DETAILED ACTION
Claims 1-20 are pending. Applicant has amended claims 1, 7 and 13.
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
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 1-4, 7-10, 13-16 and 19-20 are rejected 35 U.S.C. 103 as being unpatentable over Nagpal et al. (US 2022/0156114 A1) in view of Subramanian et al. (US 2020/0104184 A1).
As to claim 1, Nagpal teaches a computer-implementable method for performing a data center monitoring and management operation (a method; claim 1 and abstract), comprising:
receiving workload orchestration input data (The manager may … receive information about resource usage of various workload instances (which may be, for example, virtual machines or containers); paragraph [0018] and monitoring a workload may include monitoring resource usage, memory usage, latency, speed, health, etc.; paragraph [0020]);
applying the workload orchestration data to a network model (the manager 108 may use a neural network or other computational model created from monitoring workloads at the various computing environments; paragraph [0020], [0042]-[0046]);
generating a probability distribution of the workload orchestration data, the probability distribution including performance indicator information (“the manager may predict how the workload will execute at the cloud computing environment and suggest configurations of the instance” and “determine how the workload may execute at different computing environments with different architectures and provisions of computing resources”; paragraph [0020] and “performance indicator … workloads”; paragraphs [0039], [0042]-[0043]); and,
managing data center workload provisioning based upon the probability distribution of the workload orchestration data (The workload manager may also … automatically resize workloads … load balancer; paragraph [0040]).
Nagpal does not teach the performance indicator information including Key Performance Indicators, the probability distribution of the workload orchestration data including a probability distribution of the Key Performance Indicators.
However, Subramanian teaches the performance indicator information including Key Performance Indicators, the probability distribution of the workload orchestration data including a probability distribution of the Key Performance Indicators (“In some examples, pod manager 510 can specify target KPIs or other requirements for the workload request that accelerator 520 will use in place of or in addition to requirements specified by its SLA. Such parameters can be transferred to accelerator 520 as part of the request for resource configuration”; paragraph [0030] and “Accelerator 520 can receive resource configuration requests from pod manager 510 and provide the parameters (e.g., workload identifier, SLA requirement(s), required resource configuration or KPI, and so forth) of the request to resource determination module 522”; paragraph [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Subramanian to the system of Nagpal because both are in the same field of endeavor, and Subramanian teaches a method that improve application scheduling and deployment as well as application workload resource allocation in the edge and/or cloud computing environments (paragraph [0012]).
As to claim 2, Nagpal as modified by Subramanian teaches the method of claim 1, further comprising:
generating a signature of workload deployment operational data (see Nagpal: neural networks of the provisioning model may generate a fingerprint for a workload based on the workload data; paragraph [0045]); and
using the signature of the workload deployment operational data when managing the data center workload provisioning (see Nagpal: where the fingerprint forms a representation of the workload … identify other workloads … for the workload; paragraph [0045]).
As to claim 3, Nagpal as modified by Subramanian teaches the network model comprises a convolutional neural network (see Subramanian: At least because of potentially massive amount of telemetry information received, a hardware-based accelerator (e.g., a neural network, convolutional neural network, or other type of neural network) can be used to accelerate suggestions for resource allocations based on the telemetry data; paragraph [0023]); and, the generating the probability distribution uses the convolutional neural network (see Subramanian: Accelerator 116 can use an artificial intelligence (AI) model or models that use a supervised or unsupervised reinforcement learning scheme to guide its suggestions of compute resources. For example, for a workload, the AI model can consider any of measured telemetry data, performance indicators, boundedness, utilized compute resources, or evaluation or monitoring of the application performance (including the application's own evaluation of its performance); paragraph [0022]).
As to claim 4, Nagpal as modified by Subramanian teaches the method of claim 1, further comprising: applying an artificial intelligence (AI) for information technology (IT) operations (AIOps) operation when managing the data center workload provisioning (see Nagpal: paragraph [0044]).
