DETAILED ACTION
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 § 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.
Claim 21 is 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.
The following claim language is unclear and indefinite:
As per claim 21, the term “higher order workload groups” is a relative term which renders the claim indefinite. The term “higher order workload groups” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Examiner Notes
Examiner did not make a 35 USC § 101 rejection with respect to the “computer program product” of claim 20 because Applicant’s specification specifically states that it is not to be construed as a signal. (Applicant’s Specification, [0155], 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 communicated through a wire, and/or other transmission media).
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 remarks filed June 23, 2026 have been fully considered but are not persuasive. Applicant’s arguments related to newly amended language have been fully considered and are addressed in the rejections recited below.
With respect to the 35 USC § 101 rejections, Applicant argues that the claims are allowable because:
“The Office's position oversimplifies the claim by extracting isolated words such as "assessing," "generating," and "presenting" and treating them as generic acts divorced from the claim context. Properly considered as a whole, claim 1 requires assessment of workloads of a cluster using workload characterizing data, followed by generation of prompting data that specifically prompts for merging identified workloads. A human using pcn and paper docs not practically perform the claimed operation because the claim is directed to cluster workload assessment and workload-merger prompting in a computer cluster. The relevant "workloads" are not paper items or business tasks; they are computer workloads of a cluster. The claimed prompt is not a generic recommendation, but a prompt for merging identified cluster workloads.(Applicant’s Remarks, Pg. 9).
Examiner respectfully disagrees. The steps of:
assessing workloads of the plurality of workloads with use of characterizing data of the workload characterizing data; and
generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads.
But for the recitation of generic computing components, steps a-b can be completed in the human mind with the aid of pen and paper through observation, evaluation, judgment, and/or opinion. “The courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).” (MPEP 2106.04(d)(I))
In this case, the computing elements recited in steps a-b do not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment and using the computing elements as a tool to apply the judicial exception.
“Claim 1 also integrates any alleged abstract idea into a practical application. The claimed process is applied to a specific technological environment and addresses a computer-specific problem: identifying workloads in a cluster that may be merged to improve cluster operation and resource utilization. The specification explains that clusters can include computing nodes hosting container-based workloads, such as Kubernetes pods, and that running multiple service instances for different tenants can increase cluster footprint and complexity. The claimed process addresses that problem by assessing workload characterizing data and generating prompting data that prompts for merging identified workloads. Thus, the claim is not merely a field-of-use limitation. It uses workload characterizing data of actual cluster workloads to support a practical workload-merger operation in a cluster computing environment.” (Applicant’s Remarks, Pgs. 9-10).
Examiner respectfully disagrees. The MPEP recites examples indicative of additional elements that may integrate a judicial element into a practical application:
“Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).”
In this case, the additional elements do not provide an improvement, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
“The claimed process also improves computer-system operation. The specification explains that embodiments can reduce a cluster footprint and resource utilization by assessing workloads for merging, including by discovering related workloads and identifying workloads suitable for merging. It further explains that the identifying process can use discovered workload relationships to facilitate identification of workloads suitable for merging and reduce computational resources associated with that identification. Accordingly, the claimed invention is directed to improving operation of a cluster-based computer environment, rather than merely applying a generic abstract idea on a computer.” (Applicant’s Remarks, Pg. 10).
“The “improvements” analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity. That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity.” (MPEP 2106.04(d)(1)). “With respect to Step 2A, “after the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016)... However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”).” (MPEP 2106.05 (a)).
In this case the claim does not disclose the alleged technological improvement. When viewed as a whole, the claims do not cover a particular solution to a problem or a particular way; rather, the claims merely claim the idea of a solution or desired outcome. For example, the claims do not recite a particular machine and appear to merely use a generic computer as a tool to apply the abstract idea. For example, the MPEP states:
“In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691.” (MPEP 2106.05 (a) (I)).
Unlike the application at issue in Enfish, the instant application does not recite the abstract idea with a specific data structure. The judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. As discussed in the 101 rejection below, the additional elements do not provide an improvement.
“The "obtaining workload characterizing data" limitation should not be dismissed as insignificant extra-solution activity. The obtained data is not incidental to the claim. It is the operative input used to assess the plurality of workloads of the cluster, and that assessment drives generation of prompting data that prompts for workload merging. The workload characterizing data therefore forms part of the technological process by which the system identifies workloads for possible merger. It is not a peripheral data-gathering step unrelated to the claimed improvement.
Nor do the additional elements merely recite generic computer implementation. Claim 1 does not claim the abstract concept of collecting and displaying information in the abstract. It recites a particular sequence of operations applied to workloads of a cluster: obtaining workload characterizing data characterizing a plurality of cluster workloads, assessing those workloads using the characterizing data, generating prompting data based on that assessment, and presenting prompting data that prompts for merging identified workloads. The ordered combination is directed to a practical cluster-management workflow for improving workload deployment and resource usage.
Accordingly, claim 1 is patent eligible at least because it is not directed to a mental process under Step 2A, Prong One. Even assuming, arguendo, that an abstract idea is recited, claim 1 integrates the alleged abstract idea into a practical application under Step 2A, Prong Two by applying the claimed operations to cluster workloads to support merging of identified workloads in a cluster computing environment.” (Applicant’s Remarks, Pg. 10).
Examiner respectfully disagrees. The MPEP outlines limitations that do not integrate a judicial exception into a practical application:
“ • Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).”
In this case, the claims merely recite judicial exceptions and add the equivalent of “apply it” as additional elements. “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. (MPEP 2106.05(f)). MPEP 2106.05(f) also states “implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.”
“Claim 9 further recites that the assessing includes discovering a hierarchical ordering of workload groups within a first namespace, discovering a hierarchical ordering of workload groups within a second namespace, and identifying, in dependence on the hierarchical ordering of workload groups within the first and second namespaces, a first workload suitable for merging with a second workload, wherein the prompting data prompts for merging of the first and second workloads. These limitations are not practically performable in the human mind.” (Applicant’s Remarks, Pg. 11).
Applicant’s further argue this point by stating:
“Claim 9 also integrates any alleged abstract idea into a practical application. The hierarchical ordering is not merely displayed information. It is used to identify workloads suitable for merging and to generate prompting data that prompts for merger of those workloads. The claim therefore applies the assessment to a concrete cluster-management result: prompting for merger of identified workloads. This is a practical application in the management of cluster workloads and resource utilization, not a generic
mental process.
The ordered combination of claim 9 also supplies significantly more than any alleged abstract
idea. The claim recites a specific sequence of cluster-workload operations: discovering hierarchical orderings within separate namespaces, using those orderings to identify workloads suitable for merging, and generating prompting data for merging the identified workloads. This ordered combination improves
the ability of a cluster-management system to identify and present workload-merger opportunities and is not merely an instruction to apply an abstract idea on a generic computer.” (Applicant’s Remarks, Pg. 11).
Examiner respectfully disagrees. As stated in the prior Office Action, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that incorporate the judicial exception into a practical application or provide “significantly more” than the judicial exception.
Claim 11 recites processing text-based reports for respective workloads of a namespace and using that processing to ascertain Euclidean distances between workloads. In the cluster context, the text-based reports may characterize technical attributes of workloads, and the Euclidcan distances provide a computational measure of workload relatedness. The claim does not merely recite a human judgment that two items are similar. It recites a computer-implemented analysis of workload report data to derive distance values between workloads within a namespace.
Claim 11 also integrates any alleged mathematical concept into a practical application. Even if Euclidean distance is considered a mathematical concept in isolation, claim 11 does not claim Euclidean distance in the abstract. The claimed Euclidean distances are ascertained from processed text-based reports for respective workloads of a namespace and are used as part of assessing workloads of a cluster for merger prompting. The mathematical operation is therefore applied in a specific technological context to discover relationships between computer workloads within a namespace of a cluster.
The claim also recites significantly more than a mathematical concept or mental process. The additional limitations define how the workload assessment is performed: by processing text-based reports for workloads of a namespace and deriving Euclidean distances between those workloads. This is a specific computer-implemented technique for discovering workload relationships in a cluster, not merely generic data analysis. Accordingly, claim 11 is patent eligible.” (Applicant’s Remarks, Pg. 11).
Examiner respectfully disagrees. As discussed in the prior Office Action, the “assessing step” of claim 11 is analyzed with the assessing step of claim 1 which is a mental process.
“Claim 14 is not directed to a mental process. The claim requires text-based static analysis and dynamic runtime analysis of workload groups in different namespaces. Dynamic runtime analysis of cluster workloads, and runtime workload analysis to determine a provisioning resource allocation for a remaining workload group after merger, are computer operations that depend on the operation and resource behavior of workloads in a computing environment. Such operations are not practically performed in the human mind or with pen and paper.
Claim 14 also clearly integrates any alleged abstract idea into a practical application. The claim does not stop at identifying or displaying similarities. The similarity analysis and runtime workload analysis are used to determine a provisioning resource allocation for a remaining workload group after merger, and the prompting data references that provisioning resource allocation. Thus, the claim applies the analysis to a concrete post-merger cluster-management problem: how to provision resources for a remaining workload group after first and second workload groups are merged. This is a practical technological application directed to improving cluster operation and resource usage.
Claim 14 also recites significantly more than any alleged abstract idea. The claim requires a particular ordered combination of technical operations: namespace-specific workload-group discovery, similarity analysis using both static text-based and dynamic runtime analysis, post-merger provisioning analysis, and generation of prompting data referencing the determined provisioning resource allocation. This combination is not conventional generic computer implementation of a mental process. It is a specific cluster-workload management workflow for evaluating merger of workload groups and provisioning the remaining workload group after merger. (Applicant’s Remarks, Pgs. 12-13).
Examiner respectfully disagrees. As discussed in the prior Office Action claim 14 recites an additional mental process. The additional elements outlined in claim 14 do not integrate the judicial exception into a practical application, nor do they amount to “significantly more.”
“Claim 18 is not directed merely to collecting, analyzing, and displaying information. Claim 18 requires an actual cluster-management operation: merging first and second workload groups and removing the second workload group of the second namespace. These limitations go beyond mental analysis or display of results. A person cannot mentally remove a workload group from a namespace of a computer cluster. The claimed removal is a computer-environment operation that changes the workload
configuration of the cluster.
Claim 18 also integrates any alleged abstract idea into a practical application. The generated prompting data is not an end in itself. It leads to user-defined input, and the merging is performed in dependence on that input. The merger includes removing the second workload group of the second namespace. Thus, the claim applies the assessment and prompting to carry out a practical cluster-workload modification. This is precisely the type of concrete application that places the claim outside the category of merely displaying information or using a computer as a tool.
