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 .
Claim Objections
Claims 7 and 17 are objected to because of the following informalities: The word “farcicalities” appears to be a typographical error in “wherein the processor is further configured to extract the history file from systems management farcicalities (SMF) records indicating job related information,” as based on the specification on paragraph [0060], the SMF is described as “a system management facility (SMF).” Appropriate correction is required. For examination purposes, the word “farcicalities” will be interpreted as “facilities.”
Claim 19 is objected to because of the following informalities: claim 19 is substantially similar to claim 9, where both claim 19 and claim 9 are currently dependent on claim 1. Claim 19 is therefore believed to have a typographical error as the claim states “The method of claim 1, wherein…”, where claim 1 is not a method, but rather, claim 11 is a method and claim 1 is a system. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to (an) abstract idea(s) without significantly more.
Claims 1 and 11 recite:
A system for use in predicting resources required for a program, the system comprising:
a processor;
a storage device accessible by the processor;
one or more sequential input files stored in the storage device, each consisting of a dataset comprising one or more records;
a customer program configured to read and perform data manipulation on the one or more records in the one or more sequential input files in accordance with a job that references the one or more sequential input files and to
write one or more corresponding manipulated records into one or more corresponding sequential output files on the storage device; and
a sequential file prediction program that when executed by the processor configures the system to:
access a history file to determine sizes of past sequential input files of the one or more sequential input files input to the customer program and sizes of resultant past sequential output files produced by the customer program processing the sequential input files,
determine a correlation between the sizes of the past sequential input files and the resultant sizes of the past sequential output files,
utilize the correlation to predict future consumption of the scalable resources including future sizes of future sequential output files based on the current sizes of current sequential input files, and
utilize the predicted future consumption of the scalable resources to perform at least one of memory allocation or to determine scheduling for batch jobs being to be performed by the system, wherein the scalable resources include at least one of processing time, working memory or input/output time.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 1 is a machine.
Claim 11 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘performing’ limitation in #1 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “performing” in the context of this claim encompasses a person analyzing, evaluating, or performing data manipulation on the one or more records in the one or more sequential input files in accordance with a job that references the one or more sequential input files, including comparison or judgement.
The ‘determining’ limitation in #4 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “determining” in the context of this claim encompasses a person analyzing, evaluating, or determining a correlation between the sizes of the past sequential input files and the resultant sizes of the past sequential output files, including comparison or judgement.
The ‘utilizing’ limitation in #5 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “utilizing” in the context of this claim encompasses a person analyzing, evaluating, or utilizing the correlation to predict future consumption of the scalable resources, including comparison or judgement.
The ‘utilizing’ limitation in #6 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “utilizing” in the context of this claim encompasses a person analyzing, evaluating, or utilizing the predicted future consumption of the scalable resources to perform at least one of memory allocation or to determine scheduling for batch jobs to be performed by the system, including comparison or judgement.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘writing’ limitation in #2 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception. The limitation “writing” in the context of this claim encompasses merely writing one or more corresponding manipulated records into one or more corresponding sequential output files on the storage device. See MPEP 2106.05(f).
The ‘accessing’ limitation in #3 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “accessing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Additionally, one or more of the claims recite the following additional elements:
a processor (Claim 1)
a storage device accessible by the processor (Claim 1)
These additional elements are recited at a high level of generality (i.e., as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g)&(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Claims 8 and 18 merely further describe the one or more sequential input and output files of Claims 1 and 11 respectively. The claims do not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception.
Claims 9 and 19 merely further describe the customer program of Claims 1 and 11 respectively. The claims do not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception.
Claims 10 and 20 merely further describe the memory allocation for storing the sequential output files and processing time for producing the sequential output files of Claims 1 and 11 respectively. The claims do not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception.
Therefore, Claims 1, 8-11 and 18-20 are directed to (an) abstract idea(s) without significantly more.
Claims 2 and 12 recite:
wherein the processor is further configured to determine the correlation by
performing a linear regression on the sizes of the past sequential input files and the resultant consumption of scalable resources, the linear regression producing a linear function for performing the prediction of the future consumption of the scalable resources based on the current sizes of current sequential input files of the one or more sequential input files.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 2 is a machine.
Claim 12 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘performing’ limitation in #7 above, as claimed and under broadest reasonable interpretation (BRI), is a mathematical concept that covers mathematical relationships, formulas, equations, and calculations. The limitation “performing” in the context of this claim encompasses performing a linear regression on the sizes of the past sequential input files and the resultant consumption of scalable resources.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, Claims 2 and 12 are directed to (an) abstract idea(s) without significantly more.
Claims 3 and 13 recite:
wherein the processor is further configured to
perform the linear regression as NxM linear regressions for NxM combinations of N input files by M output files, where N and M are integer values ranging between 1 and a maximum number of input files and output files that is supported by the program.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 3 is a machine.
