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 .
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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Response to Arguments
The applicant’s remarks filed July 20, 2026 have been fully considered but are not persuasive. Applicant argues that the claims are allowable because:
“Though Fu discloses process execution profile indicating the usage of resources by a process, Fu does not disclose generation of a performance profile of a process, which indicates the performance characteristics of the function with respect to a given amount of allocated resources.” (Applicant’s Remarks, Pg. 9).
Examiner respectfully disagrees. Fu’s execution profiles are generated with respect to a given amount of allocated because it the captures resource utilization of allocated resources. ([0036], Each of the process execution profiles 230 indicates a variety of information related to the execution of a corresponding process 110. For example, a process execution profile 230 may indicate minimal, average, and maximum processor, memory, and/or I/O usage on a set of servers 102 for a process 110, affinity rules for grouping processes 110 with the same set of resources, anti-affinity rules for excluding processes 110 from being grouped with the same set of resources, and/or priority information for assigning a priority for processing/execution of a process 110). The utilization information captured by Fu’s execution profiles indicate performance characteristics of the function with respect to its resource utilization.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4, 6, 12-16, 18, and 20 are rejected under 35 U.S.C. 102 as being anticipated by Fu et al. (WO 2019135703).
As per claim 1, Fu teaches the invention as claimed including a method by a computing system to determine a resource configuration for a function as a service application, the method comprising:
generating, for each of one or more functions of the application, a performance profile of the function ([0036], Generating process execution profiles 230 can be performed by the local analytics unit 206 and/or the long-term analytics unit 216; Examiner Note: Fu’s processes are a part of a larger application: [0031], processes 1 l0i-l l0.sub.3 are computer software designed to perform a group of coordinated functions, tasks, or activities and may be part of a larger application running on respective client devices 108i-108.sub.3) that indicates performance characteristics of the function with respect to an amount of resources allocated to the function ([0036], Each of the process execution profiles 230 indicates a variety of information related to the execution of a corresponding process 110... a process execution profile 230 may indicate minimal, average, and maximum processor, memory, and/or I/O usage on a set of servers 102 for a process 110, affinity rules for grouping processes 110 with the same set of resources, anti-affinity rules for excluding processes 110 from being grouped with the same set of resources, and/or priority information for assigning a priority for processing/execution of a process 110);
determining a resource configuration for the application based on an optimization objective ([0031], process agents 112i-112.sub.3 may request the datacenter system 100 reserve/allocate a set of resources for processing/execution of their respective processes 1 lOi-l IO.sub.3 based on continually optimized placement policies and/or process execution profiles; and [0034], placement policies 218 are goals/objectives that are associated with a placement node 114 and/or a process 110) and the performance profiles of the one or more functions ([0036], In addition to placement policy 218 information, the placement node 114 may receive process execution profiles 230 from the placement services 118 for determining resource allocation for processes 110; and [0037], placement agent 202 determines a load estimation 224 of the local placement node 114 and the demand estimation 226 of the process 110 from the local analytics unit 206...the demand estimation 226 of the process 110 may be determined based on a process execution profile 230 associated with the process 110); and
deploying the application using the resource configuration determined for the application ([0037], next candidate/contact will repeat the process described above until placement of the process 110 is achieved. The term “process placement” used in the described embodiments may correspond to the alternative term “task scheduling”; and [0047], When the current placement node 114 determines at operation 308 that the resources managed by the placement node 114 can fulfill the demand of the process 110, the method 300 moves to operation 310 for the current placement node 114 to fulfill the placement request 212 locally (i.e., using the resources managed by the placement node 114)).
As per claim 2, Fu teaches, wherein the resource configuration for the application is determined based on executing an optimization algorithm using the optimization objective and the performance profiles of the one or more functions ([0034], the placement policies 218 are fulfilled through the use of a variety of placement algorithms, and the placement policies are 218 used by the placement agent 202 to make resource allocation/placement decisions for processes 110. A placement policy 218 is configurable and can be altered to utilize a variety of corresponding different placement algorithms; and [0036], In addition to placement policy 218 information, the placement node 114 may receive process execution profiles 230 from the placement services 118 for determining resource allocation for processes 110).