Claim 18 further recites significantly more than any alleged abstract idea. The ordered combination requires identifying namespace-specific workload groups suitable for merging, receiving responsive user input through a user interface, merging the workload groups, and removing a workload group from a namespace. This ordered combination effects a change in the operation and configuration of the cluster. The claim therefore recites a specific implementation of cluster workload merger, rather than merely a generic recommendation or abstract idea.”(Applicant’s Remarks, Pgs. 13-14).
Examiner respectfully disagrees. As stated in the prior Office Action claim 18 recites a mental process that is not integrated into a practical application or amount to “significantly more.”
With respect to the 35 USC § 103 rejections, Applicant argues that the claims are allowable because:
“As an initial matter, the rejection does not satisfy the requirements of 37 C.F.R. § 1.104. Under 37 C.F.R. § 1.104(c)(2), when a reference is complex or describes inventions other than that claimed, the particular part relied upon must be designated as nearly as practicable, and the pertinence of each reference, if not apparent, must be clearly explained. Here, the Office relies on broad "workload" terminology from two materially different technical contexts, but does not clearly explain how the relied upon portions of Sharon and Singh teach or suggest the specific claimed operation of generating prompting data that prompts for merging of identified workloads of a cluster…Sharon does not teach or suggest assessing a plurality of workloads of a cluster and generating prompting data that prompts for merging of identified workloads of the cluster.” (Applicant’s Remarks, Pg. 20).
Examiner respectfully disagrees. The combination of Sharon-Singh teaches assessing a plurality of workloads of a cluster (Sharon, [0102], the workload analyzer 706 reviews or analyzes the workload attributes) and generating prompting data that prompts for merging of identified workloads of the cluster (Singh, [0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; and [0032], Given the correlation metrics, at step 109, an affinity between an activity stream and a particular workload type can be determined and quantified, which affinity in turn can be used in making resource allocation decisions. In some cases, and as shown, an affinity determination is provided to a user interface, which user interface can be viewed and manipulated by a user).
“The Office relies on Sharon's disclosure that a workload analyzer tracks workload attributes and determines relationships between namespaces. However, the cited portions of Sharon relate to storage-command attributes and namespace relationships in a non-volatile memory device. Even if Sharon determines that two namespaces have related write workloads, Sharon uses that relationship to optimize where write data is stored. Sharon does not identify workloads of a cluster for merging. Sharon does not generate prompting data that prompts a user to merge identified workloads. Sharon does not present any such workload-merger prompt to a user.”(Applicant’s Remarks, Pg. 21).
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
“Singh, on current review, is directed to dynamic workload classification for workload-based resource allocation in a distributed virtualization system. Singh may disclose classifying workloads, recommending resource allocation operations, and presenting such recommendations through a user interface. However, the relied-upon recommendations in Singh concern resource allocation, workload placement, migration, or consolidation of workloads of a given type onto a node configured with corresponding resources. Singh does not teach the particular claimed sequence in which workload characterizing data characterizing a plurality of workloads of a cluster is assessed to generate prompting data that prompts for merging identified workloads of the cluster. The Office's reliance on Singh's disclosure of "consolidat[ing] workloads of a given type onto a node" does not establish the claimed "merging of identified workloads." Consolidating workload placement onto a node is not the same as prompting for merger of identified workloads. A placement or resource-allocation recommendation may change where workloads run or how resources are allocated, but it does not necessarily merge workloads. Claim 1 requires prompting data that prompts for merging identified workloads, not merely prompting or recommending generic resource allocation, migration, or placement.” (Applicant’s Remarks, Pg. 21).
Examiner respectfully disagrees. Under the broadest reasonable interpretation, a “merging” of workloads can include merging workloads onto a same node. Applicant’s claim language does not preclude this interpretation.
“The § 1.104 deficiency is particularly apparent in the treatment of the "merging" limitation. The Office acknowledges that Sharon does not specifically teach the claimed prompting for merging, and then relies on Singh's resource allocation and consolidation disclosures. However, the rejection does not clearly explain why Singh's consolidation of workloads of a given type onto a node corresponds to, or would have suggested, prompting for merging identified workloads of the plurality of workloads of the claimed cluster. Nor does the rejection clearly explain how Sharon's grouping of write data in open erase blocks would be transformed into a user-facing cluster-workload merger prompt by the addition of Singh's user interface. The pertinence of the cited passages is therefore not apparent, and the rejection does not provide the clear explanation required for Applicant to reasonably understand and respond to the actual theory of unpatentability. The proposed combination also does not supply a reasoned basis for arriving at the claimed invention.” (Applicant’s Remarks, Pgs. 21-22).
Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
“The Office relies on Ayyagari's disclosure of "operational hierarchies" and "execution sequences" associated with a workload. However, an operational hierarchy or execution sequence associated with a workload is not the claimed discovery of a hierarchical ordering of workload groups within a namespace. Nor is Ayyagari's disclosure of workload parsing and clustering a teaching of discovering hierarchical orderings of workload groups within both first and second namespaces and using those hierarchical orderings to identify first and second workloads suitable for merging. The rejection therefore does not show the claimed two-namespace hierarchical workload group discovery and merger-prompting sequence.
The claim is not satisfied by merely showing that workloads can be classified, clustered, migrated, or allocated to resources. Claim 9 requires a specific relationship between hierarchical orderings discovered within first and second namespaces, identification of workloads suitable for merging based on those hierarchical orderings, and prompting for merging those workloads. The cited art does not teach or suggest this ordered combination. Withdrawal of the rejection of claim 9 is respectfully requested. (Applicant’s Remarks, Pg. 23).
Examiner respectfully disagrees. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. As discussed in the prior Office Action, the combination of the applied prior art teaches this limitation including the required relationships between the claimed components.
“the rejection does not show that Sheoran teaches ascertaining Euclidean distances in dependence on processing text-based reports for respective workloads of a certain namespace, as claimed. The cited portions of Shcoran are directed to workload resource usage histories or workload representations for equivalence-class prediction, not to processing text-based reports for workloads within a namespace and deriving Euclidean distances between those workloads based on the text-based reports.
The Office's mapping effectively treats any Euclidean distance calculation applied to workloads as sufficient. That reading omits the claim's important context and data source: Euclidean distances are ascertained in dependence on processing text-based reports for respective workloads of a certain namespace. Because Sharon does not teach the text-based report processing and Sheoran does not teach applying Euclidcan distance in the claimed text-report/namespace-workload manner, the combination fails to teach or suggest claim 11. (Applicant’s Remarks, Pg. 24).
Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
“ Claim 14 is not rendered obvious by assembling general teachings of workload classification, grouping, and resource allocation. The claim recites an integrated cluster-management workflow that links namespace-specific workload group instantiations, similarity analysis, merger suitability, post-merger provisioning, and prompting data. The rejection does not show where the cited art teaches or suggests that particular ordered combination.” (Applicant’s Remarks, Pg. 25).
Examiner respectfully disagrees. Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
“The removal limitation is important. Claim 18 is not merely directed to recommending placement, moving execution, or allocating resources. It recites a merger operation in which a second workload group of a second namespace is removed as part of merging first and second namespace-specific workload groups. The cited portions of Singh do not teach this namespace-specific removal of a workload group after a merger. Sharon's grouping of write data in erase blocks likewise does not teach removing a workload group of a namespace.
The user-interface limitation also is not met by generic presentation of resource recommendations. Claim 18 requires that the merging be performed in dependence on user-defined input data entered into a user interface responsively to the prompting data. The claimed prompting data is merger prompting data, not merely a generic resource-allocation recommendation. The rejection does not show that the cited art teaches a user approving or supplying input that causes merger of first and second namespace-specific workload groups with removal of the second workload group.
Accordingly, Sharon and Singh do not teach or suggest the full claim 18 sequence of identifying namespace-specific workload groups suitable for merging, merging them, removing the second workload group of the second namespace, and performing the merging in dependence on user input entered responsively to merger prompting data. Withdrawal of the rejection of claim 18 is respectfully requested.” (Applicant’s Remarks, Pg. 26).
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In adhering to the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process, machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the analysis directed to Step 2A.
Step 2A is a two prong inquiry. Prong 1 considers whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). In this case independent claim 1 recites mental processes as applied to human activity (i.e. concepts performed in the human mind but for the recitation of generic computing components). For example, the following claimed steps are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion:
assessing workloads of the plurality of workloads with use of characterizing data of the workload characterizing data; and
generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads.
But for the recitation of generic computing components, steps a-b can be completed in the human mind with the aid of pen and paper through observation, evaluation, judgment, and/or opinion. “As the Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’ Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).” (MPEP 2106.04(a)(2) (III)). Steps a-b describe collecting, analyzing, and displaying information at a high level of generality. According to the MPEP, these steps are examples of mental processes, thus it is reasonable to identify these limitations as reciting mental processes. (MPEP 2106.04(a)(2) III (A), claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Since the claims are directed toward a judicial exception, analysis flows to Prong 2. Prong 2 considers whether the judicial exception is integrated into a practical application. In this case, the judicial exception is not integrated into a practical application because the claim language merely describes steps of collecting data, field of use/technological environment, and using a computer as a tool to apply the abstract idea and fails to describe an improvement to the functioning of a computer or other technical field. The additional elements recited in the claim do not integrate the judicial exception into a practical application for the following reasons:
The additional element of “presenting the prompting data to a user” amounts insignificant extra solution data gathering/transmission activity (MPEP § 2106.05(g));
The additional element of “obtaining workload characterizing data, wherein the workload characterizing data characterizes a plurality of workloads of a cluster” amounts to insignificant extra-solution data gathering/data transmission activity (MPEP § 2106.05(g)).
Therefore the abstract idea has not been integrated into practical application and the claims are directed to the abstract idea.
Since the claims are directed to the determined judicial exception, the analysis flows to Step 2B. Therein, the elements and combination of elements are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. In this case, the additional elements identified at Step 2A Prong 2, individually or in an order combination, also do not amount to significantly more than the abstract idea for the same reasons as given in Step 2A Prong 2 and further because:
The additional element of “obtaining workload characterizing data, wherein the workload characterizing data characterizes a plurality of workloads of a cluster amounts to well‐understood, routine, and conventional functions because these steps are claimed in a merely generic manner (e.g., at a high level of generality) and as insignificant extra-solution activity. “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network.” (MPEP 2106.05(d) (II)); and
“A claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” (MPEP 2106.05 (h)).
Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Regarding claim 3, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 4, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 5, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 6, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 7, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 8, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 9, the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 10,
The additional step of “wherein the method includes performing runtime workload analysis to determine a provisioning resource allocation for supporting a remaining workload subsequent to merger of the identified workloads” recites an additional mental process under Prong 1 since this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion but for the recitation of generic computing components. For example, a person can think and evaluate to perform a runtime workload analysis to determine resource allocation subsequent to merging a workload.
The additional element of “wherein the prompting data references the provisioning resource allocation” is analyzed with the “generating prompting data” step of claim 1 as an abstract idea because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion.
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 11,
the additional “assessing step” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion.
The step of “wherein the discovering includes ascertaining, in dependence on the processing text based reports for respective workloads of the certain namespace Euclidean distances between the respective workloads of the certain namespace” recites an additional mental process because this step recites a mathematical concept. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 12,
the additional element of “wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace and a workload group instantiation in a second namespace” is analyzed with the “generating prompting data” step of claim 1 as an abstract idea because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion.
The step of “wherein the prompting data...further specifies a determined similarity level of the workload group instantiation in the first namespace and the workload group instantiation in the second namespace” recites an additional mental process because this step recites a mathematical concept.
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 13,
The step of “wherein the assessing includes discovering workload groups comprising related workloads, and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
The additional elements of :
“wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace defining the first workload group and a workload group instantiation in a second namespace defining the second workload group”; and
“wherein the prompting data further specifies a determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group,
are analyzed with the “generating prompting data” step of claim 1 as an abstract idea because these steps can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The additional elements of a “workload” and a “namespace” are recited at a high level of generality and amounts to using a generic computing component as a tool to apply the abstract idea (MPEP § 2106.05(f)) under Prong 2.
The additional elements of :
“wherein the assessing includes performing similarity analysis to obtain the determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group” ;
wherein the similarity analysis includes text based static analysis of the first workload group and the second workload group, and
wherein the similarity analysis includes dynamic runtime analysis of the first workload group and the second workload group
are analyzed with the assessing step of claim 1 as a mental processes because these steps can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. These steps also recite a mathematical concept with regard to the similarity analysis.
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 14,
The additional element of “wherein the assessing includes discovering workload groups comprising related workloads, and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging” is analyzed with the assessing step of claim 1 as a mental process because this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
The additional elements of :
wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace defining the first workload group and a workload group instantiation in a second namespace defining the second workload group;
wherein the prompting data further specifies a determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group, wherein the assessing includes performing similarity analysis to obtain the determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group, wherein the similarity analysis includes text based static analysis of the first workload group and the second workload group, and wherein the similarity analysis includes dynamic runtime analysis of the first workload group and the second workload group; and
“wherein the prompting data references the provisioning resource allocation” is analyzed with the “generating prompting data” step of claim 1 as an abstract idea because these steps be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion. These steps also recite a mathematical concept with regard to the similarity analysis.
The additional element of “wherein the method includes performing runtime workload analysis to determine a provisioning resource allocation for supporting a remaining workload group subsequent to merger of the first workload group and the second workload group” recites an additional mental process under Prong 1 since this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion but for the recitation of generic computing components. For example, a person can think and evaluate to perform a runtime workload analysis to determine resource allocation subsequent to merging a workload.
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 17, the additional steps of :
“wherein the assessing includes discovering workload groups comprising related workloads, and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging;” and
“wherein the method includes merging the first workload group of the first namespace and the second workload group of the second namespace, wherein the merging includes removing the second workload group of the second namespace”
recite mental processes because these steps can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion.
Regarding claim 18, the additional steps of:
“wherein the assessing includes discovering workload groups comprising related workloads, and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging;” and
“wherein the method includes merging the first workload group of the first namespace and the second workload group of the second namespace, wherein the merging includes removing the second workload group of the second namespace”
recite mental processes because these steps can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion.
Regarding claim 21, the additional element of “wherein the assessing includes, for each namespace of a plurality of namespaces of the cluster, recursively grouping workloads into progressively higher order workload groups until all workloads of the namespace are assigned to a top order workload group for the namespace, and identifying the identified workloads for merging based at least in part on comparing the progressively higher order workload groups of different namespaces” recites an additional mental process under Prong 1 since this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion but for the recitation of generic computing components. The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 22, the additional elements of :
“wherein the presenting the prompting data includes presenting a user-selectable merge-analysis policy” amounts to insignificant extra-solution data gathering/data transmission activity (MPEP § 2106.05(g)); and
“wherein, in response to user selection of the user-selectable merge-analysis policy, the method includes reassessing the plurality of workloads and generating updated prompting data that changes a determined similarity level between workload group instantiations of different namespaces” recites an additional mental process under Prong 1 since this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion but for the recitation of generic computing components.
Regarding claim 23, the additional elements of :
“wherein the assessing includes performing runtime workload analysis to determine, for a remaining workload after merger of the identified workloads, at least one suggested post-merger CPU or memory resource limit based on aggregated runtime usage of the identified workloads across replicas and namespaces,” recites an additional mental process under Prong 1 since this step can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion but for the recitation of generic computing components.; and
“wherein the prompting data includes the at least one suggested post-merger CPU or memory resource limit” appears to be an attempt to generally link the use of the judicial exception to a particular technological environment or field of use. (MPEP 2106.05(h)).
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In adhering to the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process, machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the analysis directed to Step 2A.
Step 2A is a two prong inquiry. Prong 1 considers whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). In this case independent claim 1 recites mental processes as applied to human activity (i.e. concepts performed in the human mind but for the recitation of generic computing components). For example, the following claimed steps are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion:
assessing workloads of the plurality of workloads with use of characterizing data of the workload characterizing data, wherein the assessing includes identifying a first workload group of a first namespace and a second workload group of a second namespace as being suitable for merging;
generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads; and
in dependence on user-defined input data entered into a user interface responsively to the prompting data, merging the first workload group and the second workload group, wherein the merging includes removing the second workload group of the second namespace.
But for the recitation of generic computing components, steps a-c can be completed in the human mind with the aid of pen and paper through observation, evaluation, judgment, and/or opinion. “As the Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’ Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).” (MPEP 2106.04(a)(2) (III)). Steps a-c describe collecting, analyzing, and displaying information at a high level of generality. According to the MPEP, these steps are examples of mental processes, thus it is reasonable to identify these limitations as reciting mental processes. (MPEP 2106.04(a)(2) III (A), claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Since the claims are directed toward a judicial exception, analysis flows to Prong 2. Prong 2 considers whether the judicial exception is integrated into a practical application. In this case, the judicial exception is not integrated into a practical application because the claim language merely describes steps of collecting data, field of use/technological environment, and using a computer as a tool to apply the abstract idea and fails to describe an improvement to the functioning of a computer or other technical field. The additional elements recited in the claim do not integrate the judicial exception into a practical application for the following reasons:
The additional elements of a “system,” “memory,” “processor,” “user interface,” “workload,” and a “cluster” are recited at a high level of generality and amounts to using a generic computing component as a tool to apply the abstract idea (MPEP § 2106.05(f)). These additional elements also appear to be an attempt to generally link the use of the judicial exception to a particular technological environment or field of use. (MPEP 2106.05(h)).
The additional element of “presenting the prompting data to a user” amounts to insignificant extra-solution data gathering/data transmission activity (MPEP § 2106.05(g)).
The additional element of “obtaining workload characterizing data, wherein the workload characterizing data characterizes a plurality of workloads of a cluster” amounts to insignificant extra-solution data gathering/data transmission activity (MPEP § 2106.05(g)).
Therefore the abstract idea has not been integrated into practical application and the claims are directed to the abstract idea.
Since the claims are directed to the determined judicial exception, the analysis flows to Step 2B. Therein, the elements and combination of elements are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. In this case, the additional elements identified at Step 2A Prong 2, individually or in an order combination, also do not amount to significantly more than the abstract idea for the same reasons as given in Step 2A Prong 2 and further because:
The additional element of “obtaining workload characterizing data, wherein the workload characterizing data characterizes a plurality of workloads of a cluster amounts to well‐understood, routine, and conventional functions because these steps are claimed in a merely generic manner (e.g., at a high level of generality) and as insignificant extra-solution activity. “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network.” (MPEP 2106.05(d) (II)).
“A claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” (MPEP 2106.05 (h)).
Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In adhering to the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process, machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the analysis directed to Step 2A.
Step 2A is a two prong inquiry. Prong 1 considers whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). In this case independent claim 1 recites mental processes as applied to human activity (i.e. concepts performed in the human mind but for the recitation of generic computing components) and mathematical concepts. For example, the following claimed steps are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, and/or opinion:
assessing workloads of the plurality of workloads with use of characterizing data of the workload characterizing data, wherein the assessing includes processing the text-based reports to discover relationships between workloads within the namespace…; and
generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads; and
The following step recites a mathematical concept:
wherein the assessing includes processing the text-based reports to discover relationships between workloads within the namespace, including ascertaining Euclidean distances between respective workloads of the namespace in dependence on the text-based reports.
But for the recitation of generic computing components, steps a-b can be completed in the human mind with the aid of pen and paper through observation, evaluation, judgment, and/or opinion. “As the Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’ Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).” (MPEP 2106.04(a)(2) (III)). Steps a-b describe collecting, analyzing, and displaying information at a high level of generality. According to the MPEP, these steps are examples of mental processes, thus it is reasonable to identify these limitations as reciting mental processes. (MPEP 2106.04(a)(2) III (A), claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Since the claims are directed toward a judicial exception, analysis flows to Prong 2. Prong 2 considers whether the judicial exception is integrated into a practical application. In this case, the judicial exception is not integrated into a practical application because the claim language merely describes steps of collecting data, field of use/technological environment, and using a computer as a tool to apply the abstract idea and fails to describe an improvement to the functioning of a computer or other technical field. The additional elements recited in the claim do not integrate the judicial exception into a practical application for the following reasons:
The additional elements of a “computer readable storage medium,” “processing circuit,” “workload,” and a “cluster” are recited at a high level of generality and amounts to using a generic computing component as a tool to apply the abstract idea (MPEP § 2106.05(f)). These additional elements also appear to be an attempt to generally link the use of the judicial exception to a particular technological environment or field of use. (MPEP 2106.05(h)).
The additional element of “presenting the prompting data to a user generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads amounts to insignificant extra-solution data gathering/data transmission activity (MPEP § 2106.05(g)).