Claim 13 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘performing’ limitation in #8 above, as claimed and under broadest reasonable interpretation (BRI), is a mathematical concept that covers mathematical relationships, formulas, equations, and calculations. The limitation “performing” in the context of this claim encompasses performing the linear regression as NxM linear regressions for NxM combinations of N input files by M output files.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, Claims 3 and 13 are directed to (an) abstract idea(s) without significantly more.
Claims 4 and 14 recite:
wherein the processor is further configured to
perform the linear regression KxN linear regressions for N input files and K transformations for each of the scalable resources consumed, where N is an integer value ranging from 1 to a number of indicating each input file data definition (DD).
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 4 is a machine.
Claim 14 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘performing’ limitation in #9 above, as claimed and under broadest reasonable interpretation (BRI), is a mathematical concept that covers mathematical relationships, formulas, equations, and calculations. The limitation “performing” in the context of this claim encompasses performing the linear regression KxN linear regressions for N input files and K transformations for each of the scalable resources consumed.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, Claims 4 and 14 are directed to (an) abstract idea(s) without significantly more.
Claims 5 and 15 recite:
wherein the processor is further configured to determine the correlation by training a neural network by inputting the sizes of the past sequential input files,
processing the sizes of the past sequential input files based on set weights,
predicting resource consumption of the scalable resources,
computing a difference between the predicted resource consumption and the known resultant consumption of past scalable resources, and
adjusting the set weights in an attempt to minimize the difference.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 5 is a machine.
Claim 15 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘predicting’ limitation in #12 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “predicting” in the context of this claim encompasses a person analyzing, evaluating, or predicting resource consumption of the scalable resources, including comparison or judgement.
The ‘computing’ limitation in #13 above, as claimed and under broadest reasonable interpretation (BRI), is a mathematical concept that covers mathematical relationships, formulas, equations, and calculations. The limitation “computing” in the context of this claim encompasses computing a difference between the predicted resource consumption and the known resultant consumption of past scalable resources.
The ‘adjusting’ limitation in #14 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “adjusting” in the context of this claim encompasses a person analyzing, evaluating, or adjusting the set weights in an attempt to minimize the difference, including comparison or judgement.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘training’ limitation in #10 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception. The limitation “training” in the context of this claim encompasses merely training a neural network by inputting the sizes of the past sequential input files. See MPEP 2106.05(f).
The ‘processing’ limitation in #11 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception. The limitation “processing” in the context of this claim encompasses merely processing the sizes of the past sequential input files based on set weights. See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, Claims 5 and 15 are directed to (an) abstract idea(s) without significantly more.
Claims 6 and 16 recite:
wherein the processor is further configured to train the neural network based on the input file sizes and at least one of logical record length and block size of the input files, record format, timing of job execution, parameters passed to the program from the input files, names data definitions (DDs) for the input files, and duration of job execution.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 6 is a machine.
Claim 16 is a process.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘training’ limitation in #15 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception. The limitation “training” in the context of this claim encompasses merely training a neural network based on the input file sizes and at least one of logical record length and block size of the input files, record format, timing of job execution, parameters passed to the program from the input files, names data definitions (DDs) for the input files, and duration of job execution. See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, Claims 6 and 16 are directed to (an) abstract idea(s) without significantly more.
Claims 7 and 17 recite:
wherein the processor is further configured to extract the history file from systems management farcicalities (SMF) records indicating job related information.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 7 is a machine.
Claim 17 is a process.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘extracting’ limitation in #16 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “extracting” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, Claims 7 and 17 are directed to (an) abstract idea(s) without significantly more.
Claim 21 recites:
wherein determining the scheduling for batch jobs includes rescheduling a batch job of the batch jobs for a later time when system load of the system is lower based on a prediction of the processing resources necessary to execute the batch job.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes.
Claim 21 is a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘rescheduling’ limitation in #17 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “rescheduling” in the context of this claim encompasses a person analyzing, evaluating, or rescheduling a batch job of the batch jobs for a later time when system load of the system is lower based on a prediction of the processing resources necessary to execute the batch job, including comparison or judgement.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, Claim 21 is directed to (an) abstract idea(s) without significantly more.
Claim Rejections - 35 USC § 102
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.
(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.
Claim(s) 1, 7, 9-11, 17, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Cao et al. (U.S. Publication No. US 20160110224 A1), hereinafter “Cao.”
