Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) are rejected under 35 U.S.C. 103 as being unpatentable over Vuda et al. (USPN 20240291718A1) in view of Anand et al. (USPN 20230273852A1).
As per claim 1, Vuda et al. discloses a system, comprising: a processor; and a non-transitory memory storing instructions that, when executed, cause the processor (paragraph 0043 - The client 126 can include one or more processors that process software or other computer-readable instructions and include a memory to store the software, computer-readable instructions, and data.) to:
identify a problem instance for a workload associated with a plurality of data service platforms (paragraph 0067 - Presenting the topology can also include, at block 355, displaying interface elements representing anomalous relationships determined at block 337.; paragraph 0060 - Determining the network topology information can further include, at block 321, mapping service-to-service relationships and workload-to-service relationships.; paragraph 0037 - An orchestration tools, such as KUBERNETES, distributes and manages services and workloads across the nodes 133 of the cluster 105. Services are abstractions representing applications running on a set of pods 135. Services can be applications, software components, or functionalities that are made available to users or other systems. Services can have relationships with other services and workloads.; paragraph 0039 - status of the object (e.g., pod, node, workload) – services are a part of a plurality of data service platforms),
determine, using at least one machine learning model, a problem solution based on the problem instance and a catalog of problem solutions, wherein the at least one machine learning mode is trained based on: a predetermined set of problem patterns, a predetermined set of problem solutions, historical problem solutions or labelled problem solutions (paragraph 0078 - The subset model 613 can be a set of rules, an algorithm, or a trained machine learning model configured to determine candidate remediation plans (e.g., candidate remediation plans 503) for identified subsets of the network topology based on the signatures of the subsets.; paragraph 0102 - Some embodiments train the machine learning model using a training data set comprising historical subsets and corresponding modifications implemented for the historical subsets, which can be stored in database (e.g., in topology log 215).,. the modifications are the solutions/remediations, as disclosed in paragraph 0098),
execute operations included in the problem solution across the plurality of data service platforms (paragraph 0089 - Using the candidate remediation plans 503, the remediation analysis system can generate updated network topologies incorporating the modifications of the candidate remediation plans and predicted changes in the characteristics of the entities updated network topologies resulting from the modifications.; paragraph 0080 - The remediation plans can include changes to a subset of the network topology, such as rebooting a node in the subset, deploying additional pods or nodes for the subset, removing a node from the subset, allocating a node to a service, redirecting traffic to a different node, adding or reconfiguring a network load balancer, and the like. The remediation plans 617 can also include modifying workloads, such as deleting a workload, reconfiguring a workload, and scaling a workload.), and
recover the workload in accordance with a determination that the problem instance is resolved by the problem solution (paragraph 0080 - The remediation plans 617 can also include modifying workloads, such as deleting a workload, reconfiguring a workload, and scaling a workload.).
Vuda et al. fails to explicitly state wherein the problem instance is identified during either a development stage or a production stage of the plurality of data service platforms.
Vuda et al. discloses paragraph 0067 - Presenting the topology can also include, at block 355, displaying interface elements representing anomalous relationships determined at block 337; paragraph 0039 - events (e.g., errors, warnings, or anomalies).
Anand et al. discloses wherein the problem instance is identified during either a development stage or a production stage of the plurality of data service platforms paragraph 0018 - Aspects of the present disclosure discuss a system and methods implemented by the system to automatically detect anomalies in components 142 of the production computing environment 140 in real time or near real time, and further to automatically and intelligently correct a system anomaly with minimal or no system downtime.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include detecting anomalies in a production computing environment of Anand in detecting anomalous relationships and events such as errors, warning or anomalies are detected of Vuda. A person of ordinary skill in the art would have been motivated to make the modification because the disclosed systems and methods timely and automatically identifies and fixes anomalous behavior occurring in the production computing environment avoiding system downtime and consequential service interruption, as disclosed in paragraph 0007.