The additional element of “obtaining workload characterizing data, wherein the workload characterizing data characterizes a plurality of workloads of a cluster” amounts to insignificant extra-solution data gathering/data transmission activity (MPEP § 2106.05(g)).
The additional element of “wherein the workload characterizing data includes text-based reports for respective workloads of a namespace of the cluster” appears to be an attempt to generally link the use of the judicial exception to a particular technological environment or field of use. (MPEP 2106.05(h)).
Therefore the abstract idea has not been integrated into practical application and the claims are directed to the abstract idea.
Since the claims are directed to the determined judicial exception, the analysis flows to Step 2B. Therein, the elements and combination of elements are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. In this case, the additional elements identified at Step 2A Prong 2, individually or in an order combination, also do not amount to significantly more than the abstract idea for the same reasons as given in Step 2A Prong 2 and further because:
The additional elements of :
“obtaining workload characterizing data, wherein the workload characterizing data characterizes a plurality of workloads of a cluster; and
“presenting the prompting data to a user”
amounts to well‐understood, routine, and conventional functions because these steps are claimed in a merely generic manner (e.g., at a high level of generality) and as insignificant extra-solution activity. “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network.” (MPEP 2106.05(d) (II)).
“A claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” (MPEP 2106.05 (h)).
Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
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, 3-4, 7-8, 10, 17-19, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Sharon et al. (US 20200409559) in view of Singh et al. (US 20200026560).
As per claim 1, Sharon teaches the invention substantially as claimed including a computer implemented method comprising:
obtaining workload characterizing data ([0100], workload analyzer 706 tracks workload attributes that define, describe, or characterize the workloads), wherein the workload characterizing data characterizes a plurality of workloads ([0100], the workload analyzer 706 tracks workload attributes that define, describe, or characterize the workloads. As used herein, a “workload attribute” is any quality, characteristic, attribute, feature, behavior, trait, or property about a workload) of a cluster ([0095], system comprises the functionality necessary for the solution disclosed herein, including a network 114, non-volatile memory device(s) 118, a storage controller 126, one or more hosts (host 616, host 618, and host 620), a write data pipeline 702, a read data pipeline 704, a workload analyzer 706, an open erase block manager 708, storage commands 710, at least one open erase block 712, and a data allocator 714; and [0099], workload analyzer 706 tracks, or monitors, storage commands 710 received by the non-volatile memory system 102 in order to determine information about the workloads that the non-volatile memory system 102 is handling);
assessing workloads of the plurality of workloads with use of characterizing data of the workload characterizing data ([0102], the workload analyzer 706 reviews or analyzes the workload attributes).
Sharon fails to specifically teach, generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads; and presenting the prompting data to a user.
However, Singh teaches, generating prompting data in dependence on the assessing of the workloads of the plurality of workloads ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; and [0032], Given the correlation metrics, at step 109, an affinity between an activity stream and a particular workload type can be determined and quantified, which affinity in turn can be used in making resource allocation decisions. In some cases, and as shown, an affinity determination is provided to a user interface, which user interface can be viewed and manipulated by a user), wherein the prompting data prompts for merging of identified workloads of the plurality of workloads ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; [0052], a workload detection agent can predict that a workload running in a particular node of a first cluster might perform better and/or achieve improved resource utilization if that workload were situated in, for example, a different cluster. Upon such a prediction, resource reallocation operations and/or migration operations can be invoked so as to pursue the predicted improvements; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)); and
presenting the prompting data to a user ([0049], certain recommended instances of the resource allocation operations 138 and/or other information (e.g., resource usage metrics) can be presented in user interface 116 for analysis, expressed in terms pertaining to the classified workloads in the system; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)).
Singh also teaches, wherein the workload characterizing data characterizes a plurality of workloads ([0036], for each VE, I/O activity trace data for the respective VE is retrieved (at operation 162) from I/O activity attributes (e.g., as stored in a measured activity database 111); and [0038], the I/O activity attributes for each VE can be applied to the inputs of a workload detection and classification model to classify the workload or workloads running on the VE (operation 164)) of a cluster ([0024], some clusters in a distributed virtualization system might deploy hundreds of nodes or more that support several thousand or more autonomous virtualized entities (e.g., VMs, containers, etc.) that are individually tasked to perform one or more of a broad range of computing workloads).
Sharon and Singh are analogous because they are each related to workload management. Sharon teaches a method of workload management including workload classification and merging workloads of different namespaces ([0104], By grouping write data for two namespaces together in the same open erase block, the storage controller 126 uses the relationship to optimize where write data is stored on the non-volatile memory media 120;and [0112], workload analyzer 800 includes a comparator 804 configured to review the information gathered by the tracker 806 and workload attributes and deduce, or determine, relationships between namespaces with stored data in currently open erase blocks based on the workload attributes detected for the workloads associated with those namespaces. The workload analyzer 800 tracks and distinguishes namespaces using their namespace identifiers). Singh teaches a method of workload classification, management, including workload consolidation/migration, and resource allocation based on workload attributes. (Abstract, managing virtualized entities in computing systems. In a method embodiment, processing commences upon receiving I/O activity trace data associated with virtualized entities running in a computing system. Specific I/O activity attributes are extracted from the I/O activity trace data, and the I/O activity attributes are used to form a workload classification model. The workload classification model serves to assign one or more workload classifications to a respective one or more observed workloads running on the computing system. Based on the determined workload classification or classifications, recommended resource allocation operations are formed for further consideration. Considered resource allocation operations include migrations of virtualized entities from a source computing resource to a target computing resource; and [0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402). As can be observed in this example, the resource allocation operations result in node 252.sub.15 being tasked with servicing consolidated SQL server workloads and node 252.sub.34 being tasked with servicing consolidated VDI workloads)). Singh also teaches a user interface that allows users to make management decisions. ([0049], certain recommended instances of the resource allocation operations 138 and/or other information (e.g., resource usage metrics) can be presented in user interface 116 for analysis, expressed in terms pertaining to the classified workloads in the system; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)). 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, Sharon’s “workload analyzer” would be modified with Singh’s resource allocation prompting mechanism resulting in a system that allows users to view workload information and make resource allocation decisions in consideration of namespaces associated with workloads and workload information. Therefore, it would have been obvious to combine the teachings of Sharon and Singh.
As per claim 3, Sharon teaches, wherein the assessing includes performing first intra-namespace analysis to discover relationships between workloads within a first namespace, performing second intra-namespace analysis to discover relationships between workloads within a second namespace ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace), and identifying, in dependence on the first intra-namespace analysis and the second intra-namespace analysis that a first workload is suitable for merging with a second workload ([0102], the workload analyzer 706 reviews or analyzes the workload attributes and determines patters, similarities, relationships, and/or commonalities between namespaces that are based on the workload attributes; and [0119], data allocator 900 is configured to direct an open erase block manager (or other non-volatile memory media 120 physical placement manager) to group data of write commands having a first namespace identifier into a common one of two or more of the open logical erase blocks as write commands having a second namespace identifier, based on a relationship between the namespaces).
As per claim 4, Sharon teaches, wherein the assessing includes discovering related workloads within a first namespace, performing discovery of related workloads in a second namespace and identifying, in dependence on the discovering and the performing discovery, that a first workload is suitable for merging with a second workload ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace; and [0119], data allocator 900 is configured to direct an open erase block manager (or other non-volatile memory media 120 physical placement manager) to group data of write commands having a first namespace identifier into a common one of two or more of the open logical erase blocks as write commands having a second namespace identifier, based on a relationship between the namespaces), wherein the first workload is included in the first namespace, and wherein the second workload is included in the second namespace ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace).
As per claim 7, Singh teaches, wherein the assessing includes discovering workload groups comprising related workloads ([0040], workload parameters can be grouped and/or preprocessed to form hypothesis or other inputs used in heuristics to determine a workload type; [0071], workload detection model, used in conjunction with a workload classification model can be used to classify workloads to facilitate dynamic resource allocation in a distributed virtualization system; and [0073], workload classification technique 3B00 can assign instances of workload classifications 130 and determine sets of workload parameters 132 for workloads (e.g., WL3 and WL4) operating at various virtualized entities (e.g., VE3 and VE4) in distributed virtualization system 102)), and identifying, in dependence on the discovering, first and second workload groups suitable for merging ([0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)).
As per claim 8, Singh teaches, wherein the assessing includes discovering workload groups comprising related workloads ([0040], workload parameters can be grouped and/or preprocessed to form hypothesis or other inputs used in heuristics to determine a workload type; [0071], workload detection model, used in conjunction with a workload classification model can be used to classify workloads to facilitate dynamic resource allocation in a distributed virtualization system; and [0073], workload classification technique 3B00 can assign instances of workload classifications 130 and determine sets of workload parameters 132 for workloads (e.g., WL3 and WL4) operating at various virtualized entities (e.g., VE3 and VE4) in distributed virtualization system 102)), and identifying, in dependence on the discovering, first and second workload groups suitable for merging ([0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)), wherein the prompting data references the first and second workload groups ([0049], Workload detection agent 104.sub.11 can further comprise a user interface 116 to facilitate a workload-based resource analysis 140 by one or more users 118 (e.g., system administrators). For example, certain recommended instances of the resource allocation operations 138 and/or other information (e.g., resource usage metrics) can be presented in user interface 116 for analysis, expressed in terms pertaining to the classified workloads in the system).
As per claim 10, Singh teaches, wherein the method includes performing runtime workload analysis to determine a provisioning resource allocation for supporting a remaining workload subsequent to merger of the identified workloads ([0078], workload consolidation technique 4A00 as implemented in systems for distributed resource allocation using dynamically classified workloads; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)), and wherein the prompting data references the provisioning resource allocation ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads).
As per claim 17, Singh teaches, wherein the assessing includes discovering workload groups comprising related workloads [0040], workload parameters can be grouped and/or preprocessed to form hypothesis or other inputs used in heuristics to determine a workload type; [0071], workload detection model, used in conjunction with a workload classification model can be used to classify workloads to facilitate dynamic resource allocation in a distributed virtualization system; and [0073], workload classification technique 3B00 can assign instances of workload classifications 130 and determine sets of workload parameters 132 for workloads (e.g., WL3 and WL4) operating at various virtualized entities (e.g., VE3 and VE4) in distributed virtualization system 102)), and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging ([0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402); Examiner Note: Singh’s workloads comprise containers which are associated with their own namespace: [0023], Another form of virtualization in distributed systems is operating system virtualization or container virtualization. The containers implemented in container virtualization environments comprise groups of processes and/or resources (e.g., memory, CPU, disk, etc.) that are isolated from the host computer and other containers...Any of the foregoing virtualized entities can be implemented in distributed virtualization systems to facilitate execution of one or more workloads; and [0128], multiple containers that share access to a virtual disk can be assembled into a pod (e.g., a Kubernetes pod). Pods provide sharing mechanisms (e.g., when multiple containers are amalgamated into the scope of a pod) as well as isolation mechanisms (e.g., such that the namespace scope of one pod does not share the namespace scope of another pod)), wherein the method includes merging the first workload group of the first namespace and the second workload group of the second namespace, wherein the merging includes removing the second workload group of the second namespace ([0052], a workload detection agent can predict that a workload running in a particular node of a first cluster might perform better and/or achieve improved resource utilization if that workload were situated in, for example, a different cluster. Upon such a prediction, resource reallocation operations and/or migration operations can be invoked so as to pursue the predicted improvements; and [0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)).
