Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 15, 2025 and May 20, 2026 are in compliance with the provisions of 37 CFR 1.97, and accordingly, the IDS have been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 2-3, 5-10, 12-17, 19-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 7-14, 17-19 of U.S. Patent No. 12,289,364 (“Patent ‘364”) in view of Sheshadri et al. US Patent Publication No. 2022/0383324 (“Sheshadri”).
As shown below, the claims of Patent ‘364 substantially discloses the subject matter of the claims with the differences being obvious to one of ordinary skill in the art.
Instant Application
US Patent No. 12,289,364
1. One or more computing devices comprising: processing circuitry having access to memory storing executable instructions for a learning engine, the learning engine configured to:
11. A controller comprising processing circuitry having access to memory, the memory comprising instructions that, when executed, cause the processing circuitry to:
obtain first historical utilization data for a plurality of servers, the first historical utilization data comprising respective central processing unit (CPU) utilization at first times and respective data processing unit (DPU) utilization at second times,
wherein the CPU utilization is for one or more CPUs, and
wherein the DPU utilization is for one or more DPUs of one or more network interface cards (NICs);
obtain second historical utilization data for a plurality of services, the second historical utilization comprising respective resource utilization requirements for the plurality of services a process the first historical utilization data and the second historical utilization data to train a machine learning model to predict:
CPU utilization at a future time;
DPU utilization at the future time; and
a resource utilization requirement for a first service of the plurality of services at the future time.t third times; and
predict a resource utilization value for a service at a future time; for each server of a plurality of servers, predict a central processing unit (CPU) utilization at the future time and a data processing unit (DPU) utilization at the future time, wherein the DPU utilization is for a DPU of a network interface card (NIC) of the server;
schedule, based on the resource utilization value for the service at the future time, the CPU utilization at the future time for a particular server of the plurality of servers, and the DPU utilization at the future time for the DPU of the NIC of the particular server, the service to execute at least in part on the DPU of the NIC of the particular server.
12. The controller of claim 11, wherein the instructions cause the processing circuitry to: receive historical usage data for the plurality of servers and a plurality of services, the plurality of services comprising the service; and process the historical usage data to train a machine learning model.
13. The controller of claim 12, wherein the instructions cause the processing circuitry to:
predict, with the trained machine learning model, the resource utilization value for the service at the future time; and predict, with the trained machine learning model, the DPU utilization at the future time for the DPU of the NIC of the particular server.
17. The controller of claim 11, wherein to predict the resource utilization value for the service at the future time, the instructions cause the processing circuitry to: receive historical usage data associated with the service, a DPU usage, and one or more of: a CPU usage; a memory usage; or a network usage; and predict the resource utilization value for the service at the future time as a function of the historical usage data.
18. The controller of claim 17, wherein the service is associated with a first timestamp corresponding to a start of the service, and wherein the instructions cause the processing circuitry to: apply a vector autoregression machine learning model to the historical usage data to generate respective values, for a second timestamp following the first timestamp, for the DPU usage and one or more of: the CPU usage; the memory usage; or the network usage.
Claims 11 and 17 of Patent ‘365 disclose obtaining historical utilization data comprising CPU utilization and DPU utilization, “receive historical usage data associated with the service, a DPU usage, and one or more of: a CPU usage” but do not expressly disclose “at first times” and “at second times.”
Claims of Patent ‘364 do not disclose “the second historical utilization comprising respective resource utilization requirements for the plurality of services at third times” and predicting “a resource utilization requirement for a first service of the plurality of services at the future time.”
Claim 1 of the application does not further define the first and second times. One of ordinary skill in the art would have recognized that the obtaining of historical utilization data disclosed by claims of Patent ‘365 occur at some particular point in times, and it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have collected usage data at first and second times in order to have received updates to the usage data for training the machine learning model and providing updated predictions.
