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 . This action is made non-final.
This action is in response to the application and claims filed December 14, 2023. Claims 1-20 are pending in the case and have been examined. Claims 1-20 are rejected.
Claim Rejections - 35 USC § 112
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 11, and 18-20 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 11 recites the limitation "the labeled dataset" in page 78, claim 11 line 1. There is insufficient antecedent basis for this limitation in the claim. Claim 11 depends on claim 9, which does not previously recite a labeled dataset, but claim 10 does.
Claim 18 recites the limitation "the labeled dataset" in page 79, claim 18 line 1. There is insufficient antecedent basis for this limitation in the claim. Claim 18 depends on claim 16, which does not previously recite a labeled dataset, but claim 17 does.
Claim 19 recites the limitation "select an initial set of parameters based on the respective range associated with each parameter" in page 79, claim 19 lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Unlike claims 1, and 9, claim 16 does not recite that any parameter is associated with a respective range.
Claim 20 recites the limitation "select, using the machine learning algorithm, a set of predicted parameters based on the respective range associated with each parameter" in page 80, claim 20 lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Unlike claims 1, and 9, claim 16 does not recite that any parameter is associated with a respective range.
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 therefore, subject to the conditions and requirements of this title.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 are directed to a computer implemented method (a process), Claims 9-15 are directed to a processor comprising one or more circuits (a machine), and Claims 16-20 is directed to a system (a manufacture). Therefore, Claims 1-20 are directed to a process, machine or manufacture or composition of matter.
Regarding claim 1
Step 2A Prong 1
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “processing pipeline”, “modules”, and “machine learning algorithm”) [see MPEP 2106.04(a)(2)(III)].
“Generating a set of tuned parameters for the processing pipeline” (e.g., a human can take data then determine improved/optimized parameters of those values to then input into a machine learning system for faster/optimized training of that system)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “processing pipeline”, “modules”, and “machine learning algorithm” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The Examiner notes that this is used throughout the claim limitations and is rejected thusly for each claim which recites the same language.
Regarding the “receiving data associated with a processing pipeline” this additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of inputting data for use in the claimed process (see MPEP 2106.05(g)).
Regarding “the processing pipeline including a plurality of modules and a plurality of parameters associated with the modules” which is recited at a high-level of generality such that they amount to no more than generally linking the use of abstract idea to a particular technological environment or field of use using a generic computer component (See MPEP 2106.05(h)). It is merely limiting the claimed parameter tuning to the technological environment of a processing pipeline having modules and associated parameters, without reciting any technological implementation or improvement to the processing pipeline.
Regarding “the received data including a set of parameters of the plurality of parameters to be tuned, at least one parameter of the set of parameters being associated with a respective range” this additional element is recited at a high level of generality and amounts to extra-solution activity of what information is being supplied for later analysis, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Regarding “using a machine learning algorithm that uses the received data associated with the processing pipeline as input” which is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of a “processing pipeline”, “modules”, and “machine learning algorithm” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receiving data associated with a processing pipeline” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding “the processing pipeline including a plurality of modules and a plurality of parameters associated with the modules” which is recited at a high-level of generality such that they amount to no more than generally linking the use of abstract idea to a particular technological environment or field of use using a generic computer component (See MPEP 2106.05(h)). It is merely limiting the claimed parameter tuning to the technological environment of a processing pipeline having modules and associated parameters, without reciting any particular technological implementation or improvement to the processing pipeline.
