Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 05/04/2026.
Status of Claims
Claims 1-5,7,8,10-20 have been amended by Applicant.
Claims 1-20 are currently pending and have been rejected as follows.
Response to Amendments / Arguments
Applicant’s 05/04/2026 amendment necessitated new grounds of rejection in this act.
= Objection to dependent Claims 2,15 in the prior act is withdrawn in view of Applicant’s amendment as suggested by Examiner.
= 101 rejection is maintained with a detailed analysis and explanation below.
Ill. Rejections under 35 U.S.C. §101
Examiner reincorporates all findings and rationales at Non-Final Act 02/04/2026 p.2 last ¶-p.7 ¶3. Examiner now addresses the newly submitted arguments of Remarks 05/04/2026.
Subject matter eligibility argument A: Remarks 05/04/2026 p.14 last ¶ -p.16 ¶ 2 argues that as amended, independent Claim 1 is directed to a computer-implemented cloud services provider deployment modification mechanism requiring generating a particular directed flow graph that models infrastructure components and their technical dependencies, performing a maximum flow analysis on that graph to attribute cloud services provider resource-usage costs to execution of commerce-platform services, generating a data package that includes resource deployment adjustments based on the attributed costs obtained by tracing through the directed graph, and then configuring deployment by ingesting that data which are argued as specific computer operations on graph-based data structures and automated deployment-modification mechanism, and therefore are not reasonably performable as a mental process or fundamental economic practice or other organizing human activity. Further, Applicant argues that amended independent claim 1 involves generating and processing multiple, distinct data sets associated with a directed flow graph, including nodes and edges that model an infrastructure of a commerce platform system and involves performing a maximum flow analysis of the directed flow graph to attribute cloud services provider resource costs to execution of services, that allegedly require algorithmic processing of complex graph data structures and therefore cannot be reasonably performed as a mental process or a method of organizing human activity.
-> Examiner considered the subject matter eligibility argument A but respectfully disagrees fining it unpersuasive, because here, the claims’ character as a whole is still that of “modeling and analyzing a commerce platform infrastructure provided by” [business entities] namely “cloud services provider systems to a commerce platform system”, as summarized by the preamble of independent Claims 1,14,20 and Title of the Application as filled, by “receiving” or considering “information indicative of one or more costs”, price or fees as fundamental economic concept of “of cloud services provider resource usage” [as fundamental economic concept of resource demand] “by the commerce platform system over a period of time” (independent Claims 1,14,20); as well as “information indicative of execution of services” (i.e. exemplified as payment services for subscription services, on-demand marketplaces, read in light of Original Specification ¶ [0020]) “of the commerce platform system over the period of time” (independent Claims 1,14,20). Such data is analyz[ed], to extract cloud services provider resource usage” [as fundamental economic concept of demand] and [consumption or] “execution information of the services” “over the period of time” (independent Claims 1,14,20) and for model[ing] “an infrastructure of the commerce platform system, the directed graph being a flow graph” (independent Claims 1,14,20). This is then used for “performing”, “maximum flow analysis of the directed graph to attribute the one or more costs of the cloud services provider resource usage to execution of the services” [or utilization] “of the commerce platform system at the cloud services provider system” as well as “based on the attributed one or more costs” [prices or fees] “obtained via tracing through the directed graph for which the maximum flow analysis is performed”, “generating”, [a report, or document as read in light of Original Specification ¶ [0069] which is was rephrased by Applicant as] “a data package having resource deployment adjustments that alters cloud services provider system costs for one or more of the cloud services provider resource usage information and commerce platform system execution information over the period of time”.
Applicant characterizes these as multiple, distinct data sets and data structures, yet, no matter of how such data is narrowed or manipulated, its underlining concepts of receiving resource usage or demand and associated costs, and respective services for modeling and reporting about what can be alter[ed] for such costs, and such “usage information” fall squarely within the fundamental practices or certain methods or organizing human activities of MPEP 2106.04(a)(2) II A implementable through equally abstract mathematical relationships or “edges” between “nodes” that “model” said “usage”, “execution” and “costs” as part of “maximum flow analysis” on a “directed graph” as mathematical concepts expressed in words and, consistent with the abstract mathematical relationships of MPEP 2106.04(a)(2) I A, which at their turn1 are used by pen and paper or, at most, aided by computer as part of the equally abstract observation, set forth here by visual tracing or tracking on the graph, followed by evaluation, set forth here through cognitive flow analysis on said graph, and judgement, set forth here by reading to acquire knowledge, learn, assimilate, soak in, absorb or “ingesting” such information as part of a report, text document or package for subsequent decision making for what to deploy and possibly of what to is capable to be modif[ied]. When tested per MPEP 2106.04(A)(2) III ¶2 and 2106.04(a)(2) III C #1,#2,#3, such paper aided or computer-aided, cognitive processes do not preclude the claims to recite, describe or set forth the abstract exception, with emphasis on MPEP 2106.04(a)(2) III C #2 which states that even performing such cognitive processes in a computer environment does not preclude the claims to recite, describe or set forth the abstract exception. Here, such computer environment is one associated with “a cloud services provider systems to a commerce platform”.
While, independent Claims 1,14,20 call at last limitation, for “configuring deployment of the services of the commerce platform system on the hardware computing resources of the cloud services provider system based on the data package by ingesting the data package by a system deployment element capable” [or intended2] of” modifying cloud services provider deployments” [based on prior considerations of costs, services execution and resource usage], said last limitation is a mere drafting effort to narrow the abstract “modeling” “and” “flow” analyzing” “of commerce platform infrastructure provided by” [business entities of] “cloud services provider” “to a commerce platform”, with consideration for fundamental and equally abstract “costs”, services’ “execution” and “resource usage”. To be also clear, here, the “system deployment element” [is merely intended or] “capable of modifying cloud services provider deployments”, as read in light of Original Specification ¶ [0069] 5th sentence. A correct claim construction or interpretation of said phase, under the broadest reasonable interpretation test of MPEP 2111 reveals that the “system deployment element capable of modifying cloud services provider deployments” is not necessarily the result of “ingesting the data package by” the “system deployment element”.
In any event here, the “configuring deployment of the services of the commerce platform system” “by” [including, assimilating, learning, absorbing, or even more narrowly interpreted as inserting, loading, installing etc. as non-limiting synonyms for], “ingesting the data package” [report or document as read in light of Original Specification ¶ [0069] 3rd sentence] “ by a system deployment element capable of modifying” [best practices of] “cloud services provider deployments”, at last limitation of independent claims 1,14,20, represents a mere attempt at narrowing the abstract exception to a field of use or technological environment which, per MPEP 2106.05(h) does not preclude the claims to recite, describe or set forth the abstract exception. A more granular analysis will be resented next as follows. For now, argument A is unpersuasive.
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Subject matter eligibility argument B: Remarks 05/04/2026 p.17 ¶4-p.18 ¶1 argues that
even if the prior Office Action correctly identified the judicial exception, the four limitations of:
(i) generating a directed graph (a flow graph) that models technical dependencies of cloud-hosted infrastructure,
(ii) performing a maximum flow analysis to attribute cloud resource-usage costs to execution of services,
(iii) generating a data package having resource deployment adjustments based on the attributed costs obtained by tracing through the directed graph, and
(iv) configuring deployment of the services on cloud-provider hardware by ingesting the data package by a system deployment element capable of modifying cloud services provider deployments,
allegedly address a technical problem in cloud environments where cloud fees can become obscured by complex interdependent software deployments-and provides a technical solution that improves cloud infrastructure resource tracking and enables automated deployment modification based on the analysis results (citing Original Specification ¶ [0004]). Remarks 05/04/2026 p.18 ¶2-p.19 ¶1 then argues that by generating the specific directed graph and performing the maximum flow analysis of the directed flow graph, this approach utilizing graph theory techniques can efficiently determine which commerce platform system products consume cloud resources at cloud services provider system(s) and their underlying costs (citing Original Specification ¶ [0026]). Also, by tracing costs of cloud services provider resource usage through the directed flow graph, it is argued that this approach can attribute costs to execution of services and generate data package for configuring deployment of the services on hardware computing resources of the cloud services provider system, thereby driving subsequent cloud deployment modifications based on such data package, to improve software development and product deployment and to reduce cloud spend (citing Specification ¶ [0004], ¶ [0054]).
-> Examiner fully considered the subject matter eligibility argument B but respectfully disagrees finding it unpersuasive because limitations (i) and (ii) as raised by Applicant at Remarks 05/04/2026 p.18 ¶1, still set forth the abstract concepts as identified and mapped above, while next two limitations (iii) and (iv) as also raised by Applicant at Remarks 05/04/2026 p.18 ¶1, would represent use of software to tailor information (limitation iii) and a narrowing of the abstract exception to a field of use or technological environment (limitation iv) respectively. Yet, MPEP 2106.05(f)(2)(v) is clear that requiring use of software to tailor information and provide it on a computer, is a mere example of invoking computer or machinery as tools to apply the abstract exception that does not integrate it into a practical application. Similarly, MPEP 2106.05(h) is also clear that narrowing the abstract exception to a field of use or technological environment also does not integrate the abstract exception into a practical application.
It is also clear that determining product consumption and then underlying or tracing or attributing costs, or cloud fees to services execution and resource usage for decision making on what to deploy to reduce cloud spend as repeatedly and preponderantly argued by Applicant above by reliance on Original Specification ¶ [0004], ¶ [0026], ¶ [0054] remain within the realm of the abstract exception right from the onset. Thus, both the problem and its underlining solution appear abstract right from the onset. Here, at Remarks 05/04/2026 p.18 ¶1, the Applicant seems to conflate the solution of the obscured cloud fees, as a patent eligible improvement. Yet as found
by Non-Final Act 02/04/2026 p.3 -p.4 ¶1, the term fundamental is not used in the sense of necessarily being old or well-known, but rather as referring to building blocks of modern economy, as explicitly stated by MPEP 2106.04(a)(2) II A. Here, the product consumption and then underlying or tracing or attributing costs, or cloud fees to services execution and resource usage for decision making on what to deploy, represent such building blocks of economy no matter if the cloud fees do allegedly become obscured by complex interdependent software deployments as argued by Applicant at Remarks 05/04/2026 p.18 ¶1. Further, as stated by MPEP 2106.04(d)(1), “improvement in the judicial exception itself is not an improvement in technology”. Similarly, MPEP 2106.04 I. cites “Myriad, 569 U.S. at 591, 106 USPQ2d at 1979” to underline that even a “groundbreaking, innovative, or even brilliant discovery” [akin to what is argued here at Remarks 05/04/2026 p.17 ¶4-p.18 ¶1] “does not by itself satisfy the §101 inquiry" as corroborated by “SAP Am, Inc v InvestPic, LLC, No 2017-2081, 2018 BL 275354 (Fed. Cir.Aug.02, 2018)”: “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant”, [akin to what is argued here Remarks 05/04/2026 p.17 ¶4-p.18 ¶1] “those features are not enough for eligibility because their innovation is innovation in ineligible subject matter” [here improving abstract “Certain Methods Of Organizing Human Activity”]. “An advance of that nature is ineligible for patenting”. Simply said here, as in “SAP” supra, “no matter how much of [such] an advance in the field” “the claims [would] recite the advance [would still] lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm. This finding was corroborated by “Versata Dev Grp, Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015” undelaying the difference between improvement to entrepreneurial goal or objective versus improvement to actual technology. MPEP 2106.04. Here, the modeled and reported adjustments that alters cloud services provider system costs for one or more of the cloud services “provider resource usage information over the period of time represent such entrepreneurial improvement in an environment governed by obscured cloud fees. Moreover, the “configuring deployment” “by ingesting the data package by a system deployment element” represents a narrowing of the above abstract concepts to a technological environment or field of use, which, as tested per MPEP 2106.05(h) does not integrate the abstract exception into a practical application.
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Subject matter eligibility argument C: Remarks 05/04/2026 p.19 ¶4-p.20 ¶ 1 argues that “generating a directed graph being a flow graph, comprising nodes and edges that model an infrastructure of the commerce platform system; performing a maximum flow analysis of the directed flow graph; generating a data package having resource deployment adjustments based on a result of the maximum flow analysis; and configuring deployment of services by ingesting the data package by a system deployment element capable of modifying cloud services provider deployments” as an order combination is not a well-understood, routine, or conventional. Then,
Remarks 05/04/2026 p.20 ¶2-p.21 ¶3 again cites Original Specification ¶ [0004] to again argue cloud fees "may become obscured by the complex interdependent nature of the software system deployed by the cloud services provider system," and that "[a] technical problem exists for tracking usage of remote system resources at one or more cloud computing systems based on existing and potential software deployments of a company” which is addressed through a specific graph-based modeling and maximum flow analysis that attributes cloud services provider resource usage costs to execution of services of the commerce platform system and generates a data package for configuring subsequent deployments. Then, Remarks 05/04/2026 p. 21 ¶4- p.22 ¶1 argues that similar to DDR Holdings, the current “generating a directed graph being a flow graph that model an infrastructure of a commerce platform system, performing a maximum flow analysis of the directed flow graph to attribute cloud services provider resources usage costs to execution of services, and generating a data package having resource deployment adjustments that is ingested by a system deployment element capable of modifying cloud services provider deployments” of independent Claims 1,14,20, achieves a result that cannot be accomplished through routine or conventional steps citing Original Specification ¶ [0026], ¶ [0054], and ¶ [0076].
