DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the RCE, Remarks, and Amendments filed 04/06/2026.
Claims 1, 8, 15 are amended.
Claims 4-7, 11-14 and 18-20 are canceled.
Claims 1-3, 8-10, and 15-17 have been examined and are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/06/2026, has been entered.
(AIA ) Examiner Note
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Rejections - 35 USC § 103 (AIA )
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claims 1-3, 8-10, and 15-17 are rejected under 35 U.S.C. 103 as obvious over Paul et al. (U.S. 2021/0042140 A1; hereinafter, "Paul") in view of McLaughlin (U.S. 2017/0324765 A1; hereinafter, "McLaughlin") in view of Gupta (U.S. 2014/0278333 A1 A1; hereinafter, "Gupta")
Claims 1, 8, 15 (currently amended)
Pertaining to claims 1, 8, 15 exemplified in the limitations of method claim 1, Paul as shown teaches the following:
A computer implemented method, with a workload allocation engine, of allocating a workload in a cloud architecture, the computer-implemented method comprising:
identifying, for an enterprise, a data center, a plurality of servers within the data center, and a plurality of virtual machines (VMs) running on the plurality of servers (Paul, see at least Fig. 3 and associated disclosure, e.g. [0055], teaching: “…FIG. 3 is an illustration of a data center virtual environment 500 being monitored and controlled by a system 100, according to certain embodiments herein. Certain data center virtual environments may comprise a list of server clusters 501, resource pool 502, etc. Server cluster 501 may further comprise hosts 503 or virtual machines 504. Each virtual machine 504 may comprise multiple applications/processes 505….”
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generating, based on at least one predetermined factor, a plurality of clusters of the plurality of servers (Paul, see citations noted supra, e.g. Fig. 3 “server clusters 501a and 501b” and per at least [0056]-[0067] teaching “each level of virtual infrastructure”, including “one or more host servers 403a” [cluster of servers], may be selected and viewed via “dashboard views 403” according to various criteria [factors]; e.g. as noted per at least [0056]: “…As shown, the IPM+IOT engine 400 comprises multiple dashboard views 403. In general, dashboard views 403 provide comprehensive views of performance for one or more [i.e. a cluster of] host servers 403a, applications 403b, and virtual machines 403c being monitored, with specialized views for each level of virtual infrastructure… Dashboards views 403 may comprise single purpose screens that provide a user with a quick summary of a select component or group of components. For instance, certain dashboards views 403 can provide numerical and/or graphical representations of utilization metrics associated with the single object (e.g., datacenter, cluster, host server, resource pool, virtual machine, datastore or the like) or collection [plurality] of objects [server cluster being noted as an object]. The dashboard views 403 can be further associated with additional views that provide statistical information for one or more objects being viewed in the dashboard. For example, in some embodiments, embedded views can comprise one or more of the following: …Top 5 Ready View: shows the top five virtual machines in terms of percent readiness for CPU cycles for the selected host or virtual machines… Dashboard views may also comprise the diagnostic tool 403f that provides architectural representation of the monitored virtual environments… The diagnostic tool 403/ also represents the impact of running one application or software from one virtual machine to another or from one host server to another host server…”);
tracking, for each cluster of the plurality of clusters, a plurality of time-series variables wherein each cluster of the plurality of clusters comprises a respective set of servers of
the plurality of servers, and the plurality of time-series variables indicates utilization of the respective set of servers (Paul, see citations noted supra, e.g. at least [0056]-[0067] also in view of at least [0071]-[0074], teaching e.g.: “…FIG. 5 illustrates a flowchart of power reduction in a server. As shown in steps 601, 602 client agent 200 receives performance data, power consumption data [time-series variables indicates utilization of the respective set of servers] of host servers [e.g. a cluster of servers], virtual machines and the applications running inside virtual machines and sends this to IPM+IOT engine 400. The data is stored [tracked] in data center database 402. The analytics engine 404 performs analysis over this time series data [a plurality of time-series data is being tracked] and passes it to the prediction module 405…”; Also note [0041]: “…Conceptually, the power used by the virtual machine is measured by tracking the hardware resources (such as the CPU, disk, memory, network, etc) used by the virtual machine….”);
