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 Amendment
Applicant’s Amendment, filed May 29, 2026, has been fully considered and entered. Accordingly, Claims 1-20 are pending in this Application. Claims 1, 7, 10, 14, and 17 have been amended. Claims 1, 10, and 17 are Independent Claims.
Claim Interpretation
It is the position of the Examiner that the computer readable storage medium of Claims 17-20 is limited to non-transitory computer readable storage media in view of paragraph [0023] of the Specification.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-6, 10, 12, 13, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li (PG Pub. No. 2018/0351816 A1), and further in view of Neary (PG Pub. No. 2009/0254907 A1).
Regarding Claim 1, Li discloses a computer-implemented method of facilitating processing within a computing environment, the computer-implemented method comprising:
obtaining a parameterization for a parallelized file system operation of a file system of the computing environment (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System);
using machine learning to adjust one or more parameters of the parameterization based on the determined performance metrics of the executed parallelized file system operation to obtain a tuned parameterization (see Li, paragraph [0027], where parameter tuning logic has three sets of inputs: the tuning goal, the state values, and the parameter values … there are many ways to implement the parameter tuning logic … neural networks and reinforcement learning, and other similar machine learning and artificial intelligence methods can also be used); and
executing the parallelized file system operation with the tuned parameterization, wherein the adjusting one or more parameters of the parameterization enhances performance of the parallelized file system operation within the computing environment (see Li, paragraph [0029], where parameter tuning instructions are transmitted back to the subject system periodically; the parameter tuning instructions are then executed to change the according parameters periodically).
Li does not disclose:
wherein the parameterization specifies parallel execution of the parallelized file system operation by partitioning file system structure into multiple portions and assigning the portions to multiple processing threads of the computing environment; and
executing the parallelized file sysytem operation by dsitributing the portions among the procsesing threads of the computing environment, and determining performance metrics of the executing parallelized file system operation with the parameterization
Li in view of Neary discloses:
wherein the parameterization parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System) specifies parallel execution of the parallelized file system operation by partitioning file system structure into multiple portions and assigning the portions to multiple processing threads of the computing environment (see Neary, paragraph [0013], where one aspect of the present invention involves a method for assigning work to a plurailty of threads; the method involves identifying a primative data attribute and partitioning a work load into a plurality of partitions using the primative data attribute; the method further involves assigning a first partition of the plurality of partitions to a first thread of the plurality of threads and assigning a second partition of the plurality of partitions to a second thread of the plurality of threads; see also paragraph [0042], where the partitioning model can be applied to any application … for example, consier the problem of multithreading a grep (global regular expression printer) function to perform a parallel search of a directory hierarchy to locate a particular file); and
executing the parallelized file sysytem operation by dsitributing the portions among the procsesing threads of the computing environment (see Neary, paragraph [0013], where one aspect of the present invention involves a method for assigning work to a plurailty of threads; the method involves identifying a primative data attribute and partitioning a work load into a plurality of partitions using the primative data attribute; the method further involves assigning a first partition of the plurality of partitions to a first thread of the plurality of threads and assigning a second partition of the plurality of partitions to a second thread of the plurality of threads; see also paragraph [0042], where the partitioning model can be applied to any application … for example, consier the problem of multithreading a grep (global regular expression printer) function to perform a parallel search of a directory hierarchy to locate a particular file), and determining performance metrics of the executing parallelized file system operation with the parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the cluster performance tuning in Li with the workload partitioning and multithreading techniques in Neary for the benefit of improved performance of concurrent multithreaded applications (see Neary, Abstract).
Regarding Claim 3, Li in view of Neary discloses the computer-implemented method of Claim 1, wherein the parameterization comprises an initial parameterization based on one or more expected average parameter values (see Li, paragraph [0006], where a parameter can be set to a certain value, and the value of these parameters change how the systems behave and offer a means to customize the system to meet different user requirements).
Regarding Claim 4, Li in view of Neary discloses the computer-implemented method of Claim 1, wherein determining performance of the parallelized file system operation with the parameterization comprises collecting data associated with the executing of the parallelized file system operation with the parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds), and storing the collected data (see Li, paragraph [0036], where system can further comprise storing the state values, the parameter values, and parameter tuning instructions in a data store).