With regards to claim 1, Cao teaches:
A system for use in predicting resources required for a program (Paragraph 23, “a historical job(s) is used to determine a characteristic of input and output of the target job and thereby a resource overhead of the target job is calculated. Therefore, before the target job is processed, an alert for the target job can be generated based on the calculated resource overhead.” The calculated resource overhead for a target job being calculated correlates to a system for use in predicting resources required for a program), the system comprising:
a processor;
a storage device accessible by the processor (Paragraphs 16 and 19, “As shown in FIG. 1, computer system/server 12 is shown in the form of a general-purpose computing device. The components of computer system/server 12 can include, but are not limited to, at least one processor or processing unit 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16... System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 can further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”).” The processor being coupled to system memory which includes computer system readable media correlates to a processor and a storage device accessible by the processor);
one or more sequential input files stored in the storage device, each consisting of a dataset comprising one or more records (Paragraphs 27, 36 and 41, “In the processing of the target job and/or historical jobs in each stage, a job can have at least one task. Such tasks can be assigned to at least one computing node for parallel processing. If one job includes two or more stages, the input of a task in the first stage can be input data provided by a user/developer, and the input of a task in an intermediate stage can be the output data of the task in the previous stage, and the output data of a task(s) in the final stage can be regarded as the final output of the job… In some embodiments, the input data of historical intermediate tasks can be stored in files, in which the input data can be stored in rows or columns… Usually when the target job is extracted, the input data of the target job, together with the input data property, is submitted to the corresponding storage system, such as a distributed file system. Thus, the input data property can be directly obtained from the storage system.” The job having multiple stages and tasks, with the output of a previous task including rows and columns being used as the input of a later task, correlates to one or more sequential input files each consisting of a dataset comprising one or more records. The input data and input data properties being submitted to a distributed file system and being directly obtained from the storage system correlates to one or more sequential input files stored in the storage device);
a customer program configured to read and perform data manipulation on the one or more records in the one or more sequential input files in accordance with a job that references the one or more sequential input files and to write one or more corresponding manipulated records into one or more corresponding sequential output files on the storage device (Paragraphs 27, 32, and 35-36 “In the processing of the target job and/or historical jobs in each stage, a job can have at least one task. Such tasks can be assigned to at least one computing node for parallel processing. If one job includes two or more stages, the input of a task in the first stage can be input data provided by a user/developer, and the input of a task in an intermediate stage can be the output data of the task in the previous stage, and the output data of a task(s) in the final stage can be regarded as the final output of the job… In particular, an intermediate stage can be any of the stages of the target job, including the first stage of the target job. In some cases, the intermediate stage can not be the last stage of the target job, because the final output of the task in the final stage is usually stored in a corresponding file system, rather than in the local storage of the computing node… In some embodiments, the output data of each of the intermediate tasks can usually be stored in a form of at least one record… In some embodiments, the input data of historical intermediate tasks can be stored in files, in which the input data can be stored in rows or columns.” The output of one task being used as input for a later task within a job, where the input data can be stored as rows or columns, would involve processing of the input data and therefore correlates to a customer program configured to read and perform data manipulation on the one or more records in the one or more sequential input files in accordance with a job that references the one or more sequential input files. The computing node being assigned the tasks of a job, where the output of each intermediate task can be stored in a file system or local storage correlates to writing one or more corresponding manipulated records into one or more corresponding sequential output files on the storage device); and
a sequential file prediction program that when executed by the processor configures the system to:
access a history file to determine sizes of past sequential input files of the one or more sequential input files input to the customer program and sizes of resultant past sequential output files produced by the customer program processing the sequential input files (Paragraphs 22, 27 and 34 and 36, “The term “target job” refers to the job to be processed; and the term “historical job” refers to the job that has already been processed… In the processing of the target job and/or historical jobs in each stage, a job can have at least one task. Such tasks can be assigned to at least one computing node for parallel processing. If one job includes two or more stages, the input of a task in the first stage can be input data provided by a user/developer, and the input of a task in an intermediate stage can be the output data of the task in the previous stage, and the output data of a task(s) in the final stage can be regarded as the final output of the job… A historical job can have at least one historical intermediate task. In order to statistically determine the second association precisely, the input data size and the output data size of all of these historical intermediate tasks are considered as long as they have the same logical as the processing of the intermediate task(s) of the target job… In some embodiments, the input data of historical intermediate tasks can be stored in files, in which the input data can be stored in rows or columns.” A job having at least one task, where the output of one stage is used as the input for a input of the next stage, correlates to a sequential input file. The input data size and output data size of the historical intermediate tasks being considered, which have already been processed with the input data stored in files, would involve accessing the file and therefore correlates to a sequential file prediction program that when executed by the processor configures the system to access a history file to determine sizes of past sequential input files of the one or more sequential input files input to the customer program and sizes of resultant past sequential output files produced by the customer program processing the sequential input files)
determine a correlation between the sizes of the past sequential input files and the resultant sizes of the past sequential output files (Paragraph 34, “A historical job can have at least one historical intermediate task. In order to statistically determine the second association precisely, the input data size and the output data size of all of these historical intermediate tasks are considered as long as they have the same logical as the processing of the intermediate task(s) of the target job. In one embodiment, the second association can refer to a fixed ratio of the input to output. In another embodiment, it can refer to a function of the input and output. The second association can indicate the characteristic of input and output of the target job in the intermediate stage in other manners and the embodiments of the present invention are not restricted in this regard.” The input data size and the output data size of the historical intermediate tasks being considered to determine a second association correlates to determining a correlation between the sizes of the past sequential input files and the resultant sizes of the past sequential output files), utilize the correlation to predict future consumption of the scalable resources including future sizes of future sequential output files based on the current sizes of current sequential input files (Paragraph 39, “As discussed above, the resource overhead associated with the processing of the target job, e.g., the overhead of computing resource, storage resource and I/O resource is related to the input and output of the target job in each stage. Therefore, in the calculation of the resource overhead, one or some of the first association, second association, and third association calculated above can be considered, so as to determine the resource overhead in the corresponding stages.” The resource overhead for the target job being calculated with consideration for at least the second association correlates to utilizing the correlation to predict future consumption of the scalable resources including future sizes of future sequential output files based on the current sizes of current sequential input files),
and utilize the predicted future consumption of the scalable resources to perform at least one of memory allocation or to determine scheduling for batch jobs to be performed by the system (Paragraph 40 and 46, “At step S203, an alert for the target job is generated in response to the resource overhead exceeding a predetermined threshold… According to the generated alert, the system administrator or supervisor can expand the storage space of the corresponding storage node cluster to avoid the occurrence of a fault when the target job is processed.” The system administrator using the alert for a target job to expand the storage space of a corresponding storage node cluster correlates to utilizing the predicted future consumption of the scalable resources to perform at least one of memory allocation), wherein the scalable resources include at least one of processing time, working memory or input/output time (Paragraph 39, “As discussed above, the resource overhead associated with the processing of the target job, e.g., the overhead of computing resource, storage resource and I/O resource is related to the input and output of the target job in each stage.” The resource overhead being associated with at least the overhead of a computing resource correlates to wherein the scalable resources include at least one of processing time).
