DETAILED ACTION
Claims 1-20 are presented for examination.
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 .
Claim Rejections - 35 USC § 102
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 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, 8-9 and 15-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wadekar et al. (US 2023/0168929 A1).
As to claim 1, Wadekar teaches a method of resource allocation in a cloud computing environment, executable by a processor, the method comprising:
receiving a request for resource allocation from a user in a cloud computing environment (Device 220 is configured to receive one or more reservation requests from one or more users/vendors; paragraph [0042], [0058] and when user 230 wants to deploy an application, he sends a resource request, such as resource request 240 (e.g., request for 10 CPU cores for running the application) to resource manager; paragraph [0065]);
determining a profile for the user based on one or more metrics (Reservation record includes data corresponding to users of devices. Each user is associated with a particular vendor, … users and resource reservations are managed at the tenant level, and tenant-level access is referred to as role-based access; paragraph [0060] and [0067]);
assigning a workload allocation to the user based on the determined profile matching one or more clusters of other users (resource manager determines that cluster has 10 available CPU cores, resource manager will allocate the 10 available CPU cores to user. Accordingly, user uploads the application to cloud system and executes the application on the cloud system; paragraph [0065]);
monitoring a usage value of the assigned workload allocation to the user (in response to an application being deployed as a tenant on one or more of set of clusters, and the deployed application being executed with reserved resources, the resources required for processing the application are monitored by resource manager via a monitoring engine. In some embodiments, monitoring engine records real-time metrics in a time series database; paragraph [0078]); and
upgrading the user immediately to a higher workload allocation based on the usage value exceeding a threshold value (assigning more intra-tenant resources to optimize the performance of the application; paragraph [0081], exceeds the intra-tenant limit threshold, increase the intra-tenant limit; paragraphs [0084]-[0085], [0096]).
As to claim 2, Wadekar teaches the method of claim 1, further comprising downgrading the user gradually based on determining that the user is underutilizing the allocated resources (reclaiming overly-reserved resources; paragraph [0081]).
As to claim 8, it is the same as the method claim 1 above except this is a computer system claim, and therefore is rejected under the same ground of rejection.
As to claim 9, see rejection of claim 2 above.
As to claim 15, it is the same as the method claim 1 above except this is a non-transitory computer readable medium claim, and therefore is rejected under the same ground of rejection.
As to claim 16, see rejection of claim 2 above.
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.
Claims 3-7, 10-14 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wadekar et al. (US 2023/0168929 A1) in view of Chawla (US 2023/0179540 A1).
As to claim 3, Wadekar does not teach the method of claim 2, wherein the determination that the user is underutilizing the allocated resources corresponds to a determination that the user is idle or inactive for a predetermined amount of time.
However, Chawla teaches determine that the user is underutilizing the allocated resources for a predetermined amount of time (At operation 515, the device can identify one or more machines with low resource usage (e.g., consumption or utilization) over a period of time associated with one or more users; paragraph [0135]).
Although Chawla does not teach that the user is idle or inactive, it would have been obvious that a device with low resource usage occurs only when the device is not being used by the application, in other words, the user is idle or inactive for a period of time.
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 teaching of Chawla to the system of Wadekar because both are directed to optimizing resources allocation and usage of users, and Chawla teaches a method of effectively allocating computing resources to end users (abstract).
As to claim 4, Wadekar does not teach wherein the profile for the user is determined based on calculating a nearest neighbor distance between the user and a plurality of other users in the one or more clusters.
However, Chawla teaches the profile for the user is determined based on calculating a nearest neighbor distance between the user and a plurality of other users in the one or more clusters (groups, profile; paragraph [0085] and [0089]).
As to claim 5, Wadekar as modified by Chawla teaches the method of claim 1, further comprising assigning a new user to one of the one or more clusters based on identifying a role associated with the new user matches a largest number of users within the cluster (see Chawla: paragraph [0089]).
As to claim 6, Wadekar teaches the method of claim 1, wherein the metrics comprise a CPU usage amount, a memory usage amount, a user role (paragraphs [0060], [0067], [0107]).
Wadekar does not teach the metrics comprise past usage for a given time period, a time and a day of the week, and types of API requests made by the user.
Chawla teaches the metrics comprise past usage for a given time period, a time and a day of the week, and types of API requests made by the user (paragraph [0100]).
As to claim 7, Wadekar as modified by Chawla teaches the method of claim 1, wherein the profile for the user is determined based on the metrics having predefined weight values (see Chawla: 1% high or 1% low, 10%, 20%, etc. paragraph [0100], [0103]).
As to claims 10-14, see rejections of claims 3-7 above, respectively.
As to claims 17-20, see rejections of claims 3-6 above, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vasic et al. (US 2013/0185729 A1) teaches systems, methods, and apparatus for managing resources assigned to an application or service. A resource manager maintains a set of workload classes and classifies workloads using workload signatures. In specific embodiments, the resource manager minimizes or reduces resource management costs by identifying a relatively small set of workload classes during a learning phase, determining preferred resource allocations for each workload class, and then during a monitoring phase, classifying workloads and allocating resources based on the preferred resource allocation for the classified workload.
Borthakur (US 9,356,883 B1) teaches At least one workflow comprising end-user interactions with an application implemented using provider network resources is identified by a resource allocation service of the provider network. The service collects performance metrics associated with the end- user workflow. If a performance metric meets a threshold criterion, a re-evaluation of the resources assigned to the application is initiated. Configuration changes to modify the set of provider network resources assigned to the application are implemented in accordance with a result of the resource re-evaluation.
LI et al. (CN 109684065 A) teaches a resource scheduling method, wherein the method comprises: according to the pre-defined scheduling platform policy, and resource description information and user scheduling policy in the resource scheduling request, determining the target scheduling policy according to pre-constructing the label with label condition relation, calculating the node attribute information. and label the target scheduling strategy, determining the candidate computing node, label in the preference requirement of target according to the scheduling strategy, selecting target computing node from the candidate computing nodes for resource distribution system distributes calculation resource according to the scheduling request. Through the technical solution of this invention can flexibly dispatch the relevant scheduling policy, so as to meet the individual scheduling demand of user.
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/DIEM K CAO/Primary Examiner, Art Unit 2196
DC
October 29, 2025