-> Examiner fully considered the subject matter eligibility argument C but respectfully disagrees fining it unpersuasive. First, it is noted that the limitations of “generating a directed graph being a flow graph, comprising nodes and edges that model an infrastructure of the commerce platform system” and “performing a maximum flow analysis of the directed flow graph” (independent Claims 1,14,20) as argued by Remarks 05/04/2026 p.20 ¶ 1 remain abstract right from the onset. They do not represent any additional elements aside from what has been already identified above as abstract through visual observation and cognitive computer-aided evaluation and judgement.
Next, the limitation “generating a data package having resource deployment adjustments based on a result of the maximum flow analysis” (independent Claims 1,14,20), the Examiner carefully scrutinizes the term “data package” by reincorporating the findings and rationales of Non-Final Act 02/04/2026 p.3 ¶1 which found that the Applicant has amended independent Claims 1,14,20, to replace the term “report” for “data package”. Yet replacing the term “report” for “data package”, as a claim drafting effort, does not necessarily render the claims less abstract and eligible because, according to MPEP 2106 II, ¶1, “Evaluating eligibility based on the BRI also ensures that patent eligibility under 35 U.S.C. 101 does not depend simply on the draftsman’s art”3. Here, when read in light of Original Specification ¶ [0076] 5th sentence, the “report” is disclosed to comprise a “data package” which is broad enough to encompass a mere text-based document. Thus here, when tested per MPEP 2106.04(a)(2) III, the Examiner finds that the aid of “commerce platform system” in “generating” document or “data package” having the adjust[ments] of “costs” for respective “usage information”, can be equally interpreted as a computer aid performing analogous mental process of evaluation and judgment that would have otherwise be performed by a skilled artisan, and further displayed or observed by the skill artisan on the canvas or user interface of the computer tool, in a manner not meaningfully different than what was long achieved by a pen and paper report. MPEP 2106.04(a)(2) III C is also clear that: # 1. Performing a mental process on a generic computer, # 2. Performing a mental process in a computer environment and # 3. Using a computer as a tool to perform a mental process, all do not preclude the claims from reciting, describing or setting forth the abstract exception. Add to this finding, the fact that no matter which of the “report” or “data package” terminology is used, its contents still refer to the abstract concepts of “costs” and “usage information” or consumption, and it becomes clear that it does not preclude the claims to recite, describe or set forth the abstract exception. Yet, even if considered, in the arguendo, as an additional element, the recitation of “generating, based on the attributed one or more costs obtained via tracing through the directed graph for which the maximum flow analysis is performed, by the commerce platform system, a data package having resource deployment adjustments that alters cloud services provider system costs for one or more of the cloud services provider resource usage information and commerce platform system execution information over the period of time” at Claims 1,14, 20 would represent use of software (here “data package”) to tailor (here “adjust”) information and provide on a generic computer, which per MPEP 2106.05(f)(2)(v) is an example of invoking computer elements or other machinery to apply the abstract exception which does not provide significantly more.
Finally, with respect to the limitation “configuring deployment of services by ingesting the data package by a system deployment element capable of modifying cloud services provider deployments” (independent Claims 1,14,20), Examiner again follows the guidelines of MPEP 2106.05(d) II 2nd bullet point and caries over the conclusions reached on the MPEP 2106.05(f) and/or (h) tests above to Step 2B, and submits that for the same reasons articulated above, said computer-based additional elements also do not provide significantly more, when considering MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence, without the need to rely on the well-understood, routine and conventional test of MPEP 2106.05(d). This is because the conventionality test of MPEP 210.05(d) is merely one of the many options of MPEP 2106.05(a)-(h) to test whether the additional elements provide significantly more. In this respect, MPEP 2106.05I ¶1 states that the conventional consideration overlaps with the mere instructions to apply an exception consideration test, (MPEP 2106.05(f)), the insignificant extra-solution activity consideration test (MPEP 2106.05(g)) etc. In this case, Examiner found “configuring deployment of services by ingesting the data package by a system deployment element capable” [or intended] of modifying cloud services provider deployments” (independent Claims 1,14,20) is a draftsman’s effort in narrowing the aforementioned abstract processes in the claims to a field of use or technological environment which, according to MPEP 2106.05(h) does not integrate the abstract exception into a practical application, and for the same reason also does not provide significantly more without having to rely on the conventionality test of MPEP 2106.05(d).
Yet, assuming arguendo, that further evidence would be required to demonstrate conventionality of any asserted additional, computer-based elements, the Examiner would further rely on MPEP 2106.05(d) guidelines to demonstrate that said additional elements are also well-understood, routine, conventional. In such case, the Examiner would rely as evidence on Applicant’s own Specification as instructed by MPEP 2106.05(d) I 2 by citing Original Specification ¶ [0069] 3rd-5th sentences reciting at high level: … “the reports may be generated for total cloud spend, product cloud spend, service cloud spend, code path cloud spend, team cloud spend, etc. based on tracing through the generated flow graph for which the maximum flow problem has been solved. In embodiments, the reports may be user interfaces (e.g., web based reports accessible to those within a commerce platform). In embodiments, the reports may comprise a data package (e.g. an XML document, a text based document, etc.) that are transmitted to a metrics tracking system of the commerce platform, which utilizes the data package to configure an interface of the metrics tracking system when rendering a summary/snapshot of the reported data. In embodiments, such data packages may further be used to configure systems of the cloud service provider, such as when a detected cloud spend increase from a prior report exceeds a threshold (E.g. increase of X, increase of Y%, etc.), such as by ingesting the data package by a system deployment element capable of modifying CSP deployments”.
As per Applicant’s reliance on the Federal Circuit ruling on DDR Holdings, as raised at Remarks 05/04/2026 p.21 ¶4, the Examiner submits that current legal findings of the present claims are irreconcilably different than what was found eligible in DDR because here, the argued above limitations, focus on graph model[ing] and “flow” “analysis”. At no point do the amended claims provide anything remotely analogous to the systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third-party merchant, as was the case in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 113 USPQ2d 1097 (Fed. Cir. 2014), as cited by MPEP 2106.05(d) and argued by Applicant at Remarks 05/04/2026 p.21 ¶4. Digging deeper into DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1248,113 USPQ2d at 1099, the Examiner finds the Court ruled that the eligible claim had additional elements that amounted to significantly more than the abstract idea, because it modified conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, which differed from conventional operation of Internet hyperlink protocol that transported the user away from the host’s webpage to the 3rd party’s webpage when the hyperlink was activated. Yet here, there is nothing similar to such patent eligible technological arrangement. Hence, the comparison of the current claims to DDR Holdings is flawed and erroneous.
In conclusion, Examiner has provided a preponderance of legal and/or factual evidence demonstrating that the claims still recite, describe or at a minimum set forth the abstract exception, (Step 2A prong one) with the additional elements not integrating the abstract exception (Step 2A prong two) or providing significantly more (Step 2B). Thus, the claims are patent ineligible.
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IV. Rejections under 35 U.S.C. §103
Remarks 05/04/2026 p.24-p.25 argues that Chen does not teach the newly amended:
- “generating, based on the extracted cloud services provider resource usage information and commerce platform system execution information, by the commerce platform system, a directed graph to model an infrastructure of the commerce platform system, the directed graph being a flow graph”
- “performing a maximum flow analysis of such a directed graph”, and
- “configuring deployment of the services of the commerce platform system on the hardware computing resources of the cloud services provider system based on the data package by ingesting the data package by a system deployment element capable of modifying cloud services provider deployments”.
The prior art arguments is considered but moot in view of new grounds of rejections as necessitated by amendment. Examiner now relies on Dhoolam et al, US 11150995 B1 teaching:
- “generating, based on the extracted cloud services provider resource usage information and commerce platform system execution information, by the commerce platform system, a directed graph to model an infrastructure of the commerce platform system, the directed graph being a flow graph” (Dhoolam Figs. 2-3 and column 9 lines 21-25: in Fig.2 includes directed edges (shown as arrowed lines between vertices), the directed edges indicate the number of nodes that are executed by computing resources in the given rack 206 and are a member of the given deployment group 208. column 9 lines 38-54: Therefore, by making the vertices of the graph racks and deployment groups, various constraints may be satisfied by maximizing a flow between source 202 and sink 204 of the graph in Fig.2. the source 202 represent an origin and/or starting point for the selection algorithm/maximum flow algorithm for determining the set of nodes. For example, the directed edge from the source 202 to the racks 206 represents the total number of nodes that may be placed in a single rack. Similarly, the directed edges between the deployment groups and sink 204 represent the total number of nodes that may be placed within a single deployment group. A search function may be used to search the graph for possible selection of nodes to include in the set of nodes that satisfy the requirements based at least in part on the graph. The search function include a depth-first search or breadth-first search)
- “performing, by the commerce platform system, a maximum flow analysis”;
(Dhoolam column 1 lines 37-43: creating consistent replicas of customer data across a plurality of computer systems requires additional resources and requires consensus on customer data across the plurality of computer systems with increased cost and time required to provision computer systems to maintain replicas of customer data. To address this, Dhoolam column 8 lines 16-20 discloses a placement system 116 uses a maximum network flow algorithm such as Ford-Fulkerson algorithm to determine the set of nodes 104 that satisfy the one or more constraints.
Dhoolam column 7 line 67-colun 8 line 15: Specifically, the placement system generate a graph or similar representation of configuration of nodes 104 host computer systems, and other computing resources in the computing resource service provider environment and use various algorithms to traverse the graph and determine a solution. For example, the placement service draw a vertex for each rack and deployment group, a directed edge from each rack vertex to deployment group vertex with a capacity representing the number of nodes, and draw a source and a sink vertex. The graph representing the configuration of the host computer systems and configuration of nodes 104 is described in connection with Figs.2 and 3. The placement system 116 may use a maximum network flow algorithm such as the Ford-Fulkerson algorithm to determine the set of nodes 104 that satisfy at least a portion of the one or more constraints)
- “configuring deployment of the services of the commerce platform system on the hardware computing resources of the cloud services provider system based on the data package”
(Dhoolam column 9 lines 34-41: software deployments may be sent to one deployment group at a time to reduce the impact of the deployment on the entire set of computing resources operated by the computing resource service provider. Therefore, by making the vertices of the graph racks and deployment groups, the constraints may be satisfied by maximizing a flow between a source 202 and a sink 204 of the graph illustrated in Fig. 2) “by ingesting the data package by a system deployment element capable4 of modifying cloud services provider deployments” (Dhoolam column 8 lines 56-62: deployment system 110…installing [or ingesting]… executable content [or data package] included in the deployment… column 12 lines 7-22: replication group service 402 or component thereof utilize this information to determine a rate at which data replication groups 412 are to be added to the deployment groups 410 and/or a rate at which new deployment groups are to be generated. The process for adding data replication groups 412 to the deployment groups 410 include identifying, by the placement system, a set of physical hosts suitable for hosting a new data replication group, initializing nodes on the set of physical hosts to be included in the new data replication group, initializing consensus protocol among the nodes, validating the health of the data replication group 412 (e.g., determining status of heartbeat messages), and updating replication group data 408 to indicate that the new data replication group is ready to receive traffic and is a member of the deployment group 410. For example, at column 17 lines 11-17: if the data replication group requires 7 nodes to have sufficient capacity, the number of nodes included in the placement information must be greater than or equal to 7. If there is insufficient capacity 708, the system executing process 700 transmit an exception 710 that indicate that there is insufficient remaining capacity to generate new data replication groups. As a result, the data replication group service may provision new nodes or perform other operations to include capacity.
Dhoolam column 14 lines 48-54: By increasing an edge capacity from the source to the racks, the placement algorithm 508 may find additional nodes that satisfy the constraints. The placement algorithm 508 may first attempt to find nodes within a brick (e.g., servers within the same rack or set of racks connected by a top of rack switch) and if an insufficient number of nodes are found may then attempt to find nodes within a zone. A zone, described in greater detail below, may include a logical grouping of servers or other computing resources).