With regards to Claim 11, the system of Claim 1 performs the same steps as the method of Claim 11, and Claim 11 is therefore rejected using the same rationale set forth above in the rejection of Claim 1.
With regards to claim 7, Cao teaches the system of claim 1 above. Cao further teaches:
wherein the processor is further configured to extract the history file from systems management farcicalities (SMF) records indicating job related information (Paragraphs 26 and 34, “Likewise, a historical job refers to the job that has been processed, e.g., a job(s) recorded in a log(s) of a job processing system, such as the Hadoop system… A historical job can have at least one historical intermediate task. In order to statistically determine the second association precisely, the input data size and the output data size of all of these historical intermediate tasks are considered as long as they have the same logical as the processing of the intermediate task(s) of the target job.” The input data size of the historical jobs being considered would involve accessing the respective log for the historical job that is stored in a job processing system and therefore correlates to wherein the processor is further configured to extract the history file from systems management facilities (SMF) records indicating job related information).
With regards to Claim 17, the system of Claim 7 performs the same steps as the method of Claim 17, and Claim 17 is therefore rejected using the same rationale set forth above in the rejection of Claim 7.
With regards to claim 9, Cao teaches the system of claim 1 above. Cao further teaches:
wherein the customer program used during processing of the past sequential input files of the historical data is the same as the customer program used during processing of the future sequential input files (Paragraphs 27 and 50, “In the processing of the target job and/or historical jobs in each stage, a job can have at least one task. Such tasks can be assigned to at least one computing node for parallel processing… When the target job has a plurality of intermediate tasks, the plurality of intermediate tasks are usually assigned to at least one computing node for execution in accordance with a predetermined strategy, and their output data can be stored in the local disk of the computing nodes.” The node processing both the target and historical jobs can be the same node which correlates to wherein the customer program used during processing of the past sequential input files of the historical data is the same as the customer program used during processing of the future sequential input files).
With regards to Claim 19, the method of Claim 9 performs the same steps as the method of Claim 19, and Claim 19 is therefore rejected using the same rationale set forth above in the rejection of Claim 9.
With regards to claim 10, Cao teaches the system of claim 1 above. Cao further teaches:
wherein the processor is further configured to use at least one of the predicted future sizes of future sequential output files, CPU time, and memory consumption to determine at least one of memory allocation for storing the sequential output files and processing time for producing the sequential output files (Paragraphs 39-40 and 46, “Paragraph 39, “As discussed above, the resource overhead associated with the processing of the target job, e.g., the overhead of computing resource, storage resource and I/O resource is related to the input and output of the target job in each stage. Therefore, in the calculation of the resource overhead, one or some of the first association, second association, and third association calculated above can be considered, so as to determine the resource overhead in the corresponding stages… At step S203, an alert for the target job is generated in response to the resource overhead exceeding a predetermined threshold. For example, if the storage resource overhead determined at step S202 is higher than the available storage resource overhead, a corresponding alert is generated. Alternatively or additionally, in one embodiment, if the overhead of read/write resource determined at step S202 exceeds a predetermined read/write quantity, a corresponding alert can be generated… According to the generated alert, the system administrator or supervisor can expand the storage space of the corresponding storage node cluster to avoid the occurrence of a fault when the target job is processed.” The resource overhead, which includes a storage overhead, for the target job being calculated correlates to the predicted future sizes of future sequential output files. The system administrator expanding the storage space of the corresponding storage node cluster in response to the alert generated when the resource overhead exceeds a threshold correlates to wherein the processor is further configured to use at least one of the predicted future sizes of future sequential output files, CPU time, and memory consumption to determine at least one of memory allocation for storing the sequential output files and processing time for producing the sequential output files).
