DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1, 5-8, 12-15, and 19-24 are pending.
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, 5-8, 12-15, and 19-24 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a method (i.e., a process), claim 8 is directed to a non-transitory computer-readable medium (i.e., machine or manufacture), and claim 15 is directed to a computing device (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 8, and 15 (claim 1 being representative) is:
transmitting an instruction to allocate one or more sessions to the determined first number, wherein the one or more sessions are selected to be allocated to the determined first number based on a predicted logoff time of each of said one or more sessions;
determining a first start time for the first maintenance window, wherein the first start time corresponds to the predicted logoff time of the one or more sessions such that the one or more sessions are predicted to log off before the first start time of the maintenance window;
detecting whether an actual logoff time of the one or more sessions on at least one of the determined first number is later than the predicted logoff time thereby causing a delay in the first start time;
in response to detecting that the actual logoff time of the one or more sessions on at least one of the determined first number is later than the predicted logoff time, evaluating, using a second capacity risk model and a current time value, whether powering off the first number with the delay in the first start time maintains capacity risk below the capacity risk level;
in response to the second capacity risk model indicating that the capacity risk remains below the capacity risk level, powering off the first number and performing maintenance on the first number during the first maintenance window, wherein performing the maintenance starts at the first start time and is completed in a time span after the first start time; (Examiner notes that while the underlined limitation was removed from claim 1, is still present in independent claims 8 and 15).
determining a next number in the pool to undergo maintenance during a next maintenance window, wherein a length of the next maintenance window is established based on the time span taken to perform the maintenance on the first number during the first maintenance window;
determining a next start time for the next maintenance window, wherein the next number and the next start time are determined based on the first risk model and on the capacity risk level; and
performing maintenance on the next number during the next maintenance window, starting at the next start time.
The abstract idea steps italicized above are directed to automating maintenance in a virtualized computing environment. This is a process that, under its broadest reasonable interpretation (BRI), could be performed mentally, including with pen and paper. This is further supported by paragraphs 0014 – 0016 of applicant’s specification as filed. If a claim limitation, under its BRI, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to evaluating and calculating computational capacity, which constitutes a process that, under its BRI, covers mathematical concepts. This is further supported by paragraphs 0031 – 0035 of applicant’s specification as filed. If a claim limitation, under its BRI, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: by a session allocator executing on one or more processors, to one or more hypervisors executing on the determined first number of hosts.
Claim 8 recites the following additional elements: A non-transitory computer-readable medium; one or more processors; by a session allocator executing on one or more processors, to one or more hypervisors executing on the determined first number of hosts.
Claim 15 recites the following additional elements: A computing device; one or more processors; a non-transitory computer-readable medium coupled to the one or more processors; by a session allocator executing on one or more processors, to one or more hypervisors executing on the determined first number of hosts.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs 0075 – 0081 of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Claims 5, 7, 12, 14, 19, and 21-24 further narrows the abstract idea with additional steps and/or description, in addition to including additional elements: hosts. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Regarding claims 6, 13, and 20, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Accordingly, claims 1, 5-8, 12-15, and 19-24 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5-8, 12-15, and 19-21 are rejected under 35 U.S.C. § 103 as being unpatentable over Maldaner (US 20110276695) in view of Madishetti (US 20210103644).