As to claim 7, Nagpal teaches a system comprising:
a processor (processor 212; Fig. 3 and paragraph [0029]);
a data bus coupled to the processor (communication fabric 322; Fig. 3 and paragraph [0029]); and,
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for (One or more non-transitory computer readable media … cause a computing device to; claim 10 and paragraph [0030]):
receiving workload orchestration input data (The manager may … receive information about resource usage of various workload instances (which may be, for example, virtual machines or containers); paragraph [0018] and monitoring a workload may include monitoring resource usage, memory usage, latency, speed, health, etc.; paragraph [0020]);
applying the workload orchestration data to a network model (the manager 108 may use a neural network or other computational model created from monitoring workloads at the various computing environments; paragraph [0020], [0042]-[0046]);
generating a probability distribution of the workload orchestration data, the probability distribution including performance indicator information (“the manager may predict how the workload will execute at the cloud computing environment and suggest configurations of the instance” and “determine how the workload may execute at different computing environments with different architectures and provisions of computing resources”; paragraph [0020] and “performance indicator … workloads”; paragraphs [0039], [0042]-[0043]); and,
managing data center workload provisioning based upon the probability distribution of the workload orchestration data (The workload manager may also … automatically resize workloads … load balancer; paragraph [0040]).
Nagpal does not teach the performance indicator information including Key Performance Indicators, the probability distribution of the workload orchestration data including a probability distribution of the Key Performance Indicators.
However, Subramanian teaches the performance indicator information including Key Performance Indicators, the probability distribution of the workload orchestration data including a probability distribution of the Key Performance Indicators (“In some examples, pod manager 510 can specify target KPIs or other requirements for the workload request that accelerator 520 will use in place of or in addition to requirements specified by its SLA. Such parameters can be transferred to accelerator 520 as part of the request for resource configuration”; paragraph [0030] and “Accelerator 520 can receive resource configuration requests from pod manager 510 and provide the parameters (e.g., workload identifier, SLA requirement(s), required resource configuration or KPI, and so forth) of the request to resource determination module 522”; paragraph [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Subramanian to the system of Nagpal because both are in the same field of endeavor, and Subramanian teaches a method that improve application scheduling and deployment as well as application workload resource allocation in the edge and/or cloud computing environments (paragraph [0012]).
As to claim 8, see rejection of claim 2 above.
As to claim 9, see rejection of claim 3 above.
As to claim 10, see rejection of claim 4 above.
As to claim 13, Nagpal teaches a non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for (One or more non-transitory computer readable media … cause a computing device to; claim 10):
receiving workload orchestration input data (The manager may … receive information about resource usage of various workload instances (which may be, for example, virtual machines or containers); paragraph [0018] and monitoring a workload may include monitoring resource usage, memory usage, latency, speed, health, etc.; paragraph [0020]);
applying the workload orchestration data to a network model (the manager 108 may use a neural network or other computational model created from monitoring workloads at the various computing environments; paragraph [0020], [0042]-[0046]);
generating a probability distribution of the workload orchestration data, the probability distribution including performance indicator information (“the manager may predict how the workload will execute at the cloud computing environment and suggest configurations of the instance” and “determine how the workload may execute at different computing environments with different architectures and provisions of computing resources”; paragraph [0020] and “performance indicator … workloads”; paragraphs [0039], [0042]-[0043]); and,
managing data center workload provisioning based upon the probability distribution of the workload orchestration data (The workload manager may also … automatically resize workloads … load balancer; paragraph [0040]).
Nagpal does not teach the performance indicator information including Key Performance Indicators, the probability distribution of the workload orchestration data including a probability distribution of the Key Performance Indicators.
However, Subramanian teaches the performance indicator information including Key Performance Indicators, the probability distribution of the workload orchestration data including a probability distribution of the Key Performance Indicators (“In some examples, pod manager 510 can specify target KPIs or other requirements for the workload request that accelerator 520 will use in place of or in addition to requirements specified by its SLA. Such parameters can be transferred to accelerator 520 as part of the request for resource configuration”; paragraph [0030] and “Accelerator 520 can receive resource configuration requests from pod manager 510 and provide the parameters (e.g., workload identifier, SLA requirement(s), required resource configuration or KPI, and so forth) of the request to resource determination module 522”; paragraph [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Subramanian to the system of Nagpal because both are in the same field of endeavor, and Subramanian teaches a method that improve application scheduling and deployment as well as application workload resource allocation in the edge and/or cloud computing environments (paragraph [0012]).
As to claim 14, see rejection of claim 2 above.
As to claim 15, see rejection of claim 3 above.