With regards to Claim 20, the method of Claim 10 performs the same steps as the method of Claim 20, and Claim 20 is therefore rejected using the same rationale set forth above in the rejection of Claim 10.
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.
Claim(s) 2-4, 8, 12-14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cao in view of Zhang et al. (U.S. Publication No. US 20200167891 A1), hereinafter “Zhang.”
With regards to claim 2, Cao teaches the system of claim 1 above. Cao further teaches:
wherein the processor is further configured to determine the correlation by performing an analysis on the sizes of the past sequential input files and the resultant consumption of scalable resources (Paragraph 34, “A historical job can have at least one historical intermediate task. In order to statistically determine the second association precisely, the input data size and the output data size of all of these historical intermediate tasks are considered as long as they have the same logical as the processing of the intermediate task(s) of the target job. In one embodiment, the second association can refer to a fixed ratio of the input to output. In another embodiment, it can refer to a function of the input and output. The second association can indicate the characteristic of input and output of the target job in the intermediate stage in other manners and the embodiments of the present invention are not restricted in this regard.” The input data size and the output data size of the historical intermediate tasks being considered to determine a second association correlates to performing an analysis on the sizes of the past sequential input files and the resultant consumption of scalable resources), the analysis producing a prediction of the future consumption of the scalable resources based on the current sizes of the current sequential input files of the one or more sequential input files (Paragraph 39, “As discussed above, the resource overhead associated with the processing of the target job, e.g., the overhead of computing resource, storage resource and I/O resource is related to the input and output of the target job in each stage. Therefore, in the calculation of the resource overhead, one or some of the first association, second association, and third association calculated above can be considered, so as to determine the resource overhead in the corresponding stages.” The resource overhead for the target job being calculated with consideration for at least the second association correlates to the analysis producing a prediction of the future consumption of the scalable resources based on the current sizes of the current sequential input files of the one or more sequential input files).
Cao does not explicitly teach that the analysis is a linear regression and that the analysis produces a linear function for performing the prediction of the future consumption of the scalable resources. However, linear regression and linear functions for performing the prediction of the future consumption of the scalable resources are a popular method of analysis for predicting the future consumption of scalable resources as evidenced by Zhang (Paragraph 53, “An estimated amount of memory may be reserved for one or more image analysis jobs and partially suspending the one or more image analysis jobs upon memory requirements for the one or more image analysis jobs exceeding the estimated amount of memory. An upper bound of the estimated amount of memory may be estimated/predicted using a linear regression model with image resolution as an independent variable and a prediction interval. The linear regression model may be trained using a target function that penalizes under estimation of the estimated amount of memory as compared to over estimation.” The linear regression model being trained using a target function predicting the estimated amount of memory used for a job correlates to linear regression and a linear function for performing the prediction of the future consumption of the scalable resources).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Cao with a linear regression and a linear function for performing the prediction of the future consumption of the scalable resources as taught by Zhang because the amount of memory to be reserved may be predicted without actually running a job. An estimated amount of memory may be reserved for one or more image analysis jobs, with a linear regression model being trained using a target function that penalizes under estimation of the estimated amount of memory as compared to over estimation. Under estimating the amount of memory usage may cause job failures or performance degradation (Zhang: paragraphs 53-54 and 60).
With regards to Claim 12, the system of Claim 2 performs the same steps as the method of Claim 12, and Claim 12 is therefore rejected using the same rationale set forth above in the rejection of Claim 2.
With regards to claim 3, Cao in view of Zhang teaches the system of claim 2 above. Zhang further teaches:
wherein the processor is further configured to perform the linear regression as NxM linear regressions for NxM combinations of N input files by M output files, where N and M are integer values ranging between 1 and a maximum number of input files and output files that is supported by the program (Paragraphs 58-59 and 62, “In one aspect, a graph 510 of linear regression, using image resolution (e.g., pixels and/or voxels) as the dependent variable, may be used for approximation of memory (e.g., a gigabyte “GB” of memory). As illustrated in graph 510, using linear regression of the following equation: y=ƒ(x)=a*x+b, where a and b are computed using training data (xi, yi) . . . (xn, yn to minimize target function sum (F(xi)−yi).sup.2, as illustrated in graph 520 of FIG. 5B, where the data values are for illustration purpose only… Where {xi, y}.sub.i=1.sup.n may be an observed sample, where xi is the image size and yi is used memory.” The image sizes xi to xn correlates to N input files, and the yi to yn used memory correlates to M output files. The linear regression being performed for all y and x values correlates to performing the linear regression as NxM linear regressions for NxM combinations of N input files by M output files, where where N and M are integer values ranging between 1 and a maximum number of input files and output files that is supported by the program).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Cao with wherein the processor is further configured to perform the linear regression as NxM linear regressions for NxM combinations of N input files by M output files, where N and M are integer values ranging between 1 and a maximum number of input files and output files that is supported by the program as taught by Zhang because the amount of memory to be reserved may be predicted without actually running a job. An estimated amount of memory may be reserved for one or more image analysis jobs, with a linear regression model being trained using a target function that penalizes under estimation of the estimated amount of memory as compared to over estimation. Under estimating the amount of memory usage may cause job failures or performance degradation. The image resolution or image size may be used for approximation of memory used by the image analysis job (Zhang: paragraphs 53-54, 58 and 60).
