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 .
Response to Amendment
The amendments filed 10/07/2025 have been entered.
Claims 1-2, 4-11, and 13-20 remain pending within the application.
The amendments filed 10/07/2025 are sufficient to overcome each and every objection previously set forth in the Non-Final Office Action mailed 07/09/2025. The objections have been withdrawn.
The amendments filed 10/07/2025 are sufficient to overcome the 112(b) rejections previously set forth in the Non-Final Office Action mailed 07/09/2025. The rejections have been withdrawn.
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.
The 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 includes the steps of:
A computer-implemented method comprising:
providing a first list including at least one input variable of a predictive model and a second list including a plurality of input variables of the predictive model;
for each input variable of the plurality of input variables in the second list, determining contribution of each of the plurality of input variables to prediction of the predictive model with respect to the at least one input variable in the first list;
updating the first list by moving an input variable from the second list into the first list based on the determined contribution of each input variable of the plurality of input variables;
rendering one or more input variables in the updated first list based on an order of the input variables in the updated first list.
wherein determining contribution of the input variable to prediction of the predictive model with respect to the at least one input variable in the first list further comprises: determining a first performance of the predictive model with the at least one input variable being enabled;
determining a second performance of the predictive model with the at least one input variable together with a first input variable of the plurality of input variables being enabled;
determining, based on comparison of the first performance and the second performance, the contribution of the input variable; and
determining accuracy of the first performance and the second performance by comparing predictions of the first performance and the second performance, respectively, of the predictive model with ground truths of data records.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The broadest reasonable interpretation of the following limitations falls within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. The claim(s) recite(s) in part:
“for each input variable of the plurality of input variables in the second list, determining contribution of the input variable to prediction of the predictive model with respect to the at least one input variable in the first list”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining the contribution of input variables in a list to a prediction of a predictive model, recited at a high level of generality, with respect to another variable in another list encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“updating the first list by moving an input variable from the second list into the first list based on the determined contribution of each input variable of the plurality of input variables”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because moving an input variable from one list to another based on a determined contribution of the input variable encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“wherein determining contribution of the input variable to prediction of the predictive model with respect to the at least one input variable in the first list further comprises: determining a first performance of the predictive model with the at least one input variable being enabled”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining a performance of a predictive model, recited at a high level of generality, with certain input variables being enabled encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“determining a second performance of the predictive model with the at least one input variable together with a first input variable of the plurality of input variables being enabled”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining a performance of a predictive model, recited at a high level of generality, with certain input variables being enabled encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“determining, based on comparison of the first performance and the second performance, the contribution of the input variable”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining contribution of an input variable based on a comparison of performance encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“determining accuracy of the first performance and the second performance by comparing predictions of the first performance and the second performance, respectively, of the predictive model with ground truths of data records”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining of two performances by comparing their respective predictions with ground trust data encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The judicial exception is not integrated into a practical application. In particular, The claim(s) recite(s) in part:
“A computer-implemented method comprising”. As drafted and under its broadest reasonable interpretation, this limitation recites additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
“providing a first list including at least one input variable of a predictive model and a second list including a plurality of input variables of the predictive model”. As drafted and under its broadest reasonable interpretation, this limitation recites receiving input information, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
“rendering one or more input variables in the updated first list based on an order of the input variables in the updated first list”. As drafted and under its broadest reasonable interpretation, this limitation recites outputting information, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application. Therefore, no meaningful claim limits are imposed practicing the abstract idea. Accordingly, at Step 2A, prong two, the additional elements do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed, the claim limitations reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer. The claim reciting the additional elements of rendering information amount to outputting information. The claim reciting the additional elements of “receiving” and/or “transmitting” amount to receiving/transmitting information.
“Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)) MPEP § 2106.05(d)(II)(i).
The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. Thus, the claim does not provide an inventive concept.
The claim is ineligible.
