CTNF 19/013,402 CTNF 82490 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-21 and 25 are pending for examination in the instant application. Information Disclosure Statement The information disclosure statement (IDS) submitted on 01/08/2025 and 05/19/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 07-30-03-h AIA Claim Interpretation Claim 20 recite “One or more machine-readable media storing instructions which, when executed by one or more hardware processors, perform operations comprising”. In light of specification paragraphs [0337], applicant excludes the computer readable medium as medium being a “transmission medium”. Examiner however will suggest the applicant to add the phrase “non-transitory” in claim 20 to clarification purposes and to avoid any further ambiguity. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-4 and 6-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rameshkumar et al. (Pub. No.: US 2017/0286252 A1), hereinafter “Ram” in view of Chen et al. (Patent No.: US 9396028 B2), hereinafter “Chen” and further in view of Nagpal et al. (Patent No.: US 10691491 B2), hereinafter “Nag” . As to claim 1. Ram discloses, an apparatus (Ram, Abstract and fig.1) comprising: processing circuitry to perform operations comprising, upon receipt of a primary workload to be scheduled on a data center (Ram, fig.1, [0012], cloud scout manager monitors and drives model-based scheduling): determining resource requirements associated with the primary workload, the resource requirements comprising, for each of one or more microarchitecture resources, an amount of the resource consumed by the primary workload in the absence of resource contention (Ram, [0023], and [0027-0029], Model logic and workload profiling build workload profiles and models that represent per-workload behavior); a performance associated with running the primary workload on the compute node (Ram, fig.6, [0023-0025], model(s) built from monitored operating information are used to predict data-center behavior under scenarios and to facilitate allocation/scheduling); and selecting one of the compute nodes for placement of the primary workload based at least in part on the computed performance (Ram, fig.4, [0025], indicate logic provides predicted behavior to job scheduler/resource manager to facilitate placement decisions); and a network interface to transmit the primary workload to the selected one of the compute nodes (Ram, fig.8, [0007], System architecture includes communications interface and orchestration to enact placement). Ram however is silent to disclose explicitly, determining resource availabilities for a cluster of compute nodes, the resource availability for each compute node comprising an amount of each of the one or more microarchitecture resources that is available to the primary workload on the compute node (Chen, col.2 lines 5-13, identifying a workload in the set of pending workloads that is scheduled to utilize hypothetic resources, wherein hypothetic resources are idle computer resources that are currently not available, but can be made available to execute workloads through provisioning actions; holding the identified workload from dispatch to hypothetic resources for a holding period, wherein the holding period is a customizable duration of time.) Therefore, before the effective filing date of the instant application it would have been obvious to one of the ordinary skilled in the art to incorporate the teachings of “Chen” into those of “Ram” to provide a computer-implemented method for workload scheduling and resource provisioning. In accordance with the present invention, the computer implemented method includes the steps of scheduling a set of pending workloads for execution on computer resources in a computing environment to enhance the process of workload scheduling. Ram and Chen however are silent to disclose explicitly, operating a workload signature model on representations of the determined resource requirements and the resource availabilities to predict, for each compute node. Nag however discloses explicitly, operating a workload signature model on representations of the determined resource requirements and the resource availabilities to predict, for each compute node, a performance associated with running the primary workload on the compute node (Nag, col.7, lines 52-61, described instances of the resource performance predictive model according to the herein disclosed techniques. Specifically, instances of the predictive model manager can facilitate adapting pre-trained resource performance predictive models to dynamic hyperconverged computing environments. As can be observed, the training system 124 in the training hyperconverged computing environment 122 and the target system 128 in the target hyperconverged computing environment 126 can support an instance of such a predictive model manager.). Therefore, before the effective filing date of the instant application it would have been obvious to one of the ordinary skilled in the art to incorporate the teachings of “Nag” into those of “Ram and Chen” to provide a