With regards to Claim 13, the system of Claim 3 performs the same steps as the method of Claim 13, and Claim 13 is therefore rejected using the same rationale set forth above in the rejection of Claim 3.
With regards to claim 4, Cao in view of Zhang teaches the system of claim 2 above. Zhang further teaches:
wherein the processor is further configured to perform the linear regression KxN linear regressions for N input files and K transformations for each of the scalable resources consumed, where N is an integer value ranging from 1 to a number of indicating each input file data definition (DD) (Paragraphs 14, 58-59 and 62, “For example, image analysis is an important type of big data analytics. Such analysis may include medical image analysis (e.g., anatomy segmentation, computer aided diagnosis), general three-dimensional (“3D”) image analysis (e.g., surveillance video analysis) and two-dimensional (“2D”) image analysis (e.g., scene reconstruction, event detection, object recognition)… In one aspect, a graph 510 of linear regression, using image resolution (e.g., pixels and/or voxels) as the dependent variable, may be used for approximation of memory (e.g., a gigabyte “GB” of memory). As illustrated in graph 510, using linear regression of the following equation: y=ƒ(x)=a*x+b, where a and b are computed using training data (xi, yi) . . . (xn, yn to minimize target function sum (F(xi)−yi).sup.2, as illustrated in graph 520 of FIG. 5B, where the data values are for illustration purpose only… Where {xi, y}.sub.i=1.sup.n may be an observed sample, where xi is the image size and yi is used memory.” The image sizes xi to xn correlates to N input files, and the yi to yn used memory for each image analysis procedure such as scene reconstruction would involve at least one transformation on the image and therefore correlates to K transformations. The linear regression being performed for all y and x values correlates to performing the linear regression KxN linear regressions for N input files and K transformations for each of the scalable resources consumed, where N is an integer value ranging from 1 to a number of indicating each input file data definition (DD)).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Cao with wherein the processor is further configured to perform the linear regression KxN linear regressions for N input files and K transformations for each of the scalable resources consumed, where N is an integer value ranging from 1 to a number of indicating each input file data definition (DD) as taught by Zhang because the amount of memory to be reserved may be predicted without actually running a job. An estimated amount of memory may be reserved for one or more image analysis jobs, with a linear regression model being trained using a target function that penalizes under estimation of the estimated amount of memory as compared to over estimation. Under estimating the amount of memory usage may cause job failures or performance degradation. The image resolution or image size may be used for approximation of memory used by the image analysis job (Zhang: paragraphs 53-54, 58 and 60).
With regards to Claim 14, the system of Claim 4 performs the same steps as the method of Claim 14, and Claim 14 is therefore rejected using the same rationale set forth above in the rejection of Claim 4.
With regards to claim 8, Cao teaches the system of claim 1 above. Cao does not explicitly teach:
wherein the one or more sequential input files and sequential output files contain transactional data for the customer, and the one or more records contain information about a transactional event.
However, Zhang teaches:
wherein the one or more sequential input files and sequential output files contain transactional data for the customer, and the one or more records contain information about a transactional event (Paragraphs 51, “Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95…” The workloads layer supporting multiple workloads and functions which include transaction processing correlates to wherein the one or more sequential input files and sequential output files contain transactional data for the customer. The processing of a transaction would involve transaction data to be processed and therefore correlates to the one or more records contain information about a transactional event).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Cao wherein the one or more sequential input files and sequential output files contain transactional data for the customer, and the one or more records contain information about a transactional event as taught by Zhang because workload layers can support a variety of workloads and functions such as mapping and navigation, software development and lifecycle management, virtual classroom education delivery, data analytics processing, transaction processing and complex image analysis. Workloads and functions for managing memory for complex image analysis in a computing environment may include such operations as data analytics, data analysis, and notification functionality. These workloads and functions may work in conjunction with other portions of the various abstraction layers, such as those in hardware and software, virtualization, management, and other workloads to achieve caching and data-aware placement for acceleration of machine learning applications involving deep learning models and managing memory for workloads and functions in a computing environment (Zhang: paragraphs 51-53).
With regards to Claim 18, the system of Claim 8 performs the same steps as the method of Claim 18, and Claim 18 is therefore rejected using the same rationale set forth above in the rejection of Claim 8.
Claim(s) 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Cao in view of Chen et al. (U.S. Publication No. US 20190012210 A1), hereinafter “Chen.”
