with the plurality of resources in the resource group satisfy a second condition and that the value being incorporated into the baseline is a invariant defined by a tuple including, the grouping property and the invariance identifying property. Allen further does not explicitly disclose that anomaly detection is done in a cloud environment. However, Rieke discloses: Iteratively processing, each invariance identifying property in a set of invariance identifying properties, determining whether values of the invariance identifying property associated with each resource included in the plurality of resources in the resource group satisfies a second equivalence rule (Rieke: paragraph [0107] provides for that relationships derived from asset attributes are evaluated by the system, which can involve determining certain values associated with the attributes. Rieke paragraph [0108] further provides that the evaluation of attributes involves evaluators that process the attribute values, which can be considered conditions that the values must satisfy. Rieke [0115]-[0116] further provide iterative processing of multiple invariance identifying properties (attributes) for each resource (asset/event) in the system. Rieke paragraph [0126] provides for evaluation of multiple properties per resource.)
successfully determinate that each resource included in the plurality of resources in the resource group satisfies the second equivalence rule (Rieke: [0122] provides successful determination that resources satisfy evaluation criteria before proceeding with baseline operations. The baseline creation process inherently requires confirming compliance across all selected assets before establishing the baseline for anomaly detection, as described in paragraphs [0139] and [0054] where baselines are created as a combination of one or more assets and one or more relationships evaluated against one or more sets of events.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Allen, which provides a method for grouping resources based on similar attributes and using baselines for anomaly detection, with the teachings of Rieke, which introduces the concept of evaluating multiple invariance identifying properties against specific conditions. One of ordinary skill in the art would recognize the ability to incorporate Rieke's attribute evaluation into Allen's resource grouping and baseline creation process to enhance the accuracy of resource categorization. One of ordinary skill in the art would be motivated to make this modification in order to create more precise resource groups, leading to more accurate baselines. Allen in view of Rieke does not explicitly disclose that the value being incorporated into the baseline is a invariant defined by a tuple including, the grouping property and the invariance identifying property. Allen in view of Rieke further does not explicitly disclose that anomaly detection is done in a cloud environment. However, Ahad discloses: Wherein the value is a invariant is defined by a tuple including, (i) the grouping property and the first equivalence rule associated with the grouping property, (Ahad: [0010]-[0011], [0094]-[0097], [0132]-[0135] and [0119] Provides for using tuples to define baseline behavior (normal operating conditions) for the system as well as component types and assemblies that define how resources are grouped.)
(ii) the invariance identifying property and the second equivalence rule associated with the invariance identifying property, wherein at least the second equivalence rule corresponds to the values of the invariance identifying property associated with the plurality of resources in the resource group being equivalent (Ahad: [0132]-[0136] Provides for defining metrics and their associated bounds (minimum/maximum values, soft limits) which teaches to identifying properties. Further provides what makes values "equivalent" (normal) by specifying ranges that constitute normal operation. Values that fall within the defined range are considered equivalent (all normal), while those outside the range are anomalous.)
Performing anomaly detection using a baseline in a cloud environment (Ahad: [0013], [0077]-[0083], [0119]-[0123] Provides for anomaly detector (ADRC) that uses defined baselines to detect anomalies in the cloud environment.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Allen in view of Rieke, which provides a method for grouping resources based on multiple attributes and evaluating them against specific conditions, with the teachings of Ahad, which introduces defining invariants as tuples of properties and rules and applying anomaly detection in cloud environments. One of ordinary skill in the art would recognize the ability to incorporate Ahad's tuple-based invariant definition and cloud-focused anomaly detection into the combined system to enhance its applicability and precision in modern computing environments. One of ordinary skill in the art would be motivated to make this modification in order to create a more robust anomaly detection system capable of formally defining normal behavior using comprehensive tuples.
In reference to claim 2, the method of claim 1, further comprising: identifying the first equivalence rule associated with the grouping property, (Allen: paragraph [0032] provides for grouping assets based on shared state and event information, which teaches the use of a first equivalence rule for determining similarity of the grouping property.)
