DETAILED ACTION
This action is in response to the Applicant Response filed 12 March 2026 for application 17/453,387 filed 18 February 2021.
Claim(s) 1, 8, 15 is/are currently amended.
Claim(s) 1-20 is/are pending.
Claim(s) 1-20 is/are rejected.
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 Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of claim 1-20 have been fully considered but are not persuasive. Applicant argues that the claims provide an improvement which integrated the claim into a practical application. Specifically applicant argues that the claims are focused on sequentially allocating time series data and executing a planning algorithm to improve computational efficiency. Examiner respectfully disagrees. The MPEP states that it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a). As noted below, the claim recites limitations which allocate data for the models, evaluate the models and select a model based on the evaluation score. This process for selecting a model is an abstract idea, and any improvement demonstrated by the recited claims is, at best, an improvement in the abstract idea. Therefore, the claims do not integrate the abstract idea into a practical application and the 35 U.S.C. 101 rejection of claims 1-20 is maintained.
Applicant’s arguments regarding the 35 U.S.C. 103 rejections of claims 1-20 have been fully considered but are not persuasive.
Applicant first argues that Sabharwal does not teach the term "joint optimizer" and, therefore, does not teach the recited claims. Examiner respectfully disagrees. As recited in the claims, the joint optimizer is a process to train a plurality of pipelines wherein the training starts with a minimum allocation of data to trigger data allocation using upper bounds. Sabharwal teaches the same process to train a plurality of pipelines with data allocation using upper bounds (Sabharwal, section 3). Therefore, Sabharwal does, in fact, teach a joint optimizer.
Applicant next argues that individually, the references do not teach all of the limitations of the claims and further asserts that the combination does not teach all limitations. Examiner respectfully disagrees. Examiner agrees that individually the references do not teach all of the limitations of the claims. However, as noted in detail below, the combination of the joint optimizer in Sabharwal and the model selection based on time series data in Li does teach all of the limitations. Obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it would be obvious to extend the joint optimizer of Sabharwal to include processing time series data to select a model as recited in Li as the need for time series prediction modeling grows (Li, Abstract).
Therefore, claims 1-20 stand rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 15 recites sequentially, incrementally allocate, by a machine learning component, in association with an allocation component, an evaluation component, and the joint optimizer component of a time series forecasting machine learning pipeline ranking service, additional time series data from a time series data set for testing by one or more candidate machine learning pipelines while also including a limitation “program instruction to sequentially, incrementally allocate …” It is unclear why this limitation is included in the claim. Correction or clarification is required.
Examiner’s Note: For the purposes of examination, the limitation will be interpreted as a typographical error and the claim will be interpreted as if the limitation is not included.
Claims 16-20 are rejected under 35 U.S.C. 112(b) due to their dependence, either directly or indirectly, on claim 15.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for ranking time series forecasting machine learning pipelines.
The limitation of incrementally allocating … additional time series data from a time series data set for testing by one or more candidate machine learning pipelines, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of providing … intermediate evaluation scores ... following each time series data allocation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of automatically selecting … one or more machine learning pipelines from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computing environment, one or more processors, time series data pipelines. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – time series forecasting machine learning pipelines, joint optimizer, candidate machine learning pipelines, time series data allocation using upper bounds (TDAUB), machine learning component, allocation component, evaluation component, joint optimizer component, time series forecasting machine learning pipeline ranking service. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites training by a joint optimizer a plurality of candidate machine learning pipelines which is simply generic training to perform the abstract idea of model ranking and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites wherein the candidate machine learning pipelines comprise a plurality of time series data pipelines, and wherein the training starts with a minimum allocation of time series data that is a threshold selected to trigger the time series data allocation using upper bounds (TDAUB) which is simply additional information regarding the candidate machine learning pipelines, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computing environment, one or more processors, time series data pipelines amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
time series forecasting machine learning pipelines, joint optimizer, candidate machine learning pipelines, time series data allocation using upper bounds (TDAUB), machine learning component, allocation component, evaluation component, joint optimizer component, time series forecasting machine learning pipeline ranking service amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the candidate machine learning pipelines do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for ranking time series forecasting machine learning pipelines.
