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 .
Examiner Notes
Examiner has not made a 101 rejection regarding the “computer program product” of claim 17 because Applicant’s specification states that a computer readable medium is not a signal. (Applicant’s Specification, [0024], A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals).
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Response to Arguments
Applicant’s arguments with respect to the 35 USC § 103 rejections have been fully considered but are not persuasive. Applicant’s argue that the claims are allowable because:
“Examiner asserts that the combination of Litke and Watson discloses or renders obvious the limitation requiring "the storage has a number of functions that match functionality requirements from the application storage requirements." Applicant respectfully submits that this conclusion is premised on an overly broad and unsupported interpretation of the term "functions" that is inconsistent with the claim language and Applicant's specification.” (Applicant’s Remarks, Pg. 8);
“Applicant respectfully reemphasizes that Applicant has defined "functions" for "the storage" to include at least one of encryption, snapshot, clone, a storage size, an input/output type, an input/output size, a storage type, or other functions for storage. In this case, Examiner relies on Watson's disclosure of storage attributes or class-of service parameters, such as performance levels, data protection options, deduplication, compression, or transport protocols, to satisfy the claimed "functions." However, Watson merely teaches selecting a storage class or storage array based on abstract characteristics or preferences. Such attributes identify potential capabilities of a storage resource, but they are not described as functions performed by the storage to meet application-defined functionality requirements... In other words, Applicant respectfully submits that equating selectable attributes or supported features with claimed storage functions improperly collapses distinct claim language and effectively reads the "functions" limitation out of the claim” (Applicant’s Remarks, Pg. 10); and
“Litke and Watson at most teach choosing an appropriate storage resource based on requirements, but they do not teach or suggest storage having a number of functions that correspond to and satisfy application functionality requirements as claimed and as explained in Applicant's specification.” (Applicant’s Remarks, Pgs. 10-11).
Examiner respectfully disagrees. Applicant’s claim language does not specify particular storage “functions.” Page 10 of Applicant’s remarks describe “functions” “to include at least one of encryption, snapshot, clone, a storage size, an input/output type, an input/output size, a storage type, or other functions for storage.” The underlined portions from Applicant’s remarks indicate that the claimed “functions” include storage attributes and capabilities. Watson teaches provisioning a storage resource that have “a number of functions that match functionality requirements from the application storage requirements" and includes some “functions” described in Applicant’s specification. ([0040], provides generic storage facilities that are selected by the attributes of the storage desired such as, by way of example only, class of service, types of data protection, storage interconnect fabric, and type of storage (e.g., block, file, or object); and [0072], intelligently choose the type of storage array best suited to an application. In one or more illustrative embodiments, this can be accomplished by utilizing a generic storage class that specifies only class of service attributes that could be interpreted across different array types. By way of example, these class of service attributes may comprise: (i) required volume performance expressed in terms of desired input-output operations per second (TOPS) or host read-write bandwidth; (ii) facilities required for data protection (e.g., replication to a given target array); (iii) features that can be array specific such as deduplication, compression, thin or thick volumes, and the like; (iv) preferred host transport protocol (e.g., NVME/TCP, FC, iSCSI, NFS, SMB/CIFS)).
The newly cited Khosrowpour reference also teaches provisioning a storage resource that have “a number of functions that match functionality requirements from the application storage requirements." (Column 3, Lines 3-6, operations may include selecting a particular profile from a plurality of predefined profiles based at least in part on the particular workload type; Column 8, Lines 45-48, identify a profile with configuration settings to improve performance. Thus, a profile may be selected at runtime to improve performance for a particular application; and Column 12, Lines 47-60, The profile 132(M) may modify parameters 136 of the memory 118, such as how much of the memory 118 is allocated for paging, and other memory-related settings...The profile 132(M) may modify parameters 140 associated with the cache 122, such as a size of the cache 122, under what conditions the contents of the cache 122 are written to the physical storage 124, and the like. The profile 132(M) may modify parameters 142 associated with the pagefile 123, such as a size of the pagefile 123, under what conditions paging occurs, and the like. The profile 132(M) may modify parameters 144 associated with the physical storage 124).
In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “"functions" for "the storage" to include at least one of encryption, snapshot, clone, a storage size, an input/output type, an input/output size, a storage type, or other functions for storage”) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s other arguments are related to newly amended claim language and are fully addressed in the rejections below.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Litke et al. (US 20220350490) in view of Watson et al. (US 20240134526) and Khosrowpour et al. (US 10771580).