Regarding Claim 5, Li in view of Neary discloses the computer-implemented method of Claim 4, wherein using machine learning to adjust one or more parameters of the parameterization comprises determining a parameter delta for a parameter of the one or more parameters of the parameterization, and based on the parameter delta exceeding a parameter delta threshold, adjusting the parameter of the one or more parameters of the parameterization to, at least in part, obtain the tuned parameterization (see Li, paragraph [0073], where configuration options for each parameter depend on the subject system and can include, but are not limited to, name of the parameter, how it should be set, valid range or set of values, collection interval, time limit of changing the parameter, conditions in which the parameter needs being tuned, and preprocessing instructions … the conditions in which the parameter needs being tuned is a collection of conditions, and when they are met, the parameter needs being tuned; samples for such conditions include, but are not limited to, when a certain state value is lower or higher than a threshold, the value of a certain parameter is lower or higher than a threshold, a job of a certain name has started, etc., or any combination of them).
Regarding Claim 6, Li in view of Neary discloses the computer-implemented method of Claim 5, wherein using machine learning to adjust one or more parameters of the parameterization comprises using a machine learning model for mapping the collected data to the one or more parameters of the parameterization, the one or more parameters effecting a desired operational characteristic for executing the parallelized file system operation (see Li, paragraph [0037], where computing of parameter tuning instructions can further comprise: using one or more machine learning or artificial intelligence methods to analyze the state values and the parameter values at intervals; and training one or more models using the state values, the parameter values, and the parameter tuning instructions from one or more subject systems at intervals; and generating parameter tuning instructions at intervals).
Regarding Claim 10, Li discloses a computer system for facilitating processing within a computing environment, the computer system comprising:
a memory (see Li, Claim 16, wherein the parameter tuning logic comprises … a memory coupled with and readable by the processor); and
at least one processor in communication with the memory (see Li, wherein the parameter tuning logic comprises a processor), wherein the computer system is configured to perform a method, the method comprising:
obtaining a parameterization for a parallelized file system operation of a file system of the computing environment (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System);
using machine learning to adjust one or more parameters of the parameterization based on the determined performance metrics of the executed parallelized file system operation to obtain a tuned parameterization (see Li, paragraph [0027], where parameter tuning logic has three sets of inputs: the tuning goal, the state values, and the parameter values … there are many ways to implement the parameter tuning logic … neural networks and reinforcement learning, and other similar machine learning and artificial intelligence methods can also be used); and
executing the parallelized file system operation with the tuned parameterization, wherein the adjusting one or more parameters of the parameterization enhances performance of the parallelized file system operation within the computing environment (see Li, paragraph [0029], where parameter tuning instructions are transmitted back to the subject system periodically; the parameter tuning instructions are then executed to change the according parameters periodically).
Li does not disclose:
wherein the parameterization specifies parallel execution of the parallelized file system operation by partitioning file system structure into multiple portions and assigning the portions to multiple processing threads of the computing environment; and
executing the parallelized file sysytem operation by dsitributing the portions among the procsesing threads of the computing environment, and determining performance metrics of the executing parallelized file system operation with the parameterization
Li in view of Neary discloses:
wherein the parameterization parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System) specifies parallel execution of the parallelized file system operation by partitioning file system structure into multiple portions and assigning the portions to multiple processing threads of the computing environment (see Neary, paragraph [0013], where one aspect of the present invention involves a method for assigning work to a plurailty of threads; the method involves identifying a primative data attribute and partitioning a work load into a plurality of partitions using the primative data attribute; the method further involves assigning a first partition of the plurality of partitions to a first thread of the plurality of threads and assigning a second partition of the plurality of partitions to a second thread of the plurality of threads; see also paragraph [0042], where the partitioning model can be applied to any application … for example, consier the problem of multithreading a grep (global regular expression printer) function to perform a parallel search of a directory hierarchy to locate a particular file); and
executing the parallelized file sysytem operation by dsitributing the portions among the procsesing threads of the computing environment (see Neary, paragraph [0013], where one aspect of the present invention involves a method for assigning work to a plurailty of threads; the method involves identifying a primative data attribute and partitioning a work load into a plurality of partitions using the primative data attribute; the method further involves assigning a first partition of the plurality of partitions to a first thread of the plurality of threads and assigning a second partition of the plurality of partitions to a second thread of the plurality of threads; see also paragraph [0042], where the partitioning model can be applied to any application … for example, consier the problem of multithreading a grep (global regular expression printer) function to perform a parallel search of a directory hierarchy to locate a particular file), and determining performance metrics of the executing parallelized file system operation with the parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the cluster performance tuning in Li with the workload partitioning and multithreading techniques in Neary for the benefit of improved performance of concurrent multithreaded applications (see Neary, Abstract).