As per claim 18, Singh teaches, wherein the assessing includes discovering workload groups comprising related workloads ([0040], workload parameters can be grouped and/or preprocessed to form hypothesis or other inputs used in heuristics to determine a workload type; [0063], a set of observed workloads 326 might include certain representative workloads (e.g., WL1, WL2, . . . , WLN) identified by respective instances of observed workload type identifiers 126; and [0073], workload classification technique 3B00 can assign instances of workload classifications 130 and determine sets of workload parameters 132 for workloads (e.g., WL3 and WL4) operating at various virtualized entities (e.g., VE3 and VE4) in distributed virtualization system 102)), and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging ([0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)), wherein the method includes merging the first workload group of the first namespace and the second workload group of the second namespace ([0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)), wherein the merging includes removing the second workload group of the second namespace ([0052], a workload detection agent can predict that a workload running in a particular node of a first cluster might perform better and/or achieve improved resource utilization if that workload were situated in, for example, a different cluster. Upon such a prediction, resource reallocation operations and/or migration operations can be invoked so as to pursue the predicted improvements), wherein the merging is performed in dependence on user defined input data entered into a user interface, the user defined input data entered into a user interface responsively to the prompting data ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; [0032], an affinity determination is provided to a user interface, which user interface can be viewed and manipulated by a user; and [0049], Workload detection agent 104.sub.11 can further comprise a user interface 116 to facilitate a workload-based resource analysis 140 by one or more users 118 (e.g., system administrators). For example, certain recommended instances of the resource allocation operations 138 and/or other information (e.g., resource usage metrics) can be presented in user interface 116 for analysis, expressed in terms pertaining to the classified workloads in the system).
As per claim 19, Sharon teaches the invention substantially as claimed including a system comprising:
a memory ([0095], system comprises the functionality necessary for the solution disclosed herein, including … non-volatile memory device(s) 118, a storage controller 126);
at least one processor in communication with the memory ([0095], system comprises the functionality necessary for the solution disclosed herein, including a …one or more hosts (host 616, host 618, and host 620)); and
program instructions executable by one or more processor via the memory to perform a method comprising:
obtaining workload characterizing data ([0100], workload analyzer 706 tracks workload attributes that define, describe, or characterize the workloads), wherein the workload characterizing data characterizes a plurality of workloads ([0100], the workload analyzer 706 tracks workload attributes that define, describe, or characterize the workloads. As used herein, a “workload attribute” is any quality, characteristic, attribute, feature, behavior, trait, or property about a workload) of a cluster ([0095], system comprises the functionality necessary for the solution disclosed herein, including a network 114, non-volatile memory device(s) 118, a storage controller 126, one or more hosts (host 616, host 618, and host 620), a write data pipeline 702, a read data pipeline 704, a workload analyzer 706, an open erase block manager 708, storage commands 710, at least one open erase block 712, and a data allocator 714; and [0099], workload analyzer 706 tracks, or monitors, storage commands 710 received by the non-volatile memory system 102 in order to determine information about the workloads that the non-volatile memory system 102 is handling);
assessing workloads of the plurality of workloads with use of characterizing data of the workload characterizing data, ([0102], the workload analyzer 706 reviews or analyzes the workload attributes) wherein the assessing includes identifying a first workload group of a first namespace and a second workload group of a second namespace as being suitable for merging ([0102], the workload analyzer 706 reviews or analyzes the workload attributes and determines patters, similarities, relationships, and/or commonalities between namespaces that are based on the workload attributes; and [0119], data allocator 900 is configured to direct an open erase block manager (or other non-volatile memory media 120 physical placement manager) to group data of write commands having a first namespace identifier into a common one of two or more of the open logical erase blocks as write commands having a second namespace identifier, based on a relationship between the namespaces).
Sharon fails to specifically teach, generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads; and presenting the prompting data to a user; and in dependence on user-defined input data entered into a user interface responsively to the prompting data, merging the first workload group and the second workload group, wherein the merging includes removing the second workload group of the second namespace.
However, Singh teaches, generating prompting data in dependence on the assessing of the workloads of the plurality of workloads ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; and [0032], Given the correlation metrics, at step 109, an affinity between an activity stream and a particular workload type can be determined and quantified, which affinity in turn can be used in making resource allocation decisions. In some cases, and as shown, an affinity determination is provided to a user interface, which user interface can be viewed and manipulated by a user), wherein the prompting data prompts for merging of identified workloads of the plurality of workloads ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; [0052], a workload detection agent can predict that a workload running in a particular node of a first cluster might perform better and/or achieve improved resource utilization if that workload were situated in, for example, a different cluster. Upon such a prediction, resource reallocation operations and/or migration operations can be invoked so as to pursue the predicted improvements; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)); and
presenting the prompting data to a user ([0049], certain recommended instances of the resource allocation operations 138 and/or other information (e.g., resource usage metrics) can be presented in user interface 116 for analysis, expressed in terms pertaining to the classified workloads in the system; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)); and
in dependence on user-defined input data entered into a user interface responsively to the prompting data, merging the first workload group and the second workload group ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; and [0091], The user interacting with view 444 can either accept the recommended action by clicking the “Go” button, or decline the recommended action by clicking the “Ignore” button.), wherein the merging includes removing the second workload group of the second namespace ([0052], a workload detection agent can predict that a workload running in a particular node of a first cluster might perform better and/or achieve improved resource utilization if that workload were situated in, for example, a different cluster. Upon such a prediction, resource reallocation operations and/or migration operations can be invoked so as to pursue the predicted improvements; and [0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)).
The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 23, Singh teaches, wherein the assessing includes performing runtime workload analysis to determine, for a remaining workload after merger of the identified workloads, at least one suggested post-merger CPU or memory resource limit based on aggregated runtime usage of the identified workloads across replicas and namespaces ([0032], an affinity between an activity stream and a particular workload type can be determined and quantified, which affinity in turn can be used in making resource allocation decisions. In some cases, and as shown, an affinity determination is provided to a user interface, which user interface can be viewed and manipulated by a user; [0043], if the predicted CPU usage is 100 GHz, a threshold at 85% of 100 GHz might be established so as to trigger a resource allocation operation (e.g., user alert, remediation action, etc.) when the then-current CPU usage breaches that threshold. Current resource usage can be used in conjunction with workload classification techniques when performing distributed resource allocation; and [0090], various workload-based resource allocation operations can be generated (operation 180.sub.5). As one example of workload-based resource allocation operations, the workload parameters can be used to present various metrics pertaining to the resource capacity for serving the workloads (operation 412).), and wherein the prompting data includes the at least one suggested post-merger CPU or memory resource limit ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; [0043], if the predicted CPU usage is 100 GHz, a threshold at 85% of 100 GHz might be established so as to trigger a resource allocation operation (e.g., user alert, remediation action, etc.) when the then-current CPU usage breaches that threshold. Current resource usage can be used in conjunction with workload classification techniques when performing distributed resource allocation; and [0091], The user interacting with view 444 can either accept the recommended action by clicking the “Go” button, or decline the recommended action by clicking the “Ignore” button).
Claims 5, 6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sharon-Singh as applied to claim 1 and in further view of Ayyagari et al. (US 20190354403).
As per claim 5, Sharon teaches, wherein the first workload is included in the first namespace, and wherein the second workload is included in the second namespace ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace).
The combination of Sharon-Singh fails to specifically teach, wherein the assessing includes discovering a hierarchical ordering of workload groups within a first namespace, and identifying, in dependence on the hierarchical ordering of workload groups within a first namespace, that a first workload is suitable for merging with a second workload, wherein the first workload is included in the first namespace.
However, Ayyagari teaches, wherein the assessing includes discovering a hierarchical ordering of workload groups within a first namespace ([0062], workload deployment program 300 utilizes logical controls to activate or inhibit the execution of executable procedures within an embedded computing entity based on constraints, operational hierarchies, and execution sequences associated with the workload; Examiner Note: Ayyagari teaches workloads within a namespace: [0004], Proxy nodes are used to distribute workloads, handle workload requests within a namespace, and direct the transfer of objects that comprise the workload among nodes), and identifying, in dependence on the hierarchical ordering of workload groups within a first namespace, that a first workload is suitable for merging with a second workload ([0078], workload analysis program 500 accesses a set of information related to processing of the workload. In various embodiments, workload analysis program 500 accesses information related to a workload that includes: a plurality of utilized objects, a set of locations of utilized objects provided by workload deployment program 300, a list of embedded computing entities utilized by the workload, and functional resources/dictates of one or more embedded computing entities of the workload, such as a hierarchy of operations or identifying a set of objects associated with one or more pipelined tasks; [0080], In response to analyzing a workload and various embedded computing entities, workload analysis program 500 obtains one or more additional executable procedures for inclusion within an embedded computing entity utilized during executing the workload, and/or compiles a new embedded computing entity from executable procedures; [0085], workload analysis program 500 utilizes clustering algorithms to determine whether to include the unidentified portion of the workload with another portion of the workload; [0089], in response to parsing a workload, workload analysis program 500 classifies objects related to the workload to identify executable procedures that are utilized to process the one or more objects of the workload; and [0090], Upon classifying an object, analysis suite 107 updates the metadata of an object with one or more classifications. Similarly, workload analysis program 500 can utilize analysis suite 107 to identify and classify new executable procedures for inclusion in an instance of table 250).
