DETAILED ACTION
This office action is in response to application filed on 7/26/2024.
Claims 1 – 20 are pending.
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) 1 – 3, 5, 6 8 – 10, 12, 13, 15 – 17, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Higginson et al (US 20230153165, hereinafter Higginson), in view of Cheng et al, “Wide and Deep Learning for Recommender Systems”, arXiv: 1606.07792, 6/24/2016, pages 1 – 4 (prior art part of IDS dated 6/4/2025, hereinafter Cheng).
As per claim 1, Higginson discloses: A system comprising: a memory that stores instructions; and one or more processors coupled to the memory and configured to execute the instructions to perform operations comprising:
providing attribute data for software as wide input to a wide part of a trained machine learning model; (Higginson [0078]: “The system determines entity attributes for an entity that utilizes computational resources (operation 204). For example, the entity attributes may be retrieved from a data repository and/or received in a request. The entity attributes may include a level of granularity associated with components utilizing the computational resources (e.g., virtual machine, database, application, application server, database server, transaction, etc.), a metric representing the utilization of the computational resources (e.g., processor, memory, network, I/O, storage, and/or thread pool usage), and/or a user or organization representing a customer or owner of the components. The entity attributes may describe a topology associated with a particular workload at a specified level of granularity.”)
providing time-series resource usage data for the software as deep input to a deep part of the trained machine learning model; (Higginson [0080]: “The entity attributes are matched to a time-series model that is trained on historical time-series data for the entity (operation 206). For example, the entity attributes may be used as keys in a lookup of the time-series model in a model repository and/or an environment in which the time-series model is deployed. As an example, a set of entity attributes may describe the entity topology at a particular level of granularity, such as: 4 nodes of a node cluster, each node including 8 processors, 3 nodes including processors of type A with X number of processor cores each, 1 node including processors of type B with Y number of processor cores each. Another, more generalized, level of granularity associated with an entity topology may include: 1 node running a virtual machine and accessing a database of a type D and 1 sibling node in the same node cluster. The system compares a specified topology with stored topologies associated, respectively, with stored time-series models trained on historical time-series data for the respective topologies.”)
receiving, from the trained machine learning model, a forecast resource usage for a plurality of future time periods for the software; based on the forecast resource usage for the plurality of future time periods, determining an amount of resources to provide for the software for a future time period of the plurality of future time periods; (Higginson [0081]: “The time-series model is then applied to additional time-series data for the entity to generate a forecast of the utilization of the computational resources by the entity (operation 208). For example, recently collected utilization metrics for the entity are inputted into the time-series model, and the time-series model generates output representing predictions of future values for the utilization metrics.”)
and based on the determined amount of resources, causing a resource cluster to be allocated to the software during the future time period. (Higginson [0082] - [0083]: “The forecast is output in association with the entity (operation 210). For example, the predicted future values may be displayed and/or outputted in a chart, table, log, file, and/or other representation. In turn, a representative of the entity and/or a manager of the computational resources can use the predicted future values to adjust allocation of resources to the entity and/or provision additional resources in anticipation of increased workload on the resources. Operations 202-208 may be repeated for remaining entities that utilize the computational resources. For example, a time-series model may be retrieved for each entity that utilizes resources in a cloud and/or distributed system, and a forecast of the entity’s resource utilization is generated and outputted to facilitate subsequent management, allocation, and/or provisioning of the resources.”)
Higginson did not explicitly disclose:
the wide part performing a weighted integration of the wide input;
the deep part of the trained machine learning model comprising a single layer transformer;
However, Cheng teaches:
the wide part performing a weighted integration of the wide input; (Cheng page 2, left column, section 3.1, The Wide Component.)
the deep part of the trained machine learning model comprising a single layer transformer; (Cheng page 2, right column, section 3.2, The Deep Component.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cheng into that of Higginson in order to have the wide part performing a weighted integration of the wide input and the deep part of the trained machine learning model comprising a single layer transformer. Cheng page 1, abstract teaches that wide learning and deep learning has their own strengths and weaknesses, and combining them would combine the benefits of memorization and generalization systems and resulting in a better prediction system, and such combination would enhance the overall appeals of all references and is therefore rejected under 35 USC 103.
As per claim 2, the combination of Higginson and Cheng further teach:
The system of claim 1, wherein the operations further comprise: generating the trained machine learning model by providing a training set comprising historical resource usage data for a plurality of applications and databases. (Higginson [0059])
As per claim 3, the combination of Higginson and Cheng further teach:
The system of claim 1, wherein each future time period of the plurality of future time periods is an hour and the plurality of future time periods is twenty-four future time periods. (Higginson [0059])
As per claim 5, the combination of Higginson and Cheng further teach:
The system of claim 1, wherein the operations further comprise preparing the time-series resource usage data using min-max scaling prior to providing the time-series resource usage data to the deep part of the trained machine learning model. (Higginson [0059])
As per claim 6, the combination of Higginson and Cheng further teach:
The system of claim 5, wherein the operations further comprise determining a score that represents a cluster workload in each time period by finding a weighted sum of resources used by the cluster in the time period. (Higginson [0048])
As per claim 8, it is the non-transitory computer-readable medium variant of claim 1 and is therefore rejected under the same rationale.