Thus, the prior art of Dhoolam et al, US 11150995 B1 teaches the contested limitations.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) describe or set forth the abstract “modeling and analyzing a commerce platform infrastructure” as summarized in preamble of independent Claims 1,14,20 which falls under abstract Certain Method of Organizing Human Activities grouping implemented through equally abstract computer-aided Mental Processes5 using what appear to be equally abstract mathematical “flow” relationship expresses in words between nodes and edges. First, when tested per MPEP 2106.04(a)(2) II, the claims describe or set forth the abstract Certain Method of Organizing Human Activities grouping namely, fundamental economic practices, as tested per MPEP 2106.04(a)(2) II A and/or commercial interactions, tested per MPEP 2106.04(a)(2) II B, represented as “services” of business relations [MPEP 2106.04(a)(2) II B], reflected by a “services provider system” vis-à-vis “resource usage information” and “costs for” “the cloud services provider usage information” (independent Claims 1,14,20) and “increase” in such “cost” (dependent Claims 5,9,19), “increased usage” (dependent Claim 10), “consumption” of “services” (dependent Claim 7), “cost is based on a size of one or more tables within the database” (dependent Claim 8), “cost of the cloud service consumed between the two nodes” (dependent Claim 12), “attributing costs to individual products, software development groups, customers of the commerce platform system, or a combination thereof” (dependent Claim 13). Examiner also points to MPEP 2106.04(a)(2) II A ¶2 to stress that the term fundamental is not used in the sense of necessarily being old or well-known6. In a similar vein, Examiner points to MPEP 2106.04 I mid-¶3 to stress that narrow forms of abstract idea that have limited applications were still held ineligible. It then follows that here, limiting “services” to “cloud services” and “on the hardware computing resources of the cloud services provider”, and limiting “services provider” to “cloud services provides”, and a general recitation of “a system deployment element capable” [or intended] “of modifying cloud services provider deployments” does not necessarily preclude the claims from reciting, describing or setting forth such fundamental economic practices business relations, commercial interactions, and management of such interactions. In fact, MPEP 2106.04(a)(2) II, ¶6, 4th sentence, is clear that certain activity between a person and a computer may still fall within the "certain methods of organizing human activity" grouping. It then follows that here: “rendering, through the GUI on a user device, the cloud services provider cost information” at dependent Claims 3,16 may still fall within certain methods of organizing human activity grouping. It also appears that here, the claims set forth mitigative forms of organizing human activities such as “configuring the commerce platform system service of the commerce platforms system to a state associated with the prior period of time” in response to “detecting an increase of a cloud system cost attributable to a commerce platform system service update that exceeds a predefined threshold” at dependent Claims 5,19; and similarly “configuring usage of the specific service area of the cloud service provider system to a previous usage configuration of the specific service area of the cloud services provide system” in response to “detecting that an increased usage of a specific service area of the cloud services provider system by the commerce platform is inconsistent with an anticipated increased usage of the cloud services provider resources by the commerce platform system” at dependent Claim 10; “wherein the scorecard generates an alert as a result of an increase in a cost of a commerce platform system service” at dependent Claim 9.
Speaking of such mitigative actions, it can also be argued that such Certain Method of Organizing Human Activities can be implementable by computer-aided observation, evaluation and judgement of the equally abstract Mental Processes7 [MPEP 2106.04(a)(2) III] to expand upon the use of use pen and paper, especially relevant here given the recitation of the “directed graph” of dependent Claims 11-13, or even through use of computer aids, tested per MPEP 2106.04(a)(2) III C. For example, as tested per MPEP 2106.04(a)(2) III C #1,#3, the high level of generality of expression “by commerce platform system”, could be viewed as a generic computer or tool to aid in performing the mental processes of observation, evaluation and judgement. In a similar vein, the “cloud services” environment and the associated “services on the hardware computing resources of the cloud services provider” as tested per MPEP 2106.04(a)(2) III #2, could be argued as a computer environment upon which the abstract mental observation, evaluation and judgment are performed. None of these preclude the claims from reciting, describing or setting forth the abstract idea. As one example, MPEP 2106.04(a)(2) III A, 5th bullet, cites Electric Power Group v Alstom, S.A 830 F.3d 1350, 1353-54,119 USPQ2d 1739, 1741-42 (Fed Cir 2016) to state that a claim of collecting information, analyzing it, and displaying certain results of the collection and analysis, recites the mental processes.
- Here, such collection of information are set forth by: “receiving”, “first data generated by a cloud services provider system, wherein the first data comprises information indicative of one or more costs of cloud services provider resource usage by the commerce platform system over a period of time”; “receiving”, “second data for one or more systems of the commerce platform system executed by hardware computing resources of the cloud services provider system, wherein the second data comprises information indicative of execution of services of the commerce platform system over the period of time” at independent Claims 1,14,20; “receiving, instructions for a reconfiguration based at least in part on the resource deployment adjustments” at dependent Claims 2,15.
- Here, such analysis or evaluation and judgment are set forth by: “analyzing, by the commerce platform system, the first data and the second data to extract cloud services provider resource usage information and commerce platform system execution information of the services on the hardware computing resources of the cloud services provider system over the period of time”, “generating” “based on the extracted cloud services provider resource usage information and commerce platform system execution information”, “directed graph to model an infrastructure of the commerce platform system, the directed graph being a flow graph comprising: a super sink node representing the commerce platform system nodes between the source node and the super sink node representing interconnected services of the commerce platform system and the hardware computing resources of the cloud services provider system; and edges representing technical dependencies between the nodes, wherein flows along the edges model one or more costs of commerce platform system service usage at the cloud services provider system based at least in part on the extracted cloud services provider resource usage information and commerce platform system execution information”; “performing”, “a maximum flow analysis of the directed graph that models costs of commerce platform service usage at the cloud services provider to attribute costs of the cloud services provider resource usage information to execution of the services of the commerce platform system at the cloud services provider” at independent Claims 1,14,20; “comparing cloud service provider system costs from a period of time from which the data was generated with the cloud service provider system costs from a prior period of time from which a prior data package was generated; detecting an increase of a cloud service provider system cost attributable to a commerce platform system service update that exceeds a predefined threshold” at dependent Claims 5, 19; “detecting that an increased usage of a specific service area of the cloud services provider system by the commerce platform system is inconsistent with an anticipated increased usage of cloud services provider resources by the commerce platform system service”; at dependent Claim 10, “the analyzing of the directed graph comprises solving the maximum flow analysis for the flow graph having one or more adjusted nodes and/or edges of the flow graph” at dependent Claim 11; “performing the maximum flow analysis using the flow graph having the one or more adjusted nodes or edges of the flow graph to determine an adjusted cloud service provider resource usage of the commerce platform system across the directed graph, wherein the directed graph is decomposed into a plurality of spanning trees during the maximum flow analysis for attributing costs to individual products, software development groups, customers of the commerce platform system, or a combination thereof” at dependent Claim 13
- Here, such displaying of certain results of collection and analysis is set forth by: “rendering”, “the cloud services provider cost information” at dependent Claims 3,16; “render the data as a scorecard detailing cloud spend for at least one of a commerce platform system service or a team” at dependent Claims 4,17; “wherein the scorecard comprises a monthly snapshot of consumption of cloud services provider resources by a set of commerce platform system services associated with one or more of a commerce platform system developer or a team” at dependent Claims 7,18; “wherein the scorecard generates an alert as a result of an increase in a cost of a commerce platform system service”; at dependent Claim 9; and the results of the analysis of the graph at dependent Claims 11-13.
Thus, there is preponderance of legal evidence showing that the claims’ character is abstract.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, no individual or combination of the additional, computer-based elements integrate the abstract idea into a practical application. For example, when tested per MPEP 2106.05(f)(2)(i), “computer processing system” of independent Claim 14, and “memory” instruct[ed] “processor” of independent Claim 20, are found to merely apply the above abstract, business processes as identified above, as mere invocation of tools, which, according to MPEP 2106.05(f)(2)(i), do not integrate the abstract idea into a practical application. The same analysis and results would apply to “the commerce platform system” of independent Claims 1,14,20, which, if not already considered as an aid of the abstract idea identified above, would at most represent, as tested per MPEP 2106.05(f)(2)(i), an example of merely applying the above abstract, business processes above, as mere invocation of a tool which, again would not integrate the abstract idea into a practical application. In fact, MPEP 2106.05(f)(2) ¶1, 2nd sentence is clear that use of a computer or other machinery in its ordinary capacity for economic or other combination of tasks such as to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. It would then follow that the capabilities of the “commerce platform system” in “receiving” “data” as recited at independent Claims 1,14 and recitations in passive diathesis of “wherein the data is generated by a service execution tracking system of the commerce platform” at dependent Claim 6 would not integrate the abstract idea into a practical application. Additionally or alternatively, it could also be argued that recitation of “wherein the data is generated by a service execution tracking system of the commerce platform system” at dependent Claim 6, and any computerized capabilities of “detecting an increase of a cloud service provider system cost” and “usage of a specific service area” as recited throughout dependent Claims 5,10,19, would represent, as tested per MPEP 2106.05(f)(2) iii, a process for monitoring audit log data, executed on a general-purpose computer8, which again would represent, in the arguendo, mere invocation of computers or machinery to perform an abstract process, which, as tested per MPEP 2106.05(f)(2) iii, would not integrate the abstract idea into a practical application. In a similar vein, the capabilities the “graphical user interface (GUI)” to “render” “the cloud services provider cost information” and “data” at dependent Claims 3,4,16, 17, if not already an aid of the abstract idea identified above, it could be argued, as tested per MPEP 2106.05(f)(2) v. as a mere requirement to use of software to tailor information and provide it to user on a generic computer9, which again would represent an example of invoking computers or machinery as tool to perform an existing process, without integrating the abstract idea into a practical application. The same test applies to and “generating based on the attributed one or more costs obtained via tracing through the directed graph for which the maximum flow analysis is performed a data package” (or report, or document as read in light of Original Specification ¶ [0069]) “having resource deployment adjustments that alters cloud services provider system costs for one or more of the cloud services provider resource usage information and commerce platform system execution information over the period of time”; at independent Claims 1,14,20.
As per the general degree of automation, recited as “automatically configuring the commerce platform system service of the commerce platforms system to a state associated with the prior period of time” at dependent Claims 5,19 and, “automatically configuring usage of the specific service area of the cloud service provider to a previous usage configuration of the specific service area of the cloud services provider” at dependent Claim 10, the Examiner points to MPEP 2106.05(f)(2)(iii), finding that a process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer. Alternatively, the computerized environment of any alleged additional elements, as tested per MPEP 2106.05(h) would represent a mere narrowing of the abstract exception, to a field of use or technological environment, which would not integrate the abstract idea into practical application. For example, MPEP 2106.05(h) vi. cites the same Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), to state that narrowing combination of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to a field of use or technological environment does not integrate the abstract ide into a practical application. It then follows that here narrowing the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, as identified and mapped above, to data related to a cloud-based environment of computerized functions or elements, including “services on the hardware computing resources of the cloud services provider system” would similarly not integrate the abstract idea into a practical application. Similarly, narrowing the abstract modeling and analyzing as identified above to a field of use or technological environment characterized by “configuring deployment of the services of the commerce platform system on the hardware computing resources of the cloud services provider system based on the data package by ingesting the data package by a system deployment element” at independent Claims 1,14,20 does not integrate the abstract exception into a practical application when tested per MPEP 2106.05(h).
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea, [see MPEP 2106.05(f)] and/or provide a narrowing of the abstract idea to a field of user or technological environment [MPEP 2106.05(h)]. Specifically, Examiner points to MPEP 2106.05 (d) II and carries over the finings tested per MPEP 2106.05 (f) and (h) and submits that the additional computer-based elements also do not provide significantly more. Examiner submits that the above tests show the applying of the abstract idea [MPEP 2106.05 (f)] and narrowing the abstract idea to a field of use or technological environment [MPEP 2106.05 (h)], suffice in showing that the additional computer-based elements also do not provide significantly more without having to rely on the conventionality test [MPEP 2106.05(d)]. Yet assuming arguendo that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, Examiner would point to MPEP 2106.05(d) to demonstrate that said additional elements remain well-understood, routine, conventional. In such case, Examiner would rely as evidence on Applicant’s own Original Specification, publications and/or case law.
Specifically, per MPEP 2106.05(d)(I)(2) Examiner points to Applicant’s own:
* Original Specification ¶ [0013] 2nd-3rd sentences: It will be apparent to one of ordinary skill in the art having the benefit of this disclosure, that the embodiments described herein may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form”,…
* Original Specification ¶ [0014] 2nd sentence: “These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art”
* Original Specification ¶ [0069] 3rd-5th sentences reciting at high level of generality: “As discussed herein, the reports may be generated for total cloud spend, product cloud spend, service cloud spend, code path cloud spend, team cloud spend, etc. based on tracing through the generated flow graph for which the maximum flow problem has been solved. In embodiments, the reports may be user interfaces (e.g., web based reports accessible to those within a commerce platform). In embodiments, the reports may comprise a data package (e.g. an XML document, a text based document, etc.) that are transmitted to a metrics tracking system of the commerce platform, which utilizes the data package to configure an interface of the metrics tracking system when rendering a summary/snapshot of the reported data. In embodiments, such data packages may further be used to configure systems of the cloud service provider, such as when a detected cloud spend increase from a prior report exceeds a threshold (E.g. increase of X, increase of Y%, etc.), such as by ingesting the data package by a system deployment element capable of modifying CSP deployments”.
* Original Specification ¶ [0073] reciting at a high level of generality: “Figure 5 is one embodiment of a computer system that may be used to support the systems and operations discussed herein. It will be apparent to those of ordinary skill in the art, however that other alternative systems of various system architectures may also be used”.
* Original Specification ¶ [0077] reciting at high level of generality “It will be apparent to those of ordinary skill in the art that the system, method, and process described herein can be implemented as software stored in main memory or read only memory and executed by processor”.
* Original Specification ¶ [0078] last two sentences: “Conventional methods may be used to implement such a handheld device. The implementation of embodiments for such a device would be apparent to one of ordinary skill in the art given the disclosure as provided herein”.
* Original Specification ¶ [0081] reciting at a high level of generality: “It will be appreciated by those of ordinary skill in the art that any configuration of the system may be used for various purposes according to the particular implementation. The control logic or software implementing the described embodiments can be stored in main memory, mass storage device, or other storage medium locally or remotely accessible to processor”.
* Original Specification ¶ [0082] reciting at a high level of generality “It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled”.