Claim 2, which depends upon claim 1, recite(s) in part:
“providing the first list and the second list of input variables of the predictive model comprises”. As drafted and under its broadest reasonable interpretation, this limitation recites receiving input information, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
“determining variable importance of each of the input variables from the first list and the second list of the predictive model”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining variable importance of each of input variables encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“initiating the first list by adding an input variable with a largest variable importance into the first list”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because adding an input variable with a largest variable importance into a list encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“initiating the second list by adding the input variables of the predictive model other than the input variable with the largest variable importance into the second list in accordance with the determined variable importance”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because adding an input variable in accordance with the determined variable importance into a list encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
The claim reciting the additional elements of mere data gathering do not integrate the judicial exception into practical application. The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application.
The claim reciting the additional elements of providing input information amount to receiving data. The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 4, which depends upon claim 1, recite(s) in part:
“wherein the first performance and the second performance indicate accuracy of the predictive model”. As drafted and under its broadest reasonable interpretation, this limitation further clarifies the mental evaluation of determining a performance of a predictive model recited in claim 3, by introducing accuracy of the predictive model recited at a high level of generality, and thus falls under the same analysis.
The claim does not integrate the judicial exception into practical application.
The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 5, which depends upon claim 1, recite(s) in part:
“wherein moving an input variable from the second list into the first list based on the determined contribution of the plurality of input variables comprises: moving a first input variable with a largest contribution from the second list into the first list”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because moving an input variable with the largest contribution from one list to another encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
The claim does not integrate the judicial exception into practical application.
The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 6, which depends upon claim 5, recite(s) in part:
“determining whether the first input variable has a largest variable importance in the second list; and in accordance with a determination that the first input variable does not have the largest variable importance in the second list”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining whether an input variable has a largest variable importance in a list encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“displaying at least one of: correlation information of the first input variable with the at least one input variable in the first list; and correlation information of a second input variable with the at least one input variable in the first list, the second input variable having the largest variable importance in the second list”. As drafted and under its broadest reasonable interpretation, this limitation recites displaying output data, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The claim reciting the additional elements of mere data gathering do not integrate the judicial exception into practical application. The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application.
The claim reciting the additional elements of displaying outputs amount to outputting information. The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 7, which depends upon claim 6, recite(s) in part:
“the correlation information of the second input variable with the at least one input variable in the first list is represented by one or more edges between a node representing the second input variable and nodes representing the at least one input variable in the first list”. As drafted and under its broadest reasonable interpretation, this limitation further clarifies the mere data gathering and output of displaying information recited in claim 6, by introducing the correlation information of the second input variable with the at least one input variable in the first list is represented by one or more edges between a node representing the second input variable and nodes representing the at least one input variable in the first list, and thus falls under the same analysis.
The claim reciting the additional elements of mere data gathering do not integrate the judicial exception into practical application. The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application.
The claim reciting the additional elements of displaying outputs amount to outputting information. The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 8, which depends upon claim 1, recite(s) in part:
“wherein rendering one or more of input variables in the updated first list further comprises:”. As drafted and under its broadest reasonable interpretation, this limitation recites outputting information, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
“determining whether the second list is empty”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because determining whether a list is empty encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“in accordance with a determination that the second list is empty, visualizing the input variables in the first list based on the order of the input variables in the first list ”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because visualizing the input variables in the first list based on the order of the input variables in the first list encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. For example, one could visualize input variables by drawing a visual representation by pen and paper. See MPEP 2106.04(a)(2), subsection III.B.
The claim reciting the additional elements of mere data gathering do not integrate the judicial exception into practical application. The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application.
The claim reciting the additional elements of rendering information amount to outputting information. The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 9, which depends upon claim 8, recite(s) in part:
“wherein visualizing the input variables in the first list based on the order of the input variables in the first list comprises: “ As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because visualizing the input variables in the first list based on the order of the input variables in the first list encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. For example, one could visualize input variables by drawing a visual representation by pen and paper. See MPEP 2106.04(a)(2), subsection III.B.