The combination of Sharon-Singh and Ayyagari are analogous because they are each related to workload management. Sharon teaches a method of workload management including workload classification and merging. Singh teaches a method of workload classification, management, including workload consolidation/migration, and resource allocation based on workload attributes. Ayyagari teaches a method of workload analysis, classification and display. ([0094], workload analysis program 500 utilizes one or more visualization programs to depict, via UI 122, the parsing and analysis of one or more portions of the workload to a user that initiated the workload. In an example, workload analysis program 500 presents a representation of the workload, based on one or more aspects of analysis suite 107, to a user that generates the workload. Workload analysis program 500 may receive: one or more characteristics of a portion of the workload, modifications of characteristics of a portion of the workload, and/or modification of the analysis of the workload. Alternatively, workload analysis program 500 may receive a request from a user for further analysis and/or optimizations of a portion of the workload based on changes input by the user). 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 “workload analyzer” of the combination of Sharon-Singh would be modified with Ayyagari’s workload parsing and display mechanisms resulting in a system that allows users to view hierarchical workload information and make resource allocation decisions based on said workload information. Therefore, it would have been obvious to combine the teachings of the combination of Sharon-Singh and Ayyagari.
As per claim 6, Sharon teaches, wherein the first workload is included in the first namespace, and wherein the second workload is included in the second namespace ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace).
The combination of Sharon-Singh fails to specifically teach, wherein the assessing includes discovering a hierarchical ordering of workload groups within a first namespace, discovering a hierarchical ordering of workload groups within a second namespace and identifying, in dependence on the hierarchical ordering of workload groups within the first namespace and in dependence on the hierarchical ordering of workload groups within the second namespace, that a first workload is suitable for merging with a second workload, wherein the first workload is included in the first namespace.
However, Ayyagari teaches, wherein the assessing includes discovering a hierarchical ordering of workload groups within a first namespace ([0062], workload deployment program 300 utilizes logical controls to activate or inhibit the execution of executable procedures within an embedded computing entity based on constraints, operational hierarchies, and execution sequences associated with the workload; Examiner Note: Ayyagari teaches workloads within a namespace: [0004], Proxy nodes are used to distribute workloads, handle workloads requests within a namespace, and direct the transfer of objects that comprise the workload among nodes), discovering a hierarchical ordering of workload groups within a second namespace ([0078], workload analysis program 500 accesses a set of information related to processing of the workload. In various embodiments, workload analysis program 500 accesses information related to a workload that includes: a plurality of utilized objects, a set of locations of utilized objects provided by workload deployment program 300, a list of embedded computing entities utilized by the workload, and functional resources/dictates of one or more embedded computing entities of the workload, such as a hierarchy of operations or identifying a set of objects associated with one or more pipelined tasks) and identifying, in dependence on the hierarchical ordering of workload groups within the first namespace and in dependence on the hierarchical ordering of workload groups within the second namespace, that a first workload is suitable for merging with a second workload ([0078], workload analysis program 500 accesses a set of information related to processing of the workload. In various embodiments, workload analysis program 500 accesses information related to a workload that includes: a plurality of utilized objects, a set of locations of utilized objects provided by workload deployment program 300, a list of embedded computing entities utilized by the workload, and functional resources/dictates of one or more embedded computing entities of the workload, such as a hierarchy of operations or identifying a set of objects associated with one or more pipelined tasks; [0080], In response to analyzing a workload and various embedded computing entities, workload analysis program 500 obtains one or more additional executable procedures for inclusion within an embedded computing entity utilized during executing the workload, and/or compiles a new embedded computing entity from executable procedures; [0085], workload analysis program 500 utilizes clustering algorithms to determine whether to include the unidentified portion of the workload with another portion of the workload; [0089], in response to parsing a workload, workload analysis program 500 classifies objects related to the workload to identify executable procedures that are utilized to process the one or more objects of the workload; and [0090], Upon classifying an object, analysis suite 107 updates the metadata of an object with one or more classifications. Similarly, workload analysis program 500 can utilize analysis suite 107 to identify and classify new executable procedures for inclusion in an instance of table 250).
The same motivation used in the rejection of claim 5 is applicable to the instant claim.
As per claim 9, Sharon teaches, wherein the first workload is included in the first namespace, and wherein the second workload is included in the second namespace ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace).
Sharon fails to specifically teach, wherein the assessing includes discovering a hierarchical ordering of workload groups within a first namespace, discovering a hierarchical ordering of workload groups within a second namespace and identifying, in dependence on the hierarchical ordering of workload groups within the first namespace and in dependence on the hierarchical ordering of workload groups within the second namespace, a first workload suitable for merging with a second workload,..., wherein the prompting data prompts for merging of the first and second workloads.
However, Singh teaches, wherein the prompting data prompts for merging of the first and second workloads ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; [0052], a workload detection agent can predict that a workload running in a particular node of a first cluster might perform better and/or achieve improved resource utilization if that workload were situated in, for example, a different cluster. Upon such a prediction, resource reallocation operations and/or migration operations can be invoked so as to pursue the predicted improvements; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)).
The combination of Sharon-Singh fails to specifically teach, wherein the assessing includes discovering a hierarchical ordering of workload groups within a first namespace, discovering a hierarchical ordering of workload groups within a second namespace and identifying, in dependence on the hierarchical ordering of workload groups within the first namespace and in dependence on the hierarchical ordering of workload groups within the second namespace, a first workload suitable for merging with a second workload.
However, Ayyagari teaches, wherein the assessing includes discovering a hierarchical ordering of workload groups within a first namespace ([0062], workload deployment program 300 utilizes logical controls to activate or inhibit the execution of executable procedures within an embedded computing entity based on constraints, operational hierarchies, and execution sequences associated with the workload; Examiner Note: Ayyagari teaches workloads within a namespace: [0004], Proxy nodes are used to distribute workloads, handle workload requests within a namespace, and direct the transfer of objects that comprise the workload among nodes), discovering a hierarchical ordering of workload groups within a second namespace ([0078], workload analysis program 500 accesses a set of information related to processing of the workload. In various embodiments, workload analysis program 500 accesses information related to a workload that includes: a plurality of utilized objects, a set of locations of utilized objects provided by workload deployment program 300, a list of embedded computing entities utilized by the workload, and functional resources/dictates of one or more embedded computing entities of the workload, such as a hierarchy of operations or identifying a set of objects associated with one or more pipelined tasks) and identifying, in dependence on the hierarchical ordering of workload groups within the first namespace and in dependence on the hierarchical ordering of workload groups within the second namespace, a first workload suitable for merging with a second workload ([0078], workload analysis program 500 accesses a set of information related to processing of the workload. In various embodiments, workload analysis program 500 accesses information related to a workload that includes: a plurality of utilized objects, a set of locations of utilized objects provided by workload deployment program 300, a list of embedded computing entities utilized by the workload, and functional resources/dictates of one or more embedded computing entities of the workload, such as a hierarchy of operations or identifying a set of objects associated with one or more pipelined tasks; [0080], In response to analyzing a workload and various embedded computing entities, workload analysis program 500 obtains one or more additional executable procedures for inclusion within an embedded computing entity utilized during executing the workload, and/or compiles a new embedded computing entity from executable procedures; [0085], workload analysis program 500 utilizes clustering algorithms to determine whether to include the unidentified portion of the workload with another portion of the workload; [0089], in response to parsing a workload, workload analysis program 500 classifies objects related to the workload to identify executable procedures that are utilized to process the one or more objects of the workload; and [0090], Upon classifying an object, analysis suite 107 updates the metadata of an object with one or more classifications. Similarly, workload analysis program 500 can utilize analysis suite 107 to identify and classify new executable procedures for inclusion in an instance of table 250).
The same motivation used in the rejection of claim 5 is applicable to the instant claim.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sharon-Singh as applied to claim 1 and in further view of Sheoran et al. (US 20220129316).
As per claim 11, Sharon teaches, wherein the assessing includes discovering relationships between workloads within a certain namespace of the cluster (0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace), wherein the discovering includes processing text based reports ([0112], workload analyzer 800 includes a comparator 804 configured to review the information gathered by the tracker 806 and workload attributes and deduce, or determine, relationships between namespaces with stored data in currently open erase blocks based on the workload attributes detected for the workloads associated with those namespaces. The workload analyzer 800 tracks and distinguishes namespaces using their namespace identifiers) for respective workloads of the certain namespace ([0101], the workload analyzer 706 uses the namespace identifier included in the storage commands 710 to track how namespaces having a first set of workload attributes relate to namespaces having other workload attributes, particularly similar workload attributes; [0102], the workload analyzer 706 reviews or analyzes the workload attributes and determines patterns, similarities, relationships, and/or commonalities between namespaces that are based on the workload attributes. In certain embodiments, these patterns, similarities, relationships, and/or commonalities are referred to as hints and can be used to optimize the performance of the non-volatile memory device(s) 118. In certain embodiments, the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace).
The combination of Sharon-Singh fails to specifically teach, wherein the discovering includes ascertaining, in dependence on the processing text based reports for respective workloads of the certain namespace Euclidean distances between the respective workloads of the certain namespace.
However, Sheoran teaches, wherein the discovering includes ascertaining, in dependence on the processing text based reports for respective workloads of the certain namespace Euclidean distances between the respective workloads of the certain namespace ([0019], different distance metric generation systems are used, each one using different features or characteristics of workloads to determine a representation of the workload (e.g., a vector representation of the workload) based on the resource usage of the workload. For each distance metric generation system, a number of clusters into which a set of workload resource usage histories are grouped is determined based on the workload representations generated by the distance metric generation system; [0021], the distances are determined using a baseline distance metric that is the Euclidean distance on the workload representations; and [0037], The term “workload” refers to one or more computational tasks to perform. Examples of workloads include individual sets of commands or operations, applications or services running inside containers ).
The combination of Sharon-Singh and Sheoran are analogous because they are each related to workload management. Sharon teaches a method of workload management including workload classification and merging. Singh teaches a method of workload classification, management, including workload consolidation/migration, and resource allocation based on workload attributes. Singh also teaches a user interface that allows users to make management decisions. Sheoran teaches a method of workload management that includes workload grouping and resource allocation based on similarities and distance algorithms. (Abstract, scheduling system includes an equivalence-class-based resource usage prediction system that receives a workload request and predicts an equivalence class for that workload request based on resource usage over time by the workload request or metadata associated with the workload request. The scheduling system also includes a workload assignment system that assigns the workload request to one or more of the computing nodes based on the predicted equivalence class; [0004], [0014], allows multiple different workloads with similar characteristics to be assigned to the same equivalence class and treated similarly by the scheduling system (e.g., scheduled based on an expected amount of resources used by the workload over time)). 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 “workload analyzer” of the combination of Sharon-Singh would be modified to include Sheoran’s workload classification mechanisms including its distance algorithms resulting in a system that allows users to view and classify workload information and make resource allocation decisions based on said workload information. Therefore, it would have been obvious to combine the teachings of the combination of Sharon-Singh and Sheoran.
