Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-10, 11, 12-20 are directed to a method, computer program product and system of training a ML model. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 11 and 12 these claims recite
receiving training data comprising a trial subset of training data;
probing the trial subset of training data using a machine learning system and multiple robust measures of scale formulas to select an upper bound for data outlier detection and to select a lower bound for data outlier selection,
detecting one or more outliers in the training data using the selected upper bound and the selected lower bound;
generating modified training data using the detected outliers; and
training the machine learning system utilizing the modified training data.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0039-0055], etc. Fig. 1.
The claim also recites Mathematical Concepts with mathematical formulas. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 11, 12 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 11 and 12: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “receiving…”, “probing…”, “detecting…”, “generating…”, training…”. etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 13
Claim 2, 13 merely recite other additional elements that determine data bounds with the formulas and training the ML with the determiend training data and determine a score for the trained ML model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 14
Claim 3, 14 merely recite other additional elements that optimal the formula with optimizing the parameters during training the ML model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 15
Claim 4, 15 merely recite other additional elements that generating the modified data with the detected outlier which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 16
Claim 5, 16 merely recite other additional elements that defining the missing value imputer which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 17
Claim 6, 17 merely recite other additional elements that defining the pipeline ML system which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 18
Claim 7, 18 merely recite other additional elements that generating test group and testing accuracy for the ML system which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8, 19
Claim 8, 19 merely recite other additional elements that generating modified training data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9, 20
Claim 9, 20 merely recite other additional elements that generating automated ML system which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 10
Claim 10 merely recite other additional elements that define multiple robust measures of scale formulas which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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.
Claims 1-5, 8, 11-16, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (Lin) US 2023/0117088 in view of Miguelanez et al. (Miguelanez) US 20060085155
In regard to claim 1, Lin disclose A computer-implemented method comprising: ([0007]-0023])
receiving training data comprising a trial subset of training data; ([0007]-[0023] [0041]-[0043] [0051]-[0061] receiving training dataset with pieces of data)
probing the trial subset of training data using a machine learning system and a formula to obtain an upper bound for data outlier detection and to obtain a lower bound for data outlier selection, ([0007]-[0023] [0041]-[0048] [0058]-[0068] detect and determine for the target data with a piece of data using AI model and with a predefined the outlier detect equation, such as IQR to obtain an upper and an lower boundary for data outlier detection and selection)
detecting one or more outliers in the training data using the selected upper bound and the selected lower bound; ([0009]-[0023] [0045]-[0048] [0058]-[0070] [0077]-[0082] detect outliers in the training dataset using the selected upper and lower boundary)
generating modified training data using the detected outliers; ([0060]-[0072] the training dataset is updated based on the detected outliers. Note: please further define how training data is modified to help move forward the prosecution, call to discuss if necessary) and
training the machine learning system utilizing the modified training data. ([0041]-[0048] [0058]-[0070] [0077]-[0082] training the AI model using the training data)
But Lin fail to explicitly disclose “probing the trial subset of training data using the machine learning system and multiple robust measures of scale formulas to select the upper bound and to select the lower bound;”
Miguelanez disclose probing the trial subset of training data using multiple robust measures of scale formulas to select the upper bound and to select the lower bound; ([0046]-[0063] [0072]-[0078] analyzing each datum using multiple outliner detection algorithms to select upper and lower threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Miguelanez’s outliner detection into Lin’s invention as they are related to the same field endeavor of data outliner detection. The motivation to combine these arts, as proposed above, at least because Miguelanez’s multiple outlier detection algorithms would help to provide more outlier detection methods into Lin’system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more outlier detection methods would improve quality of data provided to analysis tools.
In regard to claim 2, Lin and Miguelanez disclose The computer-implemented method of claim 1,
Lin disclose training the machine learning system with the trial training data for the various outlier detection parameters; ([0041]-[0048] [0058]-[0070] [0077]-[0082] training the AI model using the training data for various outlier detection parameters)
and determining an accuracy score for the machine learning system trained with the trial training data for the various outlier detection parameters using the training data; (([0041]-[0052] [0057]-[0070] [0077]-[0082] [0146]-[0157] an accuracy of the model trained with the training set is calculated for various outlier detection parameters using the test set data)
But Lin fail to explicitly disclose “wherein the probing comprises selecting an optimal formula from the multiple robust measures of scale formulas by: determining a trial upper and lower bounds using at least multiple of the multiple robust measure of scale formulas; generating trial training data using the trial upper and lower bounds for the at least multiple robust measure of scale formulas; the at least multiple robust measure of scale formulas; and selecting the optimal formula from the at least multiple robust measure of scale formulas using the accuracy score.”