* Original Specification ¶ [0083] reciting at high level of generality “for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and PatentApplication44 10142.P036 **** ****described in order to best explain the principles and practical applications of the various embodiments, to thereby enable others skilled in the art to best utilize the various embodiments with various modifications as may be suited to the particular use contemplated”.
The conventionality of maximum flow analysis is further corroborated by
* US 20150199724 A1 at ¶ [0038] last 3 sentences, ¶ [0063]. Additionally,
Per MPEP 2106.05(d)(II), the additional computer-based elements, can also be viewed as performing the well-understood, routine or conventional functions of:
* receiving or transmitting data over a network10 [here “commerce platform system” in “receiving” “first data” that “comprises information indicative of one or more costs of cloud services provider resource usage by the commerce platform system over a period of time” and “receiving” “a second data” that “comprises information indicative of execution of services of the commerce platform system over the period of time” as recited at independent Claims 1,14]
* sorting information11 / electronically extracting data12 [here “by the commerce platform” analyze to “extract cloud services provider resource usage information and commerce platform system execution information of the services over the period of time”; at Claims 1,14];
* arranging a hierarchy of groups13 [here “wherein the directed graph comprises the source node associated with the cloud services provider system, the super sink node associated with the commerce platform system, and a plurality of intermediate nodes that represent infrastructure of the commerce platform system and are associated with the services of the commerce platform system, and wherein the edges between any two nodes in the directed graph are directed and are labeled with a timestamp, a type of cloud service, a consumer of the cloud service, an identifier of the cloud service, and a cost of the cloud service consumed between the two nodes” at dependent Claim 12]
* gathering statistics14, electronic recordkeeping15 and recording a customer’s order16 [here “commerce platform system” in “receiving” “first data” that “comprises information indicative of one or more costs of cloud services provider resource usage by the commerce platform system over a period of time” and “receiving” “a second data” that “comprises information indicative of execution of services of the commerce platform system over the period of time” as recited at independent Claims 1,14; “the directed graph comprises the source node associated with the cloud services provider system, the super sink node associated with the commerce platform system, and a plurality of intermediate nodes that represent infrastructure of the commerce platform system and are associated with the services of the commerce platform system, and wherein the edges between any two nodes in the directed graph are directed and are labeled with a timestamp, a type of cloud service, a consumer of the cloud service, an identifier of the cloud service, and a cost of the cloud service consumed between the two nodes” at Claim 12]
All of these fail to provide anything significantly more than what is already well-understood, routine and conventional in light MPEP 2106.05(d). In conclusion, Claims 1-20 although directed to statutory categories (here “method” or process at Claims 1-13, “non-transitory computer-readable storage medium” or computer product at Claims 14-19 and “system” or machine at Claim 20) they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more (Step 2B). Claims 1-20 are thus not patent eligible.
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Claim Rejections - 35 USC § 102
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2,11,14-15,20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by:
Dhoolam et al, US 11150995 B1 hereinafter Dhoolam. As per,
Claims 1,14,20 Dhoolam teaches or suggests: “A method for modeling and analyzing a commerce platform infrastructure provided by cloud services provider systems to a commerce platform system, the method comprising”: / “A non-transitory computer readable storage medium having instructions stored thereon, which when executed by a computer processing system, cause the computer processing system to perform operations for modeling and analyzing a commerce platform system infrastructure provided by cloud services provider systems to a commerce platform system, the operations comprising”: / “A system for modeling and analyzing a commerce platform system infrastructure provided by cloud services provider systems to a commerce platform system, the system comprising: a memory that stores one or more instructions; and a processor coupled with the memory to execute the one or more instructions to perform operations” (Dhoolam column 1 lines 12-27: Within multi-tier ecommerce systems, combinations of different types of resources are allocated to customers and/or their applications, such as whole physical or virtual machines, CPUs, memory, network bandwidth, or I/O capacity. Block-level storage devices implemented by a storage service may be made accessible, from physical or virtual machines implemented by another service. Computer systems that provide services to customers employ various techniques to protect the computer systems from a number of service requests that could potentially overload the computer systems. Further, these computer systems may also employ various techniques to preserve customer data and customer experience during periods when the computer systems are overloaded or even experiencing failures. column 21 lines 45-67: The environment can include a variety of data stores and other memory and storage media as discussed above. These can reside in a variety of locations, such as on a storage medium local to (and/or resident in) one or more of the computers or remote from any or all of the computers across the network. In a particular set of embodiments, the information may reside in a storage-area network (SAN) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers, servers, or other network devices may be stored locally and/or remotely, as appropriate. Where a system includes computerized devices, each such device can include hardware elements that may be electrically coupled via a bus, the elements including, for example, at least one central processing unit (CPU or processor), at least one input device (e.g., a mouse, keyboard, controller, touch screen, or keypad), and at least one output device (e.g., a display device, printer, or speaker). Such a system may also include one or more storage devices, such as disk drives, optical storage devices, and solid-state storage devices, such as random access memory (RAM) or read-only memory (ROM), as well as removable media devices, memory cards, flash cards, etc.), “comprising”
- “receiving, by the commerce platform system, a first data generated by a cloud services provider system, wherein the first data comprises information indicative of one or more costs of cloud services provider resource usage by the commerce platform system over a period of time”;
(Dhoolam column 10 lines 55-59: computing resource service provider operate replication group service 402 to manage data replication groups 412 in deployment group 410, which per column 11 lines 2731 is distributed across regions, geographic boundaries, physical boundaries, or other logical groupings of computing resources in a distributed computing environment.
Dhoolam column 4 lines 51-57, column 4 line 65-column 5 line 1: the computing resource service provider issues a request to store state information on behalf of customer. Yet, per
Dhoolam column 10 lines 61-64: receiving such traffic increases latency or violate the terms of service-level agreement SLA between service provider and customer. Specifically, per, Dhoolam column 1 lines 39-43 there is increased cost and time required to provision the computer systems to maintain replicas of customer data. To address this, column 9 lines 33-37 discloses a deployment strategy where software deployments are sent to one deployment group at a time to reduce the impact of deployment on entire set of computing resources operated by the computing resource service provider. Also per column 5 lines 52-57: number of nodes 104 in data replication group 112 may be reduced if a particular service and/or customer requires reduced latency and response time. In contrast, if a customer and/or service requires higher fault tolerance & data durability, number of nodes 104 in data replication group 112 may be increased)
- “receiving, by the commerce platform system, a second data for one or more systems of the commerce platform system executed by hardware computing resources of the cloud service provider system, wherein the second data comprises information indicative of execution of services of the commerce platform system over the period of time”;
(Dhoolam column 8 lines 23-34: during execution of backtracking search the system 116 incrementally build a set of nodes that satisfy the constraints. The backtracking cause placement system to generate a tree structure where each node represents potential solutions that satisfies the constraints and traverse the search tree recursively, from root down, in depth-first order. At each node of the tree the placement system evaluate the potential solution.
Dhoolam column 13 lines 47-63 in Fig.5, the placement algorithm 508 include a plurality of passes or determinations, illustrated as elements inside the dashed rectangle, used to obtain the placement information. For example, a first execution of the placement algorithm may be network-locality (1,1) 512, and network-locality (1,1) 512 may represent a capacity from a source vertex of the graph to rack vertices included in the graph and a capacity from deployment groups vertices to a sink vertex. As described above in connection with Figs.2 and 3, the representation of network-locality (1,1) 512 may be considered c(S, Ri)=1 and c(Gi, T)=1. By attempting to find a set of nodes that satisfy network-locality (1,1) 512 (e.g., a flow along the graph with capacity of one from source and to sink) the placement system may ensure that if a solution is found, the solution satisfies one or more constraints described above). column 5 lines 48-51: the number of nodes 104 in data replication group 112 vary depending on latency & durability requirements of the customer, other services of the computer system, or replication group service 102)
- “analyzing, by the commerce platform system, the first data and the second data to extract cloud services provider resource usage information and commerce platform system execution information of the services on the hardware computing resources of the cloud services provider system over the period of time” (Dhoolam column 1 lines 20-35: the computer systems that provide services to customers employ techniques to protect the computer systems from a number of service requests that could overload the computer systems and employ techniques to preserve customer data and customer experience during periods when the computer systems are overloaded or even experiencing failure. In general, a computer system is considered to be in an overloaded state if it is not able to provide the expected quality of service for at least some portion of customer requests it receives. column 10 lines 59-62: provisioning a data replication group 412 such that it may implement a consensus protocol and begin to receive traffic increase latency or violate the terms of a service-level agreement SLA. For example per, column 5 lines 48-57 since the number of nodes 104 in data replication group 112 vary depending on latency and durability requirements of the customer, the number of nodes 104 in data replication group 112 may be reduced if a particular service and/or customer requires reduced latency and response time. In contrast, if a customer and/or service requires higher fault tolerance and data durability, the number of nodes 104 in the data replication group 112 may be increased. Indeed per, column 7 lines 61-63: placement system 116 may use a backtracking search to search all available nodes which may be included in the data replication group 112. For instance, in the example of column 14 lines 22-54: if less than the desired number of nodes are returned (e.g., the search of the graph returns less than the number of nodes required for the data replication group satisfying the constraints), then the placement algorithm may increase the capacity of one or more edges and search for another solution. For example, if after searching the condition network-locality (2,1) 518 less than 7 nodes are returned, the placement algorithm proceed to network-locality (2,1) 520 to search for 7 nodes that satisfy the constraints. By increasing the capacity of the edge from the deployment groups to the sink, the placement algorithm 508 may find additional nodes that satisfy the constraints. Alternatively, if less than the desire number of deployment groups are returned (e.g., the search of the graph returns nodes that are not distributed between a sufficient number of deployment groups), then the placement algorithm may increase the capacity of one or more edges and search for another solution. For example, if after searching the condition network-locality (2,1) 518 the nodes are distributed between less than 4 deployment groups, the placement algorithm may proceed to zone-locality (3,1) 524 to search for a number of nodes that are distributed between four or more deployment groups. By increasing an edge capacity from the source to the racks, the placement algorithm 508 may find additional nodes that satisfy the constraints. The placement algorithm 508 may first attempt to find nodes within a brick (e.g., servers within the same rack or set of racks connected by a top of rack switch) and if an insufficient number of nodes are found may then attempt to find nodes within a zone. A zone, described in greater detail below, may include a logical grouping of servers or other computing resources.
- “generating, based on the extracted cloud services provider resource usage information and commerce platform system execution information, by the commerce platform system, a directed graph to model an infrastructure of the commerce platform system, the directed graph being a flow graph” (Dhoolam Figs. 2-3 and column 9 lines 21-25: in Fig.2 includes directed edges (shown as arrowed lines between vertices), the directed edges indicate the number of nodes that are executed by computing resources in the given rack 206 and are a member of the given deployment group 208. column 9 lines 38-54: Therefore, by making the vertices of the graph racks and deployment groups, various constraints may be satisfied by maximizing a flow between source 202 and sink 204 of the graph in Fig.2. the source 202 represent an origin and/or starting point for the selection algorithm/maximum flow algorithm for determining the set of nodes. For example, the directed edge from the source 202 to the racks 206 represents the total number of nodes that may be placed in a single rack. Similarly, the directed edges between the deployment groups and sink 204 represent the total number of nodes that may be placed within a single deployment group. A search function may be used to search the graph for possible selection of nodes to include in the set of nodes that satisfy the requirements based at least in part on the graph. The search function include a depth-first search or breadth-first search) “comprising”:
“a source node representing the cloud services provider system”;
(Dhoolam column 9 lines 38-41: maximizing a flow between source 202 and sink 204 of the graph illustrated in Fig.2. Similarly, Fig.3, 302->304)
“a super sink node representing the commerce platform system”;
(Dhoolam column 10 lines 13-16: there is a path p from t source 302 to sink 304 in the graph such that cf(u,v)>0 for All edges (u,v)∈p the algorithm find cf(p)=min {cf(u,v):(u,v)∈p} )
“nodes between the source node and the super sink node representing interconnected services of the commerce platform system and the hardware computing resources of the cloud services provider system” (Dhoolam column 1 lines 56-63: Fig.2 [below] illustrates an environment in which a placement system of a replication group service determines placement information for nodes of a data replication group in accordance with an embodiment; Fig.3 [below] illustrates an environment in which a placement system of a replication group service determines placement information for nodes of a data replication group in accordance with an embodiment. Dhoolam column 3 lines 58-61: Each node 104 may be executed by physical host, and each node 104 may participate in a plurality of data replication groups.
Dhoolam column 15 lines 17-28: The sets of racks 612A-612B may be physical hardware that hosts servers or, simply a logical grouping of the servers. Examples of logical groupings other than by rack include servers grouped together based on data center location, servers in different fault isolation groups (i.e., logical divisions of resources such that failure of one fault zone may not affect servers in the other zones; e.g., grouped by geographic regions, data centers, hard drive clusters, backup generators, etc.), servers grouped together based on performance characteristics (e.g. throughput, input/output operations per second, etc.), etc.) “and”
“edges representing technical dependencies between the nodes, wherein flows along the edges model one or more costs of commerce platform system service usage at the cloud services provider system based at least in part on the extracted cloud services provider resource usage information and the commerce platform system execution information”
(Dhoolam column 10 lines 7-16: The graph in Fig.3 defined as G(V,E), for each edge from u to v capacity is defined as c(u,v) and the flow is defined as f (u,v). when searching for a solution based at least in part on the graph, the flow along an edge cannot exceed capacity along the edge. Further, while there is a path p from source 302 to sink 304 in the graph such that cf(u,v)>0 for all edges (u,v)∈p the algorithm may find cf(p)=min {cf (u,v): (u,v)∈p}.