As per claims 3,13,21, Vuda et al. discloses wherein the problem solution is determined based on: determining, based on the problem instance, a problem pattern that exists in the workload, wherein the problem pattern is among a catalog of problem patterns each of which is associated with a respective problem solution in the catalog of problem solutions; and selecting, from the catalog of problem solutions, the problem solution that is associated with the problem pattern (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claims 4,14, Vuda et al. discloses wherein: the problem pattern is a juggler pattern where the workload is deployed with a less number of consumer instances across consumer applications than a total number of partitions from which messages are to be consumed; and the problem solution comprises at least one of: adding at least one additional consumer application, where partitions assigned to the at least one additional consumer application are co-assigned to existing consumer applications, or restarting a consumer application in accordance with a determination that the consumer application has a temporary issue causing the juggler pattern (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claims 5,15, Vuda et al. discloses wherein: the problem pattern is a time slicer pattern where the workload is deployed with consumer applications provisioned with a less number of processor cores than a number of consumer instances configured per consumer application; and the problem solution comprises at least one of: increasing a total quantity of consumer applications, where additional consumer instances in additional consumer applications are assigned with dedicated processor cores to process messages independent of other consumer instances, or increasing a total quantity of processor cores, where each consumer application provides dedicated processor cores for consumer instances running on the consumer application (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claims 6,16, Vuda et al. discloses wherein: the problem pattern is a headline pattern where a same topic in the workload is consumed by multiple consumer applications more than a predetermined threshold; and the problem solution comprises at least one of: stopping and re-starting at least one of the multiple consumer applications, or scaling up or down a total quantity of consumer instances (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claims 7,17, Vuda et al. discloses wherein: the problem pattern is a know-all pattern where one consumer application is consuming from multiple topics more than a predetermined threshold; and the problem solution comprises at least one of: stopping and re-starting the consumer application, or scaling up or down a total quantity of consumer instances (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claims 8,18, Vuda et al. discloses wherein: the problem pattern is a quiescent topic pattern where a topic is not consumed by any consumer application for a time period longer than a predetermined threshold; and the problem solution comprises at least one of: starting at least one consumer application in accordance with a determination that the at least one consumer application is inactive, or deleting the topic and re-claiming its associated space (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claims 9,19, Vuda et al. discloses wherein: the problem pattern is a diehard client pattern where a client application is implemented using an unsupported version of client library, wherein the client application is a producer application or a consumer application; and the problem solution comprises at least one of: stopping and re-starting the client application, or sending a notification for planning and upgrading the client application to use a supported version of client library or migrate to a newer data service platform (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claim 11, Vuda et al. discloses wherein: the problem solution is determined based on metadata of the workload; and the metadata of the workload comprises data related to: one or more clusters in the workload, a list of topics hosted on the one or more clusters, a number of partitions of each topic, partition assignment strategy for each topic, a list of consumer applications consuming each topic, and configurations of the one or more clusters and the topics (paragraphs 0079 - The anomaly detection criteria 615 can be a library of rules and metrics that trigger the remediation analysis system 501 to identify entities (e.g., nodes and edges) of the network topology for inclusion in a subset. The anomaly detection criteria 615 can include threshold values for individual characteristics or combinations of characteristics of the entities. For example, a particular anomaly detection criteria 615 may indicate that a node is unhealthy if a load is greater than the first threshold or response time is less than the second threshold.; paragraph 0080 - The remediation plan library 617 can be one or more datasets associating remediation plans (e.g., remediation plan 503) with corresponding subsets. – the cited problem pattern and problem solution are part of the anomaly detection criteria and remediation plan, respectively).