“displaying nodes on a spiral representing the input variables in the first list; and displaying one or more edges between the nodes representing correlations of the input variables in the first list”. As drafted and under its broadest reasonable interpretation, this limitation recites displaying output data, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The claim reciting the additional elements of mere data gathering do not integrate the judicial exception into practical application. The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application.
The claim reciting the additional elements of displaying output data amount to outputting information. The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claims 10-11 and 13-18 are substantially similar to claims 1-2 and 4-9 respectively, and thus are rejected on the same basis as claims 1-2 and 4-9 respectively.
Claims 19 is substantially similar to claim 1, and thus are rejected on the same basis as claims 1.
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, 10-13, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over BEZZUBTSEVA et al. (Pub. No.: US 2018/0039911 A1), hereafter Yandex, in view of KORMILITSIN et al. (Pub. No.: US 2023/0334360 A1), hereafter Koch.
Regarding claim 1, Yandex discloses:
A computer-implemented method comprising (Yandex, Fig. 1, ¶[0060]),
providing a first list including at least one input variable of a predictive model and a second list including a plurality of input variables of the predictive model (Yandex, Fig. 4A and 4B teaches a selected-sub-set of features as a first list including at least one input variable of a predictive model and not-yet-selected features as a second list including a plurality of input variables of the predictive model),
for each input variable of the plurality of input variables in the second list, determining contribution of the input variable to prediction of the predictive model with respect to the at least one input variable in the first list (Yandex, Fig. 4A and 4B teaches determining the significance scores as contribution of the non-yet-selected feature with respect to at least one already-selected feature),
updating the first list by moving an input variable from the second list into the first list based on the determined contribution of each input variable of the plurality of input variables (Yandex, Fig. 4B element 414 teaches updating the list of selected-sub-set of features by moving a feature from the second list, i.e. the given selected feature, based on the feature significance score as the determined contribution)
… one or more input variables in the updated first list based on … the input variables in the updated first list (Yandex, Fig. 4B element 416 teaches storing the selected-sub-set of features as the updated first list),
wherein determining contribution of the input variable to prediction of the predictive model with respect to the at least one input variable in the first list further comprises: determining a first performance of the predictive model … (Yandex, Fig. 4A element 408 and ¶[0097] teaches determining a first performance of the predictive model),
determining a second performance of the predictive model with the at least one input variable together with a first input variable of the plurality of input variables being enabled (Yandex, Fig. 4A element 410 and ¶[0101] teaches determining a second performance of the predictive model with the at least one input variable together with a first input variable of the plurality of input variables being enabled).
determining, based on comparison of the first performance and the second performance, the contribution of the input variable (Yandex, Fig. 4B, element 412 and ¶[0103] teaches determining the contribution of the input variable, i.e. the feature significance score of the not-yet-selected features, based on a comparison of the first and second performance of redundancy and relevancy).
While Yandex teaches storing the one or more input variables in the updated first list based on … the input variables in the updated first list, they do not teach rendering an order of the updated list.
Koch teaches:
rendering one or more input variables in… updated … list based on an order of the input variables in .. updated … list (Koch, ¶[0078] teaches outputting ranked candidate features to a peripheral device as rendering one or more input variables in an updated list based on an order of the input variables in the updated list).
Yandex and Koch are analogous art because they are from the same field of endeavor, feature selection and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yandex to include rendering one or more input variables in… updated … list based on an order of the input variables in .. updated … list, based on the teachings of Koch. One of ordinary skill in the art would have been motivated to make this modification in order to increase predictive accuracy, as suggested by Koch (¶[0096]).
While Yandex teaches determining a first performance of the predictive model and determining a second performance of the predictive model with the at least one input variable together with a first input variable of the plurality of input variables being enabled, Yandex does not disclose determining the two performances with the at least one input variable being enabled for one of the performances.
