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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 23 January 2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-30 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-10, 12-21, and 23-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian (US 11,507,430) and further in view of Abdulaal (US 2022/0138019).
Regarding claim 1, Subramanian teaches: A processor, comprising: one or more circuits to use one or more neural networks that receive as inputs both respective characteristics of one or more workloads (col. 5:35-37, “Accelerator 220 provides to AI model 222 the workload parameters from pod manager 210”) and one or more current system conditions (col. 5:37-43, “and also information related to the workload from workload table 224. Workload table 224 keeps track of previously performed workloads and their characteristics such as one or more of: boundedness (e.g., utilization of one or more of: processor, memory, network, storage, or cache), applied resource allocations, telemetry data, or workload performance characteristic(s)”) to predict one or more computing resources to perform one or more workloads (col. 5:43-45, “AI model 222 runs its inference model and produces a predicted best or recommended resource configuration for that workload”) based at least on analyzing, concurrently, the respective characteristics of the one or more workloads and one or more current system conditions (col. 4:65-67 and col. 5:1-3, “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)”) and to cause allocation of the one or more computing resources for execution of the one or more workloads (col. 5:49-50, “Pod manager 210 can choose to accept or reject the recommended resource configuration”).
Subramanian does not teach as clearly as Abdulaal teaches: predict one or more computing resources to perform one or more workloads (¶ 2, “generating performance predictions associated with the compliant hardware configurations using the workload features, a portion of the hardware specification information associated with the compliant hardware configurations, and a second machine learning model; generating a recommendation using the performance predictions, and the recommendation specifies a hardware configuration of the compliant hardware configurations”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of predict one or more computing resources to perform one or more workloads, as taught by Abdulaal, in the same way to the inputs, as taught by Subramanian. Both inventions are in the field of forecasting neural network or machine learning computing needs, and combining them would have predictably resulted in solving the problem of “poor resource allocation and utilization on the hardware platforms,” as indicated by Abdulaal (¶ 1).
Regarding claim 2, Abdulaal teaches: The processor of claim 1, wherein the one or more neural networks are further to recommend one or more nodes of a data center to perform the one or more workloads (¶ 25, “The recommendation may include a data node identifier and one or more hardware component identifiers”), wherein the one or more nodes comprise the one or more computing resources (¶ 3, “a data node of the data nodes includes a processor and memory”).
Regarding claim 3, Subramanian teaches: The processor of claim 2, further comprising: the one or more circuits to receive selection of the recommended one or more nodes of the data center (col. 3:53-55, “Pod manager 114 can accept or reject the resource allocation suggestion from accelerator 116”) and to allocate the recommended one or more nodes of the data center for the one or more workloads (claim 5, “causing at least a portion of the computing resources to perform the workload using the recommendation of the computing resource allocation”).
Regarding claim 4, Subramanian teaches: The processor of claim 1, further comprising: the one or more circuits to determine that the one or more computing resources are not sufficient to handle the one or more workloads during execution of the one or more workloads on the one or more computing resources (col. 10:45-47, “While the workload is running and potentially after its completion, telemetry data is collected on one or a variety of nodes or monitoring point or points” and col. 11:47-49, “A lowest reward (e.g., zero or a negative award value penalty) can be given if the SLA requirements are not met”), and to automatically allocate one or more additional computing resources for the one or more workloads (col. 8:1-3, “A different suggested configuration can be applied depending on the life stage of the workload”).
Regarding claim 5, Abdulaal teaches: The processor of claim 1, further comprising: the one or more circuits to process a first input associated with the one or more workloads (¶ 4, “The method includes obtaining, by the recommendation engine, a workload; generating workload features associated with the workload”) and a second input associated with available computing resources of a data center(¶ 4, “determining compliant hardware configurations of the data cluster using the workload features”) to predict the one or more computing resources to perform the one or more workloads from the available computing resources of the data center (¶ 4, “generating performance predictions associated with the compliant hardware configurations using the workload features”).
Regarding claim 6, Subramanian teaches: The processor of claim 5, wherein the first input comprises an industry associated with the one or more workloads (col. 9:41-50, “Resource determination module 522 can use an AI or ML model dedicated to determining and suggesting resource allocations for particular categories of applications. For example, resource determination module 522 can use an AI or ML model for any workload request from any database category of applications, a separate AI or ML model for any workload request from a social networking category of applications, and a separate AI or ML model for any workload request from an image processing category of applications”) and type of operation associated with the one or more workloads (col. 9:54-56, “Resource determination module 522 can use an AI or ML model for a particular resource (e.g., compute, storage, memory, networking)”).