Allen does not explicitly disclose the use of a second equivalence rule, however Rieke teaches:
the second equivalence rule associated with a invariance identifying property, wherein the grouping property is different than the first invariance identifying property(Rieke: paragraph [0107] provides for the evaluation of relationships based on attributes derived from assets or events, which teaches the use of a second equivalence rule for determining similarity of the invariance identifying property.)
In reference to claim 3, the method of claim 2, wherein rule, the first equivalence rule corresponds to the values of the grouping property associated with the plurality of resources being in a first predetermined range of values associated with the grouping property, (Allen: paragraph [0032] provides for grouping assets based on shared state and event information (values of the grouping property satisfying the first equivalence rule). Allen paragraphs [0081] and [0109] further provides for normalizing attribute values within a predetermined range, which teaches the claim's description of an equivalence rule involving a predetermined range of values.)
In reference to claim 4, The method of claim 1, wherein the first equivalence rule corresponds to the values of the grouping property associated with the plurality of resources being identical, (Rieke: paragraph [0108] provides for that relationships can be defined by identical attributes (first equivalence rule corresponds to values being identical.))
In reference to claim 5, the second equivalence rule corresponds to the values of the first invariance identifying property associated with the plurality of resources in the resource group being in a second predetermined range of values associated with the first invariance identifying property, (Allen: paragraphs [0081] and [0109] provides for normalizing attribute values within a predetermined range, which teaches the claim's description of an equivalence rule involving a predetermined range of values (second equivalence rule corresponding to values being in a second predetermined range.))
In reference to claim 6, the method of claim 2, wherein the second equivalence rule corresponds to the values of the first invariance identifying property associated with the plurality of resources in the resource group being identical, (Rieke: paragraph [0108] provides for relationships can be defined by identical attributes (second equivalence rule corresponds to values being identical.))
In reference to claim 8, incorporating a second invariant in the baseline, wherein the second invariant is defined by the grouping property and the second invariance identifying property, (Allen: paragraph [0088] provides for establishing multiple baseline values (first invariant, second invariant, etc.) for attributes of devices within a cluster (resource group), where the attributes include the state and event information used for grouping (grouping property) and defining normal/expected behavior (first invariance identifying property, second invariance identifying property, etc.))
Allen does not explicitly disclose selecting a second invariance identifying property from the set of invariance identifying properties, determining whether values of the second invariance identifying property associated with the plurality of resources satisfy a third condition and a response to a successful determination of whether values of the second invariance identifying property associated with the plurality of resources satisfy a third condition, however, Rieke teaches:
The method of claim 1, further comprising: selecting a second invariance identifying property from the set of invariance identifying properties, (Rieke paragraph [0008] provides for the process of selecting specific attributes from an asset attribute database.)
Determining whether values of the second invariance identifying property associated with the plurality of resources in the resource group satisfy a third equivalence rule, (Rieke: paragraph [0107] provides for the evaluation of relationships based on attributes derived from assets or events.)
Rieke also discloses a response to a successful determination of whether values of the second invariance identifying property associated with the plurality of resources satisfy a third condition (Rieke: paragraph [0097] provides for details how after successful creation of relationships that are selected and used in a baseline.)
In reference to claim 10, The method of claim 1, further comprising: detecting a trigger signal to initiate anomaly detection of the cloud environment; performing anomaly detection of the cloud environment in response to detecting the trigger signal, (Allen: paragraph [0041] provides for specific actions triggering the use of the anomaly detection system.) Generating an alert signal responsive to detecting an anomaly in the cloud environment, (Allen paragraph [0014] provides for the one or more actions could be an alert.)