Koch discloses:
determining a first performance of … predictive model with … least one input variable being enabled … (Koch, ¶[0060-0061] teaches determining a performance 810(1) of a predictive model in the absence of a first feature, as the first performance of a predictive model, as well as determining a second performance 710 with one feature and a plurality of features enabled).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yandex to include determining a first performance of … predictive model with … least one input variable being enabled, based on the teachings of Koch. One of ordinary skill in the art would have been motivated to make this modification in order to increase predictive accuracy, as suggested by Koch (¶[0096]).
While Yandex teaches determining accuracy of the first performance and the second performance (Yandex, ¶[0079] teaches the feature significance score calculated from the first and second performance to indicate the accuracy of the predictive model), they do not disclose determining accuracy of … performance … by comparing predictions of the … performance… of the predictive model with ground truths of data records.
Koch discloses:
determining accuracy of … performance … by comparing predictions of the … performance… of the predictive model with ground truths of data records (Koch, ¶[0073] teaches determining accuracy of the performance of a model by comparing with a hold out dataset, i.e. ground truth data records),
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yandex to include determining accuracy of … performance … by comparing predictions of the … performance… of the predictive model with ground truths of data records, based on the teachings of Koch. One of ordinary skill in the art would have been motivated to make this modification in order to increase predictive accuracy, as suggested by Koch (¶[0096]).
Regarding claim 2, Yandex, in view of Koch, discloses the computer-implemented method of claim 1 providing the first list and the second list of input variables of the predictive model. Yandex further discloses:
wherein providing the first list and the second list of input variables of the predictive model comprises: determining variable importance of each of the input variables from the first list and the second list of the predictive model (Yandex, Fig. 4A element 404 and ¶[0091] teaches determining the relevance as variable importance of each of the features of the model),
initiating the first list by adding an input variable with a largest variable importance into the first list (Yandex, Fig. 4A element 406 and ¶[0093] teaches initiating the first list of selected-sub-set of features by adding an input variable with a largest variable importance into the first list),
initiating the second list by adding the input variables of the predictive model other than the input variable with the largest variable importance into the second list in accordance with the determined variable importance (Yandex, Fig. 4A element 406 and 408, and ¶[0093-0094] teaches initiating a second list of not-yet-selected features other than the first selected feature).
Regarding claim 4, Yandex, in view of Koch, discloses the computer-implemented method of claim 1. Yandex further discloses:
wherein the first performance and the second performance indicate accuracy of the predictive model (Yandex, ¶[0079] teaches the feature significance score calculated from the first and second performance to indicate the accuracy of the predictive model).
Regarding claim 5, Yandex, in view of Koch, discloses the computer-implemented method of claim 1 moving an input variable from the second list into the first list based on the determined contribution of the plurality of input variables. Yandex further discloses:
wherein moving an input variable from the second list into the first list based on the determined contribution of the plurality of input variables comprises: moving a first input variable with a largest contribution from the second list into the first list (Yandex, Fig. 4B element 414 and ¶[0105] teaches moving a first input variable with a largest contribution from the second list into the first list).
Claims 10, 11, 13, and 14 are substantially similar to claims 1, 2, 4, and 5 respectively, and thus are rejected on the same basis as claims 1, 2, 4, and 5 respectively.
Claims 19 is substantially similar to claim 1, and thus are rejected on the same basis as claims 1.
Claims 6-9, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over YANDEXUBTSEVA et al. (Pub. No.: US 2018/0039911 A1), hereafter Yandex, in view of KORMILITSIN et al. (Pub. No.: US 2023/0334360 A1), hereafter Koch, in further view of ALKHODARI et al. (Pub. No.: US 2023/0355119 A1), hereafter Alkhodari.
