DETAILED ACTION
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 .
Claims 1-18 are pending for examination.
Claim Objections
Claim 11 objected to because of the following informalities: dependent claim 11 is reciting the same limitation as claim 5. The claim should be deleted, or If it is a system claim, appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim language in the following claims is not clearly understood:
As per claim 1, line 3, it is unclear whether “recommended resources” are referring to “suboptimal resources” in line 2 (i.e. consistent term should be used with “the” or “said”)
Line 7, is it unclear whether “a resource” is referring to one of the “recommended resources” in line 3 and 6 or “suboptimal resources” in line 2 (i.e. consistent term should be used with “the” or “said”)
Line 11, it is unclear whether “remedial measures” are referring to the same “remedial measures” in line 10 (i.e. consistent term should be used with “the” or “said”)
As per claim 2, line 3, it is unclear whether “recommended resources” and “each recommended resource” are referring to the “recommended resources” in claim 1 (i.e. consistent term should be used with “the” or “said”)
As per claim 3, line 3, the term “the categorical parameters” and “the encoded categorial variables” lack antecedent basis.
Line 4-6, it is unclear whether “each cluster”, “a cluster”, “cluster center”, “child cluster centers” are related to “an initial set of cluster centers” in line 4 (i.e. consistent term should be used with “the” or “said”)
As per claims 7-9, 13-15, they have the same deficiencies as claims 1, 2 and 3. Appropriate corrections are required.
As per claims 2-6, 8-12, 14-18, they depend from rejected claims and do not resolve the deficiencies thereof and are therefore rejected for at least the same reasons.
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.
Claim(s) 1-2, 6-8, 12-14, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. US Pub 2022/0107858 (hereafter Jain) in view of Manousakis et al. US Pub 2024/0126352 (hereafter Manousakis).
As per claim 1, Jain teaches the invention substantially as claimed including an automated computer-implemented method for identifying and correcting suboptimal resources of a data center, the method comprising: executing machine learning clustering to classify recommended resources into different classes according to resource parameters of the recommended resources (para[0024, 0055-0058, 0100], monitoring, detecting and remediating, by machine learning, incidents (suboptimal) on resources of a datacenter, where the incident report identifies (recommends) and categorizes resources into incident types (VM incident, storage incident, network incident etc.) and descriptions (parameters) of the incidents (i.e. VM 1 is unhealth in datacenter 1, network switch is down in datacenter 1);
executing machine learning to construct a priority model for each class of recommended resources; in response to receiving a request to determine priority of a resource, determining a class of the classes the resource belongs to, and using the priority model of the class to compute a priority of the resource;
and executing remedial measures to correct the resource based on the priority, wherein executing remedial measures includes deleting the resource, restarting the resource, and migrating the resource to a different host (para[0058-0060], determine a root cause of the multi-resource outage incidents, where the root cause (resource) has the higher priority than the dependent resources, and the recommended actions are perform to mitigate the incidents, including restarting, suspending and adjusting fan speed of the problematic resources).
Jain does not explicitly teach executing machine learning to construct a priority model for each class of recommended resources; in response to receiving a request to determine priority of a resource, determining a class of the classes the resource belongs to, and using the priority model of the class to compute a priority of the resource; remedial measures includes deleting the resource, and migrating the resource to a different host.
However, Manousakis teaches executing machine learning to construct a priority model for each class of recommended resources; in response to receiving a request to determine priority of a resource, determining a class of the classes the resource belongs to, and using the priority model of the class to compute a priority of the resource; remedial measures includes deleting the resource, and migrating the resource to a different host (para[0027, 0040, 0042, 0053, 0058-0060, 0100], FIG. 3, in response to detecting cooling capacity reduction/loss in one or more devices, resource manager uses a machine learning model to dynamically create the priority tiers of the (recommended) devices (power supply, storage, computational device etc.), and apply the remedial measures (shutdown or migrate computation/storage/device to different datacenter) based on their priorities).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Manousakis’ teaching to Jain’s invention in order to provide a thermal management devices of the datacenter to balance the supply and demand of thermal management within the datacenter and prevent overheating, data loss, or hardware damage of the resources, while minimizing impacts to the processing and storage capabilities of the datacenter when a reduction in cooling capacity is detected (para[0014, 0036]).
As per claim 2, Jain and Manousaki teach the method of claim 1, and Jain teaches wherein executing machine learning clustering to classify recommended resources into different classes comprises: forming a data frame of recommended resources, the data frame including categorical parameters of each recommended resource and categorical variables of dependent resources of each resource; and encoding the categorical variables into numerical values, the categorical parameters and the encoded categorical variables forming the resource parameters of each recommended resource (para[0025-0028, 0054-0059], FIG. 3-4, monitoring system monitors the performance or health of the resources and when the measured performance exceeds a predetermined threshold, incident report is generated, and a listing of incident reports identifies incident types (VM, storage, network incident), description of the incident (i.e. VM is unhealthy in DC 1, data is unavailable in storage account 1 in DC 1, network switch is down in DC 1).
As per claim 6, Jain teaches adding the resource to the class, and retraining the priority model for the class with the resource added (para[0037, 0042, 0046], FIG. 2, training data is fed and used to train the model, using the past and new incidents of the resources (when resources are added to classes), including identifiers of the type of incident detected (i.e. VM, storage, network, power related incident)).