As per claim 1, Litke teaches the invention substantially as claimed including a computer implemented method for managing storage for an application, the computer implemented method comprising:
receiving, by a number of processor units, a request for a storage for the application ([0023], the processing logic receives a request for a first persistent storage volume claims (PVC) from an application in a first namespace; Examiner Note: Litke’s “processor logic” comprises a number of processor units: [0021], Method 400 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, a processor, a processing device, a central processing unit (CPU), a system-on-chip (SoC), etc.), software (e.g., instructions running/executing on a processing device), firmware (e.g., microcode), or a combination thereof), wherein application storage requirements for the application is identified in response to receiving the request for storage of the application ([0023], the processing logic receives a request for a first persistent storage volume claims (PVC) from an application in a first namespace, the request including storage requirements of the first PVC; and [0024], the processing logic generates a second PVC in a second namespace in view of the storage requirements of the first PVC....The second namespace may be a temporary namespace generated on the fly when a user or application creates a PVC (e.g., the first PVC) in the user's own namespace. The second PVC may be created within the second namespace with properties that match the storage requirements of the first PVC (e.g., same size, storage class, etc.)); and
returning, by the number of processor units, the storage ([0016], user PVC 222 may include an indication of data source 224 to be used to populate the PVC 222; and [0025], the processing logic determines a type of the data source from the data source reference and instantiates a pod with instructions to populate the physical storage volume using the data source) having a storage profile for use by the application to access the storage ([0017], processing logic to identify a type of data source that the data source 224 includes. For example, the populator controller 212 may determine whether the data source 224 is an http endpoint, a database, a network server, local storage, etc. The populator controller 212 may then instantiate populator pod 214 based on the determined type of the data source 224.. one or more processes of the populator pod 214 for populating the persistent volume 230 may be specific for the determined type of the data source 224), wherein the storage profile ... is identified based on the application storage requirements for the application ([0016], user PVC 222 may include an indication of data source 224 to be used to populate the PVC 222. The indication of the data source 224 may be a database, an http end point, local storage, network or cloud storage, or any other type of persistent storage, memory, etc.; and [0017], populator controller 212 may include processing logic to identify a type of data source that the data source 224 includes. For example, the populator controller 212 may determine whether the data source 224 is an http endpoint, a database, a network server, local storage, etc. The populator controller 212 may then instantiate populator pod 214 based on the determined type of the data source 224).
Litke fails to specifically teach, wherein the storage profile describes the storage for use by the application; and wherein the storage has a number of functions that match functionality requirements from the application storage requirements.
However, Watson teaches, wherein the storage profile describes the storage for use by the application ([0072], To allow initial provisioning of a persistent storage volume, vCSI controller 400 can intelligently choose the type of storage array best suited to an application. In one or more illustrative embodiments, this can be accomplished by utilizing a generic storage class that specifies only class of service attributes that could be interpreted across different array types. By way of example, these class of service attributes may comprise: (i) required volume performance expressed in terms of desired input-output operations per second (TOPS) or host read-write bandwidth; (ii) facilities required for data protection (e.g., replication to a given target array); (iii) features that can be array specific such as deduplication, compression, thin or thick volumes, and the like; (iv) preferred host transport protocol (e.g., NVME/TCP, FC, iSCSI, NFS, SMB/CIFS)), ....and wherein the storage has a number of functions that match functionality requirements from the application storage requirements ([0040], provides generic storage facilities that are selected by the attributes of the storage desired such as, by way of example only, class of service, types of data protection, storage interconnect fabric, and type of storage (e.g., block, file, or object); and [0072], intelligently choose the type of storage array best suited to an application. In one or more illustrative embodiments, this can be accomplished by utilizing a generic storage class that specifies only class of service attributes that could be interpreted across different array types. By way of example, these class of service attributes may comprise: (i) required volume performance expressed in terms of desired input-output operations per second (TOPS) or host read-write bandwidth; (ii) facilities required for data protection (e.g., replication to a given target array); (iii) features that can be array specific such as deduplication, compression, thin or thick volumes, and the like; (iv) preferred host transport protocol (e.g., NVME/TCP, FC, iSCSI, NFS, SMB/CIFS)).
Watson also teaches, returning, by the number of processor units, the storage having the storage profile for use by the application to access the storage ([0034], facilitating the Kubernetes provisioning models that automatically deploy infrastructure (including storage) based on application profiles and storage requirements expressed as code by the application developers; [0072], To allow initial provisioning of a persistent storage volume, vCSI controller 400 can intelligently choose the type of storage array best suited to an application) wherein the storage profile...is identified based on the application storage requirements for the application ([0072], To allow initial provisioning of a persistent storage volume, vCSI controller 400 can intelligently choose the type of storage array best suited to an application. In one or more illustrative embodiments, this can be accomplished by utilizing a generic storage class that specifies only class of service attributes that could be interpreted across different array types; and [0076], uses the storage application requirements, such as performance and redundancy requirements, to make the best selection among available storage types).