training a statistical modeling engine using historical carbon emissions data (Paul, see citations noted supra, including also at least [0051] teaching his “power module 203 performs regression over this data and finds out the coefficients k1, k2, etc… and intercepts… e.g. coefficients representing CPU, memory, disk, network usage…” which is a training step for a regression technique. See also [0074], teaching the predicted power consumption [e.g. carbon emissions data1] is based on a trained regression technique [trained statistical method]; such regression technique must be trained on historical data, and therefore as is within the level of a person of ordinary skill in the art requires a training step, e.g. as performed by power module 203 noted per [0051], e.g. to determine coefficients of the model, etc…; Therefore, even if not the terminology of the claim is not explicitly stated, Examiner finds it would have been obvious to a person of ordinary skill in the art before the effective filing date to perform a training step of Paul’s regression technique to “find out the coefficients and intercepts” for the model because generic training of a regression models is within the level of skill of a person of ordinary skill in the art and such a person would be motivated to do this training to enable Paul’s teachings, e.g.: “…The power module 203 performs regression [using a statistical method] over this data [historical carbon emissions data] and finds out the coefficients k1 , k2 , k3 , k4 and intercept as ko ·k0 is considered as the static power of the host server and k1, k2 , k3 , k4 are the coefficients of CPU, memory, disk, and network usage…”; and the analytics engine then “…uses the power coefficients k1, k2 , k3 , k4 of the each of the destination servers to calculate the expected power consumption based on the resource demand of the virtual machines on the respective server…”, because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.)
predicting, for the workload, using the trained statistical modeling engine, carbon emissions generated by each cluster of the plurality of clusters, wherein the predicting of the carbon emissions generated by each cluster of the plurality of clusters is based on the plurality of time-series variables (Paul, again see citations noted supra, including again at least [0071]-[0074], e.g.: “…The prediction model 405 also predicts the energy requirement at three different levels. It predicts energy consumption [e.g. a type of carbon emissions data] of the host servers, for virtual machines and their applications [the workload]…”; Examiner notes that although Paul may not use the term “carbon emissions” the Examiner understands that a person of ordinary skill in the art before the effective filing date of the claimed invention would understand this term to include values such as CO2 equivalent, etc… which may be represented by energy consumption values based on the source of the energy consumed and/or source of the supplied power during energy use because, as already noted supra per footnote, carbon emissions, e.g. amount of carbon emitted to produce the energy consumed and/or power used, is a straightforward conversion usually done by multiplying the amount of energy used by an emission factor such as, per kg CO2 equivalent / kWh used for a particular type of energy source like coal, LNG, wood, etc..; therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have recognized Paul’s energy consumption prediction is a type of carbon emissions prediction via the aforementioned conversion and under motivation per business or government regulation to account for carbon emissions would have readily made such a conversion thereby predicting a carbon emission from the predicted energy requirement because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.);
[…]
determining a relationship between the workload and a previously-assigned workload wherein the workload and the previously-assigned workload are associated with a
same application (Paul, see at least [0014]-[0016] in view of at least [0030]: “…the systems and methods provide a monitoring Solution that understands the relationships [determining a relationship] and interactions between components in a datacenter host server and virtual infrastructure and application/processes [workloads] running inside virtual machine (VM) [same application],…”),
[…];
Although Paul teaches the limitations upon which these claims depend, and per [0030] teaches determining relationships between applications [workloads] executing on various virtual machines and servers in order to determine whether to migrate or shift virtual machines and their applications [workloads], e.g. as discussed per at least [0071]-[0074], to select the server with minimum power consumption among all the servers in destination list of a virtual machine, for the purpose of, e.g. per [Abstract]: determining “…optimal destination servers to migrate each of the VMs with corresponding applications [workloads] from the underutilized host servers” in an effort to “optimize application level resource and energy consumption [carbon emissions] by data center servers”, he may not explicitly teach the nuance as recited below regarding a particular determined relationship between applications [workloads]. However, regarding this feature, Paul in view of McLaughlin teaches the following:
and, the relationship is such that a number of data transfers between the workload