Dhoolam column 13 line 65-column 14 line 11: placement algorithm 508 attempt to find a solution that satisfies the constraints by varying capacity of the edge from source and/or to sink. in Fig.5, the placement system attempt to find a solution using placement algorithm 508 under various conditions including network-locality (1,1) 512, network-locality (1,2) 514, network-locality (1,3) 516, network-locality (2,1) 518, network-locality (2,2) 520, network-locality (2,3) 522, zone-locality (3,1) 524, zone-locality (3,2) 526, and zone-locality (3,3) 528, each representing a different capacity of edges in the graph; i.e. network-locality (2,3) 522 represents edge of capacity 2 from source to the rack and edge capacity of 3 from the deployment groups to the sink.
Dhoolam column 14 lines 22-54: Returning to Fig.5, if the search of the graph returns less than the number of nodes required for the data replication group satisfying the constraints, then the placement algorithm may increase the capacity of one or more edges and search for another solution. For example, if after searching condition network-locality (2,1) 518 less than 7 nodes are returned, the placement algorithm may proceed to network-locality (2,1) 520 to search for 7 nodes that satisfy the constraints. By increasing the capacity of the edge from the deployment groups to the sink, the placement algorithm 508 may find additional nodes that satisfy the constraints.
Alternatively, if less than the desire number of deployment groups are returned (e.g., the search of the graph returns nodes that are not distributed between a sufficient number of deployment groups), then the placement algorithm may increase the capacity of one or more edges and search for another solution. For example, if after searching the condition network-locality (2, 1) 518 the nodes are distributed between less than 4 deployment groups, the placement algorithm may proceed to zone-locality (3,1) 524 to search for a number of nodes that are distributed between 4 or more deployment groups. By increasing an edge capacity from the source to the racks, the placement algorithm 508 may find additional nodes that satisfy the constraints. The placement algorithm 508 may first attempt to find nodes within a brick (e.g., servers within the same rack or set of racks connected by a top of rack switch) and if an insufficient number of nodes are found may then attempt to find nodes within a zone. A zone, described in greater detail below, may include a logical grouping of servers or other computing resources)
- “performing, by the commerce platform system, a maximum flow analysis of the directed graph to attribute the one or more costs of the cloud services provider resource usage to execution of the services of the commerce platform system at the cloud services provider system”; (Dhoolam column 1 lines 37-43: creating consistent replicas of customer data across a plurality of computer systems requires additional resources and requires consensus on customer data across the plurality of computer systems with increased cost and time required to provision computer systems to maintain replicas of customer data. To address this, Dhoolam column 8 lines 16-20 discloses a placement system 116 uses a maximum network flow algorithm such as Ford-Fulkerson algorithm to determine the set of nodes 104 that satisfy the one or more constraints.
Dhoolam column 7 line 67-colun 8 line 15: Specifically, the placement system generate a graph or similar representation of configuration of nodes 104 host computer systems, and other computing resources in the computing resource service provider environment and use various algorithms to traverse the graph and determine a solution. For example, the placement service draw a vertex for each rack and deployment group, a directed edge from each rack vertex to deployment group vertex with a capacity representing the number of nodes, and draw a source and a sink vertex. The graph representing the configuration of the host computer systems and configuration of nodes 104 is described in connection with Figs.2 and 3. The placement system 116 may use a maximum network flow algorithm such as the Ford-Fulkerson algorithm to determine the set of nodes 104 that satisfy at least a portion of the one or more constraints)
- “generating, based on the attributed one or more costs obtained via tracing through the directed graph for which the maximum flow analysis is performed, by the commerce platform system, a data package having resource deployment adjustments that alters cloud services provider system costs for one or more of the cloud services provider resource usage information and commerce platform system execution information over the period of time”;
(Dhoolam column 1 lines 39-43 there is increased cost and time required to provision the computer systems to maintain replicas of customer data.To address this column 8 lines 12-15: placement system 116 uses a maximum network flow algorithm such as Ford-Fulkerson to determine set of nodes 104 that satisfy at least a portion of the constraints. For example, column 3 lines 47-55: the number of nodes 104 of data replication group 112 may be selected such that probability of a majority of nodes 104 of data replication group 112 failing is below some threshold. This may be accomplished by placement system 116 of replication group service 102 determining [or generating] placement information [or data package] 114 for nodes 104 of the data replication groups. Similar column 7 lines 39-44: Based at least in part on the various constraints and soft
constraints, the placement system 116 may generate placement information 114 that may cause at least a quorum of nodes 104 to be executed by the same host computer system as the computing resource supported by the data replication group 112.
Dhoolam column 11 line 67-column 12 line 22: replication group data 408 include information corresponding to number of data replication groups 412 or nodes of data replication groups 412 in deployment group 410, a maximum number of data replication groups 412 or nodes to be included in deployment group 410, and frequency or number of requests for data replication groups 412 received by replication group service 402. The replication group service 402 or component thereof utilize this information to determine a rate at which data replication groups 412 are to be added to deployment groups 410 and/or a rate at which new deployment groups are to be generated. The process for adding data replication groups 412 to the deployment groups 410 include identifying, by the placement system, a set of physical hosts suitable for hosting a new data replication group, initializing nodes on the set of physical hosts to be included in the new data replication group, initializing consensus protocol among the nodes, validating the health of the data replication group 412 (e.g., determining status of heartbeat messages), and updating replication group data 408 to indicate that the new data replication group is ready to receive traffic and is a member of the deployment group 410. For example, at column 17 lines 11-17: if the data replication group requires 7 nodes to have sufficient capacity, the number of nodes included in the placement information must be greater than or equal to 7. If there is insufficient capacity 708, the system executing process 700 transmit an exception 710 that indicate that there is insufficient remaining capacity to generate new data replication groups. As a result, the data replication group service may provision new nodes or perform other operations to include capacity. Similarly, column 5 lines 51-57: the number of nodes 104 in data replication group 112 may be reduced if a particular service and/or customer requires reduced latency and response time. In contrast, if a customer and/or service requires higher fault tolerance and data durability, the number of nodes 104 in the data replication group 112 may be increased.
Dhoolam Fig.5 column 13 lines 4-7: request 502 may be a request for certain number of nodes used to create a new data replication group or repair or replace nodes of an existing data replication group. column 13 lines 48-64: A illustrated by Fig. 5, placement algorithm 508 include plurality of passes or determinations (illustrated in Fig.5 as elements inside the dashed rectangle) used to obtain the placement information. For example, a first execution of t placement algorithm may be network-locality (1,1) 512, and network-locality (1,1) 512 may represent a capacity from a source vertex of the graph to one or more rack vertices included in the graph and a capacity from deployment groups vertices to a sink vertex. As described above in connection with Figs.2-3, the representation of network-locality (1,1) 512 may be considered c(S, Ri)=1 and c(Gi, T)=1. By attempting to find a set of nodes that satisfy network-locality (1,1) 512 (e.g., a flow along the graph with capacity of one from source and to sink) the placement system may ensure that if a solution is found, the solution satisfies one or more constraints described above).
- “configuring deployment of the services of the commerce platform system on the hardware computing resources of the cloud services provider system based on the data package”
(Dhoolam column 9 lines 34-41: software deployments may be sent to one deployment group at a time to reduce the impact of the deployment on the entire set of computing resources operated by the computing resource service provider. Therefore, by making the vertices of the graph racks and deployment groups, the constraints may be satisfied by maximizing a flow between a source 202 and a sink 204 of the graph illustrated in Fig. 2) “by ingesting the data package by a system deployment element capable17 of modifying cloud services provider deployments” (Dhoolam column 8 lines 56-62: deployment system 110…installing [or ingesting]… executable content [or data package] included in the deployment… column 12 lines 7-22: replication group service 402 or component thereof utilize this information to determine a rate at which data replication groups 412 are to be added to the deployment groups 410 and/or a rate at which new deployment groups are to be generated. The process for adding data replication groups 412 to the deployment groups 410 include identifying, by the placement system, a set of physical hosts suitable for hosting a new data replication group, initializing nodes on the set of physical hosts to be included in the new data replication group, initializing consensus protocol among the nodes, validating the health of the data replication group 412 (e.g., determining status of heartbeat messages), and updating replication group data 408 to indicate that the new data replication group is ready to receive traffic and is a member of the deployment group 410. For example, at column 17 lines 11-17: if the data replication group requires 7 nodes to have sufficient capacity, the number of nodes included in the placement information must be greater than or equal to 7. If there is insufficient capacity 708, the system executing process 700 transmit an exception 710 that indicate that there is insufficient remaining capacity to generate new data replication groups. As a result, the data replication group service may provision new nodes or perform other operations to include capacity.
Dhoolam column 14 lines 48-54: By increasing an edge capacity from the source to the racks, the placement algorithm 508 may find additional nodes that satisfy the constraints. The placement algorithm 508 may first attempt to find nodes within a brick (e.g., servers within the same rack or set of racks connected by a top of rack switch) and if an insufficient number of nodes are found may then attempt to find nodes within a zone. A zone, described in greater detail below, may include a logical grouping of servers or other computing resources)
Claims 2,15 Dhoolam teaches all the limitations in claims 1,14 above.
Dhoolam further teaches “further comprising”:
- “receiving, instructions for a reconfiguration based at least in part on the resource deployment adjustments”; (Dhoolam column 10 lines 36-42: receive request for new data replication group. In response to the request, for a set of nodes from the placement system the placement system determine a number of nodes to form the data replication group based on node placement result 310. Similarly, column 13 lines 4-7: request 502 may be a request for a certain number of nodes to be used to create a new data replication group or repair or replace nodes of an existing data replication group. Similarly column 16 lines 48-52: receive a request for a data replication group 704. The request may indicate a number of nodes to be included or added to the data replication group and may indicate a customer associated with the data replication group) “and”
- “executing a reconfiguration at the cloud services provider system based on the instructions, the reconfiguration comprising a change to one or more of the cloud services provider resource usage information or the commerce platform system execution information”
(Dhoolam teaches several examples as follows column 5 lines 51-57: the number of nodes 104 in data replication group 112 may be reduced if a particular service and/or customer requires reduced latency and response time. In contrast, if a customer and/or service requires higher fault tolerance and data durability, number of nodes 104 in data replication group 112 is increased.
Dhoolam column 14 lines 31-54: By increasing capacity of the edge from the deployment groups to the sink, the placement algorithm 508 may find additional nodes that satisfy the constraints. Alternatively, if less than the desire number of deployment groups are returned (e.g., the search of the graph returns nodes that are not distributed between a sufficient number of deployment groups), then the placement algorithm may increase the capacity of one or more edges and search for another solution. For example, if after searching the condition network-locality (2,1) 518 the nodes are distributed between less than 4 deployment groups, the placement algorithm may proceed to zone-locality (3,1) 524 to search for a number of nodes that are distributed between four or more deployment groups. By increasing an edge capacity from the source to the racks, the placement algorithm 508 may find additional nodes that satisfy the constraints. The placement algorithm 508 may first attempt to find nodes within a brick (e.g., servers within the same rack or set of racks connected by a top of rack switch) and if an insufficient number of nodes are found may then attempt to find nodes within a zone. A zone, described in greater detail below, may include a logical grouping of servers or other computing resources.
Dhoolam column 17 lines 11-17: if data replication group requires 7 nodes to have sufficient capacity, the number of nodes included in the placement information must be ≥ 7. If there is insufficient capacity 708, the system executing process 700 transmit an exception 710 that indicate that there is insufficient remaining capacity to generate new data replication groups. As a result, the data replication group service may provision new nodes or perform other operations to include capacity. Similarly, column 5 lines 51-57: the number of nodes 104 in data replication group 112 may be reduced if a particular service and/or customer requires reduced latency and response time. In contrast, if a customer and/or service requires higher fault tolerance and data durability, the number of nodes 104 in the data replication group 112 may be increased.
Claim 11 Dhoolam teaches all the limitations in claim 1 above.
Dhoolam further teaches “wherein the resource deployment adjustments are applied to one or more nodes or edges of the flow graph”
Dhoolam column 9 lines 42-64: source 202 represent an origin and/or starting point for the selection algorithm/maximum flow algorithm for determining the set of nodes. For example, the directed edge from the source 202 to the racks 206 represents the total number of nodes that may be placed in a single rack. Similarly, the directed edges between the deployment groups and the sink 204 may represent the total number of nodes that may be placed within a single deployment group. A search function may be used to search the graph for possible selection of nodes to include in the set of nodes that satisfy the requirements based at least in part on the graph. The search function include a depth-first search or a breadth-first search. The placement system may search for nodes that satisfy the constraints by at least including the constraints in the graph. For example, one constraint may require that there be no more than three nodes per rack and three nodes per deployment group. By setting maximum flow between source 202 and the racks 206 to three and the maximum flow from the rack 206 to the deployment group 208 to three, the placement system may ensure that a solution found using a maximum flow algorithm will only have three nodes per rack and three nodes per deployment group.