Litke and Watson are analogous because they are each related to container provisioning. Litke teaches a method for container provisioning including storage provisioning based on application storage requirements. ([0023], processing logic receives a request for a first persistent storage volume claims (PVC) from an application in a first namespace, the request including storage requirements of the first PVC. The first namespace may be a sandbox (i.e., allocated set of resource) of a container-orchestration system (e.g., Kubernetes™) in which one or more applications may be executed. In one example, the container-orchestration system may provide an application programming interface (API) for a user or application to provide information, such as storage requirements, a data source, etc. for the first PVC. The processing logic may then generate the first PVC in the first namespace with the provided information, a data source, and a namespace transfer request to transfer a persistent storage volume to the first PVC from a different namespace). Watson teaches a method of container provisioning including provisioning a particular storage class that is appropriate for the container. ([0034], An overall goal of our CSI plugins and CSM modules is to make capabilities of storage arrays available to Kubernetes applications, while facilitating the Kubernetes provisioning models that automatically deploy infrastructure (including storage) based on application profiles and storage requirements expressed as code by the application developers; and [0048], allowing intelligent volume placement to choose from storage arrays of different types). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the provisioning method of Litke would be modified with the Watson’s mechanism for storage class selection during container provisioning resulting in a system that provisions containers with appropriate storage. Therefore, it would have been obvious to combine the teachings of Litke and Watson.
The combination of Litke-Watson fails to specifically teach wherein the storage profile for the storage is determined using a storage machine learning model trained based on performance data that describes performance metrics for the storage and storage profiles comprising the storage profile.
However, Khosrowpour teaches, wherein the storage profile for the storage is determined using a storage machine learning model trained based on performance data that describes performance metrics for the storage and storage profiles comprising the storage profile (Column 3, Lines 22-25, executing a classifier (e.g., a machine learning algorithm) that gathers data associated with an application and selects a profile to configure resources of the computing device; and Column 4, Lines 56-67, create a classifier using a machine learning algorithm such as, for example, Random Forest, Neural Network, or the like. Combinations of different hardware platforms and different storage configurations are used to execute different types of workloads using different types of profiles and data associated with the workload characteristics is gathered. The data is used to train the classifier to identify which profile (among multiple profiles that were tested) provides the highest performance (e.g., fastest execution time) for a particular workload executing on a particular hardware platform having a particular storage configuration).
The combination of Litke-Watson and Khosrowpour are analogous because they are each related resource provisioning. Litke teaches a method for container provisioning including storage provisioning based on application storage requirements. Watson teaches a method of container provisioning including provisioning a particular storage class that is appropriate for the container. Khosrowpour teaches storage allocation including using machine learning models to select a storage profile. (Abstract, After gathering the data, a classifier may analyze the data and determine a particular workload type from a predefined set of workload types associated with the selected application. The computing device may select a particular profile from a plurality of predefined profiles based at least in part on the particular workload type, and modify, based on the particular profile, a plurality of parameters to create a plurality of modified parameters. The modified parameters may reduce an execution time of performing the operations to the input/output stack; and Column 2, Lines 62-67, data is used to train the classifier to identify which profile (among multiple profiles that were tested) provides the highest performance (e.g., fastest execution time) for a particular workload executing on a particular hardware platform having a particular storage configuration). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the provisioning methods of the combination of Litke-Watson would be modified with the Khosrowpour’s predictive storage allocation mechanism resulting in a system that provisions storage in accordance with storage profiles determined by applying machine learning models. Therefore, it would have been obvious to combine the teachings of the combination of Litke-Watson and Khosrowpour.
As per claim 2, Watson teaches, further comprising:
creating, by the number of processor units, a new storage meeting the application storage requirements in response to an absence of the storage profile meeting the application storage requirements ([0074], a class of service change is desired, it can be handled in a similar manner to volume migration, by updating the storage class with different class of service parameters in the aspirational state 410, and leaving a blank storage system selection in the storage class so that the system would select a new appropriate storage system; and [0075], vCSI controller 400 can be configured to dynamically enable or disable replication. In such a use case, a new storage class name is supplied that matches aspirational state 410, either adding replication parameters, or removing existing replication parameters); and
creating, by the number of processor units, a new storage profile meeting the application storage requirements in response to the absence of the storage profile meeting the application storage requirements ([0075], vCSI controller 400 can be configured to dynamically enable or disable replication. In such a use case, a new storage class name is supplied that matches aspirational state 410, either adding replication parameters, or removing existing replication parameters).