Regarding “the received data including a set of parameters of the plurality of parameters to be tuned, at least one parameter of the set of parameters being associated with a respective range” this additional element is recited at a high level of generality and amounts to pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding “using a machine learning algorithm that uses the received data associated with the processing pipeline as input” which is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 2
Step 2A Prong 1
Claim 2 does not recite an abstract idea but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “receiving a labeled dataset that includes an input dataset and associated labels” this additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of inputting data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “generating additional data samples by performing data augmentation on the labeled dataset, wherein the data augmentation generates variations of the received dataset” this additional element is recited at a high level of generality and amounts to extra-solution activity of what information is being supplied for later analysis, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “passing the labeled dataset and the additional data samples to the machine learning algorithm as input for generating the set of tuned parameters” this additional element is recited at a high level of generality and amounts to extra-solution activity of sending data, i.e. post-solution activity of mere data gathering for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of
“receiving a labeled dataset that includes an input dataset and associated labels” this additional element is recited at a high level of generality and amounts to extra-solution activity of pre-solution activity of inputting data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “generating additional data samples by performing data augmentation on the labeled dataset, wherein the data augmentation generates variations of the received dataset” this additional element is recited at a high level of generality and amounts to extra-solution activity of pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “passing the labeled dataset and the additional data samples to the machine learning algorithm as input for generating the set of tuned parameters” this additional element is recited at a high level of generality and amounts to extra-solution activity of post-solution activity of mere data gathering for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 3
Step 2A Prong 1
Claim 3 does not recite an abstract idea but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the labeled dataset includes a set of input images and associated labels” this additional element is recited at a high level of generality and amounts to extra-solution activity of defining the type of data, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “wherein the processing pipeline, when executed, produces labeled images” this additional element is recited at a high level of generality and amounts to extra-solution activity of what information is being supplied for analysis and output, i.e. post-solution activity of data outputting for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of “wherein the labeled dataset includes a set of input images and associated labels” this additional element is recited at a high level of generality and amounts to extra-solution pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “wherein the processing pipeline, when executed, produces labeled images” this additional element is recited at a high level of generality and amounts to extra-solution post-solution activity of data outputting for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 4
Step 2A Prong 1
Claim 4 does not recite an abstract idea but is directed to the abstract idea identified in its parents’ claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein generating the set of tuned parameters further comprises: randomly selecting an initial set of parameters based on the respective range associated with the at least one parameter” this additional element is recited at a high level of generality and amounts to extra-solution activity of selecting initial parameter values, within previously specified ranges, as preparatory input for the subsequent parameter tuning process, i.e. pre-solution activity of insignificant application for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of “wherein generating the set of tuned parameters further comprises randomly selecting an initial set of parameters based on the respective range associated with the at least one parameter” this additional element is recited at a high level of generality and amounts to extra-solution pre-solution activity of insignificant extra solution activity for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 5
Step 2A Prong 1
Claim 5 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “processing pipeline”, “modules”, and “machine learning algorithm”) [see MPEP 2106.04(a)(2)(III)].
“evaluating a performance of the processing pipeline based on results generated by execution of the one or more modules” (e.g., a human can evaluate the performance of a processing pipeline based on results produced by the pipeline)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “selecting, using the machine learning algorithm, a set of predicted parameters based on the respective range associated with the at least one parameter” this additional element is recited at a high level of generality and amounts to extra-solution activity of defining the type of data, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)). The limitation merely selects a set of predicted parameter values, within the previously defined parameter ranges, for subsequent use in executing and evaluating the processing pipeline.
Regarding the “executing one or more of the modules in the processing pipeline using the set of selected parameters” which is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The limitation merely uses the generically recite processing pipeline modules as a tool to execute the pipeline using the selected parameter values, without reciting any particular technological implementation or improvement.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of “selecting, using the machine learning algorithm, a set of predicted parameters based on the respective range associated with the at least one parameter” this additional element is recited at a high level of generality and amounts to extra-solution pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “wherein the processing pipeline, when executed, produces labeled images” this additional element is recited at a high level of generality and amounts to extra-solution post-solution activity of data outputting for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 6
Step 2A Prong 1
Claim 6 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “processing pipeline”, “modules”, and “machine learning algorithm”) [see MPEP 2106.04(a)(2)(III)].
“generating a performance score based on results generated by execution of the one or more modules” (e.g., a human can evaluate the performance of a processing pipeline results and then rank those results giving them a score to identify the best and worst ones)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “passing the performance score to the machine learning algorithm for generating a subsequent iteration of predicted parameters” this additional element is recited at a high level of generality and amounts to extra-solution activity of selecting initial parameter values, within previously specified ranges, as preparatory input for the subsequent parameter tuning process, i.e. pre-solution activity of insignificant application for use in the claimed process (see MPEP 2106.05(g)). The limitation merely passes the generated performance score as input to the machine learning algorithm for use in a subsequent iteration of the parameter tuning process.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of “passing the performance score to the machine learning algorithm for generating a subsequent iteration of predicted parameters” this additional element is recited at a high level of generality and amounts to extra-solution pre-solution activity of insignificant extra solution activity for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 7
Step 2A Prong 1
Claim 7 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “processing pipeline”, “modules”, and “machine learning algorithm”) [see MPEP 2106.04(a)(2)(III)].