and the previously-assigned workload and an increase in at least one of a physical distance or a logical2 distance between the workload and the previously-assigned workload increase a network latency and carbon emissions associated with a combination of the workload and the previously-assigned workload (McLaughlin, see at least Figs. 4-8, [0006]-[0009] and at least [0026]-[0038], teaching, e.g.: “…In the example implementations of FIGS. 5-7, 723 applications [workloads] were analyzed and grouped into clusters based on preexisting risk parameters [relationships]… any number of applications or services could be grouped, based on any parameters …Consequently, similar applications/services [i.e. similar workloads have a type of logical distance in relation to each other] will be grouped together at the same node [i.e. a same node represents a short physical distance between each other and low latency between applications on the same node], as is shown in more detail in FIG. 6… Now referring to FIG. 8, but with continued reference to FIG. 7, generally,… the clusters are determined by sorting the nodes based on one or more defining characteristic (e.g., a specific risk parameter). For example, all of the nodes included in first cluster 702 may include applications [workloads] with a medium criticality rating, etc… system 100 is configured to understand the relationships and interactions between components in the physical host server and virtual infrastructure… In addition, it reduces data center overall server power consumption [reduces carbon emissions] by analysis and prediction of parameters related to power consumption of host servers and virtual machines and executes power capping actions…”; Examiner notes that McLaughlin’s aforementioned teachings regarding “relationships and interactions between components…in the virtual infrastructure” appears to refer to his applications [workloads] or at the very least implies he is talking about his applications executing on virtual machines and therefore there is some suggestion that his system recognizes relationships and interactions between applications [workloads] where an interaction between applications inherently has some latency where the latency of interaction between applications on the same VM is understood by a person of ordinary skill in the art to increase if these applications are separated and moved to different VMs executing on physically different and separated servers. Applicant’s “physical distance” and “logical distance” is only discussed in his Specification3 at [0027] as follows: “…an “affinity” refers to a relationship between two workloads in which the physical and/or logical distance between these two workloads impacts the amount of carbon emissions generated by the combination of the workloads and/or the performance of these workloads…” and reads on McLaughlin’s teachings as noted supra, and therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date that separating McLaughlin’s applications [workloads], i.e. applications which require interaction with each other, e.g. to different physically separate servers, will increase their physical distance, increase the latency of their interaction, and hence increase the amount of power necessarily consumed to maintain interaction hence increasing the carbon footprint for their interaction.)
and, assigning, based on the determining of the relationship between the workload and the previously-assigned workload, a highest weight to a second cluster among the plurality of clusters, wherein the second cluster having the highest weight operates the previously-assigned workload; and assigning the workload to the second cluster, based on the highest weight assigned to the second cluster, and the predicted carbon emissions associated with the second cluster that is lower than that of remaining clusters of the plurality of clusters, wherein the assigned workload is performed by the second cluster; … the increase in the network latency associated with the combination of the workload and the previously-assigned workload is prevented based on the assigning of the workload to the second cluster (McLaughlin, see citations noted supra, including again at least [0026]-[0038], teaching: “….the clusters are determined by sorting the nodes based on one or more defining characteristic (e.g., a specific risk parameter) [a highest weight]. For example, all of the nodes included [in] cluster 702 may include applications [assigned workloads] with a medium criticality [highest] rating [weighting], etc…”; where again, as noted supra, separating McLaughlin’s applications [workloads], i.e. applications which require interaction with each other, e.g. to different physically separate servers, will increase their physical distance, increase the latency of their interaction, and hence increase the amount of power necessarily consumed to maintain interaction hence increasing the carbon footprint for their interaction. Therefore, the applications [workloads] are assigned to a particular cluster [e.g. a second cluster] based on their rating [weighting] and the predicted power consumption [carbon emissions] being lower for the particular [second] assigned cluster than if executed on VMs operating on other server clusters; Examiner notes that the phrase “the increase in the network latency associated with the combination of the workload and the previously-assigned workload is prevented based on the assigning of the workload to the second cluster” is a statement of an intended consequence from what naturally occurs by following the previously recited steps and therefore is also a consequence of performing the steps as noted by the combination of prior art);
