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 .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Mar. 14, 2024.
Claims 1-20 are pending in the case. Claims 1, 7 and 13 are independent claims.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over NAGARAJEGOWDA et al. (hereinafter Nag) U.S. Patent No. 2023/0136532 in view of Kaira et al. (hereinafter Kaira) U.S. Patent Publication No. 2022/0004174.
With respect to independent claim 1, Nag teaches a computer-implementable method for performing a data center monitoring and management operation, comprising:
monitoring a workload executing on a data center asset (see e.g., Para [3][35]-[37] – “performing a data center monitoring and management operation, comprising: identifying a plurality of assets within a data center; monitoring usage of the plurality of assets within the data center; generating data center asset profile data based upon the monitoring; identifying a plurality of asset configurations related to the asset profile data; ranking the plurality of asset configurations based upon the data center asset profile data … data center asset data 222 broadly refers to information associated with a particular data center asset 244, such as an information handling system 100, or an associated workload … the data center asset data 222 may likewise include certain performance and configuration information associated with a particular workload, as described in greater detail herein. In various embodiments, the data center asset data 222 may include certain public or proprietary information related to data center asset 244 configurations associated with a particular workload.”);
analyzing utilization of the data center asset when the data center asset executes the workload (see e.g., Para [32]-[37] – “the data center monitoring and management console 118 may be implemented to include a monitoring module 120, a management monitor 122, an analysis engine 124, an offering comparison engine 126, and an offering selection engine 128, or a combination thereof In certain embodiments, the monitoring module 120 may be implemented to monitor the procurement”);
Nag does not expressly show training a machine learning model using the utilization of the data center asset when executing the workload, the training the machine learning model including performing a feature clustering operation using the utilization of the data center asset to provide separate groups of machine learning features and generating a data center asset utilization forecast using the machine learning model. However, Nag expressly teaches that configuration recommendation based on usage analysis (see e.g., Para [3]-[5][81]-[86][105]). Furthermore, Kaira teaches similar features (see e.g., Para [55][63]-[79] – “creating models per group and validating convergence of machine learning models … A machine learning model is used to determine parameters from the training dataset 202 (e.g., a set of labeled data points or data streams) that characterize machine groupings. The machine learning model may be implemented using any suitable data grouping model or clustering mode … he clustering may be performed using a distance calculation 208 that is capable of quantifying the distance between multiple data samples … the grouping function 208 is applied to the existing training dataset 202. For example, the grouping function is used to split the existing training dataset 202 into smaller training datasets or groups based on machine characteristics.” ”At the deployment phase, the resulting models 210a-n are then deployed and used to perform inference or classification on live data streams to generate predictions 212 associated with those data streams”) Both Nag and Kaira are directed to usage data analysis and future recommendation/prediction. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Nag and Kaira in front of them to modify the system of Nag to include the above feature. The motivation to combine Nag and Kaira comes from Kaira. Kaira discloses the motivation to apply machine learning to usage analysis so that prediction can be more accurate (see e.g. Para [3][55][63]-[79] [183]– “Predictive analytics using machine learning and artificial intelligence can be leveraged for a wide variety of use cases and applications, which generally involve predicting some type of future event or circumstance based on patterns of data captured for past events.”). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to dependent claim 2, the modified Nag teaches the training the machine learning model includes performing a regression analysis operation on the separate groups of machine learning features (see e.g. Para [77][78] – “the predictive models may be trained using a variety of different types and combinations of artificial intelligence and/or machine learning, such as logistic regression, random forest, decision trees, classification and regression trees (CART), gradient boosting (e.g., extreme gradient boosted trees), k-nearest neighbors (kNN), Naïve-Bayes, support vector machines (SVM), deep learning (e.g., convolutional neural networks), and/or ensembles thereof (e.g., models that combine the predictions of multiple machine learning models to improve prediction accuracy), among other examples.”).
With respect to dependent claim 3, the modified Nag teaches the separate groups of machine learning features comprise homogeneous groups of machine learning features (see e.g. Para [54]-[60] – “The predictive analytics model management system 100 leverages the concept of collaborative filtering to group data streams based on data characteristics of the different machines or streams, train a predictive analytics model for each group … analyzing the data streams to characterize similarities of their underlying data; [0057] (ii) creating groups of streams based on the similarities”).
With respect to dependent claim 4, the modified Nag teaches the separate groups of machine learning features are separated based upon hierarchical features of utilization patterns of the utilization of the data center asset (see e.g. Para [71]-[72] – “the training dataset 202 is clustered using known clustering algorithms, such as k-means clustering … the clustering can be refined with even more granularity if other characteristics of the data are known or otherwise determined. For example, with respect to spot welds, the electric current and voltage configurations of welding guns often vary depending on the type of material or metal that is being welded. As a result, the type of metal can be used as a characteristic to cluster the data points in the training dataset and obtain clusters for each metal combination”).
With respect to dependent claim 5, the modified Nag teaches the monitoring the workload uses telemetry regarding the workload provided by the data center asset; data center asset utilization analysis information is generated using the telemetry regarding the workload provided by the data center asset (see e.g. Para [51][209] – “ the data streams generated by the controllers 110a-d can be ingested and analyzed—at the edge or in the cloud—to train predictive analytics models using machine learning algorithms. ”” receiving a data stream captured at least partially by one or more sensors, wherein the data stream comprises a set of feature values corresponding to an unlabeled instance of a feature set”).
With respect to dependent claim 6, the modified Nag teaches the training the machine learning model uses the telemetry regarding the workload and the data center asset utilization analysis information (see e.g. Para [69][77][78] – “the training dataset 202 contains data from multiple streams/machines … the grouping function 208 is applied to the existing training dataset 202. For example, the grouping function is used to split the existing training dataset 202 into smaller training datasets or groups based on machine characteristics. The resulting training datasets are then used to train and create machine learning models 210a-n”).
Claim 7 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 8 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 9 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 10 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 11 is rejected for the similar reasons discussed above with respect to claim 5.
Claim 12 is rejected for the similar reasons discussed above with respect to claim 6.
Claim 13 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 14 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 15 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 16 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 17 is rejected for the similar reasons discussed above with respect to claim 5.
Claim 18 is rejected for the similar reasons discussed above with respect to claim 6.
With respect to dependent claim 19, the modified Nag teaches the computer executable instructions are deployable to a client system from a server system at a remote location (see e.g. Claim 19 and Para [34] [117]– “the data center monitoring and management environment 200 may include a repository of data center monitoring and management data 220. In certain embodiments, the repository of data center monitoring and management data 220 may be local to the information handling system 100 executing the data center monitoring and management console 118 or may be located remotely. In various embodiments, the repository of data center monitoring and management data 220 may include certain information associated with data center asset data 220, data center asset configuration rules 224, data center infrastructure data 226, data center remediation data 228, and data center personnel data 230.”).
With respect to dependent claim 20, the modified Nag teaches the computer executable instructions are provided by a service provider to a user on an on-demand basis (see e.g. Claim 20).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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