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 in response to the amendment and remarks filed July 10th, 2026. In the
amendment, claims 1, 8, and 15 were amended and no claims were added or cancelled. As such, claims 1-20 are presented for examination.
Response to Arguments
Applicant’s arguments, see Pages 19-22, filed July 10th, 2026, with respect to the rejected claims 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. The rejected claims 1-20 under 35 U.S.C. 103 have been withdrawn.
Applicant’s arguments with respect to the rejection under 35 U.S.C. 101 are not persuasive for the following reasons:
35 U.S.C 101:
Applicant argues that claim 1 as amended is not directed to an abstract idea under Prong One because it recites a specific computer-implemented workflow that generates prediction distributions, clusters the distributions, merges records into blocks, and generates refitted distributions, and cannot practically be performed in the human mind (Pages 13-14 of Remarks). The Examiner respectfully disagrees. As set forth in Step 2A Prong 1, each step falls within the abstract idea groupings, with selecting features, generating combination values, grouping, assigning colors, and predicting being mental processes and discretizing values, fitting distributions, and clustering and refitting being mathematical concepts. The newly amended limitations were likewise found to recite abstract ideas, because merging records into a block based on their shared cluster assignment and reducing a plurality of distributions into a smaller number of groups are mental processes that can be performed in the mind or with pen and paper. Reciting the abstract idea as a sequence of steps carried out on a generic computer does not remove those steps from the abstract idea groupings. Therefore, claim 1 recites an abstract idea under Step 2A Prong 1.
Applicant argues that claim 1 is directed to a practical application in the field of a QA system and a post-modeling visualization system and reflects an improvement to computer functioning or other technology, pointing to specification paragraphs 12-14 and 42 (Pages 15-18 of Remarks). The Examiner respectfully disagrees. As set forth in Step 2A Prong 2 and Step 2B, the recitation of a machine learning model and processors performing the prediction merely indicates a mere instruction to apply the exception under MPEP 2106.05(f), and the outputting and visualization step is insignificant extra-solution activity amounting to necessary data outputting under MPEP 2106.05(g)(3). Providing a better way to visualize data is not an improvement to the functioning of a computer or of the machine learning model itself and is therefore not a technological improvement. The exception is therefore not integrated into a practical application and the claim does not amount to significantly more under Step 2B.
Applicant argues that independent claims 8 and 15 and dependent claims 2 through 7, 9 through 14, and 16 through 20 are eligible for reasons similar to claim 1 and by virtue of their dependency (Page 19 of Remarks). The Examiner respectfully disagrees. Claims 8 and 15 recite the same abstract ideas as claim 1 with additional elements recited at a high level as mere instructions to apply, and the dependent claims add only further mathematical concepts, mental processes, or insignificant extra-solution activity, none of which integrates the exception or amounts to significantly more. Therefore, the rejection of claims 1-20 under 35 U.S.C. 101 is maintained.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 4: "A processor output a visualization" should read "A processor outputs a visualization".
Paragraph 20: "Processors set 110" should read "Processor set 110."
Paragraph 37: "which can be used to evaluate the model. can be calculated as y - y_p" should read "which can be used to evaluate the model, can be calculated as y - y_p".
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step2A Prong 1: The claim recites, inter alia:
selecting… a top N features for a machine learning (ML) model trained on training data: This limitation recites a mental process because it involves selecting the most relevant features, which can be performed by human judgement.
discretizing… values of each continuous feature of the top N features into a set of categories: This limitation recites a mathematical concept because it involves converting continuous numerical values into categorical bins using math rules.
generating… a set of combination values that each represent a unique combination of feature values in a row representing a record within the training data: This limitation recites a mental concept because it involves creating unique combination values based on features in a record which can be performed by pen and paper and human judgement.
grouping… the predicted target values based on the combination value for each respective record of the training data: This limitation recites a mental process because it involves organizing data into groups based on shared characteristics.
fitting… a distribution for each grouping of the predicted target values associated with a respective combination value generating a set of distributions and associated distribution curves: This limitation recites a mathematical concept because it involves applying statistical modeling to approximate data with probability distributions.
clustering and refitting… the set of distributions using a clustering algorithm to compress a number of distributions resulting in a set of clusters and a refitted distribution for each cluster of the set of clusters, wherein each refitted distribution is based on records associated with each distribution of the associated cluster: This limitation recites a mathematical concept because it involves applying a clustering algorithm and statistical modeling to group and adjust distributions.
assigning… a different color to each feature of the top N features and a different shade of the respective different color for each category of the set of categories for a respective feature of the top N features: This limitation recites a mental process of assigning visual labels (colors/shades) to categories which can be performed by pen and paper and evaluation or judgement to select the top N relevant features.
predicting… a target value for each record within the training data generating predicted target values: This limitation recites a mental process because it involves predicting a value for each record in the training data.