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sharon-Singh as applied to claim 1 and in further view of Taigi et al. (US 20210336899).
As per claim 12, Sharon teaches, further specifies a determined similarity level of the workload group instantiation in the first namespace and the workload group instantiation in the second namespace ([0112], this derived, or implied relationship, between these namespaces is a hint, or prediction, that an observed relationship, similarity, or commonality will continue to exist between the two namespaces such that placing data for both on the same open erase block will be advantageous for the non-volatile memory system 102).
The combination of Sharon-Singh fails to specifically teaches, wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace and a workload group instantiation in a second namespace.
However, Taigi teaches, wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace and a workload group instantiation in a second namespace ([0046], Informing users of the reasons for groupings allows automated grouping processes to be understood and verified; and [0052], model 520 is able to output a label for a set of input features; Examiner Notes: 1) Taigi’s work group labels are displayed to users for approval/verification: [0019], Once a group (e.g., all groups) has a workload that has been assigned a label or labels, the label or labels for that workload may be applied to all workloads in the group; and [0054], Labels output by model 520 may, in some instances, be used to retrain model 520. For example, if label 540 is approved by a user (e.g., if an administrator confirms that label 540 is accurate), then label 540 may be used to generate an additional training data instance for labeled training data 510 that is used to retrain model 520; and 2) Taigi teaches namespace workloads: [0028], the teachings herein also apply to other types of virtual computing instances (VCIs) or data compute nodes (DCNs), such as containers, which may be referred to as Docker containers... namespace containers, etc.),
The combination of Sharon-Singh and Taigi are analogous because they are each related to workload management. Sharon teaches a method of workload management including workload classification and merging. Singh teaches a method of workload classification, management, including workload consolidation/migration, and resource allocation based on workload attributes. Singh also teaches a user interface that allows users to make management decisions. Tiagi teaches a method of workload classification, grouping, and management based on workload features. (Abstract, determining a plurality of sets of features comprising a respective set of features for each respective workload of a first subset of a plurality of workloads. Embodiments include identifying a group of workloads based on similarities among the plurality of sets of features. Embodiments include receiving label data from a user comprising a label for the group of workloads. Embodiments include associating the label with each workload of the group of workloads to produce a training data set). Tiagi also teaches a user interface that allows users to make management decisions. ([0045], a user provides the labels via a user interface. For instance, the grouped VCIs may be displayed for review and/or labeling. At least a subset of the features of each VCI may be displayed for review. In some embodiments, features considered most important to a workload's inclusion in a group are displayed, such as the features that were most similar to other workloads in the group). 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 “workload analyzer” of the combination of Sharon-Singh would be modified with Taigi’s workload grouping mechanism of its “manager 138” resulting in a system that allows users to view workload information and make resource allocation decisions, including workload grouping, based on said workload information. Therefore, it would have been obvious to combine the teachings of the combination of Sharon-Singh and Tiagi.
As per claim 13, Sharon teaches wherein the assessing includes discovering workload groups comprising related workloads (0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace), and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace; and [0119], data allocator 900 is configured to direct an open erase block manager (or other non-volatile memory media 120 physical placement manager) to group data of write commands having a first namespace identifier into a common one of two or more of the open logical erase blocks as write commands having a second namespace identifier, based on a relationship between the namespaces), wherein the assessing includes performing similarity analysis to obtain the determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group ([0100], the workload analyzer 706 tracks workload attributes that define, describe, or characterize the workloads. As used herein, a “workload attribute” is any quality, characteristic, attribute, feature, behavior, trait, or property about a workload; and [0101], the workload analyzer 706 tracks workload attributes about how the workloads relate to the namespaces referenced in the storage commands 710. In a further embodiment, the workload analyzer 706 uses the namespace identifier included in the storage commands 710 to track how namespaces having a first set of workload attributes relate to namespaces having other workload attributes, particularly similar workload attributes), wherein the similarity analysis includes text based static analysis of the first workload group and the second workload group, and wherein the similarity analysis includes dynamic runtime analysis of the first workload group and the second workload group ([0101], the workload analyzer 706 uses the namespace identifier included in the storage commands 710 to track how namespaces having a first set of workload attributes relate to namespaces having other workload attributes, particularly similar workload attributes; and [0102], the workload analyzer 706 reviews or analyzes the workload attributes and determines patterns, similarities, relationships, and/or commonalities between namespaces that are based on the workload attributes. In certain embodiments, these patterns, similarities, relationships, and/or commonalities are referred to as hints and can be used to optimize the performance of the non-volatile memory device(s) 118. In certain embodiments, the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace).
The combination of Sharon-Singh fails to specifically teach, wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace defining the first workload group and a workload group instantiation in a second namespace defining the second workload group, and wherein the prompting data further specifies a determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group.
However, Taigi teaches, wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace defining the first workload group and a workload group instantiation in a second namespace defining the second workload group ([0045], the grouped VCIs may be displayed for review and/or labeling. At least a subset of the features of each VCI may be displayed for review. In some embodiments, features considered most important to a workload's inclusion in a group are displayed, such as the features that were most similar to other workloads in the group; [0046], Informing users of the reasons for groupings allows automated grouping processes to be understood and verified; and [0058], displays the grouped workloads along with features of the grouped workloads), and wherein the prompting data further specifies a determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group ([0018], workloads in the subset are then grouped into one or more groups based on feature similarity. In one example, cosine similarity between features of workloads is used to group similar workloads in the subset into a group; and [0033], monitoring appliance 140 communicates with an agent on each of hosts 105, such as agent 118 in hypervisor 116, in order to retrieve attributes of VCIs, such as VCIs 135. In some embodiments, attributes are retrieved by hypervisor 116 from endpoint monitoring components (not shown) running on every VCI and/or from network flow data, such as through a virtual switch, monitored by hypervisor 116 on each host 105... One or more of the attributes of VCIs are then used as one or more of the features of the VCIs in order to group VCIs based on similarity of features).
The same motivation used in the rejection of claim 12 is applicable to the instant claim.
As per claim 14, Sharon teaches, wherein the assessing includes discovering workload groups comprising related workloads (0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace), and identifying, in dependence on the discovering, a first workload group of a first namespace and second workload group of a second namespace suitable for merging ([0006], determine a relationship between a first namespace and a second namespace based on a plurality of workload attributes for the first namespace and the second namespace; and [0102], the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace; and [0119], data allocator 900 is configured to direct an open erase block manager (or other non-volatile memory media 120 physical placement manager) to group data of write commands having a first namespace identifier into a common one of two or more of the open logical erase blocks as write commands having a second namespace identifier, based on a relationship between the namespaces), wherein the assessing includes performing similarity analysis to obtain the determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group ([0100], the workload analyzer 706 tracks workload attributes that define, describe, or characterize the workloads. As used herein, a “workload attribute” is any quality, characteristic, attribute, feature, behavior, trait, or property about a workload; and [0101], the workload analyzer 706 tracks workload attributes about how the workloads relate to the namespaces referenced in the storage commands 710. In a further embodiment, the workload analyzer 706 uses the namespace identifier included in the storage commands 710 to track how namespaces having a first set of workload attributes relate to namespaces having other workload attributes, particularly similar workload attributes), wherein the similarity analysis includes text based static analysis of the first workload group and the second workload group ([0101], the workload analyzer 706 uses the namespace identifier included in the storage commands 710 to track how namespaces having a first set of workload attributes relate to namespaces having other workload attributes, particularly similar workload attributes; and [0102], the workload analyzer 706 reviews or analyzes the workload attributes and determines patterns, similarities, relationships, and/or commonalities between namespaces that are based on the workload attributes. In certain embodiments, these patterns, similarities, relationships, and/or commonalities are referred to as hints and can be used to optimize the performance of the non-volatile memory device(s) 118. In certain embodiments, the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace), and wherein the similarity analysis includes dynamic runtime analysis of the first workload group and the second workload group ([0101], the workload analyzer 706 uses the namespace identifier included in the storage commands 710 to track how namespaces having a first set of workload attributes relate to namespaces having other workload attributes, particularly similar workload attributes; and [0102], the workload analyzer 706 reviews or analyzes the workload attributes and determines patterns, similarities, relationships, and/or commonalities between namespaces that are based on the workload attributes. In certain embodiments, these patterns, similarities, relationships, and/or commonalities are referred to as hints and can be used to optimize the performance of the non-volatile memory device(s) 118. In certain embodiments, the workload analyzer 706 determines, or derives, a relationship between two namespaces based on workload attributes for each namespace).
Sharon fails to specifically teach, wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace defining the first workload group and a workload group instantiation in a second namespace defining the second workload group, and wherein the prompting data further specifies a determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group, wherein the method includes performing runtime workload analysis to determine a provisioning resource allocation for supporting a remaining workload group subsequent to merger of the first workload group and the second workload group, and wherein the prompting data references the provisioning resource allocation.
However Singh teaches, wherein the method includes performing runtime workload analysis to determine a provisioning resource allocation for supporting a remaining workload group subsequent to merger of the first workload group and the second workload group ([0078], workload consolidation technique 4A00 as implemented in systems for distributed resource allocation using dynamically classified workloads; [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)); and wherein the prompting data references the provisioning resource allocation ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads).
The combination of Sharon-Singh fails to specifically teach, wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace defining the first workload group and a workload group instantiation in a second namespace defining the second workload group, and wherein the prompting data further specifies a determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group.
However, Taigi teaches, wherein the prompting data specifies a workload group name having a workload group instantiation in a first namespace defining the first workload group and a workload group instantiation in a second namespace defining the second workload group ([0045], the grouped VCIs may be displayed for review and/or labeling. At least a subset of the features of each VCI may be displayed for review. In some embodiments, features considered most important to a workload's inclusion in a group are displayed, such as the features that were most similar to other workloads in the group; [0046], Informing users of the reasons for groupings allows automated grouping processes to be understood and verified; and [0058], displays the grouped workloads along with features of the grouped workloads), and wherein the prompting data further specifies a determined similarity level of the workload group instantiation in the first namespace defining the first workload group and the workload group instantiation in the second namespace defining the second workload group ([0018], workloads in the subset are then grouped into one or more groups based on feature similarity. In one example, cosine similarity between features of workloads is used to group similar workloads in the subset into a group; and [0033], monitoring appliance 140 communicates with an agent on each of hosts 105, such as agent 118 in hypervisor 116, in order to retrieve attributes of VCIs, such as VCIs 135. In some embodiments, attributes are retrieved by hypervisor 116 from endpoint monitoring components (not shown) running on every VCI and/or from network flow data, such as through a virtual switch, monitored by hypervisor 116 on each host 105... One or more of the attributes of VCIs are then used as one or more of the features of the VCIs in order to group VCIs based on similarity of features).