“generating…, an estimated parameter distribution, wherein the set of tuned parameters is generated based on the estimated parameter distribution” (e.g., a human can analyze the parameter information to estimate likely/appropriate parameter values and using that estimate to make the selection)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “using the machine learning algorithm” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of a “using the machine learning algorithm” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 8
Step 2A Prong 1
Claim 8 does not recite an abstract idea but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein generating the set of tuned parameters is distributed over multiple workers of a compute node, each worker executing and evaluating a performance of the processing pipeline independently” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The limitation merely distributes performance of the claimed parameter tuning process among multiple generic computer workers, each independently executing and evaluating the processing pipeline. The examiner notes that it could also be viewed under 2106.05(h0 since “multiple workers of a compute node” defines a technological environment in which the tuning occurs.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of a “wherein generating the set of tuned parameters is distributed over multiple workers of a compute node, each worker executing and evaluating a performance of the processing pipeline independently” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claims 9-15
Claims 9-15 recites a processor. Each of these claims corresponds to the method steps of claims 1-7, respectively, with the addition of generic hardware components such as a processor which are insufficient to render the claims subject matter eligible for the same reasons as described above.
Regarding claims 16-20
Claims 16-20 recites a system. Each of these claims corresponds to the method steps of claims 1-5, respectively, with the addition of generic hardware components such as a processor which are insufficient to render the claims subject matter eligible for the same reasons as described above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-6, 8, 9, 12-14, 16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patel et a. (US 20220138616 A1, referred to as Patel) in view of Chen et al. (US 20220036246 A1, referred to as Chen).
Regarding claim 1, Patel teaches a computer-implemented method (Abstract: Describes a method for computer implementation), comprising:
receiving data associated with a processing pipeline, the processing pipeline including a plurality of modules and a plurality of parameters associated with the modules, the received data including a set of parameters of the plurality of parameters to be tuned, at least one parameter of the set of parameters being associated with a respective range (Fig. 6, [0032-0038], [0045-0050], [0054], and [0065-0068] Describes a user configurable pipeline having a plurality of pipeline nodes/machine learning components, wherein hyperparameters are associated with respective pipeline nodes. The user may specify or modify a hyperparameter grid used for optimization, and the values associated with a hyperparameter may include continuous values sampled from a range.); and
generating a set of tuned parameters for the processing pipeline ([0012-0013], and [0055-0063]: Describes performing hyperparameters tuning to determine tuned hyperparameters and discovers a best performing pipeline path having a parameter configuration. It associates hyperparameters with respective pipeline nodes and states that parameter values may be continuous values sampled from a range)
Although Patel teaches generating a set of tuned parameters, it does not teach doing so using a machine learning algorithm that uses the received data associated with the processing pipeline as input.
Chen teaches using a machine learning algorithm that uses the received data associated with the processing pipeline as input ([0067-0068]: Describes employing a hyperparameter optimization component to select a set of optimal; hyperparameters for identified machine learning pipelines, including employing a principal component analysis and/or k nearest neighbors algorithm to automatically configure hyperparameters for the machine learning pipelines.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the hyperparameter tuning of Patel with the machine learning configuration of Chen. Doing so would have enabled the system to automatically determine suitable parameter values within the hyperparameters.
Regarding claim 4, Patel, in view of Chen teaches the computer-implemented method of claim 1.
Patel further teaches wherein generating the set of tuned parameters further comprises:
randomly selecting an initial set of parameters based on the respective range associated with the at least one parameter (Patel [0048], [0050], and [0059]: Describes that hyperparameter values may comprise continuous values sampled form a range or distribution and that an initial/early hyperparameter tuning stage may employ random search using randomly generated parameter values).
Regarding claim 5, Patel, in view of Chen, teaches the computer-implemented method of claim 1.