Dhoolam column 9 line 65-column 10 line 17: Fig.3 illustrates environment 300 in which a placement system of a replication group service may use a selection algorithm to determine a set of nodes to include in a data replication group in accordance with an embodiment. Illustrated in Fig.3 is a node placement result 310 determined based at least in part on the execution of a selection algorithm, for example, a modified Ford-Fulkerson algorithm. The modified Ford-Fulkerson algorithm is used to determine a maximum flow (e.g., a maximum number of nodes) between a source 302 and a sink 304. The graph illustrated in FIG. 3 may be defined as G(V,E), for each edge from u to v the capacity is defined as c(u,v) and the flow is defined as f (u,v). In various embodiments, when searching for a solution based at least in part on the graph, the flow along an edge cannot exceed the capacity along the edge. Furthermore, while there is a path p from the source 302 to the sink 304 in the graph such that cf(u,v)>0 for all edges (u,v)∈p the algorithm may find cf(p)=min {cf (u,v): (u,v)∈p}. As described above, the path can be found by performing a breadth-first search or a depth-first search in Gf(V,Ef) ). “and”
“wherein the analyzing the directed graph comprises solving the maximum flow analysis for the flow graph having one or more adjusted nodes or edges of the flow graph”
(Dhoolam column 9 lines 59-64: By setting maximum flow between source 202 and the racks 206 to three and the maximum flow from the rack 206 to the deployment group 208 to three, the placement system may ensure that a solution found using a maximum flow algorithm will only have three nodes per rack and three nodes per deployment group.
Dhoolam column 9 line 65-column 10 line 17: Fig.3 illustrates environment 300 in which a placement system of a replication group service may use a selection algorithm to determine a set of nodes to include in a data replication group in accordance with an embodiment. Illustrated in Fig.3 is a node placement result 310 determined based at least in part on the execution of a selection algorithm, for example, a modified Ford-Fulkerson algorithm. The modified Ford-Fulkerson algorithm is used to determine a maximum flow (e.g., a maximum number of nodes) between a source 302 and a sink 304. The graph illustrated in FIG. 3 may be defined as G(V,E), for each edge from u to v the capacity is defined as c(u,v) and the flow is defined as f (u,v). In various embodiments, when searching for a solution based at least in part on the graph, the flow along an edge cannot exceed the capacity along the edge. Furthermore, while there is a path p from the source 302 to the sink 304 in the graph such that cf(u,v)>0 for all edges (u,v)∈p the algorithm may find cf(p)=min {cf (u,v): (u,v)∈p}. As described above, the path can be found by performing a breadth-first search or a depth-first search in Gf(V,Ef) ).
Dhoolam column 13 lines 57-64: As described above in connection with FIGS. 2 and 3, the representation of network-locality (1,1) 512 may be considered c(S, Ri)=1 and c(Gi, T)=1. By attempting to find a set of nodes that satisfy network-locality (1,1) 512 (e.g., a flow along the graph with capacity of one from source and to sink) the placement system may ensure that if a solution is found, the solution satisfies one or more constraints described above.
Dhoolam column 18 lines 21-29: The placement system may then execute the selection algorithm 820. Executing the selection algorithm may include executing a search (e.g., deep-first search) of a graph generated based at least in part on the racks and deployment groups described above. The selection algorithm may return a number of nodes satisfying the one or more constraints based at least in part on a maximum flow of the graph. If a solution is determined 822, the placement system may provide placement information 824
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Rejections under 35 § U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 3-4,6,7,12,16-18 are rejected under 35 U.S.C. 103 as being unpatentable over:
Dhoolam as applied to claims 1,11,14 and in view of
Chen et al, US 20170083585 A1 hereinafter Chen. As per,
Claims 3,16 Dhoolam teaches all the limitations in parent claims 1,14 above.
Dhoolam des not teach “wherein generating the data package comprises”:
- “generating a graphical user interface (GUI) of the cloud services provider cost information attributed to the commerce platform system service usage”; “and”
- “rendering, through the GUI on a user device, the cloud services provider cost information” as claimed.
Chen however in analogous analysis of cloud computing resources teaches/suggest “wherein generating the data package comprises”:
- “generating a graphical user interface (GUI) of the cloud services provider cost information attributed to the commerce platform system service usage”; (Chen ¶ [0281] Interacting with topology map displays. ¶ [0282] in addition to the display of topology map elements representing a collection of cloud computing resources, a graphical user interface displaying a topology map may be configured to enable user interaction with the resources represented in the topology map. For example, at ¶ [0288] 1st sentence: a topology map interface may be configured to display cost information associated with one or more selected topology map elements) “and”
- “rendering, through the GUI on a user device, the cloud services provider cost information” (Chen ¶ [0288] a topology map interface display cost information associated with one or more selected topology map elements. For example, a user may select a node representing a server instance and, in response, a topology map interface may display cost information for the server instance such as, for example, a total cost incurred by the server instance, an estimated current bill amount, an average cost for the server instance per month, etc. ¶ [0289] topology map interface also display cost efficiency information for selected map elements. For example, many cloud computing services offer various types of the same computing resource based on different payment models. For example, a cloud service provider may offer three or more different types of server instances such as on-demand, reserved and spot instances, the cost benefits of which depend on how the server instances are used. In an embodiment, based on a determined type of server instance and performance information associated with the instance, a topology map interface may display information indicating whether the type of server instance being used is the most cost effective of the available types of server instances. Although the examples above illustrate display of cost information for server instances, similar information may be displayed for selected storage volumes, network interfaces, or any other cloud computing resources).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Dhoolam’s “method”/ “non-transitory medium” to have included Chen’s teachings in order to have provided an enhanced interface capable to have readily obtained a broader picture of the cloud computing resources and relationships among those resources (Chen ¶ [0005] 3rd sentence in view of MPEP 2143 G)
Further, the claimed invention could have also bene viewed as a mere combination of old elements in a similar field of endeavor dealing with analysis of computing resources in a distributed or cloud environment. In such combination each element merely would have performed same analytical and displaying function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Dhoolam in view of Chen, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable.
Claims 4,17 Dhoolam / Chen teaches all the limitations in parent claims 3,16 above.
Dhoolam does not teach “wherein generating the GUI comprises”:
- “generating data that comprises the data package indicating the cloud services provider system costs attributed to the commerce platform system service usage”; “and”
- “generating the GUI to render the data as a scorecard detailing cloud spend for at least one of a commerce platform system service or a team” as claimed.
Chen however in analogous analysis of cloud computing resources teaches/suggest
“wherein generating the GUI comprises”:
- “generating data that comprises the data package indicating the cloud services provider system costs attributed to the commerce platform system service usage”; (Chen ¶ [0288] a topology map interface may be configured to display cost information associated with one or more selected topology map elements. For example, a user may select a node representing a server instance and, in response, a topology map interface may display cost information for the server instance such as, for example, a total cost incurred by the server instance, an estimated current bill amount, an average cost for the server instance per month, etc. ¶ [0289] In an embodiment, a topology map interface may also be configured to display cost efficiency information for selected map elements. For example, many cloud computing services offer various types of the same computing resource based on different payment models. For example, a cloud service provider may offer three or more different types of server instances such as on-demand, reserved and spot instances, the cost benefits of which depend on how the server instances are used. In an embodiment, based on a determined type of server instance and performance information associated with the instance, a topology map interface may display information indicating whether the type of server instance being used is the most cost effective of the available types of server instances. Although the examples above illustrate display of cost information for server instances, similar information may be displayed for selected storage volumes, network interfaces, or any other cloud computing resources) “and”
- “generating the GUI to render the data as a scorecard detailing cloud spend for at least one of a commerce platform system service or a team” (Chen ¶ [0287] 2nd sentence: Relative to information panel 2204 in Fig.22, a side panel display 2304 includes a more detailed set of information for a selected resource, including information about relationships to other resources, a line chart indicating a CPU utilization percentage [tally, or scorecard] over time, and activity count [tally, or scorecard] for a particular time period. Similarly ¶ [0254] 3rd sentence: interface 1900, for example, comprises a dashboard which displays configuration metrics 1902 (e.g., providing information about a number of configuration changes over time), server instance metrics 1904 (e.g., providing information about a total number of running, stopped, and/or reserved server instances), storage metrics 1906 (e.g., providing information about a total number of volumes in use, a total amount of storage space used, etc.), among other indicators. ¶ [0290] last sentence: aggregate information include metrics derived from information associated with the selected resources (e.g., an average response time for a set of selected server instances, a total [tally or scorecard] cost incurred by a set of selected resources, a total number of configuration changes made with respect to the selected resources, etc.)
Rationales to have modified/combined Dhoolam / Chen are above and reincorporated, the results of the combination were predictable (MPEP 2143 A).
Claim 6. Dhoolam / Chen teaches all the limitations in parent claim 4 above.
Dhoolam teaches or suggests: “wherein the data is generated by a service execution tracking system of the commerce platform system” (Dhoolam column 7 lines 61-64: placement system 116 may use a backtracking search to search all available nodes which may be included in data replication group 112. Column 8 lines 16-34: The backtracking algorithm may also be used by placement system 116 as the selection algorithm. The backtracking algorithm may group nodes by rack and deployment group similar to the modified Ford-Fulkerson algorithm described above. The placement system 116 generate a table with 3 or fewer nodes in each row and column and ensure that the table includes at least 4 rows. During execution of the backtracking search the placement system 116 incrementally build a set of nodes that satisfy the constraints, and abandon each partial candidate (e.g., backtracks) as soon as the placement system 116 determines that particular node cannot possibly be completed to a valid solution. The backtracking algorithm cause the placement system to generate a tree structure where each node represents potential solutions (e.g., a set of nodes that satisfies the one or more constraints) and traverse the search tree recursively, from the root down, in depth-first order. At each node of the tree (e.g., each possible solution) the placement system may evaluate the potential solution).
Chen also teaches or suggests: “wherein the data is generated by a service execution tracking system of the commerce platform system” (Chen ¶ [0227] The operation described above illustrates the source of operational latency: streaming mode has low latency (immediate results) and usually has relatively low bandwidth (fewer results can be returned per unit of time) while the concurrently running reporting mode has high latency (it has to perform a lot more processing before returning any results) and usually has relatively high bandwidth (more results can be processed per unit of time. Then at ¶ [0308] cloud computing management application 1810 enables display of animated topology maps which provide visualizations of how a collection of cloud computing resources and relationships among resources change over time. Examples of such time-based topology map displays include, but are not limited to, display of topology maps at specified points in time, animated topology maps displaying an evolution of a collection of resources over a period of time, and comparison topology maps displaying differences between a topology map at two or more particular points in time. For additional details see ¶ [0311]-¶ [0316] with emphasis on ¶ [0315] 2nd sentence: as the playback of a time-lapse progresses, an indication of a date associated with each of the displayed frames of the time-lapse may be displayed in association with the topology map so that a user can better track when the associated events in the time-lapse actually occurred).
Rationales to have modified/combined Dhoolam / Chen are above and reincorporated.
Claims 7,18. Dhoolam / Chen teaches all the limitations in parent claims 4,17 above.
Dhoolam does not teach: “wherein the scorecard comprises a monthly snapshot of consumption of cloud services provider resources by a set of commerce platform system services associated with one or more of a commerce platform system developer or a team” as claimed.
Chen further teaches: “wherein the scorecard comprises a monthly snapshot of consumption of cloud services provider resources by a set of commerce platform system services associated with one or more of a commerce platform system developer or a team” (Chen ¶ [0288] 2nd sentence: the topology map interface display cost information for the server instance such as, for example, a total cost incurred by the server instance, an estimated current bill amount, an average cost for the server instance per month, etc. ¶ [0286] 4th sentence: the performance metrics may correspond to a particular time period (e.g., for the past week or past month) or display information for the entire lifespan of the resource. ¶ [0314] In one embodiment, during playback of a topology map time-lapse, a user may provide input to mark two different points in time of the playback (e.g., if a time-lapse corresponds to the changes of a topology map over a month-long time period, a user may select two particular points in time during the month). Based on the marked points in time, the user may further provide input to generate a comparison topology map displays that displays differences between the topology map at the marked points in time (e.g., indicating nodes and/or edges that are added, removed, and/or modified).
Rationales to have modified/combined Dhoolam / Chen are above and reincorporated
Claim 12 Dhoolam teaches all the limitations in claim 11 above. Further
Dhoolam teaches or suggests: “wherein the directed graph comprises”
- “the source node associated with the cloud services provider system”
(Dhoolam column 9 lines 38-41: maximizing a flow between source 202 and sink 204 of the graph illustrated in Fig.2. Similarly, Fig.3, 302->304)
- “the super sink node associated with the commerce platform system”
(Dhoolam column 10 lines 13-16: there is a path p from t source 302 to sink 304 in the graph such that cf(u,v)>0 for All edges (u,v)∈p the algorithm find cf(p)=min {cf(u,v):(u,v)∈p} ) “and”
- “a plurality of intermediate nodes that represent infrastructure of the commerce platform system and are associated with the services of the commerce platform system”,
(Dhoolam column 1 lines 56-63: Fig.2 [below] illustrates an environment in which a placement system of a replication group service determines placement information for nodes of a data replication group in accordance with an embodiment; Fig.3 [below] illustrates an environment in which a placement system of a replication group service determines placement information for nodes of a data replication group in accordance with an embodiment. Dhoolam column 3 lines 58-61: Each node 104 may be executed by physical host, and each node 104 may participate in a plurality of data replication groups.