Systems for distributed resource system management. A first computing system operates in a first computing environment. A predictive model is trained in the first computing environment to form a trained resource performance predictive model that comprises a set of trained model parameters to capture at least computing and storage IO parameters that are responsive to execution of one or more workloads that consume computing and storage resources in the first computing environment. When the trained resource performance predictive model is deployed to a second computing environment, various computing system configuration differences, and/or workload differences and/or other differences between the first computing environment and the second computing environment are detected and measured. As to claim 2. The combined system of Ram, Chen and Nag discloses the invention substantially including, wherein determining the resource requirements comprises (Ram, [0023], operating information monitored by node monitoring 151-1, infrastructure 151-2, framework monitoring 151-3, software monitoring 151-4 or application monitoring 151-5 may be collected and at least temporarily stored in database 152. In some examples, model logic 153 may build one or more model(s) 153-1 using the collected operating information stored in database 152). causing the primary workload to be temporarily run alone on one of the compute nodes and receiving measurements of the resource requirements from that compute node (Chen fig.2, block 200-214, discloses, scheduling, provisioning, and dispatch based on measured/provisioned resources and supports the overall scheduling context in which measured workload runs would be used to determine requirements. Also see Nag, fig.3, steps 308-312, which describes running workloads in a controlled environment to obtain measured resource responses that become the workload signature used for scheduling.). As to claim 3. The combined system of Ram, Chen and Nag discloses the invention substantially including, wherein determining the resource availabilities comprises receiving, from the compute nodes, measurements of the resource availabilities in the presence of background workloads on the compute nodes (Nag, fig.3 and 5A, 502-514, col.13, lines 24-34, the detected environment change event invokes a modification of the then-current model parameters (e.g., the training model parameters 134 or derivatives thereof) to dynamically adapt the resource performance predictive model to the target environment (step 512). In certain embodiments, the model parameters are modified based on the environment differences associated with the environment change event. A learning phase of the resource performance predictive model 132.sub.2 can commence using the adapted model parameters (e.g., dynamically modified model parameters 136) (step 514).). As to claim 4. The combined system of Ram, Chen and Nag discloses the invention substantially including, wherein the resource requirements and resource availabilities comprise amounts for multiple microarchitecture resources (Nag, fig.1A, Fig.2 “predictive model”, fig.3, “training flow blocks 306-312” and fig.4 “training data table”.). As to claim 6. The combined system of Ram, Chen and Nag discloses the invention substantially as applied above including, wherein selecting one of the compute nodes for placement of the primary workload comprises comparing the predicted performances associated with running the primary workload on the compute nodes against a target value of a performance metric, and wherein the node selected for placement has a measured resource availability sufficient to meet the target value when executing the primary workload (Nag, Abstract, fig.1A step 110, fig.3 training/deploy/use; Ram, fig.6, [0024-0025], indicate logic provides predictive behavior and “what-if” insights to facilitate allocation against SLA/QoS requirements. The passages teach computing predicted performance per node and comparing/using those prediction relative to target SLO/QoS values. Further, Nag, fig.5A, collects local measurement so the scheduler has measured availability data). As to claim 7. The combined system of Ram, Chen and Nag discloses the invention substantially as applied above including, wherein the target value of the performance metric is a hardware service level objective (SLO) derived from a performance guarantee associated with the primary workload pursuant to a service level agreement (SLA) (Ram, fig.1, fig.6 block 602-606, [0023-0025], requirements 154 may include predefined rules or polices associated with service level agreement (SLA) 154-1, quality of service (QoS) 154-2, or reliability, availability and serviceability (RAS) 154-3 requirements.). As to claim 8. The combined system of Ram, Chen and Nag discloses the invention substantially as applied above including, wherein selecting one of the compute nodes for placement of the primary workload is further based on a cluster-level optimization policy (Ram, fig.1, 4, [0024-0025], requirements 154 may include predefined rules or polices associated