Patel further teaches wherein generating the set of tuned parameters further comprises:
selecting, using the machine learning algorithm, a set of predicted parameters based on the respective range associated with the at least one parameter (Patel [0046-0049], and [0055]: Describes hyperparameters associated with respective set of values, including continuous values sampled form a range, and optimization over the hyperparameter search space to identify parameter values.; Chen [00657-0068]: Describes employing a machine learning algorithm, such as k-nearest neighbors, to automatically configure and select a set of hyperparameters for a machine learning pipeline.);
executing one or more of the modules in the processing pipeline using the set of selected parameters (Patel [0052-0053], and [0059-0060]: Describes running pipeline paths/machine learning components using different hyperparameter combinations or parameter values during hyperparameter tuning.); and
evaluating a performance of the processing pipeline based on results generated by execution of the one or more modules (Patel [0015, and [0058-0061]: Describes executing pipeline paths/machine learning components to generate results, evaluating those results according to a performance metric, and identifying/selecting top performing or leader pipelines/models.).
Regarding claim 6, Patel in view of Chen teaches the computer-implemented method of claim 5.
Patel further teaches wherein evaluating the performance further comprises:
generating a performance score based on results generated by execution of the one or more modules ([0031-0033]: Describes executing pipeline paths to generate results and evaluating the results according to user defined performance metrics, such as accuracy, F1-scoroe, and precision, to determine a best score/leader pipeline.; Chen [0060-0062]: Also describes training and testing machine learning pipelines and generating/recording test scores indicative of pipeline performance.); and
passing the performance score to the machine learning algorithm for generating a subsequent iteration of predicted parameters (Patel [0050], [0057], and [0059-0063]: Describes iteratively evaluating pipeline performance, feeding knowledge/results from an earlier exploration into subsequent optimization, and performing subsequent rounds of hyperparameter tuning using additional parameter values.; Chen [0067-0068]: Describes employing a machine learning algorithm to automatically configure/select pipeline hyperparameters.).
Regarding claim 8, Patel in view of Chen teaches the computer-implemented method of claim 1.
Patel further teaches wherein generating the set of tuned parameters is distributed over multiple workers of a compute node, each worker executing and evaluating a performance of the processing pipeline independently (Patel [0050-0053], and [0059]: Describes distributing hyperparameter tuning through parallel/distributed execution of multiple pipeline path tasks, wherein each task independently executes and evaluates a pipeline path using respective parameter choices. The independently executing task processes correspond to workers performing the distributed tuning operations.).
Regarding claims 9, and 12-14
These claims recites substantially the same limitations as claims 1, and 4-6, and further recites a processor comprising one or more circuits (Patel [0064]: Describes that its pipeline can be implemented in hardware, software, or a combination of those, and that computer executable instructions when executed by one or more processors, performs the described operations.; [0070-0072]: Describes the actual computer hardware platform to implement the pipeline execution.) to implement the method steps of claims 1, and 4-6 and is rejected for the same reasons as described above.
Claim 13 further recites each parameter (Patel [0046-0048]: Describes that each hyperparameter associated with a pipeline node and is associated with a corresponding set of values, wherein the values may comprise continuous values sampled from a range.)
Regarding claims 16, 19, and 20
These claims recites substantially the same limitations as claims 1, 4, and 13 (same as claim 5), and further recites a system comprising: one or more processors (Patel [0064]: Describes that its pipeline can be implemented in hardware, software, or a combination of those, and that computer executable instructions when executed by one or more processors, performs the described operations.; [0070-0072]: Describes the actual computer hardware platform to implement the pipeline execution.) to implement the method steps of claims 1, 4, and 5 and is rejected for the same reasons as described above.
Claim(s) 2, 3, 10, 11, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patel et a. (US 20220138616 A1, referred to as Patel) in view of Chen et al. (US 20220036246 A1, referred to as Chen) as applied to claims, 1, 2-6, 8, 9, 12, 13, 14, 16, 19, and 20 above, and further in view of Chen et al. (US 20210383224 A1, referred to as Chen1).
Regarding claim 2, Patel, in view of Chen teaches the computer-implemented method of claim 1, further comprising:
receiving a labeled dataset that includes an input dataset and associated labels (Patel [0033], and [0065]: Describes receiving and using a training dataset for which task results are known, where the input training examples associated with known target results/labels.);
generating additional data samples by performing data augmentation on the labeled dataset, wherein the data augmentation generates variations of the received dataset (Chen [0048]: Describes generating additional data samples by performing data augmentation on the dataset, including resampling, balancing, and synthetic minority over sampling (SMOTE), which generates synthetic/varied samples based on the original data.);
Although Patel in view of Chen teaches receiving a labeled dataset that includes an input dataset and associated labels; generating additional data samples by performing data augmentation on the labeled dataset, wherein the data augmentation generates variations of the received dataset. They do not teach passing the labeled dataset and the additional data samples to the machine learning algorithm as input for generating the set of tuned parameters.