Miguelanez disclose wherein the probing comprises selecting an optimal formula from the multiple robust measures of scale formulas by: determining a trial upper and lower bounds using at least multiple of the multiple robust measure of scale formulas; generating trial training data using the trial upper and lower bounds for the at least multiple robust measure of scale formulas; the at least multiple robust measure of scale formulas; ([0046]-[0063] [0072]-[0078] analyzing each datum using multiple outliner detection algorithms to select the most useful outlier identification algorithms, determining upper and lower thresholds using the multiple outliner detection algorithms and generating data using the upper and lower thresholds for the multiple outliner detection algorithms, data using the upper and lower thresholds form the multiple outliner detection algorithms are retained and the classification engine receives the results of the pre-processing analysis generated by the multiple outliner detection algorithms) and
selecting the optimal formula from the at least multiple robust measure of scale formulas using the accuracy score. ([0046]-[0063] [0072]-[0078] select the most useful outlier identification algorithm based on the user defined or predetermined rule such as results from an algorithm satisfying a rule or a threshold, etc. which is an implementation choice, but not an invention)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Miguelanez’s outliner detection into Lin’s invention as they are related to the same field endeavor of data outliner detection. The motivation to combine these arts, as proposed above, at least because Miguelanez’s multiple outlier detection algorithms would help to provide more outlier detection methods into Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more outlier detection methods would improve quality of data provided to analysis tools.
In regard to claim 3, Lin and Miguelanez disclose The computer-implemented method of claim 2,
Lin disclose wherein the optimal formula comprises parameters and the probing comprises optimizing the parameters during iterative training of the machine learning system with the trial subset with variations in the parameters. ([0041]-[0048] [0058]-[0070] [0077]-[0082] [0141]-[0153] Fig, 7, the predefine outlier detection method has outlier detection parameters and updating the parameters during iterative training of the model with buffer size pieces of data with updated parameters based on if the concept drift is detected)
In regard to claim 4, Lin and Miguelanez disclose The computer-implemented method of claim 1,
But Lin fail to explicitly disclose “wherein generating the modified data using the detected outliers comprises labeling the detected outliers with a missing value imputer, and the machine learning system is configured for handling the missing value imputer during training.”
Miguelanez disclose wherein generating the modified data using the detected outliers comprises labeling the detected outliers with a missing value imputer, and the machine learning system is configured for handling the missing value imputer during training. ([0084]-[0092] location of identified outliers are stored and along with any other relevant information, the missing data is replaced with idealized data during the testing)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Miguelanez’s outliner detection into Lin’s invention as they are related to the same field endeavor of data outliner detection. The motivation to combine these arts, as proposed above, at least because Miguelanez’s multiple outlier detection algorithms would help to provide more outlier detection methods into Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more outlier detection methods would improve quality of data provided to analysis tools.
In regard to claim 5, Lin and Miguelanez disclose The computer-implemented method of claim 4,
But Lin fail to explicitly disclose “wherein the missing value imputer is a not-a-number identifier.”
Miguelanez disclose wherein the missing value imputer is a not-a-number identifier. ([0084]-[0092] location of identified outliers are stored and along with any other relevant information, the missing data is identified and replaced with substitute data. Note: please do not use negative language and instead use functional language to describe the limitations.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Miguelanez’s outliner detection into Lin’s invention as they are related to the same field endeavor of data outliner detection. The motivation to combine these arts, as proposed above, at least because Miguelanez’s missing number replacement would help to provide more data modification methods into Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more data modification methods would improve quality of data provided to analysis tools.
In regard to claim 8, Lin and Miguelanez disclose The computer-implemented method of claim 3,
But Lin fail to explicitly disclose “wherein generating modified training data using the detected outliers comprises deleting training data containing the identified outliers.”
Miguelanez disclose wherein generating modified training data using the detected outliers comprises deleting training data containing the identified outliers.
([0084]-[0092] the data set has been processed to remove data classified as outliers)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Miguelanez’s outliner detection into Lin’s invention as they are related to the same field endeavor of data outliner detection. The motivation to combine these arts, as proposed above, at least because Miguelanez’s modifying data based on the outlier would help to provide more data modification methods into Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more data modification methods would improve quality of data provided to analysis tools.
In regard to claim 11, claim 11 is a computer program product claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claims 12-16, 19, claims 12-16, 19 are system claims corresponding to the method claims 1-5, 8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-5, 8.