Furthermore, Sheshadri discloses obtaining first historical utilization data for a plurality of servers, the first historical utilization data comprising central processing unit (CPU) utilization at first times (para. [0016] computing resources… CPUs. para. [0047] metrics, analytics, and other data for past server and/or computing resource usage by one or more demand devices (e.g., computing devices, servers, systems, and the like)); obtaining historical utilization comprising respective resource utilization requirements for a plurality of services at third times and predicting a resource utilization requirement for a first service of the plurality of services at the future time (para. [0013] intelligent machine learning (ML) engine that analyzes past computing resource usage during different time periods to predict potential future usage of computing resources. service provider may intelligently allocate computing resources to different computing services based on predicted requirements. para. [0049] using the metrics and/or past resource usages during particular periods and/or windows of time. ML models may therefore predict future resource usage). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the claim invention with Sheshadri’s disclosure in order to have utilized data obtained at different time periods and the prediction to allocate server and computing resources at one or more future times based on the available resources of the service provider's platform and/or system at the future time(s) (see para. [0050]).
Claims 2-3, 6-8 are unpatentable over claims 11, 14, 18-19 of Patent ‘364.
Regarding claim 5, claims of Patent ‘364 do not disclose the one or more computing devices of claim 1, wherein the first service comprises one of a network, security, storage, data processing, co-processing, or machine learning service. Sheshadri discloses a first service comprising one of a network, security, storage, data processing, co-processing, or machine learning service (para. [0012] service provider, such as an online transaction processor, may provide computing services to end users, devices, and other servers and/or online platforms. online transaction processor may provide electronic transaction processing, authentication, risk analysis and fraud detection). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the claim invention with Sheshadri’s disclosure for benefits of providing different types of services and intelligent allocation of resources to the different types of services based on predicted requirements (para. [0013]).
Claims 8-10, 13-14 are unpatentable over claims 1-5, 7-10 of Patent ‘364. The differences between claim 8 and claim 1 of Patent ‘364 would have been obvious to one of ordinary skill in the art for the reasons provided above with respect to claim 1.
Regarding claim 12, claims of Patent ‘364 do not disclose the method of claim 8, wherein the first service comprises one of a network, security, storage, data processing, co-processing, or machine learning service. Sheshadri discloses a first service comprising one of a network, security, storage, data processing, co-processing, or machine learning service (para. [0012] service provider, such as an online transaction processor, may provide computing services to end users, devices, and other servers and/or online platforms. online transaction processor may provide electronic transaction processing, authentication, risk analysis and fraud detection). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the claim invention with Sheshadri’s disclosure for benefits of providing different types of services and intelligent allocation of resources to the different types of services based on predicted requirements (para. [0013]).
Claims 15-17 and 20 are unpatentable over claims 11-14, 17-19 of Patent ‘364. The differences between claim 15 and claim 11 of Patent ‘364 would have been obvious to one of ordinary skill in the art for the reasons provided above with respect to claim 1.
Regarding claim 19, claims of Patent ‘364 do not disclose the non-transitory computer-readable media of claim 15, wherein the first service comprises one of a network, security, storage, data processing, co-processing, or machine learning service. Sheshadri discloses a first service comprising one of a network, security, storage, data processing, co-processing, or machine learning service (para. [0012] service provider, such as an online transaction processor, may provide computing services to end users, devices, and other servers and/or online platforms. online transaction processor may provide electronic transaction processing, authentication, risk analysis and fraud detection). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the claim invention with Sheshadri’s disclosure for benefits of providing different types of services and intelligent allocation of resources to the different types of services based on predicted requirements (para. [0013]).
Claims 4, 11, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of U.S. Patent No. 12,289,364 (“Patent ‘364”) in view of Sheshadri and Jasleen et al. US Patent Publication No. 2022/0222122 (“Jasleen”).