Chen1 teaches passing the labeled dataset and the additional data samples to the machine learning algorithm as input for generating the set of tuned parameters ([0041], and [0045-0048]: Describes that a data augmentation model generates an augmented training sample, the classification model processes that augmented sample, its predicted label is compared with the training label of the original training sample to generate a loss, and that loss is backpropagated to obtain an improved classification model parameter hypernetwork parameter.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of Patel, in view of Chen with the data augmentation of Chen1. Doing so would have enabled the system to provide additional and varied training samples for training and evaluating the machine learning pipeline to improve model performance and accuracy while reducing reliance on collecting additional training data.
Regarding claim 3, Patel in view of Chen, in view of Chen1 teaches the computer-implemented method of claim 2.
Chen1 teaches wherein the labeled dataset includes a set of input images ([0004], and [0027]: Describes training samples include original/input images and corresponding training/classification labels.) and associated labels and wherein the processing pipeline, when executed, produces labeled images (Chen1 [0029], and [0045-0046]:Describes that the classification model classifies an input/augmented image and generates a corresponding prediction label for the image.).
Regarding claims 10, and 11
These claims recites substantially the same limitations as claims 2, and 3, and further recites a processor comprising one or more circuits (Patel [0064]: Describes that its pipeline can be implemented in hardware, software, or a combination of those, and that computer executable instructions when executed by one or more processors, performs the described operations.; [0070-0072]: Describes the actual computer hardware platform to implement the pipeline execution.) to implement the method steps of claims 2, and 3 and is rejected for the same reasons as described above.
Regarding claims 17 and 18
These claims recites substantially the same limitations as claims 2, and 3, and further recites a system comprising: one or more processors (Patel [0064]: Describes that its pipeline can be implemented in hardware, software, or a combination of those, and that computer executable instructions when executed by one or more processors, performs the described operations.; [0070-0072]: Describes the actual computer hardware platform to implement the pipeline execution.) to implement the method steps of claims 2, and 3 and is rejected for the same reasons as described above.
Claim(s) 7, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patel et a. (US 20220138616 A1, referred to as Patel) in view of Chen et al. (US 20220036246 A1, referred to as Chen) as applied to claims, 1, 2-6, 8, 9, 12, 13, 14, 16, 19, and 20 above, and further in view of Pekel et al. (US 11544136 B1, referred to as Pekel).
Regarding claim 7, Patel in view of Chen teaches the computer-implemented method of claim 1.
Although Patel in view of Chen teaches the computer-implemented method of claim 1. They do not teach generating, using the machine learning algorithm, an estimated parameter distribution, wherein the set of tuned parameters is generated based on the estimated parameter distribution.
Pekel teaches generating, using the machine learning algorithm, an estimated parameter distribution, wherein the set of tuned parameters is generated based on the estimated parameter distribution (Col 11, lines 6-28: Describes a Bayesian hyperparameter optimizer incorporates results of prior trials to identify regions of the hyperparameter space most likely to contain suitable parameters and successively shifts sampling from a uniform distribution of trial parameters to information weighted sampling using Bayesian methods.).
It would have been obvious to one of ordinary skills in the art at the time of the claimed invention to have combined the method of Patel, in view of Chen with the weighted sampling of Pekel. Doing so would have enabled the system to more efficiently optimize hyperparameters by using the information weighed sampling to focus the search on parameter regions more likely to contain good parameter values.
Regarding claim 15, which recites substantially the same limitations as claim 7, and further recites a processor comprising one or more circuits (Patel [0064]: Describes that its pipeline can be implemented in hardware, software, or a combination of those, and that computer executable instructions when executed by one or more processors, performs the described operations.; [0070-0072]: Describes the actual computer hardware platform to implement the pipeline execution.) to implement the method steps of claim 7 and is rejected for the same reasons as described above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892 for additional art including.
US 20210089961 A1: machine learning pipeline trails with different sets of parameters
US 20220282303 A1: distributed hyperparameter tuning
US 20230132064 A1: configuring a machine learning pipeline through a configuration file containing module specific parameter grids
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/D.T.R./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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