In reference to claim 11, A computing device comprising: a processor; and a memory including instructions that, when executed with the processor, cause the computing device to, at least: generate a resource group including a plurality of resources of a environment based on a grouping property, wherein values of the grouping property associated with the plurality of resources satisfy a first equivalence rule (Allen: [0006] provides for defining assets as entities capable of generating or receiving information, which encompasses resources in a monitored environment. Allen paragraph [0032] further provides for a security system that generates cluster maps in which it groups together similar assets based on having similar state and event information. Allen paragraph [0087] even further provides that assets are grouped together into a cluster when their state and event information values are sufficiently similar to each other.) Incorporate a value in a baseline (Allen: paragraph [0088] provides for establishing baseline values for attributes of devices within a cluster. Allen paragraphs [0032] and [0087] (mentioned above) provides that the attributes used to group devices into a cluster include state information and event information. The baseline values are defined based on the normal/expected values of that same state and event information for the clustered devices.) Perform, by an anomaly detector, anomaly detection of the environment using the baseline, wherein the anomaly detector modifies a configuration of the environment upon detection of an anomaly (Allen: paragraph [0041] provides for using the baseline activities to detect when a user performs an action that is not included in the baseline, such as accessing a database at an unusual time. Allen paragraph [0104] provides for automated responses to detected anomalies.)
Allen does not explicitly disclose that the incorporating step is a response of a successful determination of whether values of the invariance identifying property associated with the plurality of resources in the resource group satisfy a second condition and that the value being incorporated into the baseline is a invariant defined by a tuple including, the grouping property and the invariance identifying property. Allen further does not explicitly disclose that anomaly detection is done in a cloud environment. However, Rieke discloses: Iteratively processing, each invariance identifying property in a set of invariance identifying properties, determining whether values of the invariance identifying property associated with each resource included in the plurality of resources in the resource group satisfies a second equivalence rule (Rieke: paragraph [0107] provides for that relationships derived from asset attributes are evaluated by the system, which can involve determining certain values associated with the attributes. Rieke paragraph [0108] further provides that the evaluation of attributes involves evaluators that process the attribute values, which can be considered conditions that the values must satisfy. Rieke [0115]-[0116] further provide iterative processing of multiple invariance identifying properties (attributes) for each resource (asset/event) in the system. Rieke paragraph [0126] provides for evaluation of multiple properties per resource.)
successfully determinate that each resource included in the plurality of resources in the resource group satisfies the second equivalence rule (Rieke: [0122] provides successful determination that resources satisfy evaluation criteria before proceeding with baseline operations. The baseline creation process inherently requires confirming compliance across all selected assets before establishing the baseline for anomaly detection, as described in paragraphs [0139] and [0054] where baselines are created as a combination of one or more assets and one or more relationships evaluated against one or more sets of events.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Allen, which provides a method for grouping resources based on similar attributes and using baselines for anomaly detection, with the teachings of Rieke, which introduces the concept of evaluating multiple invariance identifying properties against specific conditions. One of ordinary skill in the art would recognize the ability to incorporate Rieke's attribute evaluation into Allen's resource grouping and baseline creation process to enhance the accuracy of resource categorization. One of ordinary skill in the art would be motivated to make this modification in order to create more precise resource groups, leading to more accurate baselines. Allen in view of Rieke does not explicitly disclose that the value being incorporated into the baseline is a invariant defined by a tuple including, the grouping property and the invariance identifying property. Allen in view of Rieke further does not explicitly disclose that anomaly detection is done in a cloud environment. However, Ahad discloses: Wherein the value is a invariant is defined by a tuple including, (i) the grouping property and the first equivalence rule associated with the grouping property, (Ahad: [0010]-[0011], [0094]-[0097], [0132]-[0135] and [0119] Provides for using tuples to define baseline behavior (normal operating conditions) for the system as well as component types and assemblies that define how resources are grouped.)
(ii) the invariance identifying property and the second equivalence rule associated with the invariance identifying property, wherein at least the second equivalence rule corresponds to the values of the invariance identifying property associated with the plurality of resources in the resource group being equivalent (Ahad: [0132]-[0136] Provides for defining metrics and their associated bounds (minimum/maximum values, soft limits) which teaches to identifying properties. Further provides what makes values "equivalent" (normal) by specifying ranges that constitute normal operation. Values that fall within the defined range are considered equivalent (all normal), while those outside the range are anomalous.)