The limitation of allocating defined subsets of the time series data backward in time to each of the one or more candidate machine learning pipelines, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for ranking time series forecasting machine learning pipelines.
The limitation of identifying a portion of the time series data exceeding a time-based threshold as historical time series data, wherein the historical time series data is less accurate training data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for ranking time series forecasting machine learning pipelines.
The limitation of ... evaluating the one or more candidate machine learning pipelines for each allocation of the time series data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites training ... the one or more candidate machine learning pipelines for each allocation of the time series data which is simply generic training to perform the abstract idea of model ranking and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for ranking time series forecasting machine learning pipelines.
The limitation of incrementally increasing an allocation amount of training data in the one or more candidate machine learning pipelines based on an intermediate evaluation score from one or more previous allocation amounts of the training data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for ranking time series forecasting machine learning pipelines.
The limitation of determining the projected learning curve generated from each of the intermediate evaluation scores, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for ranking time series forecasting machine learning pipelines.
The limitation of ranking each of the one or more candidate machine learning pipelines based on the projected learning curve, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for ranking time series forecasting machine learning pipelines.
The limitation of sequentially, incrementally allocate … additional time series data from a time series data set for testing by one or more candidate machine learning pipelines, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of provide … intermediate evaluation scores ... following each time series data allocation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of automatically select … one or more machine learning pipelines from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – system, computing environment, one or more computers, executable instructions, time series data pipelines. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – time series forecasting machine learning pipelines, joint optimizer, candidate machine learning pipelines, time series data allocation using upper bounds (TDAUB), machine learning component, allocation component, evaluation component, joint optimizer component, time series forecasting machine learning pipeline ranking service. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites train by a joint optimizer a plurality of candidate machine learning pipelines which is simply generic training to perform the abstract idea of model ranking and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites wherein the candidate machine learning pipelines comprise a plurality of time series data pipelines, and wherein the training starts with a minimum allocation of time series data that is a threshold selected to trigger the time series data allocation using upper bounds (TDAUB) which is simply additional information regarding the candidate machine learning pipelines, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
system, computing environment, one or more computers, executable instructions, time series data pipelines amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
time series forecasting machine learning pipelines, joint optimizer, candidate machine learning pipelines, time series data allocation using upper bounds (TDAUB), machine learning component, allocation component, evaluation component, joint optimizer component, time series forecasting machine learning pipeline ranking service amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the candidate machine learning pipelines do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for ranking time series forecasting machine learning pipelines.
The limitation of allocate defined subsets of the time series data backward in time to each of the one or more candidate machine learning pipelines, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for ranking time series forecasting machine learning pipelines.
The limitation of identify a portion of the time series data exceeding a time-based threshold as historical time series data, wherein the historical time series data is less accurate training data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for ranking time series forecasting machine learning pipelines.
The limitation of ... evaluate the one or more candidate machine learning pipelines for each allocation of the time series data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites train ... the one or more candidate machine learning pipelines for each allocation of the time series data which is simply generic training to perform the abstract idea of model ranking and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for ranking time series forecasting machine learning pipelines.
The limitation of incrementally increase an allocation amount of training data in the one or more candidate machine learning pipelines based on an intermediate evaluation score from one or more previous allocation amounts of the training data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for ranking time series forecasting machine learning pipelines.
The limitation of determine the projected learning curve generated from each of the intermediate evaluation scores, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for ranking time series forecasting machine learning pipelines.
The limitation of rank each of the one or more candidate machine learning pipelines based on the projected learning curve, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for ranking time series forecasting machine learning pipelines.