In view of these teachings, the Examiner understands that the limitation in question is merely applying a known technique McLaughlin (directed towards techniques of grouping applications [the workload and the previously-assigned workload,] based on “any parameter”, including applications which have “relationships and interactions” with each other and when physically separated, e.g. to execute on different VMs on physically different servers, would result in increased latency of interaction between them, requiring more energy consumption and hence an increased carbon footprint, and whose grouping of applications [workloads] are assigned to server clusters based on such relationships where clusters are determined by sorting the nodes based on one or more defining characteristic (e.g., a specific risk parameter). For example, all of the nodes included [in] cluster 702 may include applications [assigned workloads] with a medium criticality rating [weighting], etc…”) which is applicable to a known system/method of Paul (already directed towards load balancing to “optimize application level resource and energy consumption by data center servers”) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of McLaughlin to the device/method of Paul in order to perform the limitation in question because Paul and McLaughlin are analogous art in the same field of endeavor (at least G06Q30/02) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Although Paul/McLaughlin teaches the limitations upon which these claims depend, including using a statistical method such as regression to model expected relationship between an application [load] executing on a resource within a data center, such as on a virtual machine within a cluster of servers, and the energy consumption of the resource (e.g. as noted supra: “…The prediction model 405 also predicts the energy requirement at three different levels. It predicts energy consumption [carbon emissions] of the host servers, for virtual machines and their applications [workload]…”), Paul may not explicitly teach that he looks for or identifies points in this data where his statistical model predicts non-linear relationships between such loads and predicted energy consumption. However, regarding this nuance, Paul/McLauglin in view of Gupta teaches the following:
predicting, by the statistical modeling engine, based on the carbon emissions of a first cluster of the plurality of clusters, a breakpoint4 in which a ratio of energy consumption to computation load associated with the first cluster becomes non-linear; the assigning of the workload to the second cluster comprises switching from the first cluster to the second cluster based on the predicted breakpoint (Gupta, see at least Figs. 1, 2, 11 and associated disclosure including at least [0054], e.g.: “…[0054]-[0068] The power sub-module can query a server database to retrieve a coefficient matrix of the power curve for the particular server model at a given state in some embodiments. To reflect power usage that is a non-linear function of utilization, server power curves can be modeled as configurable 11-element arrays of power consumption at 10% increments, with linear interpolation between points, in some embodiments. These models can be measured directly from a server's under-utilization or can be derived from existing benchmarks, in some embodiments. etc… For example, as shown in FIG. 6, in some embodiments, the RM module can include: (a) a workload management algorithm, (b) a power management algorithm, (c) a cooling management algorithm, and (d) a coordinated workload, power, and cooling management algorithm.”; Examiner understands the coordinated workload, power, and cooling management algorithm is used to manage [i.e. switch] workloads between servers to optimize power management, e.g. when server model predicts power changes from linear to non-linear, and/or requires additional energy consumption in the form of cooling to maintain performance, etc…);
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Gupta (directed towards techniques by which to manage server workloads based on predicted power consumption curves, such as predicting non-linear power usage; implying the switch occurs at or near the point where the server model indicates power usage transitions from linear to non-linear which indicates expected deviation increase in power usage) which is applicable to a known base device/method of Paul/McLaughlin (already directed towards systems/methods by which to group workloads of VMs [applications] to decrease latency and manage power consumption; a representation of carbon emissions) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Gupta to the device/method of Paul/McLaughlin in order to perform the limitation in question because Gupta is pertinent to the power management of servers as is Paul/McLaughlin and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 2, 9, 16 (previously presented)
Paul/McLaughlin/Gupta, teaches the limitations upon which these claims depend. Furthermore, as shown, Paul teaches the following: …wherein the enterprise includes a plurality of data centers (Paul, see at least [0009] teaching: “…Corporations that maintain datacenters struggle with soaring energy costs. These costs can be attributed in part to overprovisioning with servers constantly operating below their maximum advisable capacity (for example, U.S. data centers are wasting huge amount of energy…, and the developers of the apps running on these datacenters generally do not take energy into consideration…”; therefore, whether Paul Explicitly teaches his “Enterprise Network 300” includes a plurality of data centers, the Examiner finds that because Paul makes it known that it that Corporations [enterprise] may maintain [include] data centers [i.e. a plurality], Examiner finds it would have been obvious to a practitioner of Paul’s “Enterprise Network 300”, i.e. a person of ordinary skill in the art before the effective filing date of the claimed invention implementing Paul’s system/method, to also include a plurality of data centers for the Enterprise Network 300 because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.).