wherein records associated with distributions assigned to a common cluster are merged into a block of records and the refitted distribution for the common cluster is generated using the merged block of records: This limitation recites a mental process because it involves grouping records that share a common cluster together into a block based on their shared cluster assignment, which can be performed in the human mind or by pen and paper.
and wherein compressing the number of distributions comprises reducing a plurality of distributions associated with different combination values into a smaller number of clusters represented by the refitted distributions: This limitation recites a mental process because it involves consolidating and reducing a larger number of distributions into a smaller number of groups based on shared characteristics, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
predicting, by the one or more processors, using the ML model…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
and outputting… a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
predicting, by the one or more processors, using the ML model…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
and outputting… a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)). This falls under Well-Understood, Routine, Conventional activity -see MPEP 2106.05(d)(II)(vi).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the abstract ideas of mental processes and mathematical concepts for feature selection, discretization, combinatorial grouping, statistical distribution fitting, and clustering of data). The claim merely describes a process of applying known mathematical and organizational techniques (selecting features, discretizing values into bins, generating combinations, fitting statistical distributions, clustering/refitting distributions) to analyze and organize data along with routine visualization steps (assigning colors and outputting graphs and tables). The recitation of a machine learning model and processors merely indicates a technological environment in which the abstract ideas are applied, without improving the functioning of a computer or the machine learning model itself.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 2
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
selecting the top N features for the ML model comprises: computing… a feature importance for each feature of the ML model based on an association between changes in feature values and changes in an accuracy of the ML model: This limitation recites a mental process involving selecting the top features based on the feature importance value of each feature.
and determining… the top N features that contribute to a pre-set threshold accuracy percentage for the ML model based on the feature importance for each feature: This limitation recites a mental process because it involves the determination of feature importance meeting a threshold.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 3
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
discretizing values of each continuous feature comprises: applying… equal frequency binning to a set of values for a continuous feature generating a set of categorical values for the continuous feature: This is a mathematical concept because it involves applying equal frequency binning which is a statistical technique.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 4
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
adding… a new column to the training data with respective combination values for each record: This limitation recites a mental process because it involves organizing and recording information by adding a column of values to a dataset, which can be performed conceptually.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 5
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
responsive to a user selecting a portion of data on one of the distribution curves, highlighting… corresponding records associated with the portion of data: This limitation is a mental process because it involves highlighting records in the data based on the selection the user made.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 6
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
calculating… a residual value for each record based on the corresponding predicted target value and an actual target value: This limitation is a mathematical concept because it involves using a math equation to generate the residual value. See Paragraph 37 of the instant specification.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 7
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
fitting the distribution comprises: computing… a mean and a variance of the predicted target values for each combination value: This limitation recites a mathematical concept because it involves computing the mean and variance.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 8
Step 1: The claim recites a computer program product; therefore, it is directed to the statutory category of an article of manufacture.
Step2A Prong 1: The claim recites, inter alia:
…to select a top N features for a machine learning (ML) model trained on training data: This limitation recites a mental process because it involves selecting the most relevant features can be performed by a human using judgement.
…to discretize values of each continuous feature of the top N features into a set of categories: This limitation recites a mathematical concept because it involves converting continuous numerical values into categorical bins using math rules.
…to generate a set of combination values that each represent a unique combination of feature values in a row representing a record within the training data: This limitation recites a mental concept because it involves creating unique combination values based on features in a record which can be performed by pen and paper and human judgement.
…to group the predicted target values based on the combination value for each respective record of the training data: This limitation recites a mental process because it involves organizing data into groups based on shared characteristics.
…to fit a distribution for each grouping of the predicted target values associated with a respective combination value generating a set of distributions and associated distribution curves: This limitation recites a mathematical concept because it involves applying statistical modeling to approximate data with probability distributions.
…to cluster and refit the set of distributions using a clustering algorithm to compress a number of distributions resulting in a set of clusters and a refitted distribution for each cluster of the set of clusters, wherein each refitted distribution is based on records associated with each distribution of the associated cluster: This limitation recites a mathematical concept because it involves applying a clustering algorithm and statistical modeling to group and adjust distributions.
…to assign a different color to each feature of the top N features and a different shade of the respective different color for each category of the set of categories for a respective feature of the top N features: This limitation recites a mental process of assigning visual labels (colors/shades) to categories which can be performed by pen and paper and evaluation or judgement to select the top N relevant features.
…to predict a target value for each record within the training data generating predicted target values: This limitation recites a mental process because it involves predicting a value for each record in the training data.
wherein records associated with distributions assigned to a common cluster are merged into a block of records and the refitted distribution for the common cluster is generated using the merged block of records: This limitation recites a mental process because it involves grouping records that share a common cluster together into a block based on their shared cluster assignment, which can be performed in the human mind or by pen and paper.
and wherein compressing the number of distributions comprises reducing a plurality of distributions associated with different combination values into a smaller number of clusters represented by the refitted distributions: This limitation recites a mental process because it involves consolidating and reducing a larger number of distributions into a smaller number of groups based on shared characteristics, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[a] computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
program instructions to predict using the ML model…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
and… to output a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
[a] computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
program instructions to predict using the ML model…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
and… to output a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)). This falls under Well-Understood, Routine, Conventional activity -see MPEP 2106.05(d)(II)(vi).