Claims 1, 8, and 15
Maldaner discloses (claim 1 being representative):
(claim 1) A method for maintenance of hosts in a pool of hosts, the method comprising: {“ In certain aspects described herein are methods and systems for automating a maintenance event rollout to a group of load balanced computers.” (paragraph 0006).}
(claim 8) A non-transitory computer-readable medium having instructions stored thereon, which in response to execution by one or more processors, cause the one or more processors to perform a method for maintenance of hosts in a pool of hosts, wherein the method comprises: {“The article of manufacture includes hardware logic as well as software or programmable code embedded in a computer readable medium that is executed by a processor.” (paragraph 0243).}
(claim 15) A computing device, comprising: {“It should be understood that the systems described above may provide multiple ones of any or each of those components and these components may be provided on either a standalone machine or, in some embodiments, on multiple machines in a distributed system.” (paragraph 0243).} one or more processors; and {“ In some embodiments, the processing unit 121 can include one or more processing cores.” (paragraph 0048)} a non-transitory computer-readable medium coupled to the one or more processors and having instructions stored thereon, which in response to execution by the one or more processors, cause the one or more processors to perform or control performance of operations for maintenance of hosts in a pool of hosts, wherein the operations comprise: {“The article of manufacture includes hardware logic as well as software or programmable code embedded in a computer readable medium that is executed by a processor.” (paragraph 0243).}
determining a next start time for the next maintenance window, wherein the next number of hosts and the next start time are determined based on the first risk model and on the capacity risk level {The maintenance agent may determine whether to remove a computer for maintenance based on utilization levels to maintain spare capacity, thereby considering “risk of service unavailability” if risk is too high (paragraph 0199). The load balancer determines whether the spare capacity is above a threshold before removing another computer for maintenance (paragraph 0233). The removal and timing of maintenance are based on spare capacity calculations and thresholds derived from risk assessments (paragraph 0236).}
performing maintenance on the next number of hosts during the next maintenance window, starting at the next start time {After determining that spare capacity is sufficient and the first computer is idle, the maintenance agent removes the computer from the plurality of computers for maintenance (paragraph 0236). After successful maintenance, the maintenance agent identifies the computer as up-to-date, i.e., it shows that maintenance operations are performed during the maintenance window initiated after the corresponding start time determination (paragraph 0241).}
Maldaner does not disclose, however, Madishetti, in a similar field of endeavor directed to power saving by predicting the times remote desktops and/or remote applications, teaches:
determining a first number of hosts in the pool to undergo maintenance during a first maintenance window based on a first risk model and on a capacity risk level {The desktop management system uses a machine learning model built from user login/logoff data and performance metrics to predict user activity and determine when to activate or deactivate servers (i.e., to undergo maintenance) (paragraphs 0049, 0056). It then determines whether the current capacity is adequate and activates or deactivates servers accordingly (paragraph 0056). By using this predictive model and capacity adequacy assessment, the system determines a number of hosts in a pool to suspend or maintain during an inactive (i.e., maintenance) window based on a first risk model (i.e., ML prediction model) and a capacity risk level (i.e., adequacy of host capacity).}
transmitting, by a session allocator executing on one or more processors, to one or more hypervisors executing on the determined first number of hosts, an instruction to allocate one or more sessions to the determined first number of hosts, wherein the one or more sessions are selected to be allocated to the determined first number of hosts based on a predicted logoff time of each of said one or more sessions {A ML module builds models from “login, disconnect, and logoff” history and determines “start time, length of activity, and end time for the user” (paragraphs 0044 – 0045, 0049). Based on these predictions, the system allocates capacity across hosts by activating or deactivating application servers to support user sessions (paragraphs 0019, 0056).}
determining a first start time for the first maintenance window, wherein the first start time corresponds to the predicted logoff time of the one or more sessions such that the one or more sessions are predicted to log off before the first start time of the maintenance window {The system determines when to suspend resources based on the predicted end of user activity (i.e., logoff). (paragraphs 0049, 0053)}
detecting whether an actual logoff time of the one or more sessions on at least one of the determined first number of hosts is later than the predicted logoff time thereby causing a delay in the first start time {The system monitors actual logoff events and compares them to expected activity windows. (paragraphs 0044, 0051 – 0052)}
in response to detecting that the actual logoff time of the one or more sessions on at least one of the determined first number of hosts is later than the predicted logoff time, evaluating, using a second capacity risk model and a current time value, whether powering off the first number of hosts with the delay in the first start time maintains capacity risk below the capacity risk level {The system evaluates capacity adequacy using performance and usage data before activating/deactivating hosts. (paragraphs 0049, 0056)}
in response to the second capacity risk model indicating that the capacity risk remains below the capacity risk level, powering off the first number of hosts and performing maintenance on the first number of hosts during the first maintenance window {The system deactivates (e.g., powers off/suspends) hosts when capacity is sufficient and users are inactive. The deactivation occurs based on an evaluation of capacity adequacy. {Paragraphs 0052, 0056)}
(claims 8and 15) wherein performing the maintenance starts at the first start time and is completed in a time span after the first start time {The system defines suspension timing tied to predicted activity windows and executes suspension during that period. (paragraphs 0048, 0052 – 0053)}
determining a next number of hosts in the pool to undergo maintenance during a next maintenance window, wherein a length of the next maintenance window is established based on the time span taken to perform the maintenance on the first number of hosts during the first maintenance window {The desktop management system monitors when desktops are suspended and resumed and uses historical operational data, including connection history and performance metrics, to predict future activation and deactivation timing (paragraphs 0049, 0056). This indicates that subsequent scheduling (i.e., next number of hosts to suspend for maintenance) and timing are based on the observed duration and performance of prior suspensions and resumptions.