with service level agreement (SLA) 154-1, quality of service (QoS) 154-2, or reliability, availability and serviceability. Nag teaches fig.1a , step-110, fig.2, fig.5a e.g. model deployment/adaptation and use of scheduling). As to claim 9. The combined system of Ram, Chen and Nag discloses the invention substantially as applied above including, wherein the workload signature model comprises a machine-learned model (Ram, [0023]). As to claim 10. The combined system of Ram, Chen and Nag discloses the invention substantially as applied above including, wherein the machine-learned model is based on training data (Nag, Abstract, fig.3, fig.4) comprising, for each of a plurality of collocation scenarios between primary and background workloads, associated measured resource availability and resource requirement vectors correlated with measured performance values (Nag, fig.4, training data tables, fig.3, blocks 306-312, shows training data and model data including node configuration, resource metrics and performance metrics. Nag further shows in fig.4, training data and “workload-performance correlation that explicitly links observed resource responses to performance outcomes. Ram, fig.6, [0023-0025] discloses predict/indicate logic uses model outputs (predicted behavior) and compares them to SLA/QoS targets.). As to claim 11. The combined system of Ram, Chen and Nag discloses the invention substantially as applied above including, wherein the processing circuitry is at least in part configured by instructions stored in one or more computer-readable media to perform the operations (Ram, [0045]). As to claim 12. The combined system of Ram, Chen and Nag discloses the invention substantially as applied above including, wherein the processing circuitry comprises one or more hardware accelerators to implement at least part of the operations (Ram, [0010] and apparatus 500 / circuitry 520.). As to claim 13 is rejected for same rationale as applied to claim 1 above. As to claim 14 is rejected for same rationale as applied to claim 2 above. As to claim 15 is rejected for same rationale as applied to claim 3 above. As to claim 16 is rejected for same rationale as applied to claim 4 above. As to claim 17 is rejected for same rationale as applied to claim 6 above. As to claim 18 is rejected for same rationale as applied to claim 8 above. As to claim 19 is rejected for same rationale as applied to claim 10 above. As to claim 20 is rejected for same rationale as applied to claim 1 above . 07-21-aia AIA Claim (s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Ram, Chen and Nag” as applied to parent claims above in view of Madduri et al. (Pub. No.: US 20190196827 A1), hereinafter “Madd” . As to claim 5. The combined system of Ram, Chen and Nag discloses the invention substantially including, the multiple microarchitecture resources (Ram, fig.6, [0023], collecting per-node operating metrics). Ram, Chen and Nag however are silent to disclose explicitly, last-level chance (LLC) and memory bandwidth. Madd discloses a similar concept in the same field of endeavor including, last-level chance (LLC) and memory bandwidth (Madd, [0096], The set of shared cache units 606 may include one or more mid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), or other levels of cache, a last level cache (LLC), and/or combinations thereof.). Therefore, before the effective filing date of the instant application it would have been obvious to one of the ordinary skilled in the art to incorporate the teachings of “Madd” into those of “Ram, Chen and Nag” to provide method for performing signed multiplication of packed signed doublewords and accumulation with a signed quadword. For example, one embodiment of a processor comprises: a first source register to store a first plurality of packed signed doubleword data elements; a second source register to store a second plurality of packed signed doubleword data elements; a third source register to store a plurality of packed signed quadword data elements . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached PTO-892 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAUQIR HUSSAIN whose telephone number is (571)270-1247. The examiner can normally be reached M-F 7:00 - 8:00 with IFP. 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) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian J Gillis can be reached on 571 272-7952. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Tauqir Hussain/Primary Examiner, Art Unit 2446 Application/Control Number: 19/013,402 Page 2 Art Unit: 2446 Application/Control Number: 19/013,402 Page 3 Art Unit: 2446 Application/Control Number: 19/013,402 Page 4 Art Unit: 2446 Application/Control Number: 19/013,402 Page 5 Art Unit: 2446 Application/Control Number: 19/013,402 Page 6 Art Unit: 2446 Application/Control Number: 19/013,402 Page 7 Art Unit: 2446 Application/Control Number: 19/013,402 Page 8 Art Unit: 2446 Application/Control Number: 19/013,402 Page 9 Art Unit: 2446 Application/Control Number: 19/013,402 Page 10 Art Unit: 2446
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