Claims 3, 10, 17 (previously presented)
Paul/McLaughlin/Gupta, teaches the limitations upon which these claims depend. Furthermore, as shown, Paul teaches the following: …wherein the at least one predetermined factor is selected from a group consisting of: architecture similarity, load profile similarity, and CPU architecture (Paul, see citations noted supra, e.g. again per at least [0055]-[0065], teaching: “……Top 5 Ready View [a predetermined factor]: shows the top five virtual machines in terms of percent readiness [load profile similarity in top 5] for CPU cycles for the selected host or virtual machines… Dashboard views may also comprise the diagnostic tool 403f that provides architectural representation of the monitored virtual environments… The diagnostic tool 403/ also represents the impact of running one application or software from one virtual machine to another or from one host server to another host server…”).
Response to Arguments
Claims 1, 8, 15 are amended and claims 4-7, 11-14 and 18-20 are canceled on 04/06/2026. Applicant's arguments (hereinafter “Remarks”) also filed 04/06/2026 with RCE, have been fully considered but regarding the prior art arguments the arguments are moot in view of the new grounds of rejection necessitated by applicant’s amendments. Note the new 35 USC 103 rejections with Paul in view of McLaughlin and Gupta. Additionally, respectfully, Examiner has withdrawn the 35 USC 101 rejection in view of applicant’s amendments; Examiner finds that applicant’s claims, under step 2B of the analysis enumerated per MPEP 2106, recite an unconventional arrangement of steps to identify a situation wherein the assigning of the workload to the second cluster comprises switching from the first cluster to the second cluster based on the predicted breakpoint, etc… For this reason, the previous 35 USC 101 rejection has been withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. 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) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621
1 Examiner notes: power consumption is a type of “carbon emissions data” because converting energy and/or power consumption to carbon emissions (e.g. amount of carbon used to produce the energy consumed and/or power used) is a straightforward conversion usually done by multiplying the amount of energy used by an emission factor (e.g., kg CO2 equivalent / kWh)
) specific to the energy source.
2 Specification [0027] is the only portion of applicant’s original disclosure where “physical distance” and “logical distance” are mentioned, as follows: “…As used herein, an "affinity" refers to a relationship between two workloads in which the physical and/or logical distance between these two workloads impacts the amount of carbon emissions generated by the combination of the workloads and/or the performance of these workloads…”
3 Specification [0027]: “…As used herein, an "affinity" refers to a relationship between two workloads in which the physical and/or logical distance between these two workloads impacts the amount of carbon emissions generated by the combination of the workloads and/or the performance of these workloads…”
4 Interpreted in view of Applicant’s specification, e.g. [0006], [0008], [0010], [0026], [0029]. The phrase “predicting, by the statistical modeling engine, based on the carbon emissions of a first cluster of the plurality of clusters, a breakpoint in which a ratio of energy consumption to computation load associated with the first cluster becomes non-linear” is interpreted as a colloquialism; i.e. the breakpoint is NOT based on the carbon emissions per se but is predicted by an undisclosed “statistical modeling engine” algorithm and is ONLY the point where ratio of energy consumption to computation load associated with the first cluster becomes non-linear;
Prosecution Insights