As per claim 7, it is a computer system claim of claim 1 above, thus it is rejected for the same rationale.
As per claim 8, it is a computer system claim of claim 2 above, thus it is rejected for the same rationale.
As per claim 12, it is a computer system claim of claim 6 above, thus it is rejected for the same rationale.
As per claim 13, it is an operations manager claim of claim 1 above, thus it is rejected for the same rationale.
As per claim 14, it is an operations manager claim of claim 2 above, thus it is rejected for the same rationale.
As per claim 18, it is an operations manager claim of claim 6 above, thus it is rejected for the same rationale.
Claim(s) 3-5, 9-11, 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain in view of Manousakis as applied to claim 1 above, and further in view of Jha et al. US Pub 2019/0163720 (hereafter Jha).
As per claim 3, Jain and Manousaki teach the method of claim 1, but they do not explicitly teach wherein executing machine learning to construct a priority model for each class of recommended resources comprises: applying k-means clustering to the categorical parameters and the encoded categorical variables of the resources based on an initial set of cluster centers; and for each cluster, testing a cluster for fit to a Gaussian distribution, replacing cluster center with two child cluster centers when the cluster does not fit a Gaussian distribution, and applying k-means clustering to the two child cluster centers.
However, Jha teaches executing machine learning to construct a priority model for each class of recommended resources comprises: applying k-means clustering to the categorical parameters and the encoded categorical variables of the resources based on an initial set of cluster centers; and for each cluster, testing a cluster for fit to a Gaussian distribution, replacing cluster center with two child cluster centers when the cluster does not fit a Gaussian distribution, and applying k-means clustering to the two child cluster centers (para[0088], claim 3, testing cluster for Gaussian fit, and the cluster center is replaced by two child cluster centers if the cluster does not fit a Gaussian distribution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jha’s teaching to Jain and Manousaki’s invention in order to provide a method for predicting parameters in a database of information technology equipment to be used in determining cost of IT equipment more accurately and project future cost of IT services (para[0004-0005]).
As per claim 4, Jain and Manousaki teach the method of claim 1, and Manousaki teaches priority model (para[0042], ML model to dynamically create the priority list of resources), but they do not explicitly teach where executing machine learning to construct a priority model for each class of recommended resources comprises: for each class of the classes, partitioning the resource parameters of the resources in the class into training data and validation data; iteratively computing predictor coefficients of a priority model of the class based on the training data; computing approximate priorities using the priority model applied to the validation data associated with the class, the approximate priorities approximate the actual priority of the validation data; and discarding the predictor coefficients when a difference between the approximate priorities and corresponding priorities of the validation data exceeds a threshold.
However, Jha teaches executing machine learning to construct a model for each class of recommended resources comprises: for each class of the classes, partitioning the resource parameters of the resources in the class into training data and validation data; iteratively computing predictor coefficients of a priority model of the class based on the training data; computing approximate priorities using the model applied to the validation data associated with the class, the approximate parameters approximate the actual parameter of the validation data; and discarding the predictor coefficients when a difference between the approximate parameters and corresponding parameters of the validation data exceeds a threshold (para[0058, 0065, 0074-0076, 0090], claim 4, partitioning parameters into training data and validation data, compute coefficients, approximate parameters, and Sicard the predictor coefficients when difference exceed a threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jha’s teaching to Jain and Manousaki’s invention in order to provide a method for predicting parameters in a database of information technology equipment to be used in determining cost of IT equipment more accurately and project future cost of IT services (para[0004-0005]).
As per claim 5, Jain and Maousaki teach the method of claim 1, but they do not explicitly teach wherein determining a class of the classes the resource belongs to comprises: computing a squared distance between the resource parameters of the resource and resource parameters of each resource of the classes; determining a minimum squared distance of the squared distances; and assigning the resource to the class having the minimum squared distance to the resource.
However, determining a class of the classes the resource belongs to comprises: computing a squared distance between the resource parameters of the resource and resource parameters of each resource of the classes; determining a minimum squared distance of the squared distances; and assigning the resource to the class having the minimum squared distance to the resource (para[0005, 0080-0084, 0091], claim 5, minimum squared distance is determined, and assign resource having the minimum squired distance to the resource).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jha’s teaching to Jain and Manousaki’s invention in order to provide a method for predicting parameters in a database of information technology equipment to be used in determining cost of IT equipment more accurately and project future cost of IT services (para[0004-0005]).
As per claim 9, it is a computer system claim of claim 3 above, thus it is rejected for the same rationale.
As per claim 10, it is a computer system claim of claim 4 above, thus it is rejected for the same rationale.
As per claim 11, it is a method claim of claim 5 above, thus it is rejected for the same rationale.
As per claim 15, it is an operations manager claim of claim 3 above, thus it is rejected for the same rationale.
As per claim 16, it is an operations manager claim of claim 4 above, thus it is rejected for the same rationale.
As per claim 17, it is an operations manager claim of claim 5 above, thus it is rejected for the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAMMY EUNHYE LEE whose telephone number is (571)270-7773. The examiner can normally be reached Mon, Tues, Thur 9PM-4PM.
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/TAMMY E LEE/Primary Examiner, Art Unit 2195