Regarding claim 7, Subramanian teaches: The processor of claim 5, wherein the first input comprises information on at least two of data to be processed, a type of operation to perform on the data, a target time to complete the operation, or a type of model to be used for the operation (col. 4:65-67 and col. 5:1-3, “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)”).
Regarding claim 8, Abdulaal teaches: The processor of claim 5, wherein the second input comprises at least one of compute resources availability, network resources availability, storage resources availability, or memory resources availability (¶ 65, “The component(s) characteristics (218) may specify performance information of the associated component. The performance information may include, for example, clock speed, memory type, memory size, utilization, number of CPU cores, cache types, utilization, memory clock speed, maximum power limit, and other and/or additional performance information associated with the components without departing from the invention”).
Regarding claim 9, Subramanian teaches: The processor of claim 1, wherein the one or more circuits are further to generate a job profile comprising one or more workload details of the one or more workloads (col. 5:38-43, “Workload table 224 keeps track of previously performed workloads and their characteristics such as one or more of: boundedness (e.g., utilization of one or more of: processor, memory, network, storage, or cache), applied resource allocations, telemetry data, or workload performance characteristic(s)”).
Regarding claim 10, Subramanian teaches: The processor of claim 1, further comprising: the one or more circuits to monitor execution of the one or more workloads on the one or more computing resources (col. 10:45-47, “While the workload is running and potentially after its completion, telemetry data is collected on one or a variety of nodes or monitoring point or points”), determine whether the one or more computing resources were optimal for execution of the one or more workloads (claim 1, “determining at least one performance indicator associated with performance of the workload based at least, in part, on the received telemetry data”), and update training of the one or more neural networks (col. 2:17-20, “The AI model does not have to be trained as it will be configured to continuously learn on-the-go using, for example, reinforcement learning that develops based on rewards or penalties from resources it has suggested for use”).
Claims 12-21 recite commensurate subject matter as claims 1-10. Therefore, they are rejected for the same reasons.
Regarding claim 23, Abdulaal teaches: The data center of claim 12, wherein the one or more computing resources are components of a plurality of nodes of the data center that have heterogeneous hardware (¶ 65, “The component(s) characteristics (218) may specify performance information of the associated component. The performance information may include, for example, clock speed, memory type, memory size, utilization, number of CPU cores, cache types, utilization, memory clock speed, maximum power limit, and other and/or additional performance information associated with the components without departing from the invention”), and wherein a first node of the plurality of nodes varies from a second node of the plurality of nodes in terms of at least one of performance parameters of compute resources, performance parameters of memory resources, performance parameters of network resources, or performance parameters of storage resources (claim 6, “the hardware specification information specifies components and component characteristics associated with the hardware of the data nodes of the data cluster” and ¶ 65, “The performance information may include, for example, clock speed, memory type, memory size, utilization, number of CPU cores, cache types, utilization, memory clock speed, maximum power limit, and other and/or additional performance information associated with the components without departing from the invention”).
Claims 24-30 recite commensurate subject matter as claims 1, 2, 5-8, and 10. Therefore, they are rejected for the same reasons.
Claim(s) 11 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian and Abdulaal, as applied above, and further in view of da Silva (US 2022/0156639).
Regarding claim 11, Subramanian and Abdulaal do not teach; however, da Silva discloses: determine a confidence level that the predicted one or more computing resources are sufficient for the one or more workloads (¶ 32, “the apparatus may determine 206 a confidence value. A confidence value is a value that indicates a confidence of a prediction”) and output the confidence level (¶ 32, “the confidence value may accompany a prediction (e.g., processing workload prediction, processor type prediction, etc.) and may indicate a likelihood that the prediction is correct”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of determine a confidence level that the predicted one or more computing resources are sufficient for the one or more workloads and output the confidence level, as taught by da Silva, in the same way to predicted one or more computing resources, as taught by Subramanian and Abdulaal. Both inventions are in the field of predicting resource usage of machine learning/neural networks, and combining them would have predictably resulted in “reducing loading delay for a project or projects,” as indicated by da Silva (¶ 36).
Claim(s) 22 recite(s) commensurate subject matter as claim(s) 1. Therefore, it/they is/are rejected for the same reasons.
Conclusion
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/JACOB D DASCOMB/ Primary Examiner, Art Unit 2198