Claims 6, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (Lin) US 2023/0117088 and Miguelanez et al. (Miguelanez) US 20060085155 as applied to claim 5, further in view of Poornachandran et al. (Poornachandran) US 2022/0413943
In regard to claim 6, Lin and Miguelanez disclose The computer-implemented method of claim 5,
But Lin and Miguelanez fail to explicitly disclose “wherein the machine learning system is a pipeline machine learning system, and the pipeline machine learning system comprises multiple computational units arranged in a pipeline, wherein the multiple computational units comprise a transformer configured to enable or disable the effect of the missing value imputer on output of the machine learning system.”
Poornachandran disclose wherein the machine learning system is a pipeline machine learning system, ([0384]-[0392] pipeline ML from receiving a raw data to generating a deployable ML model) and the pipeline machine learning system comprises multiple computational units arranged in a pipeline, wherein the multiple computational units comprise a transformer configured to enable or disable the effect of the missing value imputer on output of the machine learning system. ([0180]-[0195] [0384]-[0392] [0409] [0423]-[0429] [0586] [0587] pipeline ML with multiple units in pipeline, with a transformer to activate or deactivate the desired logic on the output of the ML model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Poornachandran’s managing model training circuitry into Miguelanez and Lin’s invention as they are related to the same field endeavor of training ML model. The motivation to combine these arts, as proposed above, at least because incorporate Poornachandran’s managing model training circuitry would help to provide control mechanism for the training ML into Miguelanez and Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing control mechanism for the training ML would facilitate ML model training.
In regard to claim 17, claim 17 is a system claim corresponding to the method claim 6 above and, therefore, is rejected for the same reasons set forth in the rejections of claims 6.
Claims 7, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (Lin) US 2023/0117088 and Miguelanez et al. (Miguelanez) US 20060085155, Poornachandran et al. (Poornachandran) US 2022/0413943 as applied to claim 6, further in view of Chung US 11453994
In regard to claim 7, Lin and Miguelanez, Poornachandran disclose The computer-implemented method of claim 6, further comprising:
Lin disclose generating a test group of data from the training data; ([0008]-[0023] [0088]-[0092] generating segmentations of data from the training data)
testing a first accuracy of the machine learning system using the test group of data; ([0147]-[0156] identifying the accuracy of the ML model using the segments of data)
testing a second accuracy of the machine learning system using the test group of data; ([0147]-[0156] identifying the accuracy of the ML model using the segments of data)
the first accuracy and the second accuracy. ([0147]-[0156] identifying the accuracies of the ML model using the segments of data)
But Lin fail to explicitly disclose “the effect of the missing value imputer;”
Miguelanez disclose the effect of the missing value imputer; ([0084]-[0092] the missing data is replaced with idealized data during the testing)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Miguelanez’s outliner detection into Lin’s invention as they are related to the same field endeavor of data outliner detection. The motivation to combine these arts, as proposed above, at least because Miguelanez’s missing number replacement would help to provide more data modification methods into Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more data modification methods would improve quality of data provided to analysis tools.
But Lin and Miguelanez fail to explicitly disclose “the machine learning system with the transformer configured to enable the logic; the machine learning system with the transformer configured to disable the logic;”
Poornachandran disclose the machine learning system with the transformer configured to enable the logic; the machine learning system with the transformer configured to disable the logic; ([0066] [0180]-[0195] [0384]-[0392] [0409] [0423]-[0429] [0586] [0587] the transformer to activate or deactivate the desired logic based on the evaluation of accuracy, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Poornachandran’s managing model training circuitry into Miguelanez and Lin’s invention as they are related to the same field endeavor of training ML model. The motivation to combine these arts, as proposed above, at least because incorporate Poornachandran’s managing model training circuitry would help to provide control mechanism for the training ML into Miguelanez and Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing control mechanism for the training ML would facilitate ML model training.
But Lin and Miguelanez, Poornachandran fail to explicitly disclose “disabling the logic in the transformer if the second value is greater than the first value; and enabling the logic in the transformer if the first value is greater than the second value.”
Chung disclose disabling the logic in the transformer if the second value is greater than the first value; and enabling the logic in the transformer if the first value is greater than the second value. (col. 5, line 28-48, col. 7, line 1- 49, claim 17, disable the logic if the second value is greater than the first value and enable the logic if the first value is greater than the second value, here it discloses the enable/disable trigger condition)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chung’s logic control circuitry into Poornachandran and Miguelanez and Lin’s invention as they are related to the same field endeavor of system control. The motivation to combine these arts, as proposed above, at least because incorporate Chung’s logic control circuitry with enable and disable condition would help to provide control mechanism into Poornachandran, Miguelanez and Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing control mechanism would maintain accuracy of the system.
In regard to claim 18, claim 18 is a system claim corresponding to the method claim 7 above and, therefore, is rejected for the same reasons set forth in the rejections of claims 7.