As per claim 12, Vuda et al. discloses a computer-implemented method, comprising: identifying a problem instance for a workload associated with a plurality of data service platforms (paragraph 0067 - Presenting the topology can also include, at block 355, displaying interface elements representing anomalous relationships determined at block 337.; paragraph 0060 - Determining the network topology information can further include, at block 321, mapping service-to-service relationships and workload-to-service relationships.; paragraph 0037 - An orchestration tools, such as KUBERNETES, distributes and manages services and workloads across the nodes 133 of the cluster 105. Services are abstractions representing applications running on a set of pods 135. Services can be applications, software components, or functionalities that are made available to users or other systems. Services can have relationships with other services and workloads.; paragraph 0039 - status of the object (e.g., pod, node, workload) – services are a part of a plurality of data service platforms),
determining, using at least one machine learning model, a problem solution based on the problem instance and a catalog of problem solutions, wherein the at least one machine learning mode is trained based on: a predetermined set of problem patterns, a predetermined set of problem solutions, historical problem solutions or labelled problem solutions (paragraph 0078 - The subset model 613 can be a set of rules, an algorithm, or a trained machine learning model configured to determine candidate remediation plans (e.g., candidate remediation plans 503) for identified subsets of the network topology based on the signatures of the subsets.; paragraph 0102 - Some embodiments train the machine learning model using a training data set comprising historical subsets and corresponding modifications implemented for the historical subsets, which can be stored in database (e.g., in topology log 215).,. the modifications are the solutions/remediations, as disclosed in paragraph 0098),
executing operations included in the problem solution across the plurality of data service platforms (paragraph 0089 - Using the candidate remediation plans 503, the remediation analysis system can generate updated network topologies incorporating the modifications of the candidate remediation plans and predicted changes in the characteristics of the entities updated network topologies resulting from the modifications.; paragraph 0080 - The remediation plans can include changes to a subset of the network topology, such as rebooting a node in the subset, deploying additional pods or nodes for the subset, removing a node from the subset, allocating a node to a service, redirecting traffic to a different node, adding or reconfiguring a network load balancer, and the like. The remediation plans 617 can also include modifying workloads, such as deleting a workload, reconfiguring a workload, and scaling a workload.), and
recovering the workload in accordance with a determination that the problem instance is resolved by the problem solution (paragraph 0080 - The remediation plans 617 can also include modifying workloads, such as deleting a workload, reconfiguring a workload, and scaling a workload.).
Vuda et al. fails to explicitly state wherein the problem instance is identified during either a development stage or a production stage of the plurality of data service platforms.
Vuda et al. discloses paragraph 0067 - Presenting the topology can also include, at block 355, displaying interface elements representing anomalous relationships determined at block 337; paragraph 0039 - events (e.g., errors, warnings, or anomalies).
Anand et al. discloses wherein the problem instance is identified during either a development stage or a production stage of the plurality of data service platforms paragraph 0018 - Aspects of the present disclosure discuss a system and methods implemented by the system to automatically detect anomalies in components 142 of the production computing environment 140 in real time or near real time, and further to automatically and intelligently correct a system anomaly with minimal or no system downtime.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include detecting anomalies in a production computing environment of Anand in detecting anomalous relationships and events such as errors, warning or anomalies are detected of Vuda. A person of ordinary skill in the art would have been motivated to make the modification because the disclosed systems and methods timely and automatically identifies and fixes anomalous behavior occurring in the production computing environment avoiding system downtime and consequential service interruption, as disclosed in paragraph 0007.
As per claim 20, Vuda et al. discloses a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor (paragraph 0043 - The client 126 can include one or more processors that process software or other computer-readable instructions and include a memory to store the software, computer-readable instructions, and data.), cause at least one device to perform operations comprising:
identifying a problem instance for a workload associated with a plurality of data service platforms (paragraph 0067 - Presenting the topology can also include, at block 355, displaying interface elements representing anomalous relationships determined at block 337.; paragraph 0060 - Determining the network topology information can further include, at block 321, mapping service-to-service relationships and workload-to-service relationships.; paragraph 0037 - An orchestration tools, such as KUBERNETES, distributes and manages services and workloads across the nodes 133 of the cluster 105. Services are abstractions representing applications running on a set of pods 135. Services can be applications, software components, or functionalities that are made available to users or other systems. Services can have relationships with other services and workloads.; paragraph 0039 - status of the object (e.g., pod, node, workload) – services are a part of a plurality of data service platforms),
determining, using at least one machine learning model, a problem solution based on the problem instance and a catalog of problem solutions, wherein the at least one machine learning mode is trained based on: a predetermined set of problem patterns, a predetermined set of problem solutions, historical problem solutions or labelled problem solutions (paragraph 0078 - The subset model 613 can be a set of rules, an algorithm, or a trained machine learning model configured to determine candidate remediation plans (e.g., candidate remediation plans 503) for identified subsets of the network topology based on the signatures of the subsets.; paragraph 0102 - Some embodiments train the machine learning model using a training data set comprising historical subsets and corresponding modifications implemented for the historical subsets, which can be stored in database (e.g., in topology log 215).,. the modifications are the solutions/remediations, as disclosed in paragraph 0098),
executing operations included in the problem solution across the plurality of data service platforms (paragraph 0089 - Using the candidate remediation plans 503, the remediation analysis system can generate updated network topologies incorporating the modifications of the candidate remediation plans and predicted changes in the characteristics of the entities updated network topologies resulting from the modifications.; paragraph 0080 - The remediation plans can include changes to a subset of the network topology, such as rebooting a node in the subset, deploying additional pods or nodes for the subset, removing a node from the subset, allocating a node to a service, redirecting traffic to a different node, adding or reconfiguring a network load balancer, and the like. The remediation plans 617 can also include modifying workloads, such as deleting a workload, reconfiguring a workload, and scaling a workload.), and
recovering the workload in accordance with a determination that the problem instance is resolved by the problem solution (paragraph 0080 - The remediation plans 617 can also include modifying workloads, such as deleting a workload, reconfiguring a workload, and scaling a workload.).