Regarding claim 4, claims of Patent ‘364 do not disclose the one or more computing devices of claim 1, wherein the processing circuitry is configured to iteratively train the trained machine learning model with a current resource utilization requirement for the first service. Jasleen discloses processing circuitry configured to iteratively train a trained machine learning model with a current resource utilization requirement for a first service (para. [0017] resource allocation may become more accurate over time as collected data is fed back into the model-based resource allocation to form a closed loop system that retrains the model. para. [0028] machine learning model trained on telemetry data including application usage data and system data to predict the resource requirement of the target application at runtime). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the claim invention with Jasleen’s disclosure because it would have been desirable to retrain the model in order to have provided more accurate resource allocation.
Regarding claim 11, claims of Patent ‘364 does not disclose the method of claim 8, further comprising: iteratively training, by the one or more computing device, the trained machine learning model with a current resource utilization requirement for the first service. Jasleen discloses iteratively training a trained machine learning model with a current resource utilization requirement for a first service (para. [0017] resource allocation may become more accurate over time as collected data is fed back into the model-based resource allocation to form a closed loop system that retrains the model. para. [0028] machine learning model trained on telemetry data including application usage data and system data to predict the resource requirement of the target application at runtime). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the claim invention with Jasleen’s disclosure because it would have been desirable to retrain the model in order to have provided more accurate resource allocation.
Regarding claim 18, claims of Patent ‘364 do not disclose the non-transitory computer-readable media of claim 15, wherein the instructions cause the processing circuitry to: iteratively train the trained machine learning model with a current resource utilization requirement for the first service. Jasleen discloses iteratively training a trained machine learning model with a current resource utilization requirement for a first service (para. [0017] resource allocation may become more accurate over time as collected data is fed back into the model-based resource allocation to form a closed loop system that retrains the model. para. [0028] machine learning model trained on telemetry data including application usage data and system data to predict the resource requirement of the target application at runtime). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the claim invention with Jasleen’s disclosure because it would have been desirable to retrain the model in order to have provided more accurate resource allocation.
Allowable Subject Matter
Claims 1-20 would be allowable if the nonstatutory double patenting, set forth in this Office action, is overcome by amendment and/or the filing of a terminal disclaimer.
Sheshadri et al. US Patent Publication No. 2022/0383324 discloses obtaining historical utilization comprising respective resource utilization requirements for a plurality of services at third times and processing the historical utilization data to train a machine learning model to predict resource utilization requirement for a first service of the plurality of services at the future time (para. [0013] intelligent machine learning (ML) engine that analyzes past computing resource usage during different time periods to predict potential future usage of computing resources. service provider may intelligently allocate computing resources to different computing services based on predicted requirements. para. [0016] computing resources… CPUs. para. [0049] using the metrics and/or past resource usages during particular periods and/or windows of time. ML models may therefore predict future resource usage).
Dangi et al. US Patent Publication No. 2024/0112050 discloses obtaining historical utilization comprising respective resource utilization requirements for a plurality of services at third times and processing the historical utilization data to train a machine learning model to predict resource utilization requirement for a first service of the plurality of services at the future time (para. [0028] find out a CPU core requirement for each application or job by analyzing historical CPU core consumption data for that application or job using machine learning. core usage information can be stored in a data store. core usage information can be used by the ML model to predict or forecast the CPU core requirement for each application for a given point in time (e.g., next hour)
Hoyer et al. US Patent Publication No. 2011/0289333 discloses obtaining historical utilization comprising respective resource utilization requirements for a plurality of services at third times and processing the historical utilization data to train a machine learning model to predict resource utilization requirement for a first service of the plurality of services at the future time (para. [0014] predominant or dominant period is determined from the past chronological progression of the resource capacity required for the respective service to predict the chronological progression of the resource capacity required for a respective service. predetermined time interval is then divided into partial intervals with the length of the predominant period, wherein the predicted chronological progression of the resource capacity required for the respective service is determined for a future time interval).