Performing anomaly detection using a baseline in a cloud environment (Ahad: [0013], [0077]-[0083], [0119]-[0123] Provides for anomaly detector (ADRC) that uses defined baselines to detect anomalies in the cloud environment.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Allen in view of Rieke, which provides a method for grouping resources based on multiple attributes and evaluating them against specific conditions, with the teachings of Ahad, which introduces defining invariants as tuples of properties and rules and applying anomaly detection in cloud environments. One of ordinary skill in the art would recognize the ability to incorporate Ahad's tuple-based invariant definition and cloud-focused anomaly detection into the combined system to enhance its applicability and precision in modern computing environments. One of ordinary skill in the art would be motivated to make this modification in order to create a more robust anomaly detection system capable of formally defining normal behavior using comprehensive tuples.
In reference to claim 12, the computing device of claim 11, further comprising: identifying the first equivalence rule associated with the grouping property, (Allen: paragraph [0032] provides for grouping assets based on shared state and event information, which teaches the use of a first equivalence rule for determining similarity of the grouping property.)
Allen does not explicitly disclose the use of a second equivalence rule, however Rieke teaches:
A second equivalence rule associated with a invariance identifying property wherein the grouping property is different than the first invariance identifying property (Rieke: paragraph [0107] provides for the evaluation of relationships based on attributes derived from assets or events, which teaches the use of a second equivalence rule for determining similarity of the invariance identifying property.)
In reference to claim 13, the computing device of claim 12, wherein, the first equivalence rule corresponds to the values of the grouping property associated with the plurality of resources being in a first predetermined range of values associated with the grouping property, (Allen: paragraph [0032] provides for grouping assets based on shared state and event information (values of the grouping property satisfying the first equivalence rule). Allen paragraphs [0081] and [0109] further provides for normalizing attribute values within a predetermined range, which teaches the claim's description of an equivalence rule involving a predetermined range of values.)
In reference to claim 14, the computing device of claim 11, wherein the first equivalence rule corresponds to the values of the grouping property associated with the plurality of resources being identical, (Rieke: paragraph [0108] provides for that relationships can be defined by identical attributes (first equivalence rule corresponds to values being identical.))
In reference to claim 15, the second equivalence rule corresponding to the values of the first invariance identifying property associated with the plurality of resources in the resource group being in a second predetermined range of values associated with the first invariance identifying property, (Allen: paragraphs [0081] and [0109] provides for normalizing attribute values within a predetermined range, which teaches the claim's description of an equivalence rule involving a predetermined range of values (second equivalence rule corresponding to values being in a second predetermined range.))
In reference to claim 16, the computing device of claim 12, wherein the second equivalence rule corresponds to the values of the first invariance identifying property associated with the plurality of resources in the resource group being identical, (Rieke: paragraph [0108] provides for relationships can be defined by identical attributes (second equivalence rule corresponds to values being identical.))
In reference to claim 17, incorporating a second invariant in the baseline, wherein the second invariant is defined by the grouping property and the second invariance identifying property, (Allen: paragraph [0088] provides for establishing multiple baseline values (first invariant, second invariant, etc.) for attributes of devices within a cluster (resource group), where the attributes include the state and event information used for grouping (grouping property) and defining normal/expected behavior (first invariance identifying property, second invariance identifying property, etc.))
Allen does not explicitly disclose selecting a second invariance identifying property from the set of invariance identifying properties, determining whether values of the second invariance identifying property associated with the plurality of resources satisfy a third condition and a response to a successful determination of whether values of the second invariance identifying property associated with the plurality of resources satisfy a third condition, however, Rieke teaches:
the computing device of claim 11, further comprising: selecting a second invariance identifying property from the set of invariance identifying properties, (Rieke paragraph [0008] provides for the process of selecting specific attributes from an asset attribute database.)