Regarding Claim 12, Li in view of Neary discloses the computer system of Claim 10, wherein determining performance of the parallelized file system operation with the parameterization comprises collecting data associated with the executing of the parallelized file system operation with the parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds), and storing the collected data (see Li, paragraph [0036], where system can further comprise storing the state values, the parameter values, and parameter tuning instructions in a data store), and wherein using machine learning to adjust one or more parameters of the parameterization comprises determining a parameter delta for a parameter of the one or more parameters of the parameterization, and based on the parameter delta exceeding a parameter delta threshold, adjusting the parameter of the one or more parameters of the parameterization to, at least in part, obtain the tuned parameterization (see Li, paragraph [0073], where configuration options for each parameter depend on the subject system and can include, but are not limited to, name of the parameter, how it should be set, valid range or set of values, collection interval, time limit of changing the parameter, conditions in which the parameter needs being tuned, and preprocessing instructions … the conditions in which the parameter needs being tuned is a collection of conditions, and when they are met, the parameter needs being tuned; samples for such conditions include, but are not limited to, when a certain state value is lower or higher than a threshold, the value of a certain parameter is lower or higher than a threshold, a job of a certain name has started, etc., or any combination of them).
Regarding Claim 13, Li in view of Neary discloses the computer system of Claim 12, wherein using machine learning to adjust one or more parameters of the parameterization comprises using a machine learning model for mapping the collected data to the one or more parameters of the parameterization, the one or more parameters effecting a desired operational characteristic for executing the parallelized file system operation (see Li, paragraph [0037], where computing of parameter tuning instructions can further comprise: using one or more machine learning or artificial intelligence methods to analyze the state values and the parameter values at intervals; and training one or more models using the state values, the parameter values, and the parameter tuning instructions from one or more subject systems at intervals; and generating parameter tuning instructions at intervals).
Regarding Claim 17, Li discloses a computer program product for facilitating processing with a computing environment, the computer program product comprising:
one or more computer readable storage media and program instructions collectively stored on one or more computer readable storage media (see Li, paragraph [0123], where machine-executable instructions may be stored on one or more machine readable mediums) to perform a method comprising:
obtaining a parameterization for a parallelized file system operation of a file system of the computing environment (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System);
using machine learning to adjust one or more parameters of the parameterization based on the determined performance metrics of the executed parallelized file system operation to obtain a tuned parameterization (see Li, paragraph [0027], where parameter tuning logic has three sets of inputs: the tuning goal, the state values, and the parameter values … there are many ways to implement the parameter tuning logic … neural networks and reinforcement learning, and other similar machine learning and artificial intelligence methods can also be used); and
executing the parallelized file system operation with the tuned parameterization, wherein the adjusting one or more parameters of the parameterization enhances performance of the parallelized file system operation within the computing environment (see Li, paragraph [0029], where parameter tuning instructions are transmitted back to the subject system periodically; the parameter tuning instructions are then executed to change the according parameters periodically).
