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 .
Response to Arguments
Applicant’s arguments, see page 6, filed 20 November 2025, with respect to the claim objections have been fully considered and are persuasive. The claim objections of claims 1, 5-7, and 9 has been withdrawn.
Applicant’s arguments, see page 6, filed 20 November 2025, with respect to the rejection under 35 U.S.C. § 112 have been fully considered and are persuasive. The rejection under 35 U.S.C. § 112 of claims 1-7 and 9 has been withdrawn.
Applicant’s arguments, see page-7, filed 20 November 2025, with respect to the rejection under 35 U.S.C. § 1 have been fully considered and are not persuasive. The rejection under 35 U.S.C. § 1 of claims 1- has not been withdrawn.
Applicant contends that “the claims involve generating a model when the application workload has a predetermined difficulty of collection or has a predetermined collectability,” which applicant contends integrates any recited abstract ideas into a practical application. Remarks at 7.
The Examiner respectfully disagrees. The “alternatively collect the host workload” amounts to insignificant extra-solution activity of mere data gathering. MPEP § 2106.05(g). Specifically, the recited “collect[ing]” corresponds the “[o]btaining information” in CyberSource v. Retail Decisions, Inc. See Id. For the same reason, the recited “collect[ing]” does not amount to significantly more than an abstract idea.
Additionally, the “generat[ing] a model” amounts to a mathematical concept. The specification discloses that “[t]he workload prediction model is a model that outputs, when a future specified time zone is specified, the application workload in the specified time zone and the host workload in the specified time zone.” US 2023/0367649 at ¶ 66. Accordingly, the “generat[ing] a model” is directed to an abstract idea. MPEP § 2106.04(a)(2).
Applicant’s arguments with respect to claim(s) 1-5, 7, and 9, regarding the rejection under 35 U.S.C. § 103, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-5, 7, and 9 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, it recites “apply the specified time period and the workload to a workload prediction model to calculate a workload in the specified time period, apply the workload and the current resource allocation in the specified time period to a performance model to calculate a first performance of the VDI apparatus, and determine determines whether or not the first performance meets the performance requirement.” The calculating and determining amount to mathematical concepts and a mental process.
The calculating performs a mathematical concept to calculate “a workload in [a] specified time zone” and a “first performance of the VDI apparatus.” The determining may be a mathematical concept of computing a comparison between the calculated values and performance requirements or mentally determining whether the calculated values meet performance requirements. Accordingly, claim 1 recites an abstract idea.
Claim 1 does not recite any limitations that apply the calculating and determining into any application, much less a practical application. According, under Step 2A Prong 2 (MPEP § 2106.04(d)), the claims are not integrated into a practical application.
Under Step 2B (MPEP § 2106.05), claim 1 additional recites “
an input unit that receives a performance requirement defining performance to be satisfied by the VDI apparatus and a specified time zone indicating a time zone in which the performance requirement is satisfied;
a resource allocation collection and storage unit that stores a current resource allocation including a number of virtual machines included in a host of the VDI apparatus and a virtual calculation resource allocated to a virtual machine; [and]
a workload collection and storage unit that stores a workload of the VDI apparatus.
The limitations amount to well understood routing conventional activity, as indicated by Applicant’s disclosure. Spec ¶ 48 (“the workload prediction model generation unit 221 can generate the workload prediction model using an existing method such as long short time memory (LSTM)”).
The “alternatively collect the host workload” amounts to insignificant extra-solution activity of mere data gathering. MPEP § 2106.05(g). Specifically, the recited “collect[ing]” corresponds the “[o]btaining information” in CyberSource v. Retail Decisions, Inc. See Id. For the same reason, the recited “collect[ing]” does not amount to significantly more than an abstract idea.
Additionally, the “generat[ing] a model” amounts to a mathematical concept. The specification discloses that “[t]he workload prediction model is a model that outputs, when a future specified time zone is specified, the application workload in the specified time zone and the host workload in the specified time zone.” US 2023/0367649 at ¶ 66. Accordingly, the “generat[ing] a model” is directed to an abstract idea. MPEP § 2106.04(a)(2).
Accordingly, claim 1 is directed to ineligible subject matter under 35 U.S.C. § 101.