The limitation of sequentially, incrementally allocate … additional time series data from a time series data set for testing by one or more candidate machine learning pipelines, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of provide … intermediate evaluation scores ... following each time series data allocation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of automatically select … one or more machine learning pipelines from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer program product, computing environment, one or more computer readable storage media, program instructions, time series data pipelines. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – time series forecasting machine learning pipelines, joint optimizer, candidate machine learning pipelines, time series data allocation using upper bounds (TDAUB), machine learning component, allocation component, evaluation component, joint optimizer component, time series forecasting machine learning pipeline ranking service. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites train by a joint optimizer a plurality of candidate machine learning pipelines which is simply generic training to perform the abstract idea of model ranking and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites wherein the candidate machine learning pipelines comprise a plurality of time series data pipelines, and wherein the training starts with a minimum allocation of time series data that is a threshold selected to trigger the time series data allocation using upper bounds (TDAUB) which is simply additional information regarding the candidate machine learning pipelines, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer program product, computing environment, one or more computer readable storage media, program instructions, time series data pipelines amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
time series forecasting machine learning pipelines, joint optimizer, candidate machine learning pipelines, time series data allocation using upper bounds (TDAUB), machine learning component, allocation component, evaluation component, joint optimizer component, time series forecasting machine learning pipeline ranking service amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the candidate machine learning pipelines do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for ranking time series forecasting machine learning pipelines.
The limitation of allocate defined subsets of the time series data backward in time to each of the one or more candidate machine learning pipelines, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for ranking time series forecasting machine learning pipelines.
The limitation of identify a portion of the time series data exceeding a time-based threshold as historical time series data, wherein the historical time series data is less accurate training data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for ranking time series forecasting machine learning pipelines.
The limitation of ... evaluate the one or more candidate machine learning pipelines for each allocation of time series data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of increase an allocation amount of training data in the one or more candidate machine learning pipelines based on an intermediate evaluation score from one or more previous allocation amounts of the training data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites train ... the one or more candidate machine learning pipelines for each allocation of time series data which is simply generic training to perform the abstract idea of model ranking and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for ranking time series forecasting machine learning pipelines.
The limitation of determine the learning curve generated from each of the intermediate evaluation scores, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for ranking time series forecasting machine learning pipelines.
The limitation of rank each of the one or more candidate machine learning pipelines based on the projected learning curve, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
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.
Claim(s) 1-2, 4-9, 11-16, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabharwal et al. (Selecting Near-Optimal Learners via Incremental Data Allocation, hereinafter referred to as “Sabharwal”) in view of Cerqueira et al. (“Machine learning vs Statistical Methods for Time Series Forecasting: Size Matters,” hereinafter referred to as “Cerqueira”).
Regarding claim 1 (Currently Amended), Sabharwal teaches a method for ranking … forecasting machine learning pipelines in a computing environment by one or more processors (Sabharwal, section 4 - teaches processors, memory and software) comprising:
training by a joint optimizer a plurality of candidate machine learning pipelines, wherein the candidate machine learning pipelines comprise a plurality of … data pipelines (Sabharwal, section 3 – teaches training a plurality of classifiers using a joint optimizer), and wherein the training starts with a minimum allocation of … data that is a threshold selected to trigger the … data allocation using upper bounds (TDAUB) (Sabharwal, section 3 – teaches training with a minimum allocation of data using data allocation upper bounds);
incrementally allocating, by a machine learning component, in association with an allocation component, an evaluation component, and the joint optimizer component of a time series forecasting machine learning pipeline ranking service (Sabharwal, section 4 - teaches machine learning algorithms implemented in WEKA to perform the step of the method detailed [WEKA provides software components to perform the various steps]), additional … data from a … data set for testing by one or more candidate machine learning pipelines (Sabharwal, section 3 – teaches incrementally increasing training data for each training iteration) …;
providing, by the machine learning component in association with the allocation component, the evaluation component, and the joint optimizer component of the time series forecasting machine learning pipeline ranking service (Sabharwal, section 4 - teaches machine learning algorithms implemented in WEKA to perform the step of the method detailed [WEKA provides software components to perform the various steps]), intermediate evaluation scores by each of the one or more candidate machine learning pipelines following each … data allocation (Sabharwal, section 3 – teaches evaluating each classifier at each iteration); and
automatically selecting, by the machine learning component in association with the allocation component, the evaluation component, and the joint optimizer component of the time series forecasting machine learning pipeline ranking service (Sabharwal, section 4 - teaches machine learning algorithms implemented in WEKA to perform the step of the method detailed [WEKA provides software components to perform the various steps]), one or more machine learning pipelines from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores (Sabharwal, section 3, Figures 1, 3 - showing ranking and selecting of models based on accuracy [evaluation score] learning curves at selected intermediate training iterations; see also Sabharwal, section 5- teaches informing user of information including learning curve and data allocation).