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the maintenance managing and resources allocation features of Maldaner, to include the information technology management and maintenance features of Madishetti, to improve energy and cost efficiency in providing cloud-based desktops. (see paragraph 0004 of Madishetti).
Claims 5, 12, and 19
The combination of Maldaner and Madishetti teaches the limitations set forth above. Maldaner further discloses:
wherein the one or more sessions include remote desktop sessions that run on the first number of hosts {“Each of the computers 102 may host or provide one or more services, such as a web application, virtualized environment (such as a remote desktop session), database, virtual machines, etc. […] a computer 102 may be processing zero, one or more requests at any one time. A processing load is associated with each computer based on the number and type of request(s) the computer 102 is processing.” (paragraph 0192).}
Claims 6, 13, and 20
The combination of Maldaner and Madishetti teaches the limitations set forth above. Maldaner further discloses:
wherein the first risk model is based at least in part on historical data. {Utilization or spare capacity of the computer farm may be scaled or measured against historical records (paragraph 0196).}
Claims 7, 14, and 21
The combination of Maldaner and Madishetti teaches the limitations set forth above. Maldaner further discloses:
wherein the first number of hosts is determined based on the first risk model as being a number of hosts in the pool that are allowed to be shut down while keeping the capacity risk of the pool less than the capacity risk level. {The maintenance agent or load balancer determines whether utilization is low enough to safely remove hosts for maintenance without risking service availability. The “threshold” corresponds to a capacity risk level, and the determination of how many hosts can be removed is made on system utilization and demand patterns (paragraphs 0197-0198).}
Claims 22-24 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Maldaner and Madishetti, in further view of Neuse (US 20140019966).
Claims 22, 23, and 24
While the combination of Maldaner and Madishetti teaches the limitations set forth above, it does not explicitly teach, however, Neuse, in a similar field of endeavor directed to performance of computing systems, teaches:
accessing historical host utilization data for the pool; {The system supports collecting and storing historical utilization measurements from hosts over a long-term past via data collection agents and a data warehouse. (paragraphs 0093 – 0096)}
generating, from the historical host utilization data, a percentile-based host-in-use utilization curve over time; and {The system supports computing percentile resource consumption values across regularized time blocks and interval blocks, forming a time utilization profile. (paragraphs 0117, 0124 – 0126)}
selecting a contiguous time interval having a defined window length that does not intersect the utilization curve at or above a capacity risk threshold {The system evaluates utilization across time intervals against threshold conditions tied to capacity headroom and triggering events. (paragraphs 0076 – 0077, 0107)}
wherein the first number of hosts is computed as a difference between a total powered-on host count of the pool and a maximum host- in-use value within the selected contiguous time interval. {The difference between total host capacity and peak interval usage is computed by determining the total available capacity across all hosts and subtracting aggregated consumption values. (paragraphs 0133 – 0136)}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Maldaner and Madishetti to include the optimization of computing systems features of Neuse, to improve performance, service agreements and resource availability of cloud computing environments. (See paragraph 0003 of Neuse).
Response to Arguments
Applicant's arguments filed on 01/16/2026 have been fully considered but they are not persuasive.