Regarding claim 2, it further recites additional calculating and determining steps, which are directed to mathematical concepts and/or mental processes. Further, the storing amounts to insignificant extra-solution activity of mere data gathering and the selecting amounts to a mental process. Accordingly, claim 2 is directed to ineligible subject matter under 35 U.S.C. § 101.
Regarding claims 3 and 4, they further refine the inputs/outputs of the mathematical calculations. Accordingly, they are directed to an abstract idea and ineligible under 35 U.S.C. § 101.
Regarding claim 5, the presenting amounts to insignificant extra solution activity. See MPEP § 2106.05(g). Courts have found “[p]resenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers” to amount to insignificant extra solution activity. Id. (citing OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Here, the presenting corresponds to the presenting in OIP Technologies; therefore, claim 5 does not recite significantly more than an abstract idea and is directed to ineligible subject matter under 35 U.S.C. § 101.
Claims 7 and 9 correspond to claim 1; therefore, they are rejected for the same reason.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 7, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feller (US 2016/0300142) and further in view of Beaty (US 8,234,236) and further in view of Wood NPL (Wood T et al (2007) Black-box and gray-box strategies for virtual machine migration. In: Proc of NSDI).
Regarding claim 1, Feller teaches: A virtual desktop (VDI) resource allocation decision apparatus that decides resource allocation of a VDI apparatus (¶ 39, “System 100 includes a virtualized infrastructure 120a-m. Virtualized infrastructure 120 represents one or more computing devices and the virtualization software executing on the computing devices such that multiple services may execute on the computing devices while being isolated from each other”), the VDI resource allocation decision apparatus comprising: processing circuitry configured to:
receive a performance requirement defining performance to be satisfied by the VDI apparatus (¶ 82, “The SLA store 146 then sends a response 711 back with the SLA definitions”) and a specified time period indicating a time period in which the performance requirement is satisfied (¶ 82, “each SLA definitions include a function of metrics over a period of time, an operator, and a value”);
store a current resource allocation including a number of virtual machines included in a host of the VDI apparatus (¶ 86 “one or more metrics previously collected from a virtualized infrastructure . . . the virtualized infrastructure comprises . . . one or more master workers, and one or more slave workers” and ¶ 87, “At block 904, the server end station receives real time metrics from the virtualized infrastructure”) and a virtual calculation resource allocated to a virtual machine (¶ 62 “ach worker, whether a master or slave, and each service controller, may execute on a virtual machine, such as virtual machines 271-274. In some embodiments, the service controllers and workers execute on bare metal on the server end stations directly on processing hardware (e.g., processing hardware 235-238”);
store a workload of the VDI apparatus (¶ 32, “Services are continuously monitored and resource allocations are adjusted when needed” and ¶ 5, “each service controller is associated with one or more workloads”); and
apply the specified time period and the workload to a workload prediction model to calculate a workload in the specified time period (¶ 34, “To estimate the benefits of the adaptation actions it integrates four prediction modules (performance, power, cost, and workload) and one optimization module”), apply the workload and the current resource allocation in the specified time period to a performance model to calculate a first performance of the VDI apparatus (¶ 34, “To estimate the benefits of the adaptation actions it integrates four prediction modules (performance, power, cost, and workload) and one optimization module”), and determine whether or not the first performance meets the performance requirement (¶ 37, “embodiments of the invention provide for methods, systems, and apparatuses for determining whether a service level agreement (SLA) violation has occurred or is expected to occur, based on one or more insight models that model a particular behavior in the cloud or virtualized environment”).
Feller does not expressly teach; however, Beaty discloses: a memory that stores statistical information about workloads (col. 10:6-9, “Profiler 124 may employ library/memory 123 which stores statistical information, models and other information to assist in profiling activity/idleness”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of a memory that stores statistical information about workloads, as taught by Beaty, in the same way to the service monitoring, as taught by Feller. Both inventions are in the field of monitoring virtual system workloads, and combining them would have predictably resulted in “monitoring and efficiently handling interactive events,” as indicated by Beaty (col. 1:16-17).