While Sabharwal teaches model selection with data allocation using upper bounds, Sabharwal does not explicitly teach time series data.
Cerqueira teaches a method for ranking time series forecasting machine learning pipelines in a computing environment (Cerqueira, section 3 - teaches ranking time series forecasting models) by one or more processors (Cerqueira, section 3.1 - teaches implementing the models using the forecast R package [software requiring a computer]) comprising:
… wherein the candidate machine learning pipelines comprise a plurality of time series data pipelines (Cerqueira, section 3 - teaches ranking time series forecasting models) …;
incrementally allocating additional time series data from a time series data set for testing by one or more candidate machine learning pipelines based on seasonality patterns, trending patterns or a degree of temporal dependence of the time series data (Cerqueira, section 3.1 - teaches five statistical method and five ML algorithms for forecasting time series data; Cerqueira, section 3.1 - teaches sequentially partitioning data; Cerqueira, section 3.4 - teaches incrementally allocating time series data [temporal dependence]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Sabharwal with the teachings of Cerqueira in order to extend model selection to time series forecasting models in the field of model optimization (Cerqueira, Abstract – “Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, evidence was shown that these approaches systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows.”).
Regarding claim 2 (Original), Sabharwal in view of Cerqueira teaches all of the limitations of the method of claim 1 as noted above. Cerqueira further teaches allocating defined subsets of the time series data backward in time to each of the one or more candidate machine learning pipelines (Cerqueira, section 3.3 - teaches a subset of historical time series data used to train models).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Sabharwal and Cerqueira in order to allocate time series data to extend model selection to time series forecasting models (Cerqueira, Abstract).
Regarding claim 4 (Original), Sabharwal in view of Cerqueira teaches all of the limitations of the method of claim 1 as noted above. Sabharwal further teaches training and evaluating the one or more candidate machine learning pipelines for each allocation of the time series data (Sabharwal, section 3 – teaches training and evaluating the classifiers for each allocation of data [Combined with Cerqueira for time series data]).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Sabharwal and Cerqueira for the same reasons as disclosed in claim 1 above.
Regarding claim 5 (Original), Sabharwal in view of Cerqueira teaches all of the limitations of the method of claim 1 as noted above. Sabharwal further teaches incrementally increasing an allocation amount of training data in the one or more candidate machine learning pipelines based on an intermediate evaluation score from one or more previous allocation amounts of the training data (Sabharwal, section 3 – teaches incrementally increasing the about of training data used to train the classifier based on evaluations of classifiers using previous data allocation amounts).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Sabharwal and Cerqueira for the same reasons as disclosed in claim 1 above.
Regarding claim 6 (Currently Amended), Sabharwal in view of Cerqueira teaches all of the limitations of the method of claim 1 as noted above. Sabharwal further teaches determining the projected learning curve generated from each of the intermediate evaluation scores (Sabharwal, Figures 1, 3 - showing ranking of models based on accuracy [evaluation score] learning curves at selected intermediate training iterations; see also Sabharwal, section 5- teaches informing user of information including learning curve and data allocation).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Sabharwal and Cerqueira for the same reasons as disclosed in claim 1 above.