Dhoolam column 15 lines 17-28: The sets of racks 612A-612B may be physical hardware that hosts servers or, simply a logical grouping of the servers. Examples of logical groupings other than by rack include servers grouped together based on data center location, servers in different fault isolation groups (i.e., logical divisions of resources such that failure of one fault zone may not affect servers in the other zones; e.g., grouped by geographic regions, data centers, hard drive clusters, backup generators, etc.), servers grouped together based on performance characteristics (e.g. throughput, input/output operations per second, etc.), etc.)
Dhoolam does not recite: “wherein the edges between any two nodes in the directed graph are directed and are labeled with a timestamp” , “a consumer of the cloud service, an identifier of the cloud service, and a cost of the cloud service consumed between the two nodes”
Chen however in analogous analysis of cloud computing resources teaches/suggest:
- “wherein the edges between any two nodes in the directed graph are directed and are labeled with a timestamp” (Chen ¶ [0278] 1st sentence: a set of interconnected nodes and edges representing a collection of cloud computing resources. ¶ [0257] last sentence: the edges represent relationship among the resources. ¶ [0301] 2nd sentence noting an example of an edge connecting a 1st node representing a server instance and 2nd node representing a storage volume attached to the server instance. ¶ [0314] 2nd sentence differences between topology map at the marked points in time indicating nodes and/or edges. ¶ [0335] last two sentences: user select nodes and/or edge representing server instances and the network link between the instances. In response to user's selection of topology map elements, a circular timeline visualization may be automatically generated and displayed based on timestamped events associated with the selected elements. ¶ [0320] last sentence: edges are displayed using particular colors or graphics to indicate that the corresponding computing resources were created, deleted, and/or modified during the time period between the earlier point and time and later point in time), “a type of cloud service” (Chen ¶ [0273] 2nd sentence: a topology map visualization of edges, each representing a relationship between 2 or more cloud computing resources. ¶ [0278] 1st sentence: as illustrated in Fig.20, the topology display includes edges representing a collection of cloud computing resources. For example Fig.2 & ¶ [0258] last sentence: where search panel 2006 indicate 12 different virtual private clouds, 60 server instances, 24 subnets, etc., are available for display in the map. ¶ [0284] 2nd sentence: if a particular edge connects a first node representing a first server instance to another node representing the subnet, info about network traffic transferred to and from the server instance may be displayed. ¶ [0289] 3rd sentence: For example, a cloud service provider may offer three or more different types of server instances such as "on-demand" instances, reserved instances, and spot instances, the cost benefits of which depend on how the server instances are used), “a consumer of the cloud service” (Chen ¶ [0273] 2nd sentence: topology of edges, each representing a relationship between two or more cloud computing resources. Chen ¶ [0120] 1st sentence: each data source broadly represents a distinct source of data that can be consumed by system 108. Fig.5 Customer ID. ¶ [0150] 2nd,4th sentences: search head 210 allows vendor's administrator to search the log data for order number and corresponding customer ID number of person placing the order customer ID field value matches across the log data from the 3 systems stored at the one or more indexers 206), “an identifier of the cloud service” (Chen ¶ [0149] 1st sentence: user submits an order for merchandise using a vendor’s shopping application program 501 running on user's system. ¶ [0283] 3rd sentence: a unique identifier generated by a cloud computing service for the resource. ¶ [0285] 3rd sentence: information panel 2204 include various info about the server instance represented by the selected node including, an identifier of the server instance, a type of the resource, a name or label for the server instance, an account ID associated with the server instance etc.), “and a cost of the cloud service consumed between the two nodes” (Chen ¶ [0255] 2nd sentence noting each edge connecting 2 nodes represents a relationship between resources corresponding to the 2 nodes. For example, ¶ [0305] 2nd-4th sentences: the selected edges 2604 correspond to server instances and further includes an export panel 2604 corresponding to respective cost. ¶ [0412] at least one node of the plurality of nodes is displayed using a particular graphical element based on cost data associated with the at least one node).
Rationales to have modified/combined Dhoolam / Chen are above and reincorporated.
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Claims 5,10,19 are rejected under 35 U.S.C. 103 as being unpatentable over:
Dhoolam / Chen as applied to claim, and in view of claims 1,4,14 above, in view of
Iyer et al, US 20160358249 A1 hereinafter Iyer. As per,
Claims 5,19 Dhoolam / Chen teaches all the limitations in parent claims 4,14 above.
Chen recognizes at ¶ [0314] In one embodiment, during playback of a topology map time-lapse, a user may provide input to mark two different points in time of the playback (e.g., if a time-lapse corresponds to the changes of a topology map over a month-long time period, a user may select two particular points in time during the month). Based on the marked points in time, the user may further provide input to generate a comparison topology map displays that displays differences between the topology map at the marked points in time (e.g., indicating nodes and/or edges that are added, removed, and/or modified). ¶ [0315] In an embodiment, during playback of the topology map time-lapse, an interface displaying a topology map time-lapse may display an indication of a time associated with each portion of the playback. For example, as the playback of a time-lapse progresses, an indication of a date associated with each of the displayed “frames” of the time-lapse may be displayed in association with the topology map so that a user can better track when the associated events in the time-lapse actually occurred. ¶ [0316] In one embodiment, a cloud computing management application may enable display of an animated topology map (e.g., a time-lapse display) synchronized with other data visualizations. For example, a user may desire to view an animated, time-lapse display of a topology map in synchronization with one or more other visualizations that provide performance metrics, cost and/or billing information, or other information related to the depicted resources across the displayed points in time. Examples of other data visualizations that may be displayed in conjunction with an animated topology map include line charts (e.g., displaying CPU utilization levels, network traffic levels, and/or cost information over time). In this example, a topology mapping module may enable display of a response time line chart to the topology diagram that enables a user to more easily determine how the number of instances affects response time.
* However *
Dhoolam / Chen as a combination does not explicitly recite “further comprising”:
- “comparing the cloud services provider system costs from a period of time from which the data was generated with the cloud services provider system costs from a prior period of time from which a prior data package was generated”; (Claim 5)
- “detecting an increase of a cloud service provider system cost attributable to a commerce platform system service update that exceeds a predefined threshold”; (Claim 5) / “detecting that an increased usage of a specific service area of the cloud services provider system by the commerce platform system is inconsistent with an anticipated increased usage of cloud services provider resources by the commerce platform system” (Claim 19) “and”
- “automatically configuring the commerce platform system service area of the commerce platform system to a state associated with the prior period of time” (Claim 5) / “automatically configuring usage of the specific service area of the cloud services provider to a previous usage configuration of the specific service area of the cloud services provider system” (Claim 19).
* Nevertheless *
Iyer in analogous management of cloud providers resources teaches or suggests:
- “comparing the cloud services provider system costs from a period of time from which the data was generated with the cloud service provider system costs from a prior period of time from which a prior data package was generated”; (Claim 5) (Iyer ¶ [0006] last sentence: examining the spot price for m3.xlarge nodes in us-east-1 region from 28 Apr-6 May 2015 one may see that Spot Instances are offered at nearly a 90% discount from the on-demand price ($0.280) of same instance type. ¶ [0027] Since use of spot Instances is highly dependent on bidding an acceptable and stable bid, a user interface may be presented that asks users to set a percentage of the regular on-demand price the user is willing to pay; as well, as a timeout on that bid. For example, a 90% bid level for m1.xlarge in us-east availability zone translates to maxim bid around 31.5 /hour as of June 2015);
- “detecting an increase of a cloud service provider system cost attributable to a commerce platform system service update that exceeds a predefined threshold”; (Claim 5) / “detecting that an increased usage of a specific service area of the cloud services provider system by the commerce platform system is inconsistent with an anticipated increased usage of cloud services provider resources by the commerce platform system” (Claim 19) (Iyer ¶ [0007] 3rd-4th sentences: if demand for the Spot Instance increases and the Spot Price exceeds the bid price offered by the user, the Spot Instance will be terminated. One way to reduce the probability of this is to use higher bid prices. For example, at ¶ [0028] 4th: a user may bid at about just above 100%);
- “automatically configuring the commerce platform system service area of the commerce platform system to a state associated with the prior period of time” (Claim 5) / “automatically configuring usage of the specific service area of the cloud services provider to a previous usage configuration of the specific service area of the cloud services provider system” (Claim 19).
(Iyer ¶ [0028] 4th-5th sentences: a user may bid at about just above 100%. This generally achieve cost reduction, while occasionally falling back to on-demand instances, such as those at ¶ [0006] last sentence from 28 Apr-6 May 2015. ¶ [0021] 3rd- 4th sentences: Auto-scaling clusters use Spot instances to add more compute power when required, and scale down cluster size when load recedes. Automatic addition of compute capacity provide an opportunity to use Spot Instances for auto-scaling at significantly lower costs compared to On-Demand Instances. Similar, ¶ [0025] 4th sentence: Depending on the workload, the cluster may then automatic-ally auto-scale adding more nodes. Similar auto-scaling ¶ [0027] 3rd sentence, ¶ [0029] 1st sentence).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Dhoolam / Chen “method/non-transitory” “medium” to have included Iyer’s teachings to have more efficiently handled big data as necessitated by physical constraints and contemporary market forces under powerful big data systems, capable of powerful process petabytes of data while running on upwards of thousands of machines, while also frugally adapting for underutilized periods of time for better utilization of resources and cost reduction (Iyer ¶ [0004]-¶ [0006] and MPEP 2143 G, F). The predictability of such modification would have been further justified by the broad level of skill of one of ordinary skills in the art articulated by Iyer at ¶ [0018] 2nd sentence, ¶ [0057] 2nd sentence.
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar service provider-based field of endeavor. In such combination each element merely would have performed same benchmarking, contractual and econometric functions as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements evidenced by Dhoolam/Chen in further view of Iyer, the to be combined elements would have fitted together like puzzle pieces in logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 10. Dhoolam / Chen teaches all the limitations in parent claim 1 above.
Dhoolam / Chen does not explicitly recite as claimed: “further comprising”:
- “detecting that an increased usage of a specific service area of the cloud services provider system by the commerce platform system is inconsistent with an anticipated increased usage of cloud services provider resources by the commerce platform system”; “and”
- “automatically configuring usage of the specific service area of the cloud services provider system to a previous usage configuration of the specific service area of the cloud services provider system”
Iyer however in analogous management of providers’ resources teaches or suggests:
- “detecting that an increased usage of a specific service area of the cloud services provider system by the commerce platform system is inconsistent with an anticipated increased usage of cloud services provider resources by the commerce platform system”; (Iyer ¶ [0021] 2nd sentence: the workload in Hadoop cluster may not be uniform, and thus there may be unexpected [or inconsistent] spikes [or increases]. ¶ [0007] 3rd-4th sentences: if demand for the Spot Instance increases and the Spot Price exceeds the bid price offered by the user, the Spot Instance will be terminated. One way to reduce the probability of this is to use higher bid prices. For example, at ¶ [0028] 4th: a user may bid at about just above 100%) “and”
- “automatically configuring usage of the specific service area of the cloud services provider system to a previous usage configuration of the specific service area of the cloud services provider system” (Iyer ¶ [0028] 4th-5th sentences: bidding at about just above 100% achieve cost reduction, while occasionally falling back [previous] to on-demand instances, such as those at ¶ [0006] 6th sentence from 28 Apr-6 May 2015. Specifically, ¶ [0021] 3rd- 4th sentences: Auto-scaling clusters use Spot instances to add more compute power when required, and scale down cluster size when load recedes. Automatic addition of compute capacity provide an opportunity to use Spot Instances for auto-scaling at significantly lower costs compared to On-Demand Instances).
Rationales to have modified Dhoolam / Chen with/and Iyer are above and reincorporated.
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Claims 8,9,13 are rejected under 35 U.S.C. 103 as being unpatentable over:
Dhoolam / Chen as applied to claim, and in view of claims 7, 4 above, in view of
Fliess US 20120060142 A1 hereinafter Fliess. As per,
Claim 8 Dhoolam / Chen teaches all the limitations in parent claim 7 above.
Dhoolam / Chen does not recite: “wherein the commerce platform system services manages a database of commerce platform system service data and each of the cloud services provider system costs is based on a size of one or more tables within the database”.
Fliess however in analogous analyzing cost profiles of cloud providers teaches/suggests:
- “wherein the commerce platform system services manages a database of commerce platform system service data and each of the cloud services provider system costs is based on a size of one or more tables within the database”.
(Fliess ¶ [0106] 3rd-4th sentences: As stated supra the function of COP is to determine how to minimize cost of input application code. This cost of an application is based on several factors such as database size. Specifically, ¶ [0116] 4th-8th sentences: Using amortized analysis, some of the operations will require greater than constant cost. Thus, no constant payment will be sufficient to cover the worst case cost of an operation, in and of itself. With proper selection of payment, however, this is not a problem as the expensive operations only occur when there is sufficient payment in the pool to cover their costs For example, in capacity planning, it is often necessary to create a table before its size is known. In this case, a possible strategy is to double the size of the table when it is full).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Dhoolam/ Chen’s “method” to have included Fliess’ teachings in order to have allowed the cloud service provider to budget capacity manner in an effective manner as necessitated by market forces such as a predicted usage of 10% and reduction in cost of 10% (Fliess ¶ [0118] and MPEP 2143 G, F), while at same time having avoided slowdowns by improving coding quality through selection of highly efficient algorithms as recommended by the cost-oriented profiler COP (Fliess ¶ [0108], [0129], [0132], [0163]-[0173] in view of MPEP 2143 G). The predictability of such modification is further corroborated by the broad level of skill of one of ordinary skills in the art as demonstrated by Fliess at ¶ [0047], [0067], [0071], without overextending storage but rather with reasonably effective storage capabilities as per Fliess ¶ [0173]. Further, the claimed invention could have also be viewed as mere combination of old elements in a similar field of endeavor of analyzing could infrastructure. In such combination each element would have merely performed same analytical, econometric and organizational function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements evidenced by Dhoolam / Chen in further view of Fliess above, the to be combined elements would have fitted together like pieces of a puzzle, in a complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A).