Claims 9, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (Lin) US 2023/0117088 and Miguelanez et al. (Miguelanez) US 20060085155 as applied to claim 1, further in view of Ashrafi et al. (Ashrafi) US 2022/0292309
In regard to claim 9, Lin and Miguelanez disclose The computer-implemented method of claim 1,
But Lin and Miguelanez fail to explicitly disclose “wherein the machine learning system is an automated machine learning system, and the automated machine learning system is configured for automatically selecting an optimal machine learning module from multiple machine learning models during training of the machine learning system using the modified training data.”
Ashrafi disclose wherein the machine learning system is an automated machine learning system, and the automated machine learning system is configured for automatically selecting an optimal machine learning module from multiple machine learning models during training of the machine learning system using the modified training data. ([0042] [0071]-[0080] select an optimal ML model from outlier models during training using the modified training data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Ashrafi’s optimal ML model selection into Miguelanez and Lin’s invention as they are related to the same field endeavor of training ML model. The motivation to combine these arts, as proposed above, at least because incorporate Ashrafi’s optimal ML model selection from multiple models would help to provide optimal ML model into Miguelanez and Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing optimal ML model would facilitate ML model training.
In regard to claim 20, claim 20 is a system claim corresponding to the method claim 9 above and, therefore, are rejected for the same reasons set forth in the rejections of claim 9.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (Lin) US 2023/0117088 and Miguelanez et al. (Miguelanez) US 20060085155 as applied to claim 1, further in view of Milletari et al. (Milletari) US 11804050 and Cunningham US 20020129038
In regard to claim 10, Lin and Miguelanez disclose The computer-implemented method of claim 1,
Lin disclose InterQuartile Range, ([0063]-[0068] IQR is calculate)
But Lin fail to explicitly disclose “wherein the multiple robust measures of scale formulas are selected from the group consisting of InterQuartile Range, Robust Covariance, Local Outlier Factor, standard deviation, median absolute deviation, median absolute deviation, Cauchy distribution, biweight midvariance.”
Miguelanez disclose wherein the multiple robust measures of scale formulas are selected from the group consisting of InterQuartile Range, Local Outlier Factor, standard deviation. (([0046]-[0063] [0072]-[0078] [0126]-[0130] IQR, scale factor, standard deviation, median deviation,etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Miguelanez’s outliner detection into Lin’s invention as they are related to the same field endeavor of data outliner detection. The motivation to combine these arts, as proposed above, at least because Miguelanez’s modifying data based on the outlier would help to provide more data modification methods into Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more data modification methods would improve quality of data provided to analysis tools.
But Lin and Miguelanez fail to explicitly disclose “the group consisting of median absolute deviation, median absolute deviation, biweight midvariance.”
Milletari disclose the group consisting of median absolute deviation, median absolute deviation, biweight midvariance. (col. 13, line 32-44, biweight midvariance, median absolute deviation. Note: please remove the redundant “median absolute deviation)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Milletari’s training ML model into Miguelanez and Lin’s invention as they are related to the same field endeavor of ML model training. The motivation to combine these arts, as proposed above, at least because Milletari’s data processing for ML training would help to provide more data processing methods into Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more data processing methods would improve quality of data provided to ML model.
But Lin and Miguelanez, Milletari fail to explicitly disclose “the group consisting of Cauchy distribution, Robust Covariance,”
Cunningham disclose the group consisting of Cauchy distribution, Robust Covariance, ([0087]-[0090] [0106]-[0108] cauchy distribution, Robust Covariance)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Cunningham’s data mining into Milletari, Miguelanez, Lin’s invention as they are related to the same field endeavor of data processing. The motivation to combine these arts, as proposed above, at least because Cunningham’s data processing would help to provide more data processing methods into Milletari, Miguelane, Lin’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more data processing methods would improve clarity of data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20200065213 A1 2020-02-27 Poghosyan et al.
PROCESSES AND SYSTEMS FOR FORECASTING METRIC DATA AND ANOMALY DETECTION IN A DISTRIBUTED COMPUTING SYSTEM
Poghosyan et al. disclose Computational processes and systems are directed to forecasting time series data and detection of anomalous behaving resources of a distributed computing system data. Processes and systems comprise off-line and on-line modes that accelerate the forecasting process and identification of anomalous behaving resources. In the off-line mode, recurrent neural network (“RNN”) is continuously trained using time series data associated with various resources of the distributed computing system. In the on-line mode, the latest RNN is used to forecast time series data for resources in a forecast time window and confidence bounds are computed over the forecast time window. The forecast time series data characterizes expected resource usage over the forecast time window so that usage of the resource may be adjusted. The confidence bounds may be used to detect anomalous behaving resources. Remedial measures may then be executed to correct problems indicated by the anomalous behavior… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
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