Regarding claim 6, Yandex, in view of Koch, discloses the computer-implemented method of claim 5. Yandex further discloses:
determining whether the first input variable has a largest variable importance in the second list (Yandex, Fig. 3 and ¶[0080-0081] teaches determining whether the first input variable has the highest relevance in the not-yet-selected feature list through the iterative routine 320),
in accordance with a determination that the first input variable does not have the largest variable importance in the second list (Yandex, Fig. 3 and ¶[0080-0081] teaches a determination that the first input variable does not have the largest variable importance in the second list through the iterative routine 320),
correlation information of the first input variable with the at least one input variable in the first list (Yandex, Fig. 3 and ¶[0080-0081] teaches relevancy and redundancy as correlation information of the first input variable with the at least one input variable in the first list),
correlation information of a second input variable with the at least one input variable in the first list, the second input variable having the largest variable importance in the second list (Yandex, Fig. 3 and ¶[0080-0081] teaches relevancy and redundancy as correlation information of a second input variable, i.e., the iteratively picked feature, with the at least one input variable in the first list, the second input variable having the largest variable importance in the second list).
While Yandex teaches correlation information of the first input variable with the at least one input variable in the first list and correlation information of a second input variable with the at least one input variable in the first list, the second input variable having the largest variable importance in the second list, Yandex does not explicitly disclose displaying at least one of…correlation information between input variables.
Alkhodari teaches:
displaying at least one of: correlation information …(Alkhodari, Fig. 2, elements 206-210 and ¶[0042] teaches displaying a polar plot of corresponding features as correlation information).
Yandex, Koch and Alkhodari are analogous art because they are from the same field of endeavor, feature selection and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yandex, in view of Koch, to include displaying at least one of: correlation information, based on the teachings of Alkhodari. One of ordinary skill in the art would have been motivated to make this modification in order to select … optimal features for the training and classification process, as suggested by Alkhodari (¶[0080]).
Regarding claim 7, Yandex, in view of Koch, in further view of Alkhodari, discloses the computer-implemented method of claim 6.
Yandex discloses the correlation information of the second input variable with the at least one input variable in the first list (Yandex, Fig. 3 and ¶[0080-0081] teaches relevancy and redundancy as correlation information of a second input variable, i.e., the iteratively picked feature, with the at least one input variable in the first list), but does not disclose the correlation information … is represented by one or more edges between a node … and nodes representing … at least one input variable.
Alkhodari discloses:
the correlation information … is represented by one or more edges between a node … and nodes representing … at least one input variable (Alkhodari, Fig. 3B and ¶[0028] teaches the correlation between features to be represented through a connected edge in a polar graph, where nodes connected indicate the correlated features).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yandex, in view of Koch, to include the correlation information … is represented by one or more edges between a node … and nodes representing … at least one input variable, based on the teachings of Alkhodari. One of ordinary skill in the art would have been motivated to make this modification in order to select optimal features for the training and classification process, as suggested by Alkhodari (¶[0080]).
Regarding claim 8, Yandex, in view of Koch, discloses the computer-implemented method of claim 1. Yandex further discloses:
wherein … one or more of input variables in the updated first list further comprises: determining whether the second list is empty (Yandex, ¶[0083-0085] teaches determining if the number of features to be selected is exceeded as determining whether the second list is empty, and outputting the selected subset of features),
in accordance with a determination that the second list is empty, … the input variables in the first list based on the order of the input variables in the first list (Yandex, ¶[0083-0085] teaches outputting the input variables in the first list based on the order of the input variables in the first list).
Yandex teaches outputting one or more of input variables in the updated first list further comprises: determining whether the second list is empty and in accordance with a determination that the second list is empty, outputting the input variables in the first list based on the order of the input variables in the first list. Yandex does not disclose rendering and visualizing the output.
Alkhodari discloses:
rendering ….visualizing …input variables (Alkhodari, Fig. 6 and ¶[0053] teaches rendering and visualizing features, i.e. input variables).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yandex, in view of Koch, to rendering ….visualizing …input variables, based on the teachings of Alkhodari. One of ordinary skill in the art would have been motivated to make this modification in order to select optimal features for the training and classification process, as suggested by Alkhodari (¶[0080]).