Feller and Beaty do not teach; however, Wood NPL discloses: the workload of the VDI apparatus (page 229, Section 1 (Introduction), “Data centers—server farms that run networked applications”) includes an application workload occurring on the virtual machine and an operating system of the virtual machine (page 229, Section 1, “applications run on virtual servers that are constructed using virtual machines”), and a host workload occurring at the host (page 232, Section 3.2, “Hosting environments, for instance, run third-party applications, and in some cases, third-party installed OS distributions”); and
alternatively collect the host workload (page 231, Section 3.1, “The monitoring engine tracks the usage of each resource over a measurement interval I and reports these statistics to the control plane at the end of each interval”) and generate a model (page 232, Section 3.3, “Whereas time-series profiles are used by the hotspot detector to spot increasing utilization trends, distribution profiles are used by the migration manager to estimate peak resource requirements and provision accordingly”) when the application workload has a predetermined difficulty of collection or has a predetermined collectability (page 231, Section 3.1, “In a pure black-box approach, all usages must be inferred solely from external observations and without relying on OS-level support inside the VM”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of the workload of the VDI apparatus includes an application workload occurring on the virtual machine and an operating system of the virtual machine, and a host workload occurring at the host; and alternatively collect the host workload and generate a model when the application workload has a predetermined difficulty of collection or has a predetermined collectability, as taught by the Wood NPL, in the same way to the VDI apparatus, as taught by Feller and Beaty. Both inventions are in the field of forecasting virtual workloads, and combining them would have predictably resulted in a system configured to “resolve single server hotspots within 20 seconds and scales well to larger, data center environments,” as indicated by the Wood NPL (page 229, abstract).
Regarding claim 2, Feller teaches: The VDI resource allocation decision apparatus according to claim 1, wherein the processing circuitry is further configured to:
store settable resource allocations (¶ 58, “The resolution that each service controller applies to the particular type of issue may be preconfigured from a library of preconfigured actions (e.g., add more resources from a spare resource pool or throttle usage)”), and
select one of the settable resource allocations when the first performance calculated with the current resource allocation does not meet the performance requirement (¶ 34, “The outputs of all the prediction modules are fed into an optimization module, which decides on the optimal set of actions using a heuristic algorithm”), apply a workload in the specified time period and the selected settable resource allocation to the performance model to calculate second performance of the VDI apparatus (¶ 76, “At block 421, the resource prediction insight model is retrieved for a particular behavior. As this model is used to predict a possible abnormal state”), and determine whether or not the second performance meets the performance requirement (¶ 37, “embodiments of the invention provide for methods, systems, and apparatuses for determining whether a service level agreement (SLA) violation has occurred or is expected to occur, based on one or more insight models that model a particular behavior in the cloud or virtualized environment”).
Regarding claim 3, Feller teaches: The VDI resource allocation decision apparatus according to claim 2, wherein the workload prediction model is a model that outputs a workload in a specified time period when the specified time period is input (¶ 7, “the SLA store includes one or more SLAs, wherein each SLA includes at least a function of metrics over a period of time” and ¶ 50, “continuously analyzes the real time metrics from the virtualized infrastructure 120 with the insight models to determine wither a service is in an abnormal state or is expected to enter an abnormal state”), and the performance model is a model that outputs performance in the specified time period when a workload in the specified time period and resource allocation are input (¶ 34, “Such a system leverages modeling to come up with a good service model, which is used to predict performance”).
Regarding claim 4, Feller teaches: The VDI resource allocation decision apparatus according to claim 2, wherein the first performance and the second performance each comprise application performance experienced by a user (¶ 32, “Some methods of enforcing SLAs may include systems that that target both high resource utilization and application performance”) using the VDI apparatus and host performance observable and evaluable by the host (¶ 35, “it allows services to specify multiple levels of QoS as specific service states. For such services, the framework dynamically provisions underutilized resources to enable elevated QoS levels, thereby improving system efficiency”).
Regarding claim 5, Feller teaches: The VDI resource allocation decision apparatus according to claim 1, wherein the processing circuitry is further configured to present when the first performance satisfies the performance requirement, a resource allocation used to calculate the first performance to a VDI operator (¶ 41, “These metrics may be sent out via the message bus 160. Examples of metrics include CPU usage, RAM usage, network usage and/or statistics, storage medium usage, downtime, usage statistics, warnings, failure indicators, and so on”).
Claims 7 and 9 recite commensurate subject matter as claim 1. Therefore, they are rejected for the same reasons.
Conclusion
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 JACOB D DASCOMB whose telephone number is (571)272-9993. The examiner can normally be reached M-F 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at (571) 272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACOB D DASCOMB/Primary Examiner, Art Unit 2198
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