With regards to claim 5, Cao teaches the system of claim 1 above. Cao does not explicitly teach:
wherein the processor is further configured to determine the correlation by training a neural network by inputting the sizes of the past sequential input files, processing the sizes of the past sequential input files based on set weights, predicting resource consumption of the scalable resources, computing a difference between the predicted resource consumption and the known resultant consumption of past scalable resources, and adjusting the set weights in an attempt to minimize the difference.
However, Chen teaches:
wherein the processor is further configured to determine the correlation by training a neural network by inputting the past input files, processing the input based on set weights (Paragraphs 133-134, “The neural network 1200 is represented as multiple layers of interconnected neurons, such as neuron 1208, that can exchange data between one another. The layers include an input layer 1202 for receiving input data, a hidden layer 1204, and an output layer 1206 for providing a result… The neurons and connections between the neurons can have numeric weights, which can be tuned during training. For example, training data can be provided to the input layer 1202 of the neural network 1200, and the neural network 1200 can use the training data to tune one or more numeric weights of the neural network 1200.” The neural network being trained with training input data and neurons with numeric weights correlates to training a neural network by inputting the past input files and processing the input based on set weights), predicting an outcome, computing a difference between the predicted outcome and the known outcome, and adjusting the set weights in an attempt to minimize the difference (Paragraphs 127 and 134, “In supervised training, each input in the training data is correlated to a desired output… n some examples, the neural network 1200 can be trained using backpropagation. Backpropagation can include determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network 1200 and a desired output of the neural network 1200. Based on the gradient, one or more numeric weights of the neural network 1200 can be updated to reduce the difference, thereby increasing the accuracy of the neural network 1200.” The neural network determining a gradient based on a difference between the actual output and the desired output and adjusting the weights to reduce the difference correlates to predicting an outcome, and computing a difference between the predicted outcome and the known outcome, and adjusting the set weights in an attempt to minimize the difference).
Chen does not explicitly teach that the sizes of the past sequential input files is used as input to predict resource consumption of the scalable resources, that the predicted outcome is a predicted resource consumption and the known outcome is the known resultant consumption of past scalable resources. However, using the sizes of the past sequential input files as input is a popular method of predicting resource consumption of the scalable resources as evidenced by Cao above (Paragraphs 34 and 39, “A historical job can have at least one historical intermediate task. In order to statistically determine the second association precisely, the input data size and the output data size of all of these historical intermediate tasks are considered as long as they have the same logical as the processing of the intermediate task(s) of the target job. In one embodiment, the second association can refer to a fixed ratio of the input to output. In another embodiment, it can refer to a function of the input and output. The second association can indicate the characteristic of input and output of the target job in the intermediate stage in other manners and the embodiments of the present invention are not restricted in this regard... As discussed above, the resource overhead associated with the processing of the target job, e.g., the overhead of computing resource, storage resource and I/O resource is related to the input and output of the target job in each stage. Therefore, in the calculation of the resource overhead, one or some of the first association, second association, and third association calculated above can be considered, so as to determine the resource overhead in the corresponding stages.” The input data size of the historical intermediate tasks being considered to determine a second association correlates to using the sizes of the past sequential input files as input. The resource overhead for the target job being calculated with consideration for at least the second association correlates to using the sizes of the past sequential input files to predict resource consumption. Additionally, since the prediction result is the resource consumption, it would be reasonable to arrive at both a predicted resource consumption and a known resultant consumption of past scalable resources from the combination of Cao in view of Chen).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Cao with wherein the processor is further configured to determine the correlation by training a neural network by inputting the past input files, processing the input based on set weights, predicting an outcome, computing a difference between the predicted outcome and the known outcome, and adjusting the set weights in an attempt to minimize the difference as taught by Chen because neural networks can be trained using backpropagation, which includes determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network and a desired output of the neural network. Based on the gradient, one or more numeric weights of the neural network can be updated to reduce the difference, thereby increasing the accuracy of the neural network. This process can be repeated hundreds or thousands of times to train the neural network (Chen: paragraph 134).
With regards to Claim 15, the system of Claim 5 performs the same steps as the method of Claim 15, and Claim 15 is therefore rejected using the same rationale set forth above in the rejection of Claim 5.
With regards to claim 6, Cao in view of Chen teaches the system of claim 5 above. Chen further teaches:
wherein the processor is further configured to predict a result based on the input file sizes and at least one of logical record length and block size of the input files, record format, timing of job execution, parameters passed to the program from the input files, names data definitions (DDs) for the input files, and duration of job execution (Paragraphs 34, 36 and 39, “A historical job can have at least one historical intermediate task. In order to statistically determine the second association precisely, the input data size and the output data size of all of these historical intermediate tasks are considered as long as they have the same logical as the processing of the intermediate task(s) of the target job… In some embodiments, the input data of historical intermediate tasks can be stored in files, in which the input data can be stored in rows or columns. In such instance, the corresponding third association is determined based on the input data in at least one of the rows or columns of the file and the corresponding output record number… As discussed above, the resource overhead associated with the processing of the target job, e.g., the overhead of computing resource, storage resource and I/O resource is related to the input and output of the target job in each stage. Therefore, in the calculation of the resource overhead, one or some of the first association, second association, and third association calculated above can be considered, so as to determine the resource overhead in the corresponding stages.” The second association using the input data size correlates to the input file sizes. The third association using the rows and columns of the input file correlates to the record format. The resource overhead for the target job being calculated with consideration for at least the second and third association correlates wherein the processor is further configured to predict a result based on the input file sizes and the record format).