Determining whether values of the second invariance identifying property associated with the plurality of resources in the resource group satisfy a third equivalence rule, (Rieke: paragraph [0107] provides for the evaluation of relationships based on attributes derived from assets or events.)
Rieke also discloses a response to a successful determination of whether values of the second invariance identifying property associated with the plurality of resources satisfy a third condition (Rieke: paragraph [0097] provides for details how after successful creation of relationships that are selected and used in a baseline.)
In reference to claim 18, the computing device of claim 11, further comprising: detecting a trigger signal to initiate anomaly detection of the cloud environment; performing anomaly detection of the cloud environment in response to detecting the trigger signal, (Allen: paragraph [0041] provides for specific actions triggering the use of the anomaly detection system.) Generating an alert signal responsive to detecting an anomaly in the cloud environment, (Allen paragraph [0014] provides for the one or more actions could be an alert.)
In reference to claim 19, A non-transitory computer readable medium storing specific computer-executable instructions that, when executed by a processor, cause a computer system to perform operations comprising: identifying a set of resources in a cloud environment to be monitored generate a resource group including a plurality of resources of a environment based on a grouping property, wherein values of the grouping property associated with the plurality of resources satisfy a first equivalence rule (Allen: [0006] provides for defining assets as entities capable of generating or receiving information, which encompasses resources in a monitored environment. Allen paragraph [0032] further provides for a security system that generates cluster maps in which it groups together similar assets based on having similar state and event information. Allen paragraph [0087] even further provides that assets are grouped together into a cluster when their state and event information values are sufficiently similar to each other.) Incorporate a value in a baseline (Allen: paragraph [0088] provides for establishing baseline values for attributes of devices within a cluster. Allen paragraphs [0032] and [0087] (mentioned above) provides that the attributes used to group devices into a cluster include state information and event information. The baseline values are defined based on the normal/expected values of that same state and event information for the clustered devices.) Perform, by an anomaly detector, anomaly detection of the environment using the baseline, wherein the anomaly detector modifies a configuration of the environment upon detection of an anomaly (Allen: paragraph [0041] provides for using the baseline activities to detect when a user performs an action that is not included in the baseline, such as accessing a database at an unusual time. Allen paragraph [0104] provides for automated responses to detected anomalies.)
Allen does not explicitly disclose that the incorporating step is a response of a successful determination of whether values of the invariance identifying property associated with the plurality of resources in the resource group satisfy a second condition and that the value being incorporated into the baseline is a invariant defined by a tuple including, the grouping property and the invariance identifying property. Allen further does not explicitly disclose that anomaly detection is done in a cloud environment. However, Rieke discloses: Iteratively processing, each invariance identifying property in a set of invariance identifying properties, determining whether values of the invariance identifying property associated with each resource included in the plurality of resources in the resource group satisfies a second equivalence rule (Rieke: paragraph [0107] provides for that relationships derived from asset attributes are evaluated by the system, which can involve determining certain values associated with the attributes. Rieke paragraph [0108] further provides that the evaluation of attributes involves evaluators that process the attribute values, which can be considered conditions that the values must satisfy. Rieke [0115]-[0116] further provide iterative processing of multiple invariance identifying properties (attributes) for each resource (asset/event) in the system. Rieke paragraph [0126] provides for evaluation of multiple properties per resource.)
successfully determinate that each resource included in the plurality of resources in the resource group satisfies the second equivalence rule (Rieke: [0122] provides successful determination that resources satisfy evaluation criteria before proceeding with baseline operations. The baseline creation process inherently requires confirming compliance across all selected assets before establishing the baseline for anomaly detection, as described in paragraphs [0139] and [0054] where baselines are created as a combination of one or more assets and one or more relationships evaluated against one or more sets of events.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Allen, which provides a method for grouping resources based on similar attributes and using baselines for anomaly detection, with the teachings of Rieke, which introduces the concept of evaluating multiple invariance identifying properties against specific conditions. One of ordinary skill in the art would recognize the ability to incorporate Rieke's attribute evaluation into Allen's resource grouping and baseline creation process to enhance the accuracy of resource categorization. One of ordinary skill in the art would be motivated to make this modification in order to create more precise resource groups, leading to more accurate baselines. Allen in view of Rieke does not explicitly disclose that the value being incorporated into the baseline is a invariant defined by a tuple including, the grouping property and the invariance identifying property. Allen in view of Rieke further does not explicitly disclose that anomaly detection is done in a cloud environment. However, Ahad discloses: Wherein the value is a invariant is defined by a tuple including, (i) the grouping property and the first equivalence rule associated with the grouping property, (Ahad: [0010]-[0011], [0094]-[0097], [0132]-[0135] and [0119] Provides for using tuples to define baseline behavior (normal operating conditions) for the system as well as component types and assemblies that define how resources are grouped.)