As per claim 4, Fu teaches, further comprising:
determining whether a performance profile of a particular function of the one or more functions accurately reflects an actual performance of the particular function ([0038], an intermediate score 234, which is associated with a corresponding process execution profile 230, is generated by the local analytics unit 206. The intermediate score 234 indicates either successful execution of the process 110 (e.g., a positive value for the intermediate score 234) or unsuccessful execution of the process 110 (e.g., a negative value for the intermediate score 234)... the performance score 228 will be used for long-term analytics performed by the long-term analytics unit 216, which can add/delete/update process execution profiles 230);
updating the performance profile of the particular function in response to a determination that the performance profile of the particular function does not accurately reflect the actual performance of the particular function ([0038], the performance score 228 will be used for long-term analytics performed by the long-term analytics unit 216, which can add/delete/update process execution profiles 230. For example, in response to a low performance score 228 for a process 110 using an existing process execution profile 230, the long-term analytics unit 216 can add a new process execution profile 203 that is different from the previous process execution profile 230 (e.g., increase a scale of the process 110) and optionally delete the previous process execution profile 230);
determining an updated resource configuration for the application based on the updated performance profile of the particular function ([0038], in response to a low performance score 228 for a process 110 using an existing process execution profile 230, the long-term analytics unit 216 can add a new process execution profile 203 that is different from the previous process execution profile 230 (e.g., increase a scale of the process 110) and optionally delete the previous process execution profile 230); and
assigning the updated resource configuration to the application ([0039], each new instance of the same binary file will be executed repeatedly as new requests/tasks are received. By monitoring execution of the binary file/process 110 via the monitoring unit 208, a process execution profile 230 of the resource demands can be determined for the process 110. The process execution profile 230 can then be used to predict the resource demands of a new process 110 that will execute the same binary file. As noted above, in response to a low intermediate score 234 (e.g., an intermediate score 234 that is negative) for a process 110 using a particular process execution profile 230, a new process execution profile 230 may be added/generated. The new process execution profile 230 may seek to more accurately predict resource/execution requirements of the process 110).
As per claim 6, Fu teaches, further comprising:
determining, for each of the one or more functions, a feasible resource configuration range for the function ([0036], Each of the process execution profiles 230 indicates a variety of information related to the execution of a corresponding process 110. For example, a process execution profile 230 may indicate minimal, average, and maximum processor, memory, and/or I/O usage on a set of servers 102 for a process 110).
As per claim 12, Fu teaches, wherein the resource configuration for the application is a memory configuration for the application ([0036], a process execution profile 230 may indicate minimal, average, and maximum processor, memory, and/or I/O usage on a set of servers 102 for a process 110).
As per claim 13, this is the “non-transitory machine-readable media claim” corresponding to claim 1 and is rejected for the same reasons. The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 14, this claim is similar to claim 2 and is rejected for the same reasons.
As per claim 15, this is the “computing device claim” corresponding to claim 1 and is rejected for the same reasons. The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 16, this claim is similar to claim 2 and is rejected for the same reasons.
As per claim 18, this claim is similar to claim 4 and is rejected for the same reasons.
As per claim 20, this claim is similar to claim 4 and is rejected for the same reasons.
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.
Claims 3, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fu as applied to independent claims 1, 13, and 15 and in further view of Boutnaru (US 20180349159).
As per claim 3, Fu fails to specifically teach, wherein the optimization algorithm is one of a Tabu search algorithm, a particle swarm optimization algorithm, and a genetic algorithm.
However, Boutnaru teaches, wherein the optimization algorithm is one of a Tabu search algorithm, a particle swarm optimization algorithm, and a genetic algorithm ([0040], the system may implement one or more genetic or evolution algorithm techniques on the subset of runtime environments selected at operation 305 to create a new set of runtime environments).