As per claim 9, it is the non-transitory computer-readable medium variant of claim 2 and is therefore rejected under the same rationale.
As per claim 10, it is the non-transitory computer-readable medium variant of claim 3 and is therefore rejected under the same rationale.
As per claim 12, it is the non-transitory computer-readable medium variant of claim 5 and is therefore rejected under the same rationale.
As per claim 13, it is the non-transitory computer-readable medium variant of claim 6 and is therefore rejected under the same rationale.
As per claim 15, it is the method variant of claim 1 and is therefore rejected under the same rationale.
As per claim 16, it is the method variant of claim 2 and is therefore rejected under the same rationale.
As per claim 17, it is the method variant of claim 3 and is therefore rejected under the same rationale.
As per claim 19, it is the method variant of claim 5 and is therefore rejected under the same rationale.
As per claim 20, it is the method variant of claim 6 and is therefore rejected under the same rationale.
Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Higginson and Cheng, and further in view of Desai et al (US 20200311573, hereinafter Desai).
As per claim 4, the combination of Higginson and Cheng did not teach:
The system of claim 1, wherein the determining of the amount of resources to provide for the software for the future time period comprises determining a number of capacity units to provide for the software, each capacity unit comprising one or more central processing units and memory.
However, Desai teaches:
The system of claim 1, wherein the determining of the amount of resources to provide for the software for the future time period comprises determining a number of capacity units to provide for the software, each capacity unit comprising one or more central processing units and memory. (Desai [0005] and [0013])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Desai into that of Higginson and Cheng in order to have the determining of the amount of resources to provide for the software for the future time period comprises determining a number of capacity units to provide for the software, each capacity unit comprising one or more central processing units and memory. Higginson [0082] – [[0083] teaches the resource forecast is generated and outputted to facilitate subsequent allocation of resources. Desai has taught that the claimed limitations are merely commonly known steps for the actual allocation of resources, applicants have thus merely claimed the combination of known parts in the field to achieve predictable results and is therefore rejected under 35 USC 103.
As per claim 11, it is the non-transitory computer-readable medium variant of claim 4 and is therefore rejected under the same rationale.
As per claim 18, it is the method variant of claim 4 and is therefore rejected under the same rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Higginson and Cheng, and further in view of Koottayi et al (US 20180288063, hereinafter Koottayi).
As per claim 7, the combination of Higginson and Cheng did not teach:
The system of claim 1, wherein the time-series resource usage data comprises user connection data, user queue data, and user group data.
However, Koottayi teaches:
The system of claim 1, wherein the time-series resource usage data comprises user connection data, user queue data, and user group data. (Koottayi [0100])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Koottayi into that of Higginson and Cheng in order to have the time-series resource usage data comprises user connection data, user queue data, and user group data. Koottayi teaches the claimed limitations are merely commonly known and used data types to be collected and used to train ML and is there merely an obvious design choice to use these specific resource data and is therefore rejected under 35 USC 103.
As per claim 14, it is the non-transitory computer-readable medium variant of claim 7 and is therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Desaulniers et al (US 20230055415) teaches “A method includes training a recurrent neural network by monitoring data in a memory of a first server as the first server executes jobs and by determining an amount of computing resources used by the first server while executing the jobs and applying the recurrent neural network to data in the memory to predict an amount of computing resources that the first server will use when executing a first future job. The method also includes, in response to determining that execution of the first future job did not meet a performance criterion, making a change to the first server. The method further includes further training the recurrent neural network using a reinforcement learning technique, applying the recurrent neural network to determine that the change should be made to a second server, and in response, making the change to the second server before the second server executes a second future job.”;
Al-Asaly et al, “A deep learning-based resource usage prediction model for resource provisioning in an autonomic cloud computing environment”, teaches “an autonomic and intelligent workload forecasting method for cloud resource provisioning based on the concept of autonomic computing and a deep learning approach. In particular, to predict future demand for CPU usage and determine how to respond to workload fluctuations in the next interval, we propose an efficient deep learning model based on a diffusion convolutional recurrent neural network (DCRNN). Existing deep learning models that are widely applied cannot handle accurate real-time forecasting due to the presence of inconsistent and nonlinear workloads in cloud computing systems. The goal of the proposed deep learning model is to improve forecasting accuracy and minimize the error between the predicted and the actual workloads.”;
Bi et al, “Integrated deep learning method for workload and resource prediction in cloud systems”, teaches “Cloud computing providers face several challenges in precisely forecasting large-scale workload and resource time series. Such prediction can help them to achieve intelligent resource allocation for guaran teeing that users’ performance needs are strictly met with no waste of computing, network and storage resources. This work applies a logarithmic operation to reduce the standard deviation before smoothing workload and resource sequences. Then, noise interference and extreme points are removed via a pow erful filter. A Min–Max scaler is adopted to standardize the data. An integrated method of deep learning for prediction of time series is designed. It incorporates network models including both bi-directional and grid long short-term memory network to achieve high-quality prediction of workload and resource time series. The experimental comparison demonstrates that the prediction accuracy of the proposed method is better than several widely adopted approaches by using datasets of Google cluster trace.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES M SWIFT whose telephone number is (571)270-7756. The examiner can normally be reached Monday - Friday: 9:30 AM - 7PM.
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/CHARLES M SWIFT/Primary Examiner, Art Unit 2196
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