Regarding claim 7 (Original), Sabharwal in view of Cerqueira teaches all of the limitations of the method of claim 1 as noted above. Sabharwal further teaches ranking each of the one or more candidate machine learning pipelines based on the projected learning curve (Sabharwal, Figures 1, 3 - showing ranking of models based on accuracy [evaluation score] learning curves at selected intermediate training iterations; see also Sabharwal, section 5- teaches informing user of information including learning curve and data allocation).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Sabharwal and Cerqueira for the same reasons as disclosed in claim 1 above.
Regarding claim 8 (Currently Amended), it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Sabharwal further teaches a system for ranking time series forecasting machine learning pipelines in a computing environment, comprising:
one or more computers with executable instructions that when executed cause the system to (Sabharwal, section 4 - teaches processors, memory and software) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Sabharwal and Cerqueira for the same reasons as disclosed in claim 1 above.
Regarding claim 9 (Original), the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 2.
Regarding claim 11 (Original), the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 4.
Regarding claim 12 (Original), the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 5.
Regarding claim 13 (Currently Amended), the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 6.
Regarding claim 14 (Original), the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 7.
Regarding claim 15 (Currently Amended), it is the computer program product embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Sabharwal further teaches a computer program product for ranking time series forecasting machine learning pipelines in a computing environment, the computer program product comprising:
one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instruction comprising (Sabharwal, section 4 - teaches processors, memory and software) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Sabharwal and Cerqueira for the same reasons as disclosed in claim 1 above.
Regarding claim 16 (Original), the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 2.
Regarding claim 18 (Original), Sabharwal in view of Cerqueira teaches all of the limitations of the computer program product of claim 15 as noted above. Sabharwal further teaches
train and evaluate the one or more candidate machine learning pipelines for each allocation of time series data (Sabharwal, section 3 – teaches training and evaluating the classifiers for each allocation of data [Combined with Cerqueira for time series data]); and
increase an allocation amount of training data in the one or more candidate machine learning pipelines based on an intermediate evaluation score from one or more previous allocation amounts of the training data (Sabharwal, section 3 – teaches incrementally increasing the about of training data used to train the classifier based on evaluations of classifiers using previous data allocation amounts).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Sabharwal and Cerqueira for the same reasons as disclosed in claim 1 above.
Regarding claim 19 (Original), the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 6.
Regarding claim 20 (Original), the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira for the reasons set forth in the rejection of claim 7.
Claim(s) 3, 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabharwal in view of Cerqueira and further in view of Li et al. (“Data Stream Event Prediction Based on Timing Knowledge and State Transitions”, hereinafter referred to as “Li”).
Regarding claim 3 (Original), Sabharwal in view of Cerqueira teaches all of the limitations of the method of claim 1 as noted above. However, Sabharwal in view of Cerqueira does not explicitly teach identifying a portion of the time series data exceeding a time-based threshold as historical time series data, wherein the historical time series data is less accurate training data.
Li teaches identifying a portion of the time series data exceeding a time-based threshold as historical time series data, wherein the historical time series data is less accurate training data (Li, section 6.2.3 - teaches windowing time series data wherein at a given threshold size, the accuracy decreases).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Sabharwal in view of Cerqueira with the teachings of Li in order to more efficient method of ranking time series forecasting models in the field of time series forecasting models (Li, section 6.2.4 – “We find that, while initial increase of window size can improve precision and recall accuracy, further increase beyond a certain point actually decreases the accuracy. This is because the more recent data is more accurate for training the current model; data more ancient in the history may distract the training process in learning the current latent features.”).
Regarding claim 10 (Original), the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira and further in view of Li for the reasons set forth in the rejection of claim 3.
Regarding claim 17 (Original), the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Sabharwal in view of Cerqueira and further in view of Li for the reasons set forth in the rejection of claim 3.
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 communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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