(ii) the invariance identifying property and the second equivalence rule associated with the invariance identifying property, wherein at least the second equivalence rule corresponds to the values of the invariance identifying property associated with the plurality of resources in the resource group being equivalent (Ahad: [0132]-[0136] Provides for defining metrics and their associated bounds (minimum/maximum values, soft limits) which teaches to identifying properties. Further provides what makes values "equivalent" (normal) by specifying ranges that constitute normal operation. Values that fall within the defined range are considered equivalent (all normal), while those outside the range are anomalous.)
Performing anomaly detection using a baseline in a cloud environment (Ahad: [0013], [0077]-[0083], [0119]-[0123] Provides for anomaly detector (ADRC) that uses defined baselines to detect anomalies in the cloud environment.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Allen in view of Rieke, which provides a method for grouping resources based on multiple attributes and evaluating them against specific conditions, with the teachings of Ahad, which introduces defining invariants as tuples of properties and rules and applying anomaly detection in cloud environments. One of ordinary skill in the art would recognize the ability to incorporate Ahad's tuple-based invariant definition and cloud-focused anomaly detection into the combined system to enhance its applicability and precision in modern computing environments. One of ordinary skill in the art would be motivated to make this modification in order to create a more robust anomaly detection system capable of formally defining normal behavior using comprehensive tuples.
In reference to claim 20, The non-transitory computer readable medium storing specific computer-executable instructions of claim 19, further comprising: identifying the first equivalence rule associated with the grouping property, (Allen: paragraph [0032] provides for grouping assets based on shared state and event information, which teaches the use of a first equivalence rule for determining similarity of the grouping property.)
Wherein the first equivalence rule corresponds to the values of the grouping property associated with the plurality of resources being in a first predetermined range of values associated with the grouping property, (Allen: paragraph [0032] provides for grouping assets based on shared state and event information (values of the grouping property satisfying the first equivalence rule). Allen paragraphs [0081] and [0109] further provides for normalizing attribute values within a predetermined range, which teaches the claim's description of an equivalence rule involving a predetermined range of values (first equivalence rule corresponding to values being in a first predetermined range.))
Allen does not explicitly disclose the use of a second equivalence rule, however Rieke teaches:
a second equivalence rule associated with a first invariance identifying property, (Rieke: paragraph [0107] provides for the evaluation of relationships based on attributes derived from assets or events, which teaches the use of a second equivalence rule for determining similarity of the invariance identifying property.)
In reference to claim 21, The non-transitory computer readable medium storing specific computer-executable instructions of claim 19, wherein the grouping property is different than the invariance identifying property (Rieke: paragraph [0107] provides for the evaluation of relationships based on attributes derived from assets or events, which teaches the use of a second equivalence rule for determining similarity of the invariance identifying property.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this office action. Accordingly, THIS ACTION IS MADE FINAL. 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 extension fee 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 AIDAN EDWARD SHAUGHNESSY whose telephone number is (703)756-1423. The examiner can normally be reached on Monday-Friday from 7:30am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Nickerson, can be reached at telephone number (469) 295-9235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/usptoautomated-interview-request-air-form.
/A.E.S./Examiner, Art Unit 2432
/Jeffrey Nickerson/Supervisory Patent Examiner, Art Unit 2432