Fu and Boutnaru are analogous because they are each related to resource allocation. Fu teaches a method for optimized resource allocation utilizing configurable execution profiles and various optimization algorithms. (Abstract, receiving, by a first placement node, a placement request corresponding to a process; determining a load estimation for resources managed by the first placement node and a demand estimation for the process based on a process execution profile for the process; determining whether the placement request can be fulfilled with the resources managed by the first placement node based on the demand estimation of the process and the load estimation of the resources managed by the first placement node; executing the process with the resources managed by the first placement node in response to determining that the placement request can be fulfilled with the resources managed by the first placement node; and [0034], placement policies 218 are fulfilled through the use of a variety of placement algorithms, and the placement policies are 218 used by the placement agent 202 to make resource allocation/placement decisions for processes 110). Boutnaru teaches a method of optimized resource allocation utilizing a genetic algorithm. (Abstract, system and method for optimizing runtime environments for applications by running the applications in a plurality of runtime environments and iteratively selecting and creating new runtime environments based on a fitness score determined for the plurality of runtime environments; and [0040], the system may implement one or more genetic or evolution algorithm techniques on the subset of runtime environments selected at operation 305 to create a new set of runtime environments). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the Fu’s resource allocation method, which implements various optimization algorithms would be modified with Boutnaru’s genetic search algorithm resulting in a system that efficiently optimizes a resource allocation profile for a workload. Therefore, it would have been obvious to combine the teachings of Fu and Boutnaru.
As per claim 17, this claim is similar to claim 3 and is rejected for the same reasons. The same motivation used in the rejection of claim 3 is applicable to the instant claim.
As per claim 19, this claim is similar to claim 3 and is rejected for the same reasons. The same motivation used in the rejection of claim 3 is applicable to the instant claim.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Fu as applied to independent claims 1, 13, and 15 and in further view of Shinde et al. (US 20200134423).
As per claim 5, Fu fails to specifically teach wherein the performance profile of the particular function is determined not to accurately reflect the actual performance of the particular function based on a determination that an estimated performance measurement of the particular function indicated by the performance profile of the particular function deviates from an actual performance measurement of the particular function by more than a threshold amount.
However, Shinde teaches, wherein the performance profile of the particular function is determined not to accurately reflect the actual performance of the particular function based on a determination that an estimated performance measurement of the particular function indicated by the performance profile of the particular function deviates from an actual performance measurement of the particular function by more than a threshold amount ([0098], a potential anomaly (referred to herein as a “deviation event”) for particular hardware in the datacenter is detected when the actual usage of the hardware differs (e.g., by a threshold percentage) from the predicted utilization generated by any applicable kind of deployed ML model in hierarchy of models 300).
Fu and Shinde are analogous because they are each related to resource allocation. Fu teaches a method for optimized resource allocation utilizing configurable execution profiles and various optimization algorithms. Shinde teaches a method for resource allocation based on predicted resource utilization and monitoring resource allocations for deviations from predicted performance levels. ([0091], when the forecasts from models of the backbone switches are reasonably accurate (e.g., there is less than a threshold percentage of deviation of predictions from actual usage), the forecasted traffic from all of the models are added together to produce a datacenter-level traffic forecast. According to an embodiment, which deals well with backbone switch predictions that include a relatively higher level of deviation, an additional datacenter-level ML model is trained to take the forecasted traffic from each backbone switch as input and to produce datacenter-level traffic predictions as output. Gathered historical data regarding datacenter-level traffic is used to train and validate this datacenter-level mode; and ; and [0105], ML service 250 makes a record of all detected deviation events for each device in datacenter 200, i.e., in a device-specific utilization record. A record of a deviation event includes one or more of: a timestamp of the event, a prediction for the hardware, actual utilization of the hardware, a previous workload or OS type, a subsequent workload or OS type, a deviation threshold percentage, etc.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the resource allocation method of Fu would be modified with Shinde’s mechanism for determining the efficiency of a runtime environment configuration based on deviations from predicted performance levels resulting in a system that performs optimized resource allocation and evaluates resource allocation based on a deviation from expected performance. Therefore, it would have been obvious to combine the teachings of Fu and Shinde.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Fu as applied to independent claims 1, 13, and 15 and in further view of Zorn et al. (US 8856333).