Cao does not explicitly teach that a neural network [is trained] to provide the result. However, training the neural network is a popular method to provide prediction results as evidenced by Chen above (Paragraphs 133-134, “The neural network 1200 is represented as multiple layers of interconnected neurons, such as neuron 1208, that can exchange data between one another. The layers include an input layer 1202 for receiving input data, a hidden layer 1204, and an output layer 1206 for providing a result… The neurons and connections between the neurons can have numeric weights, which can be tuned during training. For example, training data can be provided to the input layer 1202 of the neural network 1200, and the neural network 1200 can use the training data to tune one or more numeric weights of the neural network 1200.” The neural network being trained to provide a prediction result correlates to training the neural network).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Cao with train the neural network as taught by Chen because neural networks can be trained using backpropagation, which includes determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network and a desired output of the neural network. Based on the gradient, one or more numeric weights of the neural network can be updated to reduce the difference, thereby increasing the accuracy of the neural network. This process can be repeated hundreds or thousands of times to train the neural network (Chen: paragraph 134).
With regards to Claim 16, the system of Claim 6 performs the same steps as the method of Claim 16, and Claim 16 is therefore rejected using the same rationale set forth above in the rejection of Claim 6.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Cao in view of Eberlein et al. (U.S. Publication No. US 20120005597 A1), hereinafter “Eberlein.”
With regards to claim 21, Cao teaches the system of claim 1 above. Cao does not explicitly teach:
wherein determining the scheduling for batch jobs includes rescheduling a batch job of the batch jobs for a later time when system load of the system is lower based on a prediction of the processing resources necessary to execute the batch job.
However, Eberlein teaches:
wherein determining the scheduling for batch jobs includes rescheduling a batch job of the batch jobs for a later time when system load of the system is lower based on a prediction of the processing resources necessary to execute the batch job (Paragraphs 31-33, “An overview of the expected resource consumption of a multi-user or multitenancy computer system may help the users of the system to request job executions for more appropriate times in order to avoid system overload. This is especially relevant for batch job requests with long runtimes. Generally, every user or tenant of a computer system is interested to behave cooperatively as system overload or peak resource consumption decreases the performance of the system for the user, as well as for the rest of the users… At 410, an expected duration for the requested execution of the referenced batch job is estimated based on predefined criteria. The estimation of the execution duration of the batch job may follow the process described with respect to FIG. 3. The estimation of the duration may account for the available capacity of the multitenancy system… The users of the system have access to the anonymous load chart showing the expected system load. The user may specify the most appropriate start time or execution period for the batch job based on the expected system load, and the estimated duration of the batch job execution. At 415, it is verified whether the new or rescheduled batch job request includes a start time for triggering the execution. If start time is not specified, the system may automatically select a start time for the execution of the referenced batch job at 420 based on the expected system load.” The estimated duration for the requested execution of a batch job accounts for the available capacity of resources in the system and expected resource consumption, along with the expected system load, correlates to a prediction of the processing resources necessary to execute the batch job. The system automatically selecting a start time for execution of the batch job request based on the expected system load chart and the estimated duration to avoid system overload scenarios would involve selecting execution times outside peak resource consumption times and therefore correlates to wherein determining the scheduling for batch jobs includes rescheduling a batch job of the batch jobs for a later time when system load of the system is lower based on a prediction of the processing resources necessary to execute the batch job).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Cao wherein determining the scheduling for batch jobs includes rescheduling a batch job of the batch jobs for a later time when system load of the system is lower based on a prediction of the processing resources necessary to execute the batch job as taught by Eberlein because an overview of the expected resource consumption of a multi-user or multitenancy computer system may help the users of the system to request job executions for more appropriate times in order to avoid system overload. This is especially relevant for batch job requests with long runtimes. Generally, every user or tenant of a computer system is interested to behave cooperatively as system overload or peak resource consumption decreases the performance of the system for the user, as well as for the rest of the users. The system overload also increases the duration of the batch run and therefore creates a risk that it will not be completed by a given deadline. Additionally, the users and the tenants can be stimulated through further incentives to support cooperative behavior (Eberlein: paragraph 31).
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
X. Chen et al. (U.S. Publication No. US 20230012021 A1); teaching a method of grouping correlated resource objects according to processing device utilization and assign weights to each group. The self-tuning resource allocating system calculates weights for the resource object groupings using a linear regression function and utilization statistics associated with the resource object group in a sampling period. The weight may be calculated for each feature that contributes to the system performance using a linear regression function.
Conclusion
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SELINA HU
Examiner
Art Unit 2193
/Chat C Do/Supervisory Patent Examiner, Art Unit 2193
Prosecution Insights