Li does not disclose:
wherein the parameterization specifies parallel execution of the parallelized file system operation by partitioning file system structure into multiple portions and assigning the portions to multiple processing threads of the computing environment; and
executing the parallelized file sysytem operation by dsitributing the portions among the procsesing threads of the computing environment, and determining performance metrics of the executing parallelized file system operation with the parameterization
Li in view of Neary discloses:
wherein the parameterization parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System) specifies parallel execution of the parallelized file system operation by partitioning file system structure into multiple portions and assigning the portions to multiple processing threads of the computing environment (see Neary, paragraph [0013], where one aspect of the present invention involves a method for assigning work to a plurailty of threads; the method involves identifying a primative data attribute and partitioning a work load into a plurality of partitions using the primative data attribute; the method further involves assigning a first partition of the plurality of partitions to a first thread of the plurality of threads and assigning a second partition of the plurality of partitions to a second thread of the plurality of threads; see also paragraph [0042], where the partitioning model can be applied to any application … for example, consier the problem of multithreading a grep (global regular expression printer) function to perform a parallel search of a directory hierarchy to locate a particular file); and
executing the parallelized file sysytem operation by dsitributing the portions among the procsesing threads of the computing environment (see Neary, paragraph [0013], where one aspect of the present invention involves a method for assigning work to a plurailty of threads; the method involves identifying a primative data attribute and partitioning a work load into a plurality of partitions using the primative data attribute; the method further involves assigning a first partition of the plurality of partitions to a first thread of the plurality of threads and assigning a second partition of the plurality of partitions to a second thread of the plurality of threads; see also paragraph [0042], where the partitioning model can be applied to any application … for example, consier the problem of multithreading a grep (global regular expression printer) function to perform a parallel search of a directory hierarchy to locate a particular file), and determining performance metrics of the executing parallelized file system operation with the parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds; see also paragraph [0079], where cloud tuning service 106 can provide software client agent for popular subject systems, such as the Windows Operating System, the IBM GPFS Cluster File System).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the cluster performance tuning in Li with the workload partitioning and multithreading techniques in Neary for the benefit of improved performance of concurrent multithreaded applications (see Neary, Abstract).
Regarding Claim 19, Li in view of Neary discloses the computer program product of Claim 17, wherein determining performance of the parallelized file system operation with the parameterization comprises collecting data associated with the executing of the parallelized file system operation with the parameterization (see Li, paragraph [0022], where the method comprises: identifying status values of the target system to collect, parameters of the subject system to tune, a tuning goal, and a parameter tuning logic; and deploying the parameter tuning logic in one or more clouds), and storing the collected data (see Li, paragraph [0036], where system can further comprise storing the state values, the parameter values, and parameter tuning instructions in a data store), and wherein using machine learning to adjust one or more parameters of the parameterization comprises determining a parameter delta for a parameter of the one or more parameters of the parameterization, and based on the parameter delta exceeding a parameter delta threshold, adjusting the parameter of the one or more parameters of the parameterization to, at least in part, obtain the tuned parameterization (see Li, paragraph [0073], where configuration options for each parameter depend on the subject system and can include, but are not limited to, name of the parameter, how it should be set, valid range or set of values, collection interval, time limit of changing the parameter, conditions in which the parameter needs being tuned, and preprocessing instructions … the conditions in which the parameter needs being tuned is a collection of conditions, and when they are met, the parameter needs being tuned; samples for such conditions include, but are not limited to, when a certain state value is lower or higher than a threshold, the value of a certain parameter is lower or higher than a threshold, a job of a certain name has started, etc., or any combination of them).
Regarding Claim 20, Li in view of Neary discloses the computer program product of Claim 19, wherein using machine learning to adjust one or more parameters of the parameterization comprises using a machine learning model for mapping the collected data to the one or more parameters of the parameterization, the one or more parameters effecting a desired operational characteristic for executing the parallelized file system operation (see Li, paragraph [0037], where computing of parameter tuning instructions can further comprise: using one or more machine learning or artificial intelligence methods to analyze the state values and the parameter values at intervals; and training one or more models using the state values, the parameter values, and the parameter tuning instructions from one or more subject systems at intervals; and generating parameter tuning instructions at intervals).
Claims 2, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Li and Neary as applied to Claims 1, 3-6, 10, 12, 13, 17, 19, and 20 above, and further in view of HoreKa (“NHR@KIT User Documentation”, https://www.nhr.kit.edu/userdocs/horeka/filesystems_performance, January 22, 2022).
Regarding Claim 2, Li in view of Neary discloses the computer-implemented method of Claim 1, wherein:
Li does not disclose using machine learning to adjust one or more parameters of the parameterization comprises changing the one or more parameters heuristically based on a structure of the file system. HoreKa discloses using machine learning to adjust one or more parameters of the parameterization comprises changing the one or more parameters heuristically based on a structure of the file system (see HoreKa, Improving Performance on parallel file systems … when you are designing your application you should consider that performance of parallel file systems is generally better if data is transferred in large blocks and stored in few files).