Claims 9-14
Step 1: Claims 9-14 recite a computing program product; therefore, it is directed to the statutory category of an article of manufacture.
Step 2A Prong 1: Claims 9-14 recite judicial exceptions similar to those in claims 2-7.
Step 2A Prong 2: The judicial exceptions are not integrated into practical application. The analysis at this
step mirrors that of claims 2-7 respectively, except insofar as claims 9-14 additionally recites:
program instructions to…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The judicial exception is not integrated into a practical application. The analysis at this step
mirrors that of claims 2-7, respectively, except insofar as claim 15 additionally recites:
program instructions to…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 15
Step 1: The claim recites a computing system; therefore, it is directed to the statutory category of a machine.
Step2A Prong 1: The claim recites, inter alia:
…to select a top N features for a machine learning (ML) model trained on training data: This limitation recites a mental process because it involves selecting the most relevant features can be performed by a human using judgement.
…to discretize values of each continuous feature of the top N features into a set of categories: This limitation recites a mathematical concept because it involves converting continuous numerical values into categorical bins using math rules.
…to generate a set of combination values that each represent a unique combination of feature values in a row representing a record within the training data: This limitation recites a mental concept because it involves creating unique combination values based on features in a record which can be performed by pen and paper and human judgement.
…to group the predicted target values based on the combination value for each respective record of the training data: This limitation recites a mental process because it involves organizing data into groups based on shared characteristics.
…to fit a distribution for each grouping of the predicted target values associated with a respective combination value generating a set of distributions and associated distribution curves: This limitation recites a mathematical concept because it involves applying statistical modeling to approximate data with probability distributions.
…to cluster and refit the set of distributions using a clustering algorithm to compress a number of distributions resulting in a set of clusters and a refitted distribution for each cluster of the set of clusters, wherein each refitted distribution is based on records associated with each distribution of the associated cluster: This limitation recites a mathematical concept because it involves applying a clustering algorithm and statistical modeling to group and adjust distributions.
…to assign a different color to each feature of the top N features and a different shade of the respective different color for each category of the set of categories for a respective feature of the top N features: This limitation recites a mental process of assigning visual labels (colors/shades) to categories which can be performed by pen and paper and evaluation or judgement to select the top N relevant features.
…to predict a target value for each record within the training data generating predicted target values: This limitation recites a mental process because it involves predicting a value for each record in the training data.
wherein records associated with distributions assigned to a common cluster are merged into a block of records and the refitted distribution for the common cluster is generated using the merged block of records: This limitation recites a mental process because it involves grouping records that share a common cluster together into a block based on their shared cluster assignment, which can be performed in the human mind or by pen and paper.
and wherein compressing the number of distributions comprises reducing a plurality of distributions associated with different combination values into a smaller number of clusters represented by the refitted distributions: This limitation recites a mental process because it involves consolidating and reducing a larger number of distributions into a smaller number of groups based on shared characteristics, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
one or more computer processors; one or more computer readable storage media; program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
program instructions to predict using the ML model…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
and… to output a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
one or more computer processors; one or more computer readable storage media; program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
program instructions to predict using the ML model…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
and… to output a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)). This falls under Well-Understood, Routine, Conventional activity -see MPEP 2106.05(d)(II)(vi).
Claims 16-20
Step 1: Claims 16-20 recite a computing system; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claims 16-20 recite judicial exceptions similar to those in claims 2-6.
Step 2A Prong 2: The judicial exceptions are not integrated into practical application. The analysis at this
step mirrors that of claims 2-6 respectively, except insofar as claim 15 additionally recites:
program instructions to…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The judicial exception is not integrated into a practical application. The analysis at this step
mirrors that of claims 2-7, respectively, except insofar as claim 15 additionally recites:
program instructions to…: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Conclusion
Claims 1-20 overcome the prior art but are still rejected under 35 U.S.C. 101. The prior art of record fails to teach or suggest merging records associated with distributions assigned to a common cluster into a block of records and generating a refitted distribution for the common cluster using the merged block of records, wherein compressing the number of distributions reduces a plurality of distributions associated with different combination values into a smaller number of clusters represented by the refitted distributions. Friedman teaches feature selection and rule-based prediction, Rapp clusters data and fits one distribution per cluster, and Singh assigns colors and outputs records on already clustered data, but none of these references teaches clustering the set of distributions themselves and then pooling the underlying records of the co-clustered distributions to refit a new distribution. The two-stage operation of clustering fitted distributions and refitting on the merged block of records is not taught by the prior art.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAJD MAHER HADDAD whose telephone number is (571)272-2265. The examiner can normally be reached Mon-Friday 8-5 pm.
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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