As to claim 16, see rejection of claim 4 above.
As to claim 19, Nagpal as modified by Subramanian teaches the non-transitory, computer-readable storage medium of claim 13, wherein: the computer executable instructions are deployable to a client system from a server system at a remote location (see Nagpal: The memory 314 may hold computer readable instructions, files, data, etc., for execution by one or more of the processors 312 of the computing node 300. For example, the memory 314 includes manager instructions 324 which, when executed by the processors 312 implement the manger 108 of the central computing system 106. Where the computing node 300 is implemented as a node in a computing platform or cluster monitored by the central computing system 106, the memory 314 may hold computer readable instructions for workloads instantiated at the respective computing platform by the manager 108; paragraph [0030]).
As to claim 20, Nagpal as modified by Subramanian teaches the non-transitory, computer-readable storage medium of claim 13, wherein: the computer executable instructions are provided by a service provider to a user on an on-demand basis (see Nagpal: The memory 314 may hold computer readable instructions, files, data, etc., for execution by one or more of the processors 312 of the computing node 300. For example, the memory 314 includes manager instructions 324 which, when executed by the processors 312 implement the manger 108 of the central computing system 106. Where the computing node 300 is implemented as a node in a computing platform or cluster monitored by the central computing system 106, the memory 314 may hold computer readable instructions for workloads instantiated at the respective computing platform by the manager 108; paragraph [0030]).
Claims 5, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Nagpal et al. (US 2022/0156114 A1) in view of Subramanian et al. (US 2020/0104184 A1) further in view of Shukla et al. (US 2020/0034429 A1).
As to claim 5, Nagpal as modified by Subramanian does not teach the AIOps operation generates a performance measurement vector; and, managing the data center workload provisioning uses the performance measurement vector.
However, Shukla teaches generates a performance measurement vector; and, managing the data center workload provisioning uses the performance measurement vector (The workload classification and analysis … workload signatures. By using workload vector space to represent a workload … improve accuracy of workload classification; paragraphs [0038], [0043] and [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Shukla to the system of Nagpal as modified by Subramanian because Shukla teaches a method to learn and classify workloads executing within the system, and reuse the classified data for future use (paragraph [0002] and [0027]).
As to claim 11, see rejection of claim 5 above.
As to claim 17, see rejection of claim 5 above.
Claims 6, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Nagpal et al. (US 2022/0156114 A1) in view of Subramanian et al. (US 2020/0104184 A1) further in view of Jain et al. (US 2024/0007414 A1).
As to claim 6, Nagpal as modified by Subramanian does not teaches the AIOps operation generates a new signature vector for a given set of deployment operation data; and, the data center workload provisioning is managed based upon a comparison of the new signature vector with previous signature vectors.
However, Jain teaches the AIOps operation generates a new signature vector for a given set of deployment operation data; and, the data center workload provisioning is managed based upon a comparison of the new signature vector with previous signature vectors (the monitor circuit ID3_424 creates a collected resource utilization signature to represent a collection of resource utilization data. The monitor circuit ID3_424 may create a collected resource utilization signature for a group of AI models ID3_404a-c. For example, the monitor circuit ID3_424 may create a collected resource utilization signature for the group of AI models containing AI model ID3_404a and AI model ID3_404b and a different collected resource utilization signature for the group of AI models containing AI model ID3_404b and AI model ID3_404c. The collected resource utilization signature contains information about previous candidate models ID3_512a-c, ID3_516a-c selected for a group of AI models, the expected resource utilization data for the previously selected candidate models, and the newly collected resource utilization data for the group of AI models. The analyzer circuit ID33428 may access the collected resource utilization signature to compare the newly generated candidate models ID3_512a-c, ID3_516a-c to past resource utilization data to better select a resource utilization model for the AI models in the group that best optimizes the performance of the computing node ID33400 of FIG. ID3_4 when compared to an example where the collected resource utilization signature is not utilized.; paragraph [0312]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Jain to the system of Nagpal as modified by Subramanian because Jain teaches by using CNN, accuracy of extracted information is improved (paragraph [0690]).
As to claim 12, see rejection of claim 6 above.
As to claim 18, see rejection of claim 6 above.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/DIEM K CAO/Primary Examiner, Art Unit 2196
DC
June 5, 2026
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