Li and Horeka are directed toward performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Horeka as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Regarding Claim 11, Li in view of Neary discloses the computer system of Claim 10, wherein:
Li does not disclose using machine learning to adjust one or more parameters of the parameterization comprises changing the one or more parameters heuristically based on a structure of the file system. HoreKa discloses using machine learning to adjust one or more parameters of the parameterization comprises changing the one or more parameters heuristically based on a structure of the file system (see HoreKa, Improving Performance on parallel file systems … when you are designing your application you should consider that performance of parallel file systems is generally better if data is transferred in large blocks and stored in few files).
Li and Horeka are directed toward performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Horeka as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Regarding Claim 18, Li in view of Neary discloses the computer program product of Claim 17, wherein:
Li does not disclose using machine learning to adjust one or more parameters of the parameterization comprises changing the one or more parameters heuristically based on a structure of the file system. HoreKa discloses using machine learning to adjust one or more parameters of the parameterization comprises changing the one or more parameters heuristically based on a structure of the file system (see HoreKa, Improving Performance on parallel file systems … when you are designing your application you should consider that performance of parallel file systems is generally better if data is transferred in large blocks and stored in few files).
Li and Horeka are directed toward performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Horeka as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Li and Neary as applied to Claims 1, 3-6, 10, 12, 13, 17, 19, and 20 above, and further in view of Helmich (PG Pub. No. 2019/0312926 A1) and Landis (PG Pub. No. 2007/0061441 A1).
Regarding Claim 7, Li in view of Neary discloses the computer-implemented method of Claim 1, wherein the file system is a clustered file system and the executing the parallelized file system operation with the parameterization comprises:
Li does not disclose:
obtaining multiple partitions of directories of the file system;
wherein distributing the portions comprises distributing partitions of the multiple partitions of directories of the file system among available processing threads of the computing environment; and
storing undistributed partitions in a common storage.
Helmich discloses:
obtaining multiple partitions of directories of the file system (see Helmich, paragraph [0019], where in one embodiment, a partition balancing tool measures database usage on the organization and the partition level to provide partition to node mapping with improved partition-level load balancing; see also paragraph [0024], the driver operates to query the database system for orgID to partition mapping information and stores that information in the framework (e.g., in a Hadoop embodiment, within the Hadoop Distributed File System, or HDFS)); and
wherein distributing the portions comprises distributing partitions of the multiple partitions of directories of the file system among available processing threads of the computing environment (see Helmich, paragraph [0020], where partition balancing can be as simple as allocating based on processor workload caused by the database system).
Li and Helmich are both directed toward load balancing and performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Helmich as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Li in view of Helmich does not disclose storing undistributed partitions in a common storage. Landis discloses storing undistributed partitions in a common storage (see Landis, paragraph [0452], where partition states are in three basic categories: uninstalled, inactive, and active … the inactive {stopped, saved (hibernate)} and active {starting, running, paused (standby)} categories correspond to the provisioning and operating stages).
Li, Helmich, and Landis are directed toward load balancing and performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li and Helmich with Landis as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Regarding Claim 14, Li in view of Neary discloses the computer system of Claim 10, wherein the file system is a clustered file system and the executing the parallelized file system operation with the parameterization comprises:
Li does not disclose:
obtaining multiple partitions of directories of the file system;
wherein distributing the portions comprises distributing partitions of the multiple partitions of directories of the file system among available processing threads of the computing environment; and
storing undistributed partitions in a common storage.
Helmich discloses:
obtaining multiple partitions of directories of the file system (see Helmich, paragraph [0019], where in one embodiment, a partition balancing tool measures database usage on the organization and the partition level to provide partition to node mapping with improved partition-level load balancing; see also paragraph [0024], the driver operates to query the database system for orgID to partition mapping information and stores that information in the framework (e.g., in a Hadoop embodiment, within the Hadoop Distributed File System, or HDFS)); and
wherein distributing the portions comprises distributing partitions of the multiple partitions of directories of the file system among available processing threads of the computing environment (see Helmich, paragraph [0020], where partition balancing can be as simple as allocating based on processor workload caused by the database system).
Li and Helmich are both directed toward load balancing and performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Helmich as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Li in view of Helmich does not disclose storing undistributed partitions in a common storage. Landis discloses storing undistributed partitions in a common storage (see Landis, paragraph [0452], where partition states are in three basic categories: uninstalled, inactive, and active … the inactive {stopped, saved (hibernate)} and active {starting, running, paused (standby)} categories correspond to the provisioning and operating stages).