Ma et al. US Patent Publication No. 2019/0286486 discloses obtaining historical utilization comprising respective resource utilization requirements for a plurality of services at third times and processing the historical utilization data to train a machine learning model to predict resource utilization requirement for a first service of the plurality of services at the future time (para. [0033] system resource prediction computer cluster 606, also referred to as the predictor, is responsible for predicting resource requirement for client applications at a future time based on machine learning algorithms. para. [0037] processed run-time data for each application may be used as training data 705. para. [0038] run-time data that may be used as training data 705 for each client application may include but is not limited to number of user requests in a given time interval, the number of CPU cores and memory size required by each user request, user request timestamps and duration).
Lakshimikantha et al. US Patent Publication No. 2021/0303985 discloses obtaining historical utilization data for a plurality of services; and processing the historical utilization data to train a machine learning model to predict: CPU utilization at a future time (para. [0044] resource utilization may include… disk, disk space, memory, CPU. para. [0046] CPU utilization refers to a computer's usage of processing resources. para. [0125] each request comprising information describing resource requirements required by a respective file system service; collecting, over a period of time, resource utilization data of the plurality of resources, the resource utilization data comprising an identification of a resource, a timestamp, and a measurement indicating a utilization level of the resource corresponding to the timestamp; training, using the resource utilization data, a machine learning model to predict utilization patterns of the plurality of resources; based on the predicted utilization patterns, scheduling execution of the file system services).
Jasleen et al. US Patent Publication No. 2022/0222122 discloses obtaining historical utilization data and processing the historical utilization data to train a machine learning model to predict utilization at a future time (para. [0028] machine learning model trained on telemetry data including application usage data and system data to predict the resource requirement of the target application at runtime).
Jreij et al. US Patent Publication No. 2022/0179700 discloses predicting resource utilization requirements for a service (para. [0205] generates a prediction regarding the computing resources required for providing the database services. predicts that during a first time period, a compute acceleration unit (616) will be required for providing the database services).
Levy et al. US Patent Publication No. 2024/0134970 discloses monitoring resource of a data processing unit (DPU) and scheduling a workload on the DPU based on the resource (para. [0055] resources 110 may be referred to as DPU resources. para. [0077] orchestration platform may consume the resources 110. para. [0085] DPU resource management circuit 170-b may register resources to a scheduling entity that can discover the resources and schedule a workload if the resource exists.).
Jigular et al. US Patent Publication No. 2024/0028375 discloses monitoring resource of a data processing unit (DPU) and scheduling a service on the DPU based on resource of the DPU (para. [0031] scheduling service that monitors resource usage of the host devices 106. also track resource usage of DPU devices 109. para. [0034] scheduling of workloads 130 executed using virtual resources that arc mapped to the physical resources the host device 106 and/or the DPU device 109).
The prior art of record does in teach, individually or in combination, the invention as claimed including: obtain first historical utilization data for a plurality of servers, the first historical utilization data comprising respective data processing unit (DPU) utilization at second times, wherein the CPU utilization is for one or more CPUs, and wherein the DPU utilization is for one or more DPUs of one or more network interface cards (NICs); obtain second historical utilization data for a plurality of services, the second historical utilization comprising respective resource utilization requirements for the plurality of services at third times; and process the first historical utilization data and the second historical utilization data to train a machine learning model to predict: CPU utilization at a future time; DPU utilization at the future time; and a resource utilization requirement for a first service of the plurality of services at the future time.
Examiner’s Note
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Wood et al. US Patent Publication No. 2010/0083248 (para. [0042] predicting resource usage, particularly, CPU requirements, of an application running in a virtual environment or system. para. [0049] once a prediction model is generated or created, it may be applied to resource utilization traces of other applications in order to predict what their CPU requirements are going to be).
Doddavula US Patent Publication No. 2012/0089726 (para. [0040] once a prediction model is generated or created, it may be applied to resource utilization traces of other applications in order to predict what their CPU requirements are going to be).
Bordawekar et al. US Patent Publication No. 2016/0379125 (para. [0054] system can provide a rough estimate of application resource requirements and predict system utilization levels, such as how much CPU that the application will consume when executing on a newly available provision type)
Conclusion
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/JOSHUA JOO/Primary Examiner, Art Unit 2445
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