As per claim 7, Fu teaches, wherein a performance profile of a particular function of the one or more functions is generated based on iteratively executing the application using a plurality of feasible resource configurations within a feasible resource configuration range of the particular function ([0036], Generating process execution profiles 230 can be performed by the local analytics unit 206 and/or the long-term analytics unit 216 that classify processes 110 via learning the history demands of successfully executed processes 110; and [0039], each new instance of the same binary file will be executed repeatedly as new requests/tasks are received. By monitoring execution of the binary file/process 110 via the monitoring unit 208, a process execution profile 230 of the resource demands can be determined for the process 110. The process execution profile 230 can then be used to predict the resource demands of a new process 110 that will execute the same binary file...accurate predictions rely on process execution profiles 230 that model the resource demand of processes 110 over time).
Fu fails to specifically teach, wherein a performance profile of a particular function of the one or more functions is generated based on ... test cases for the application.
However, Zorn teaches, wherein a performance profile of a particular function of the one or more functions is generated based on ... test cases for the application (Column 2, Lines 10-16, Optimization includes modification of parameters until reaching a pre-determined threshold. This optimization may use actual or simulated data to test alternatives which are permissible within the constraints of the execution template. Once an optimized execution template is determined, it may be used to generate an updated execution plan, which may then be deployed in the datacenter(s)).
Fu and Zorn are analogous because they are each related to resource allocation. Fu teaches a method for optimized resource allocation utilizing configurable execution profiles and various optimization algorithms. Zorn teaches a method of optimized resource allocation utilizing execution templates and testing said execution templates. (Abstract, Deployment and execution of a service in a multiple datacenter environment may be facilitated using datacenter execution templates. ...execution template may then be used to generate an execution plan, which is then executed by datacenters which run the service. The execution template may be optimized after deployment of the service, allowing for ongoing adjustment in response to changing business, regulatory, and operational factors; and Column 2, Lines 10-14, Optimization includes modification of parameters until reaching a pre-determined threshold. This optimization may use actual or simulated data to test alternatives which are permissible within the constraints of the execution template). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the Fu’s resource allocation method, which utilizes execution profiles, would be modified with Zorn’s mechanism for testing its execution profiles resulting in a system that is able to optimize resource allocation through testing execution profiles. Therefore, it would have been obvious to combine the teachings of Fu and Zorn.
As per claim 8, Fu teaches, wherein a performance profile of a particular function of the one or more functions is generated based on iteratively executing the particular function using a plurality of feasible resource configurations within a feasible resource configuration range of the particular function ([0036], Generating process execution profiles 230 can be performed by the local analytics unit 206 and/or the long-term analytics unit 216 that classify processes 110 via learning the history demands of successfully executed processes 110; and [0039], accurate predictions rely on process execution profiles 230 that model the resource demand of processes 110 over time) and test cases for the particular function.
Fu fails to specifically teach wherein a performance profile of a particular function of the one or more functions is generated based on... test cases for the particular function.
However, Zorn teaches, wherein a performance profile of a particular function of the one or more functions is generated based on... test cases for the particular function (Column 2, Lines 10-16, Optimization includes modification of parameters until reaching a pre-determined threshold. This optimization may use actual or simulated data to test alternatives which are permissible within the constraints of the execution template. Once an optimized execution template is determined, it may be used to generate an updated execution plan, which may then be deployed in the datacenter(s)).
The same motivation used in the rejection of claim 7 is applicable to the instant claim.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Fu-Zorn as applied to dependent claim 8 and in further view of Shavro (US 11030318).
As per claim 9, the combination of Fu-Zorn fails to specifically teach, further comprising: generating the test cases for the particular function based on modifying a configuration of the application to insert an input recorder function before the particular function and executing the application with the modified configuration to collect input data recorded by the input recorder function.
However, Shavro teaches, further comprising:
generating the test cases for the particular function based on modifying a configuration of the application to insert an input recorder function before the particular function (Column 5, Lines 64-65, sensor injects code into the application process using instrumentation and/or debugging; and Column 6, Line 67-Column 7, Line 3, monitoring behavior of the application service request includes instrumentation by the sensor (e.g., 108) to inject code at specific locations of interest) and executing the application with the modified configuration to collect input data recorded by the input recorder function (Column 6, Lines 64-65, the sensors can track data handled by the application code and monitor its processing; and Column 6, Line 67-Column 7, Line 3, monitoring behavior of the application service request includes instrumentation by the sensor (e.g., 108) to inject code at specific locations of interest).