Vuda et al. fails to explicitly state wherein the problem instance is identified during either a development stage or a production stage of the plurality of data service platforms.
Vuda et al. discloses paragraph 0067 - Presenting the topology can also include, at block 355, displaying interface elements representing anomalous relationships determined at block 337; paragraph 0039 - events (e.g., errors, warnings, or anomalies).
Anand et al. discloses wherein the problem instance is identified during either a development stage or a production stage of the plurality of data service platforms paragraph 0018 - Aspects of the present disclosure discuss a system and methods implemented by the system to automatically detect anomalies in components 142 of the production computing environment 140 in real time or near real time, and further to automatically and intelligently correct a system anomaly with minimal or no system downtime.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include detecting anomalies in a production computing environment of Anand in detecting anomalous relationships and events such as errors, warning or anomalies are detected of Vuda. A person of ordinary skill in the art would have been motivated to make the modification because the disclosed systems and methods timely and automatically identifies and fixes anomalous behavior occurring in the production computing environment avoiding system downtime and consequential service interruption, as disclosed in paragraph 0007.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Vuda et al. (USPN 20240291718A1) in view of Anand et al. (USPN 20230273852A1) in further view of Park et al. (USPN 20190102266A1).
Vuda et al. fails to explicitly state the plurality of data service platforms store messages coming from producer applications; the messages are partitioned into different partitions with different topics; messages within each partition are ordered by their offsets; and partitions of all topics are distributed across clusters.
Vuda et al. does disclose in paragraph 0037 - An orchestration tools, such as KUBERNETES, distributes and manages services and workloads across the nodes 133 of the cluster 105. Services are abstractions representing applications running on a set of pods 135. Services can be applications, software components, or functionalities that are made available to users or other systems. Services can have relationships with other services and workloads.; paragraph 0039 - status of the object (e.g., pod, node, workload) – services are a part of a plurality of data service platforms.
Park et al. discloses the plurality of data service platforms store messages coming from producer applications (paragraph 0104 – kafka distributed streaming; paragraph 0109 - The messaging system 205 maintains this message ordering for producers and consumers.); the messages are partitioned into different partitions with different topics (paragraph 0108 - Since the messaging system 205 is a distributed system, the topics 215 may be divided into a number of partitions 220 (e.g., Partitions (A) and (X)) and replicated across multiple nodes or brokers 225, e.g., each partition can be placed on a separate machine to allow for multiple consumers to read from a topic in parallel (e.g., Partitions (A), (B), and (C).); messages within each partition are ordered by their offsets (paragraph 0109 - Each message within a partition 220 may have an identifier called its offset. The offset provides the ordering of messages as an immutable sequence.); and partitions of all topics are distributed across clusters (paragraph 0108 - Since the messaging system 205 is a distributed system, the topics 215 may be divided into a number of partitions 220 (e.g., Partitions (A) and (X)) and replicated across multiple nodes or brokers 225, e.g., each partition can be placed on a separate machine to allow for multiple consumers to read from a topic in parallel (e.g., Partitions (A), (B), and (C).).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the messages received in the kafka platform of Park in discovering failure of workload of Vuda. A person of ordinary skill in the art would have been motivated to make the modification because failures are discovered in the messages, as disclosed in paragraph 0107 - The new primary server pauses the processing of the incoming data (batch or stream), reads from the secondary output topic and writes to the primary output topic for the failed outputs (e.g., while the primary server was down and the synchronization system brought online the new primary server)..
Response to Arguments
Applicant's arguments and amendments filed 04/20/2026 have been fully considered. Upon further consideration, claim 10 is now rejected. Please see the above rejection.
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/Yolanda L Wilson/Primary Examiner, Art Unit 2113
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