Li, Helmich, and Landis are directed toward load balancing and performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li and Helmich with Landis as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Li, Neary, Helmich, and Landis as applied to Claims 7 and 14 above, and further in view of Banyai (PG Pub. No. 2020/0136943 A1).
Regarding Claim 8, Li in view of Neary, Helmich and Landis discloses the computer-implemented method of Claim 7, wherein the executing further comprises:
Li does not disclose:
determining whether an average execution time of the parallelized file system operation exceeds an average execution time threshold; and
based on the average execution time of the parallelized file system operation exceeding the average execution time threshold, spawning one or more new processing threads to facilitate execution of the parallelized file system operations with the parameterization on any remaining undistributed partitions in the common storage.
Banyai discloses:
determining whether an average execution time of the parallelized file system operation exceeds an average execution time threshold (see Banyai, paragraph [0124], where dynamic and transparent scaling in response to pressure conditions and performance thresholds that provide an indication of performance degradation is on a per-Kubernetes service level based on defined performance thresholds; see also paragraph [0160 - 0161], where a compute node is under pressure if the compute node 1904 is experiencing a high resource utilization that is impacting the performance of a workload 1708 running on the compute node 1904 … orchestrator/scheduler 102 also monitors and stores workload metrics 1912; workload metrics 1912 include: number of clients, average response latency; see also paragraph [0195], Timed Workload Timer) and
based on the average execution time of the parallelized file system operation exceeding the average execution time threshold, spawning one or more new processing threads to facilitate execution of the parallelized file system operations with the parameterization on any remaining undistributed partitions in the common storage (see Banyai, paragraph [0182], where another name for shared-nothing architecture is sharding … each shard is stored in a separate database server instance, to spread load).
Li and Banyai are both directed to performance optimization and tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Banyai as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Regarding Claim 15, Li in view of Neary, Helmich and Landis discloses the computer system of Claim 14, wherein the executing further comprises:
Li does not disclose:
determining whether an average execution time of the parallelized file system operation exceeds an average execution time threshold; and
based on the average execution time of the parallelized file system operation exceeding the average execution time threshold, spawning one or more new processing threads to facilitate execution of the parallelized file system operations with the parameterization on any remaining undistributed partitions in the common storage.
Banyai discloses:
determining whether an average execution time of the parallelized file system operation exceeds an average execution time threshold (see Banyai, paragraph [0124], where dynamic and transparent scaling in response to pressure conditions and performance thresholds that provide an indication of performance degradation is on a per-Kubernetes service level based on defined performance thresholds; see also paragraph [0160 - 0161], where a compute node is under pressure if the compute node 1904 is experiencing a high resource utilization that is impacting the performance of a workload 1708 running on the compute node 1904 … orchestrator/scheduler 102 also monitors and stores workload metrics 1912; workload metrics 1912 include: number of clients, average response latency; see also paragraph [0195], Timed Workload Timer) and
based on the average execution time of the parallelized file system operation exceeding the average execution time threshold, spawning one or more new processing threads to facilitate execution of the parallelized file system operations with the parameterization on any remaining undistributed partitions in the common storage (see Banyai, paragraph [0182], where another name for shared-nothing architecture is sharding … each shard is stored in a separate database server instance, to spread load).
Li and Banyai are both directed to performance optimization and tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Banyai as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Li, Neary, Helmich and Landis as applied to Claims 7 and 14 above, and further in view of Abe (PG Pub. No. 2017/0169051 A1).
Regarding Claim 9, Li in view of Neary, Helmich, and Landis discloses the computer-implemented method of Claim 7, wherein:
the parameterization comprises multiple parameters, the multiple parameters comprising a maximum number of processing threads for use in executing the parallelized file system operation (see Li, paragraph [0006], where a computer system can have hundreds of tunable parameters; see also paragraph [0049], where example of parameters are … number of worker threads).