Regarding claim 9, Yandex, in view of Koch, in further view of Alkhodari, discloses the computer-implemented method of claim 8 and visualizing the input variables in the first list based on the order of the input variables in the first list. Yandex further discloses:
wherein … the input variables in the first list based on the order of the input variables in the first list comprises: … representing the input variables in the first list …and… representing correlations of the input variables in the first list (Yandex, ¶[0083-0085] teaches representing the input variables in the first list as selected subset of features and representing correlations of the input variables as the relevancy and redundancy of the selected subset of features).
Yandex discloses wherein … the input variables in the first list based on the order of the input variables in the first list comprises: … representing the input variables in the first list …and… representing correlations of the input variables in the first list, but does not disclose:
displaying nodes on a spiral representing … input variables … and displaying one or more edges between the nodes representing correlations of … variables.
Alkhodari discloses:
displaying nodes on a spiral representing … input variables … and displaying one or more edges between the nodes representing correlations of … variables (Alkhodari, Fig. 3B, Fig. 6, ¶[0023] teaches polar feature representation as displaying nodes on a spiral representing features, i.e. input variable, and displaying one or more edges between the nodes representing correlations of the features).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yandex, in view of Koch, to - displaying nodes on a spiral representing … input variables … and displaying one or more edges between the nodes representing correlations of … variables, based on the teachings of Alkhodari. One of ordinary skill in the art would have been motivated to make this modification in order to select optimal features for the training and classification process, as suggested by Alkhodari (¶[0080]).
Claims 15-18 are substantially similar to claims 6-9, and thus are rejected on the same basis as claims 6-9.
Response to Arguments
Applicant's arguments filed 10/07/2025 have been fully considered with regards to the 35 U.S.C. 101 rejection, but they are not persuasive.
The applicant asserts on page 22 of the remarks “Thus, the Federal Circuit in Enfish has made it clear that whether a claim involves an abstract idea is irrelevant, since the correct legal test is whether the claim as a whole is directed to an abstract idea. Applicant contends that the output of Step 2A (MPEP §2106 "Patent Subject Matter Eligibility" (Manual of Patent Examining Procedure (MPEP) Ninth Edition, Revision 01.2024)) should be that the claim(s) is not an abstract idea, and accordingly, the claims are patent eligible. Additionally, assuming in arguendo continuation to Step 2B, Applicant contends that the claim(s) consider as a whole, amounts to significantly more than the exception (i.e., more than an abstract idea, and thus not an exception), and accordingly, the claims are patent eligible”, the applicant does not provide arguments regarding the 101 rejection beyond the general assertion that the claims fully comply with the requirements of 35 U.S.C. 101. The Examiner respectfully disagrees, as the claims are directed to abstract ideas without significantly more or being integrated into a practical application. A full analysis of the claims are presented through the 101 eligibility analysis outlined in the office action mailed 07/09/2025. The broadest reasonable interpretation of each and every limitation was considered, alongside the examiners interpretation of whether or not they are directed to an abstract idea, and why.
The applicant asserts on page 24-25 of the remarks “underlined operations above, features that are unconventional and specific, and as such, the features are beyond a mere abstract idea implemented on a computer, going beyond mental evaluations. Thus, these features go beyond generic computing to provide significantly more than the judicial exception as required by current case law regarding subject matter eligibility”. The Examiner respectfully disagrees, as the underlined operations incorporate the previously rejected, presently canceled dependent claim 3, with the addition of a final step that determines an accuracy of a performance. A user could reasonably determine an accuracy of a performance by comparing predictions with ground truth data. The incorporated limitations of claim 3 are also directed to mental evaluations, as one could reasonably determine performances of predictive models, recited generically, with certain input variables being enabled and then determine the contribution of an input variable based on a comparison of performance.
Applicant's arguments filed 10/07/2025 have been fully considered with regards to the 35 U.S.C. 102/103 rejection, but they are not persuasive.
The applicant asserts on page 29 of the remarks “None of the references, alone or in co