The combination of Fu-Zorn and Shavro are analogous because they are each related to resource allocation. Fu teaches a method for optimized resource allocation utilizing configurable execution profiles and various optimization algorithms. Zorn teaches a method of optimized resource allocation utilizing execution templates and testing said execution templates. Shavro teaches a method of testing applications using instrumentation (Abstract, application service request is reconstructed to include the altered application service request parameter. The behavior of the application is analyzed while executing the reconstructed application service request to detect a security vulnerability; and Column 2, Lines 45-47, utilize instrumentation and/or automatic debugging logic to monitor application traffic to detect particular patterns). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the testing mechanisms taught by the combination of Fu-Zorn would be modified with Shavro’s instrumentation mechanism for testing resulting in a system that is able to perform application testing using instrumentation. Therefore, it would have been obvious to combine the teachings of the combination of Fu-Zorn and Shavro.
As per claim 10, Fu teaches, wherein the performance profile of the particular function is further generated based on applying a curve-fitting algorithm to a plurality of performance measurements of the particular function corresponding to the plurality of feasible resource configurations ([0034], the placement policies 218 are fulfilled through the use of a variety of placement algorithms, and the placement policies are 218 used by the placement agent 202 to make resource allocation/placement decisions for processes 110; and ...A placement policy 218 is configurable and can be altered to utilize a variety of corresponding different placement algorithms. For example, the long-term analytics unit 216 may initially assign a round robin algorithm for achieving the goals of a balanced load placement policy 218 ).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Fu-Zorn as applied to dependent claim 8 and in further view of Vyas (US 20210182114).
As per claim 11, Fu fails to specifically teach, wherein the feasible resource configuration range of a function is determined using a binary search algorithm.
However, Vyas teaches, wherein the feasible resource configuration range of a function is determined using a binary search algorithm ([0021], Scaling may also be achieved by using a binary search or a half-interval search to optimize the number of running instances 170A-D of an application 170. For example, the binary search may find the target value (e.g., quantity of application instances) by testing whether the application 170 functions properly after continuously eliminating half of the application instances (e.g., 170C-D) until the target value is reached).
Fu and Vyas are analogous because they are each related to resource allocation. Fu teaches a method for optimized resource allocation utilizing configurable execution profiles and various optimization algorithms. Vyas teaches a method of resource allocation utilizing a binary search algorithm. ([0021], Scaling may also be achieved by using a binary search or a half-interval search to optimize the number of running instances 170A-D of an application 170. For example, the binary search may find the target value (e.g., quantity of application instances) by testing whether the application 170 functions properly after continuously eliminating half of the application instances (e.g., 170C-D) until the target value is reached; and [0037], To determine how much an application can be appropriately scaled-down, an orchestrator 180 may delete instances of the application 170. As discussed above, one such method of determining the optimum number of application instances is by using a binary search). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the Fu’s resource allocation method, which utilizes various optimization algorithms, would be modified with Vyas’ mechanism resource allocation using binary searching resulting in a system that is able to optimize resource allocation through binary search algorithms. Therefore, it would have been obvious to combine the teachings of Fu and Vyas.
Conclusion
Mahloo et al. (US 20200012508) – Teaches performance management based on performance profiles: Abstract, The method (200) comprises determining (230) performance characteristics associated with a first portion of the memory block, comprised in a first memory unit of the at least one memory pool; and identifying (240) a second portion of the memory block, comprised in a second memory unit of the at least one memory pool, to which data of the first portion of the memory block may be migrated to apply performance characteristics associated with the second portion; and
Ozerov et al. (US 20230418657)- Teaches job management based on job profile information:[0028], the job management optimizer 112 includes a job handler 114 that may be configured to interact with a job history repository 116 that stores historical job data for many completed jobs. As described below, the job history repository 116 may store many completed job instances for a corresponding plurality of jobs, along with corresponding execution data, related metadata, job profile information, and other job properties.
THIS ACTION IS MADE FINAL. 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.
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/MELISSA A HEADLY/Examiner, Art Unit 2197
/BRADLEY A TEETS/Supervisory Patent Examiner, Art Unit 2197
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