Li does not disclose a horizontal limit of directories for the multiple partitions of directories of the file system, and a vertical limit of sub-directories for the multiple partitions of directories of the file system. Li in view of Helmich and Abe discloses a horizontal limit of directories (see Abe, paragraph [0051], where in some embodiments of the present invention, configuration and procedures described above effectively (more fully) use the capacity of a tape because a new virtual tape is created and used when: (i) a single tape cartridge is used; (ii) the maximum number of directories and files (W) has been reached; and (iii) unused partitions are available) for the multiple partitions of directories of the file system (see Helmich, paragraph [0019], where in one embodiment, a partition balancing tool measures database usage on the organization and the partition level to provide partition to node mapping with improved partition-level load balancing; see also paragraph [0024], the driver operates to query the database system for orgID to partition mapping information and stores that information in the framework (e.g., in a Hadoop embodiment, within the Hadoop Distributed File System, or HDFS)), and a vertical limit of sub-directories (see Abe, paragraph [0051], where in some embodiments of the present invention, configuration and procedures described above effectively (more fully) use the capacity of a tape because a new virtual tape is created and used when: (i) a single tape cartridge is used; (ii) the maximum number of directories and files (W) has been reached; and (iii) unused partitions are available) for the multiple partitions of directories of the file system (see Helmich, paragraph [0019], where in one embodiment, a partition balancing tool measures database usage on the organization and the partition level to provide partition to node mapping with improved partition-level load balancing; see also paragraph [0024], the driver operates to query the database system for orgID to partition mapping information and stores that information in the framework (e.g., in a Hadoop embodiment, within the Hadoop Distributed File System, or HDFS)).
Li and Helmich are both directed toward load balancing and performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Helmich as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li and Helmich with Abe for the benefit of making additional partitions available when directory limits are reached (see Abe, paragraph [0025]).
Regarding Claim 16, Li in view of Neary, Helmich, and Landis discloses the computer program of Claim 14, wherein:
the parameterization comprises multiple parameters, the multiple parameters comprising a maximum number of processing threads for use in executing the parallelized file system operation (see Li, paragraph [0006], where a computer system can have hundreds of tunable parameters; see also paragraph [0049], where example of parameters are … number of worker threads).
Li does not disclose a horizontal limit of directories for the multiple partitions of directories of the file system, and a vertical limit of sub-directories for the multiple partitions of directories of the file system. Li in view of Helmich and Abe discloses a horizontal limit of directories (see Abe, paragraph [0051], where in some embodiments of the present invention, configuration and procedures described above effectively (more fully) use the capacity of a tape because a new virtual tape is created and used when: (i) a single tape cartridge is used; (ii) the maximum number of directories and files (W) has been reached; and (iii) unused partitions are available) for the multiple partitions of directories of the file system (see Helmich, paragraph [0019], where in one embodiment, a partition balancing tool measures database usage on the organization and the partition level to provide partition to node mapping with improved partition-level load balancing; see also paragraph [0024], the driver operates to query the database system for orgID to partition mapping information and stores that information in the framework (e.g., in a Hadoop embodiment, within the Hadoop Distributed File System, or HDFS)), and a vertical limit of sub-directories (see Abe, paragraph [0051], where in some embodiments of the present invention, configuration and procedures described above effectively (more fully) use the capacity of a tape because a new virtual tape is created and used when: (i) a single tape cartridge is used; (ii) the maximum number of directories and files (W) has been reached; and (iii) unused partitions are available) for the multiple partitions of directories of the file system (see Helmich, paragraph [0019], where in one embodiment, a partition balancing tool measures database usage on the organization and the partition level to provide partition to node mapping with improved partition-level load balancing; see also paragraph [0024], the driver operates to query the database system for orgID to partition mapping information and stores that information in the framework (e.g., in a Hadoop embodiment, within the Hadoop Distributed File System, or HDFS)).
Li and Helmich are both directed toward load balancing and performance tuning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li with Helmich as it amounts to combining prior art methods according to known methods to yield predictable results (see MPEP 2143(I)(A)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Li and Helmich with Abe for the benefit of making additional partitions available when directory limits are reached (see Abe, paragraph [0025]).
Response to Arguments
Applicant’s Arguments, filed May 29, 2026, have been fully considered, but they are moot in light of the new grounds of rejection.
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 FARHAD AGHARAHIMI whose telephone number is (571)272-9864. The examiner can normally be reached M-F 9am - 5pm ET.
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/FARHAD AGHARAHIMI/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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