Rejections under 35 U.S.C. §101
Applicant has conflated the abstract idea, considered at Step 2A Prong One, with the additional elements, considered at Step 2A Prong Two and Step 2B. Here, examiner identified the following steps as part of the abstract idea:
determining a first number in the pool to undergo maintenance during a first maintenance window based on a first risk model and on a capacity risk level;
transmitting an instruction to allocate one or more sessions to the determined first number, wherein the one or more sessions are selected to be allocated to the determined first number based on a predicted logoff time of each of said one or more sessions;
determining a first start time for the first maintenance window, wherein the first start time corresponds to the predicted logoff time of the one or more sessions such that the one or more sessions are predicted to log off before the first start time of the maintenance window;
detecting whether an actual logoff time of the one or more sessions on at least one of the determined first number is later than the predicted logoff time thereby causing a delay in the first start time;
in response to detecting that the actual logoff time of the one or more sessions on at least one of the determined first number is later than the predicted logoff time, evaluating, using a second capacity risk model and a current time value, whether powering off the first number with the delay in the first start time maintains capacity risk below the capacity risk level;
in response to the second capacity risk model indicating that the capacity risk remains below the capacity risk level, powering off the first number and performing maintenance on the first number during the first maintenance window;
determining a next number in the pool to undergo maintenance during a next maintenance window, wherein a length of the next maintenance window is established based on the time span taken to perform the maintenance on the first number during the first maintenance window;
determining a next start time for the next maintenance window, wherein the next number and the next start time are determined based on the first risk model and on the capacity risk level; and
performing maintenance on the next number during the next maintenance window, starting at the next start time.
The session allocator executing on one or more processors, to one or more hypervisors executing on the determined first number of hosts are considered additional elements, which are merely facilitating the tasks of said abstract idea. MPEP 2106.05(f) is clear that this generic recitation does not integrate the abstract idea into practical application and/or add significantly more. This interpretation holds whether the additional elements are viewed alone or in combination, where the combination of elements is nothing more than a network-enabled computing system. (Examiner notes that the phrase "well-understood, routine, and conventional" was not used in the eligibility analysis. Instead, examiner relied on MPEP 2106.05(f), as explained above.)
Accordingly, the rejection under 35 U.S.C. §101 is maintained.
Rejections under 35 U.S.C. §103
Applicant’s arguments are not persuasive.
Madishetti teaches predicting user activity based on historical login, logoff, and disconnect data. (See paragraphs 0026, 0044 – 0045). Under the BRI, predicting when a user will require a desktop reasonably comprises predicting when the user will cease use (i.e., logoff timing).
Madishetti further teaches allocating and activating computing resources based on predicted usage patterns. (See paragraphs 0019, 0056). Such allocation of servers and desktop pools based on predicted user activity reasonably corresponds to allocating sessions across hosts based on predicted timing.
Madishetti also teaches controlling system operation timing relative to predicted activity windows. (See paragraphs 0018, 0045 – 0046). Desktops are resumed prior to predicted active periods and suspended following logoff during inactive periods, which reads on determining operational timing based on predicted session termination.
Additionally, Madishetti teaches continuous monitoring of actual user activity and updating of predictive models. (See paragraphs 0044, 0049). Such monitoring reasonably comprises detecting deviations between predicted and actual logoff behavior.
Finally, Madishetti teaches dynamic adjustment of resources based on predicted usage and current load conditions. (See paragraph 0056, as well as adaptive model generation and updating, see paragraphs 0033, 0049). This reasonably correspond to evaluating whether system capacity remains sufficient prior to reducing resources and adapting subsequent operations.
Further, Examiner directs applicant’s attention to the claim analysis above. Accordingly, the rejection under 35 U.S.C. §103 is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20170371693 A1, which teaches: One example relates to a computer system that includes a plurality of host computers each executing a hypervisor. The computer system further includes a virtualization manager having an application programming interface (API) configured to manage the hypervisor on each of the plurality of host computers, the virtualization manager configured to create a virtual container host within a resource pool that spans the plurality of host computers. The computer system further includes a plurality of container virtual machines (VMs) in the virtual container host configured to consume resources in the resource pool. The computer system further includes a daemon appliance executing in the virtual container host configured to invoke the API of the virtualization manager to manage the plurality of container VMs in response to commands from one or more clients.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS F MONTALVO whose telephone number is (703)756-5863. The examiner can normally be reached Monday - Friday 8:00AM - 5:30PM; First Fridays OOO.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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