Claim 9 Dhoolam / Chen teaches all the limitations in parent claim 4 above. Furthermore
Chen recognizes at ¶ [0289] 4th sentence: based on determined type of server instance and performance information associated with the instance, a topology map interface display information indicating whether the type of server instance being used is the most cost effective of the available types of server instances
Dhoolam/Chen however in combination does not explicitly recite: “wherein the scorecard generates an alert as a result of an increase in a cost of a commerce platform system service”.
* Nevertheless *
Fliess in analogous cost profiles analysis of cloud providers teach/suggest: “the scorecard generates an alert as a result of an increase in a cost of a commerce platform system service”
(Fliess ¶ [0216] 2nd sentence: user interface also comprises various views as described supra such as a real time monitor and analysis views. For example, at Fig.20 and ¶ [0208] 2nd sentence: cost report from COP comprises a real-time monitor that presents software project currently being profiled using charts focusing on metrics such as ownership costs, CPU and network utilization. ¶ [0118] 2nd sentence: noting an example where the trace events indicate that certain optimizations reduce cost by 10% and expected usage predicts that utilization will increase by 10% the following month. ¶ [0111] When costly bottleneck is localized, the COP recommends cost saving optimization for that algorithm, tailored to the application's business case. In one embodiment, optimization includes finding a bottleneck (a critical part of the code that is the primary consumer of the needed resource) sometimes known as a hot spot. An example of bottleneck is using the service bus as opposed to polling a queue at a rate calculated according to the peak time of day. The well-known Pareto principle can be applied to resource optimization by observing 80% of resources are typically used by 20% of operations. The COP approximates that 90% of the execution time of a computer program is spent executing 10% of the code (known as 90/10 law in this context). More complex algorithms and data structures perform well with many items, while simple algorithms are more suitable for small amounts of data, i.e. the setup, initialization time, and constant factors of the more complex algorithm can outweigh the benefit).
Rationales to modify/combine Dhoolam / Chen /Fliess are above and reincorporated.
Claim 13 Dhoolam teaches all the limitations in claim 11 above.
Dhoolam teaches “wherein the analyzing further comprises”:
- “performing the maximum flow analysis using the directed flow graph having the one or more adjusted nodes or edges of the flow graph to determine an adjusted cloud service provider resource usage by the commerce platform system across the directed graph”
(Dhoolam column 7 lines 64-column 8 line 15: placement system 116 utilizes modified maximum network flow algorithms to determine the set of nodes 104. Specifically, the placement system generate a graph or similar representation of configuration of nodes 104, host computer systems, and other computing resources in the computing resource service provider environment and use various algorithms to traverse the graph and determine a solution. For example, the placement service may draw a vertex for each rack and deployment group, a directed edge from each rack vertex to deployment group vertex with a capacity representing the number of nodes, and draw a source and a sink vertex. The graph representing the configuration of the host computer systems and configuration of nodes 104 is described in connection with Figs.2-3. The placement system 116 use a maximum network flow algorithm such as Ford-Fulkerson algorithm to determine the set of nodes 104 that satisfy at least a portion of the one or more constraints.
Dhoolam column 10 lines 5-17: modified Ford-Fulkerson algorithm is used to determine a maximum flow (e.g., a maximum number of nodes) between a source 302 and a sink 304. The graph in Fig.3 may be defined as G(V,E), for each edge from u to v the capacity is defined as c(u,v) and the flow is defined as f (u,v). In various embodiments, when searching for a solution based at least in part on the graph, the flow along an edge cannot exceed the capacity along the edge. Further, while there is a path p from source 302 to sink 304 in the graph such that cf(u,v)>0 for all edges (u,v)∈p the algorithm may find cf(p)=min {cf (u,v): (u,v)∈p}. As described above, the path can be found by performing a breadth-first search or depth-first search in Gf(V,Ef)), “wherein the directed graph is decomposed into a plurality of spanning trees during the maximum flow analysis” (Dhoolam column 8 lines 28-34: backtracking algorithm cause the placement system to generate a tree structure where each node represents potential solutions (e.g., a set of nodes that satisfies the one or more constraints) and traverse the search tree recursively, from the root down, in depth-first order. At each node of the tree (e.g., each possible solution) the placement system may evaluate the potential solution)
Dhoolam does not teach “for attributing costs to individual products, software development groups, customers of the commerce platform system, or a combination thereof” as claimed.
Fliess in analogous cost profiles analysis of cloud providers teach/suggest: “for18 attributing costs to individual products, software development groups, customers of the commerce platform system, or a combination thereof” (Fliess ¶ [0070] The cost oriented profiler (COP) mechanism of the present invention is a software development tool for analyzing the behavior of an application with respect to cost. Use of the cost oriented profiler mechanism has applications in helping drive the decisions of software architects, such as during the architecture phase of a software product where a prototype or small proof of concept project is typically built. Using the cost oriented profiler mechanism on these projects can provide the information needed to define a better cost oriented architecture. ¶ [0113] An example decision tree 119 is shown in FIG. 7. The decision tree represents the price package alternatives available to the decision maker, the uncertainty they face and evaluation measures representing how well they achieve their cost minimization objectives in the final outcome. Uncertainties (e.g., customer demand spikes) are represented by probabilities and probability distributions. Risk is represented by utility functions while trade-offs between conflicting objectives (e.g., SLA and compute model versus cost) is made using multi-attribute value functions or multi-attribute utility functions (if there is risk involved). In the decision tree 119, the optimum path is highlighted by the bolded boxes 1 and 1.1. ¶ [0188] Considering an input application, it is common for different parts of the application to execute differently. As is typical, the execution of the application depends on user actions and behavior tendencies. More importantly, effectiveness of particular code blocks may vary at different execution times depending on one or more factors, such as loads, available resources, code robustness, etc. Identifying common user behavior patterns and simulating their effect on the input application is important for determining the real and actual impact of the code on the TCO [total cost of ownership] of a software product).
Rationales to have modified/combined Dhoolam with/and Fliess are above and reincorporated.
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
- Ludwig Justin, EC2 Reserved Instance Break Even Points, SWWOMM webpages, September 9 2012
- WO 2013159291 A1 teaching workload prediction for network based computing
- Lauderdale US 20120158817 A1 teaching Distributed Computing Architecture
- Li; Li US 20170214634 A1 hereinafter Li
Li teaches several examples starting with: ¶ [0068]-¶ [0070] Fig.4 is a graph 400 of a set of nodes and links provisioned for a distributed application. The graph 400 in one embodiment is an arbitrary directed graph that illustrates node numbers and capacities of the links between the nodes. In one embodiment, a minimum cut (min_cut) function is used to partition (cut) 410 nodes of graph 400 into 2 disjoint subsets S and T joined by at least one link. The links represent communication in one embodiment, and min_cut function is used to determine under-provisioned links whose capacities should be increased in order to meet the target total capacity. Many different available min_cut functions/algorithms may be used to partition the graph 400. The illustrated cut 410 is represented by min_cut (G)=(S, T)=({s,3,4,7}, {2,5,6,t}). U.sub.k=capacity (S,T)=10+8+10=28. In one embodiment, the capacity of every min_cut of G is increased until all of them reach the target capacity, because:
1. max-flow(G)=min_cut (G) capacity=minimal under-provisioned links, and
2. there could be more than one min_cut below the target total capacity.
Li ¶ [0071] Fig.5 is a graph 500 illustrating increased link capacities to meet the target total capacity Uk+1. Note that the target total capacity Uk+1 at time k+1 has increased by 12 from the current total capacity Uk:Uk+1=28+12=40.
Li ¶ [0072] Fig.6 is a pseudocode representation of an application scale-up method 600 of determining the total increased capacity diff and the under-provisioned nodes and links whose capacities should be increased, and increasing their capacities to meet the increased total increased capacity. Method 600 utilizes the min_cut function to iteratively identify the links between node partitions S and T of an application topology and increase their capacities, until the total increased capacity is met.
Li ¶ [0073] Fig.7 is a graph 700 illustrating a current capacity along with a cost, and a maximum capacity for each link. For example, a link 705 between a source node (s) and a node (2) is allocated a bandwidth of 10, relative cost of 2, and a maximum capacity of 40. A link 710 between nodes (3) and (4) has a current bandwidth of 8, relative cost of 5, and a maximum capacity of 40. A link 715 between nodes (7) and (t) has a current bandwidth of 10, relative cost of 3, and maximum capacity of 40. In one embodiment, the scaling service indicated that a total increased capacity of 12 should be allocated among three links inversely proportional to their costs. In one embodiment, node capacities may be added to S′={3,7} and T′={2,6} based on node scaling functions f3, f7, f2, f6 defined by the scaling policy of the application.
Li ¶ [0074] Fig.8 is a graph 800 illustrating changes to graph 700 in accordance with a link-node scale-up algorithm that minimizes cost associated with links and nodes to meet the total increased capacity. The links 705, 710, and 715 have been renumbered in Fig.8 to begin with “8” as changes to their capacity are now indicated. Link 805, which had the lowest cost, had a capacity increase of 6, link 810, which had the highest cost, hand an increase of 2, and link 815 which had the next lowest cost had an increase of 4, illustrating that the highest cost node had the lowest increase in order to minimize the cost associated with the under-provisioned links.
Li ¶ [0020] Fig.14 is a graph illustrating the current capacity, cost and maximum capacity of the over-provisioned links and nodes that serve as the input to a link-node scale-down method in FIG. 16 according to an example embodiment. ¶ [0087] Fig.14 is a graph 1400 illustrating the complement graph used to determine the decreased capacities for the over-provisioned links and nodes. Link costs and maximum capacities are again shown for certain links that are part of a partition in the min_cut function. In one embodiment, a target capacity of 45 is to be met from the current total capacity of 65. The scaling service determines that four over-provisioned links will decrease their capacities by 20 in total proportional to their costs to meet the target total capacity. In one embodiment, node capacity is removed from S′={5,6,4} and T′={7} based on node scaling functions f5, f6, f4, and f7 defined by the scaling policy of the application.
Li ¶ [0023] Fig.17 is pseudocode representation of allocating the total decreased capacity among the over-provisioned links to maximize cost decreases associated with the decreased link capacities to meet a total decreased capacity according to an example embodiment. ¶ [0098] Fig. 17 is a pseudocode representation of a method 1700 of allocating total decreased capacity among the over-provisioned links to achieve the link-node scale down illustrated in graphs 1400 and 1500. The method divides the total decreased capacity (diff) among the over-provisioned links proportional to their costs and each link receives a decreased link capacity greater than zero. If the total decreased capacity is received, the procedure stops; otherwise, the residue capacity is treated as the new total decreased capacity the allocation procedure repeats, until the total decreased capacity is received or none of the links can receive any capacity reduction
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Li Figs. 4-5, 7-8, 14-15 in support of rejection arguments
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624
June 2nd, 2026
1 According to MPEP 2106.04(a): “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”.
2 USPTO’s training entitled Focus on Computer/Software-related Claims dated May 2015 slides 16-17,20-21, which cites MPEP 2111.04, with respect to the patentable weight of intended use or result. Here the term capable is interpreted as analogous to intended.
3 Alice, 573 U.S. 208, 224, 110 USPQ2d at 1984, 1985 (citing Parker v. Flook, 437 U.S. 584, 593, 198 USPQ 193, 198 (1978) and Mayo, 566 U.S. at 72, 101 USPQ2d at 1966).
4 USPTO’s training entitled Focus on Computer/Software-related Claims dated May 2015 at slides 16-17,20-21, which cites MPEP 2111.04, with respect to the patentable weight of intended use or result. Here the term capable is being interpreted as analogous to intended.
5 According to MPEP 2106.04(a): “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”.
6 OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015);
In re Smith, 815 F.3d 816, 818-19, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016);
In re Greenstein, 774 Fed. Appx. 661, 664, 2019 USPQ2d 212400 (Fed Cir. 2019) (non-precedential)
7 MPEP 2106.04(a)(2) III
8 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
9 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
10 Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)
11 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
12 Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014)
13 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
14 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93
15 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014), Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755
16 Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016)
17 USPTO’s training entitled Focus on Computer/Software-related Claims dated May 2015 at slides 16-17,20-21, which cites MPEP 2111.04, with respect to the patentable weight of intended use or result. Here the term capable is being interpreted as analogous to intended.
18 USPTO’s training entitled Focus on Computer/Software-related Claims dated May 2015 slides 16-17,20-21, which cites MPEP 2111.04, with respect to the patentable weight of intended use or result. Here the term “for attributing” is interpreted as analogous to an intended use or intended result.
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