As per claim 3, Litke teaches, wherein receiving, by the number of processor units, the request for the storage for the application comprises:
receiving, by the number of processor units, the request from an application deployment process that deploys the application in a container in a container orchestration environment ([0002], Container-orchestration system may provide an image-based deployment module for creating containers; and [0023], the container-orchestration system may provide an application programming interface (API) for a user or application to provide information, such as storage requirements, a data source, etc. for the first PVC).
As per claim 4, Litke teaches, wherein the request is a persistent volume claim ([0023], the processing logic receives a request for a first persistent storage volume claims (PVC) from an application in a first namespace) and wherein the application is used in a container orchestration environment ([0023], the container-orchestration system may provide an application programming interface (API) for a user or application to provide information, such as storage requirements, a data source, etc. for the first PVC).
Litke fails to specifically teach, wherein returning, by the number of processor units, the storage having the storage profile for use by the application to access the storage comprises: returning, by the number of processor units, a modified persistent volume claim with a recommended storage class for the storage having the storage profile.
However, Watson teaches, wherein returning, by the number of processor units, the storage having the storage profile for use by the application to access the storage comprises:
returning, by the number of processor units, a modified persistent volume claim with a recommended storage class for the storage having the storage profile ([0038], reconfiguration requires a restart of the pods within the application to reference new persistent volume claims (PVCs) and PVs. PVCs are storage requests that enable developers to dynamically request storage resources without being aware of the implementation of underlying storage devices; [0043], the virtual container storage interface driver, according to one or more illustrative embodiments, is configured to isolate pods and persistent volume claims (PVCs) from the actual storage used for them. In one or more illustrative embodiments, this is accomplished by using a generic storage class (SC) and persistent volume (PV) that can represent any driver. The volume handle in the persistent volume of the virtual container storage interface driver is a globally unique identifier (GUID), which can only be interpreted by the virtual container storage interface driver. Once a volume placement algorithm has been completed (that can select from all of the available drivers), storage can be provisioned by a specific CSI driver and array; and [0076], application developers do not need to consider the specific capabilities or limitations of each type of storage array. Instead, they delegate the choice of storage array type, and characteristics to an intelligent agent (i.e., vCSI controller 400) that uses the storage application requirements, such as performance and redundancy requirements, to make the best selection among available storage types).
The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 5, Litke teaches, wherein the storage is a physical storage connected to the persistent volume ([0018], the namespace transfer controller 242 may provide the user PVC 222 with a storage pointer of the persistent volume 230 to allow the application access to the persistent volume 230 via the user PVC 222) and wherein the application uses the persistent volume for the storage class to access the storage ([0018], once the transfer of the persistent volume 230 to the user PVC 222 is complete, the volume population controller may update a status of the user PVC 222 and the application from a pending state to an active state. The container-orchestration system 240 may then schedule the application to be executed).
As per claim 6, Watson teaches, wherein identification of the storage profile comprises:
predicting, by the number of processor units, a storage class using the application storage requirements in the request ([0076], application developers do not need to consider the specific capabilities or limitations of each type of storage array. Instead, they delegate the choice of storage array type, and characteristics to an intelligent agent (i.e., vCSI controller 400) that uses the storage application requirements, such as performance and redundancy requirements, to make the best selection among available storage types); and
selecting, by the number of processor units, the storage profile from the storage class meeting the application storage requirements the storage machine learning model trained to predictor model ([0076], application developers do not need to consider the specific capabilities or limitations of each type of storage array. Instead, they delegate the choice of storage array type, and characteristics to an intelligent agent (i.e., vCSI controller 400) that uses the storage application requirements, such as performance and redundancy requirements, to make the best selection among available storage types).
The combination of Litke-Watson fails to specifically teach, predicting, by the number of processor units, a storage class using… a storage solution predictor model trained to predict storage classes using the application storage requirements.
However, Khosrowpour teaches, predicting, by the number of processor units, a storage class (Column 2, Lines 60-65, the operations may include performing an analysis of the data and determining, by a classifier and based at least in part on the analysis, a particular workload type from a predefined set of workload types that is associated with the selected application) using… the storage machine learning model trained to predict storage classes using the application storage requirements (Column 2, Line 57-Column 3, Line 3, operations may include gathering, over a predetermined interval of time, data associated with the selected application that is performing the operations to the input/output stack. After gathering the data, the operations may include performing an analysis of the data and determining, by a classifier and based at least in part on the analysis, a particular workload type from a predefined set of workload types that is associated with the selected application. The classifier may be trained using multiple hardware platforms, multiple storage configurations, multiple workloads, and the predefined plurality of profiles to classify a workload based on input/output operations performed by a particular application and to identify a profile to increase performance of the input/output operations).