The same motivation used in the rejection of claim 12 is applicable to the instant claim.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Sharon et al. (US 20200409559) in view of Sheoran et al. (US 20220129316) and Singh et al. (US 20200026560).
As per claim 20, Sharon teaches the invention substantially as claimed including a computer program product comprising:
a computer readable storage medium ([0114], The term “computer readable medium” or “computer usable medium” as used herein refers to any medium that participates in providing instructions to a data processor for execution) readable by one or more processing circuit and storing instructions for execution by one or more processor for performing a method comprising:
obtaining workload characterizing data, wherein the workload characterizing data characterizes a plurality of workloads of a cluster, wherein the workload characterizing data includes text-based reports for respective workloads of a namespace of the cluster ();
assessing workloads of the plurality of workloads with use of characterizing data of the workload characterizing data,
Sharon fails to specifically teach, wherein the assessing includes processing the text-based reports to discover relationships between workloads within the namespace, including ascertaining Euclidean distances between respective workloads of the namespace in dependence on the text-based reports; generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads; and presenting the prompting data to a user.
However, Sheoran teaches, wherein the assessing includes processing the text-based reports to discover relationships between workloads within the namespace, including ascertaining Euclidean distances between respective workloads of the namespace in dependence on the text-based reports ([0019], different distance metric generation systems are used, each one using different features or characteristics of workloads to determine a representation of the workload (e.g., a vector representation of the workload) based on the resource usage of the workload. For each distance metric generation system, a number of clusters into which a set of workload resource usage histories are grouped is determined based on the workload representations generated by the distance metric generation system; [0021], the distances are determined using a baseline distance metric that is the Euclidean distance on the workload representations; and [0037], The term “workload” refers to one or more computational tasks to perform. Examples of workloads include individual sets of commands or operations, applications or services running inside containers).
The combination of Sharon and Sheoran are analogous because they are each related to workload management. Sharon teaches a method of workload management including workload classification and merging. Sheoran teaches a method of workload management that includes workload grouping and resource allocation based on similarities and distance algorithms. (Abstract, scheduling system includes an equivalence-class-based resource usage prediction system that receives a workload request and predicts an equivalence class for that workload request based on resource usage over time by the workload request or metadata associated with the workload request. The scheduling system also includes a workload assignment system that assigns the workload request to one or more of the computing nodes based on the predicted equivalence class; [0004], [0014], allows multiple different workloads with similar characteristics to be assigned to the same equivalence class and treated similarly by the scheduling system (e.g., scheduled based on an expected amount of resources used by the workload over time)). 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 “workload analyzer” of Sharon would be modified to include Sheoran’s workload classification mechanisms including its distance algorithms resulting in a system that allows users to view and classify workload information and make resource allocation decisions based on said workload information. Therefore, it would have been obvious to combine the teachings of the Sharon and Sheoran.
The combination of Sharon-Sheoran fails to specifically teach, generating prompting data in dependence on the assessing of the workloads of the plurality of workloads, wherein the prompting data prompts for merging of identified workloads of the plurality of workloads; and
presenting the prompting data to a user.
However, Singh teaches, generating prompting data in dependence on the assessing of the workloads of the plurality of workloads ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; and [0032], Given the correlation metrics, at step 109, an affinity between an activity stream and a particular workload type can be determined and quantified, which affinity in turn can be used in making resource allocation decisions. In some cases, and as shown, an affinity determination is provided to a user interface, which user interface can be viewed and manipulated by a user), wherein the prompting data prompts for merging of identified workloads of the plurality of workloads ([0025], the resource allocation operations can be presented to a user as a set of recommended resource allocation operations to facilitate accepting or declining any of the operations. The recommended resource allocation operations and/or other information presented for analysis can be expressed in terms pertaining to named workloads; [0052], a workload detection agent can predict that a workload running in a particular node of a first cluster might perform better and/or achieve improved resource utilization if that workload were situated in, for example, a different cluster. Upon such a prediction, resource reallocation operations and/or migration operations can be invoked so as to pursue the predicted improvements; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)); and
presenting the prompting data to a user ([0049], certain recommended instances of the resource allocation operations 138 and/or other information (e.g., resource usage metrics) can be presented in user interface 116 for analysis, expressed in terms pertaining to the classified workloads in the system; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)).
The combination of Sharon-Sheoran and Singh are analogous because they are each related to workload management. Sharon teaches a method of workload management including workload classification and merging. Sheoran teaches a method of workload management that includes workload grouping and resource allocation based on similarities and distance algorithms. Singh teaches a method of workload classification, management, including workload consolidation/migration, and resource allocation based on workload attributes. (Abstract, managing virtualized entities in computing systems. In a method embodiment, processing commences upon receiving I/O activity trace data associated with virtualized entities running in a computing system. Specific I/O activity attributes are extracted from the I/O activity trace data, and the I/O activity attributes are used to form a workload classification model. The workload classification model serves to assign one or more workload classifications to a respective one or more observed workloads running on the computing system. Based on the determined workload classification or classifications, recommended resource allocation operations are formed for further consideration. Considered resource allocation operations include migrations of virtualized entities from a source computing resource to a target computing resource; and [0080], workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402). As can be observed in this example, the resource allocation operations result in node 252.sub.15 being tasked with servicing consolidated SQL server workloads and node 252.sub.34 being tasked with servicing consolidated VDI workloads)). Singh also teaches a user interface that allows users to make management decisions. ([0049], certain recommended instances of the resource allocation operations 138 and/or other information (e.g., resource usage metrics) can be presented in user interface 116 for analysis, expressed in terms pertaining to the classified workloads in the system; and [0080], various workload-based resource allocation operations can be generated (operation 180.sub.2). As one example of workload-based resource allocation operations, the workload parameters can be used to consolidate workloads of a given type onto a node that is configured with resources that correspond to the demands of the consolidated workloads (operation 402)). 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 “workload analyzer” of the combination of Sharon- Sheoran would be modified to include would be modified to include Singh’s resource allocation prompting mechanism resulting in a system that allows users to view workload information and make resource allocation decisions in consideration of namespaces associated with workloads and workload information. Therefore, it would have been obvious to combine the teachings of the combination of Sharon-Sheoran and Singh.
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sharon-Singh as applied to independent claim 1 and in further view of Schiff et al. (US 20180260252).
As per claim 21, the combination of Sharon-Singh fails to specifically teach, wherein the assessing includes, for each namespace of a plurality of namespaces of the cluster, recursively grouping workloads into progressively higher order workload groups until all workloads of the namespace are assigned to a top order workload group for the namespace, and identifying the identified workloads for merging based at least in part on comparing the progressively higher order workload groups of different namespaces..
However, Schiff teaches, wherein the assessing includes, for each namespace of a plurality of namespaces of the cluster, recursively grouping workloads into progressively higher order workload groups until all workloads of the namespace are assigned to a top order workload group for the namespace ([0006], the processor is configured to segment the workloads by clustering the workloads into tiers, each tier including workloads performing a same role within an application, and grouping the tiers to form the groups. In some embodiments, the processor is configured to represent the computing system by a graph, which includes nodes that initially represent the workloads and edges that initially represent the communication connections, and to cluster the workloads into the tiers by iteratively clustering the nodes of the graph; and [0007], cluster the nodes into the tiers based on the similarity measures and the variance measures. In an embodiment, following clustering of the workloads into the tiers, the processor is configured to group the tiers to form the groups by assigning respective weights to the edges of the graph, and grouping the tiers based on the weights), and identifying the identified workloads for merging based at least in part on comparing the progressively higher order workload groups of different namespaces ([0007], cluster the nodes into the tiers based on the similarity measures and the variance measures. In an embodiment, following clustering of the workloads into the tiers, the processor is configured to group the tiers to form the groups by assigning respective weights to the edges of the graph, and grouping the tiers based on the weights).
The combination of Sharon-Singh and Schiff are analogous because they are each related to workload management. Sharon teaches a method of workload management including workload classification and merging. Singh teaches a method of workload classification, management, including workload consolidation/migration, and resource allocation based on workload attributes. Schiff teaches a method of workload analysis, classification, and management. (Abstract, receive attributes of communication connections of multiple workloads running in a computing system. The processor is configured to automatically segment the multiple workloads into groups based on the attributes of the communication connections; and Claim 4, cluster the workloads into the tiers by iteratively clustering the nodes of the graph). 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 “workload analyzer” of the combination of Sharon-Singh would be modified with Schiff’s workload grouping mechanism resulting in a system that manages a hierarchical grouping of workloads. Therefore, it would have been obvious to combine the teachings of the combination of Sharon-Singh and Schiff.
As per claim 22, the combination of Sharon-Singh fails to specifically teach, wherein the presenting the prompting data includes presenting a user-selectable merge-analysis policy, and wherein, in response to user selection of the user-selectable merge-analysis policy, the method includes reassessing the plurality of workloads and generating updated prompting data that changes a determined similarity level between workload group instantiations of different namespaces.
However, Schiff teaches, wherein the presenting the prompting data includes presenting a user-selectable merge-analysis policy ([0115], final and/or intermediate segmentation results can be used in a semi-automatic manner, as a recommendation tool that assists a human operator to quickly define groups of similar VMs (for example by making progressive suggestions based on the VMs and groups the user marked and grouped so far)), and wherein, in response to user selection of the user-selectable merge-analysis policy, the method includes reassessing the plurality of workloads and generating updated prompting data that changes a determined similarity level between workload group instantiations of different namespaces ([0115], final and/or intermediate segmentation results can be used in a semi-automatic manner, as a recommendation tool that assists a human operator to quickly define groups of similar VMs (for example by making progressive suggestions based on the VMs and groups the user marked and grouped so far)).
The same motivation used in the rejection of claim 21 is applicable to the instant claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is as follows:
Brown et al. (US 8645425)- Teaches workload classification and merging:
Abstract, guiding the development of workload group definition (WD) classifications for a database system having resources. WD classification information is provided where a workload group is a set of system requests having characteristics defined by a WD and a WD classification identifies one or more WDs with similar performance requirements. If WD classification is not acceptable, one or more of the following functions is performed until WD classification is acceptable: (a) splitting one or more WDs; and (b) merging two or more WDs.
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/MELISSA A HEADLY/Examiner, Art Unit 2197