The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 7, Khosrowpour teaches, further comprising:
training, by the number of processor units, the storage solution predictor model using storage profiles and performance data for a plurality of storage (Column 2, Line 65-Column 3, Line 3, classifier may be trained using multiple hardware platforms, multiple storage configurations, multiple workloads, and the predefined plurality of profiles to classify a workload based on input/output operations performed by a particular application and to identify a profile to increase performance of the input/output operations; Column 4, lines 56-67, system and techniques create a classifier using a machine learning algorithm such as, for example, Random Forest, Neural Network, or the like. Combinations of different hardware platforms and different storage configurations are used to execute different types of workloads using different types of profiles and data associated with the workload characteristics is gathered. The data is used to train the classifier to identify which profile (among multiple profiles that were tested) provides the highest performance (e.g., fastest execution time) for a particular workload executing on a particular hardware platform having a particular storage configuration).
As per claim 8, Litke teaches, wherein the application storage requirements comprise storage requirements ([0020], The PVC request 313 may include PVC storage requirements 324 that the first PVC 320 is to satisfy), [and] function requirements ([0020], The PVC request 313 may include PVC storage requirements 324 that the first PVC 320 is to satisfy).
Litke fails to specifically teach, wherein the application storage requirements comprise… performance requirements.
However, Watson teaches, wherein the application storage requirements comprise … performance requirements ([0076], they delegate the choice of storage array type, and characteristics to an intelligent agent (i.e., vCSI controller 400) that uses the storage application requirements, such as performance and redundancy requirements, to make the best selection among available storage types).
The same motivation used in the rejection of claim 1 is appliable to the instant claim.
As per claim 9, this is the “system claim” corresponding to claim 1 and is rejected for the same reasons. The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 10, this claim is similar to claim 2 and is rejected for the same reasons.
As per claim 11, this claim is similar to claim 3 and is rejected for the same reasons.
As per claim 12, this claim is similar to claim 4 and is rejected for the same reasons.
As per claim 13, this claim is similar to claim 5 and is rejected for the same reasons.
As per claim 14, this claim is similar to claim 6 and is rejected for the same reasons. The same motivation used in the rejection of claim 6 is applicable to the instant claim.
As per claim 15, this claim is similar to claim 7 and is rejected for the same reasons.
As per claim 16, this claim is similar to claim 8 and is rejected for the same reasons.
As per claim 17, this is the “computer program product claim” corresponding to claim 1 and is rejected for the same reasons. The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 18, this claim is similar to claim 2 and is rejected for the same reasons.
As per claim 19, this claim is similar to claim 3 and is rejected for the same reasons.
As per claim 20, this claim is similar to claim 4 and is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure and is as follows:
Selegean et al. (US 20220058100) -Teaches resource allocation based on storage profiles which are determined using machine learning models: [0028], storage parameters and/or properties included in the storage profile 118 (e.g., schedules of resource allocation, resource provisioning, and/or compression/decompression) may be determined using a method of machine learning or artificial intelligence. For example, the storage profile 118 may be determined or updated based at least in part on the monitored usage of the stored data 128, 130; and [0033], DSMS 112 may determine characteristics of the infrastructure associated with the system 104a,b and/or application 110a,b (e.g., the processing, memory, and network infrastructure included in the system 104a,b or allocated to the application 110a,b) and use these characteristics to determine the initial storage profile 118. For example, the initial storage profile 118 may be determined based on an anticipated file size, data transfer rate, and/or downstream use of the data 106a,b received from the new data source 102a,b. For example, the storage profile 118 may include an amount of storage space anticipated to be needed to store the data 106a,b provided by the new data source 102a,b); and
Khan et al. (US 11068296)- Teaches storage allocation using storage profiles: Column 2, Lines 64-67, a storage management component providing data for defining a data storage profile of characteristics of data storage for the infrastructure, the profiles collectively defining an application profile for the software application; Column 3, Lines 8-16, one or more data storage profiles, so as to classify a profile for which optimization will provide improved application performance; training the classifier to generate a set of third classifications using training data sets based on each of: one or more application profiles; ...and one or more data storage profiles, so as to classify a profile for which additional infrastructure resource is required to provide improved application performance.
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/MELISSA A HEADLY/Examiner, Art Unit 2197